From 2f146463206630ea13a1f0e58d26863008560808 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 11 Aug 2026 10:22:27 +0200 Subject: [PATCH 001/156] Continued cupy port --- pyproject.toml | 3 + .../bsplines/tests/test_bsplines_kers.py | 28 +- src/struphy/feec/mass.py | 9 +- src/struphy/feec/mass_kernels.py | 1299 ++++++++--------- src/struphy/feec/preconditioner.py | 40 +- src/struphy/feec/psydac_derham.py | 34 +- src/struphy/feec/tests/test_l2_projectors.py | 12 +- src/struphy/pic/tests/test_pushers.py | 18 +- .../propagators/tests/test_curl_curl.py | 4 +- .../tests/test_gyrokinetic_poisson.py | 2 +- src/struphy/propagators/tests/test_poisson.py | 2 +- 11 files changed, 698 insertions(+), 753 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 39bf76759..11f2af617 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -61,6 +61,9 @@ phys = [ "gvec>=1.1.0, <=1.4.1", "desc-opt<=0.17.1", ] +cuda = [ + "cupy-cuda12x==13.6.0", +] dev = [ "struphy[mpi]", "notebook", diff --git a/src/struphy/bsplines/tests/test_bsplines_kers.py b/src/struphy/bsplines/tests/test_bsplines_kers.py index 0ee49978b..dc62da61c 100644 --- a/src/struphy/bsplines/tests/test_bsplines_kers.py +++ b/src/struphy/bsplines/tests/test_bsplines_kers.py @@ -2,7 +2,9 @@ import time import cunumpy as xp +import numpy as np import pytest +from cunumpy.xp import to_numpy from feectools.ddm.mpi import mpi as MPI logger = logging.getLogger("struphy") @@ -40,16 +42,16 @@ def test_bsplines_span_and_basis(num_elements, degree, bcs): derham_opts = DerhamOptions(degree=degree, bcs=bcs) derham = Derham(grid, derham_opts, comm=comm) - # knot vectors - tn1, tn2, tn3 = derham.V0fem.knots - td1, td2, td3 = derham.V3fem.knots + # knot vectors (bsplines_kernels/bsplines_kernels_p are Pyccel-compiled and only accept numpy.ndarray) + tn1, tn2, tn3 = (to_numpy(t) for t in derham.V0fem.knots) + td1, td2, td3 = (to_numpy(t) for t in derham.V3fem.knots) # Random points in domain of process n_pts = 100 dom = derham.domain_array[rank] - eta1s = xp.random.rand(n_pts) * (dom[1] - dom[0]) + dom[0] - eta2s = xp.random.rand(n_pts) * (dom[4] - dom[3]) + dom[3] - eta3s = xp.random.rand(n_pts) * (dom[7] - dom[6]) + dom[6] + eta1s = to_numpy(xp.random.rand(n_pts) * (dom[1] - dom[0]) + dom[0]) + eta2s = to_numpy(xp.random.rand(n_pts) * (dom[4] - dom[3]) + dom[3]) + eta3s = to_numpy(xp.random.rand(n_pts) * (dom[7] - dom[6]) + dom[6]) # struphy find_span t0 = time.time() @@ -77,14 +79,14 @@ def test_bsplines_span_and_basis(num_elements, degree, bcs): assert xp.allclose(span2s, span2s_psy) assert xp.allclose(span3s, span3s_psy) - # allocate tmps - bn1 = xp.empty(derham.degree[0] + 1, dtype=float) - bn2 = xp.empty(derham.degree[1] + 1, dtype=float) - bn3 = xp.empty(derham.degree[2] + 1, dtype=float) + # allocate tmps (passed to raw Pyccel kernels below, which only accept numpy.ndarray) + bn1 = np.empty(derham.degree[0] + 1, dtype=float) + bn2 = np.empty(derham.degree[1] + 1, dtype=float) + bn3 = np.empty(derham.degree[2] + 1, dtype=float) - bd1 = xp.empty(derham.degree[0], dtype=float) - bd2 = xp.empty(derham.degree[1], dtype=float) - bd3 = xp.empty(derham.degree[2], dtype=float) + bd1 = np.empty(derham.degree[0], dtype=float) + bd2 = np.empty(derham.degree[1], dtype=float) + bd3 = np.empty(derham.degree[2], dtype=float) # struphy b_splines_slim val1s, val2s, val3s = [], [], [] diff --git a/src/struphy/feec/mass.py b/src/struphy/feec/mass.py index c8b63ae9d..14a04381f 100644 --- a/src/struphy/feec/mass.py +++ b/src/struphy/feec/mass.py @@ -2900,6 +2900,9 @@ def __init__( self._spans_l = self.mass_ops.derham.spline_attributes[self.space_key].quad_grid_spans self._bases_l = self.mass_ops.derham.spline_attributes[self.space_key].quad_grid_bases + # Pyccel-compiled kernel only understands NumPy arrays + self._kernel_3d_vec = PyccelKernel(mass_kernels.kernel_3d_vec, outputs=(-1,)) + # Preconditioner if precond_name is None: pc = None @@ -3136,7 +3139,7 @@ def get_dofs( pads = fem_space.coeff_space.pads if isinstance(dofs, StencilVector): - mass_kernels.kernel_3d_vec( + self._kernel_3d_vec( *spans, *fem_space.degree, *starts, @@ -3147,7 +3150,7 @@ def get_dofs( dofs._data, ) elif isinstance(dofs, PolarVector): - mass_kernels.kernel_3d_vec( + self._kernel_3d_vec( *spans, *fem_space.degree, *starts, @@ -3158,7 +3161,7 @@ def get_dofs( dofs.tp._data, ) else: - mass_kernels.kernel_3d_vec( + self._kernel_3d_vec( *spans, *fem_space.degree, *starts, diff --git a/src/struphy/feec/mass_kernels.py b/src/struphy/feec/mass_kernels.py index 8093033aa..b29255917 100644 --- a/src/struphy/feec/mass_kernels.py +++ b/src/struphy/feec/mass_kernels.py @@ -1,12 +1,16 @@ """ Integral kernels for mass matrices and L2-projections. + +This module is intentionally restricted to constructs which Pyccel can +translate directly to Fortran without requiring gFTL container modules. """ import numpy as np -from numpy import shape -# ================= 1d ================================= +# ====================================================================== +# 1D +# ====================================================================== def kernel_1d_mat( spans1: "int[:]", @@ -20,57 +24,33 @@ def kernel_1d_mat( mat_fun: "float[:]", data: "float[:,:]", ): - """ - Performs the integration of Lambda_(i1) * mat_fun(eta1) * Lambda_(j1) for the basis functions (i1, j1) available on the calling process. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1 : array[int] - Array of span indices; the span is the index of the last non-vanishing spline on each grid element - (cell). The length of the returned array is the number of elements (cells). - pi1 : int - Degree of the codomain basis functions. - pj1 : int - Degree of the domain basis functions. - starts1 : int - Starting index on the current rank. - pads1 : int - Padding (=spline degree) for ghost regions in data. - w1 : "float[:,:]" - Quadrature weights. The indexing is [global element, quadrature point]. - bi1 : "float[:,:,:,:]" - Values of codomain basis functions. The indexing is [global element, local basis function, derivative, quadrature point]. - bj1 : "float[:,:,:,:]" - Values of domain basis functions. The indexing is [global element, local basis function, derivative, quadrature point]. - mat_fun : "float[:]" - Function under the integral evaluated at quadrature points (flattened). - data : "float[:,:]" - _data array of StencilMatrix to store the results. - """ + """Assemble a 1D mass matrix.""" - # number of elements ne1 = spans1.size - - # number of quadrature points in each element - nq1 = shape(w1)[1] + nq1 = w1.shape[1] for iel1 in range(ne1): for il1 in range(pi1 + 1): - # global spline indices - i_global1 = spans1[iel1] - pi1 + il1 - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 for jl1 in range(pj1 + 1): + value = 0.0 for q1 in range(nq1): - value += w1[iel1, q1] * bi1[iel1, il1, 0, q1] * bj1[iel1, jl1, 0, q1] * mat_fun[iel1 * nq1 + q1] + value += ( + w1[iel1, q1] + * bi1[iel1, il1, 0, q1] + * bj1[iel1, jl1, 0, q1] + * mat_fun[iel1 * nq1 + q1] + ) - data[pads1 + i_local1, pads1 + jl1 - il1] += value + data[ + pads1 + i_local1, + pads1 + jl1 - il1 + ] += value def kernel_1d_vec( @@ -83,48 +63,25 @@ def kernel_1d_vec( mat_fun: "float[:]", data: "float[:]", ): - """ - Performs the integration of Lambda_(i1) * mat_fun(eta1) for the basis functions (i1) available on the calling process. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1 : array[int] - Array of span indices; the span is the index of the last non-vanishing spline on each grid element - (cell). The length of the returned array is the number of elements (cells). - pi1 : int - Degree of the basis functions. - starts1 : int - Starting index on the current rank. - pads1 : int - Padding (=spline degree) for ghost regions in data. - w1 : "float[:,:]" - Quadrature weights. The indexing is [global element, quadrature point]. - bi1 : "float[:,:,:,:]" - Values of basis functions. The indexing is [global element, local basis function, derivative, quadrature point]. - mat_fun : "float[:]" - Function under the integral evaluated at quadrature points (flattened). - data : "float[:]" - _data array of StencilVector to store the results. - """ + """Apply a 1D mass operator.""" ne1 = spans1.size - - nq1 = shape(w1)[1] + nq1 = w1.shape[1] for iel1 in range(ne1): for il1 in range(pi1 + 1): - # global spline indices - i_global1 = spans1[iel1] - pi1 + il1 - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 value = 0.0 for q1 in range(nq1): - value += w1[iel1, q1] * bi1[iel1, il1, 0, q1] * mat_fun[iel1 * nq1 + q1] + value += ( + w1[iel1, q1] + * bi1[iel1, il1, 0, q1] + * mat_fun[iel1 * nq1 + q1] + ) data[pads1 + i_local1] += value @@ -138,51 +95,30 @@ def kernel_1d_eval( coeffs_data: "float[:]", values: "float[:]", ): - """ - Evaluates sum_i1 [ coeffs_i1 * Lambda_i1(quad_eta1) ] for all quadrature points on the calling process. - - The results are written into values. - - Parameters - ---------- - spans1 : array[int] - Array of span indices; the span is the index of the last non-vanishing spline on each grid element - (cell). The length of the returned array is the number of elements (cells). - pi1 : int - Degree of the basis functions. - starts1 : int - Starting index on the current rank. - pads1 : int - Padding (=spline degree) for ghost regions in coeffs_data. - bi1 : "float[:,:,:,:]" - Values of basis functions. The indexing is [global element, local basis function, derivative, quadrature point]. - coeffs_data : "float[:]" - _data array of StencilVector holding the spline coefficients of the function to be evaluated. - values : "float[:]" - Output array (flattened over elements and quadrature points) holding the evaluated function values; - it is set to zero at the start of the kernel, i.e. it is overwritten, not added to. - """ + """Evaluate a 1D spline function at quadrature points.""" values[:] = 0.0 ne1 = spans1.size - - nq1 = shape(bi1)[3] + nq1 = bi1.shape[3] for iel1 in range(ne1): for il1 in range(pi1 + 1): - # global spline indices - i_global1 = spans1[iel1] - pi1 + il1 - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 - for q1 in range(nq1): - values[iel1 * nq1 + q1] += coeffs_data[pads1 + i_local1] * bi1[iel1, il1, 0, q1] + coeff = coeffs_data[pads1 + i_local1] + for q1 in range(nq1): + values[iel1 * nq1 + q1] += ( + coeff * bi1[iel1, il1, 0, q1] + ) -# ================= 2d ================================= +# ====================================================================== +# 2D +# ====================================================================== def kernel_2d_mat( spans1: "int[:]", @@ -204,69 +140,60 @@ def kernel_2d_mat( mat_fun: "float[:,:]", data: "float[:,:,:,:]", ): - """ - Performs the integration of Lambda_(i1, i2) * mat_fun(eta1, eta2) * Lambda_(j1, j2) for the basis functions (i1, i2, j1, j2) available on the calling process. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1, spans2 : array[int] - Arrays of span indices in direction 1 and 2; the span is the index of the last non-vanishing spline - on each grid element (cell). The length of each array is the number of elements (cells) in that direction. - pi1, pi2 : int - Degree of the codomain basis functions in direction 1 and 2. - pj1, pj2 : int - Degree of the domain basis functions in direction 1 and 2. - starts1, starts2 : int - Starting index on the current rank, in direction 1 and 2. - pads1, pads2 : int - Padding (=spline degree) for ghost regions in data, in direction 1 and 2. - w1, w2 : "float[:,:]" - Quadrature weights in direction 1 and 2. The indexing is [global element, quadrature point]. - bi1, bi2 : "float[:,:,:,:]" - Values of codomain basis functions in direction 1 and 2. The indexing is - [global element, local basis function, derivative, quadrature point]. - bj1, bj2 : "float[:,:,:,:]" - Values of domain basis functions in direction 1 and 2, same indexing convention as bi1, bi2. - mat_fun : "float[:,:]" - Function under the integral evaluated at quadrature points (flattened in each direction). - The indexing is [flattened quadrature point in direction 1, flattened quadrature point in direction 2]. - data : "float[:,:,:,:]" - _data array of StencilMatrix to store the results. - """ + """Assemble a 2D mass matrix.""" ne1 = spans1.size ne2 = spans2.size - nq1 = shape(w1)[1] - nq2 = shape(w2)[1] + nq1 = w1.shape[1] + nq2 = w2.shape[1] for iel1 in range(ne1): for iel2 in range(ne2): + for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): - # global spline indices + i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 for jl1 in range(pj1 + 1): for jl2 in range(pj2 + 1): + value = 0.0 for q1 in range(nq1): + bi_1 = bi1[iel1, il1, 0, q1] + bj_1 = bj1[iel1, jl1, 0, q1] + w_1 = w1[iel1, q1] + for q2 in range(nq2): - wvol = w1[iel1, q1] * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] - bi = bi1[iel1, il1, 0, q1] * bi2[iel2, il2, 0, q2] - bj = bj1[iel1, jl1, 0, q1] * bj2[iel2, jl2, 0, q2] + wvol = ( + w_1 + * w2[iel2, q2] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2 + ] + ) - value += wvol * bi * bj + value += ( + wvol + * bi_1 + * bi2[iel2, il2, 0, q2] + * bj_1 + * bj2[iel2, jl2, 0, q2] + ) - data[pads1 + i_local1, pads2 + i_local2, pads1 + jl1 - il1, pads2 + jl2 - il2] += value + data[ + pads1 + i_local1, + pads2 + i_local2, + pads1 + jl1 - il1, + pads2 + jl2 - il2 + ] += value def kernel_2d_vec( @@ -285,61 +212,48 @@ def kernel_2d_vec( mat_fun: "float[:,:]", data: "float[:,:]", ): - """ - Performs the integration of Lambda_(i1, i2) * mat_fun(eta1, eta2) for the basis functions (i1, i2) available on the calling process. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1, spans2 : array[int] - Arrays of span indices in direction 1 and 2; the span is the index of the last non-vanishing spline - on each grid element (cell). The length of each array is the number of elements (cells) in that direction. - pi1, pi2 : int - Degree of the basis functions in direction 1 and 2. - starts1, starts2 : int - Starting index on the current rank, in direction 1 and 2. - pads1, pads2 : int - Padding (=spline degree) for ghost regions in data, in direction 1 and 2. - w1, w2 : "float[:,:]" - Quadrature weights in direction 1 and 2. The indexing is [global element, quadrature point]. - bi1, bi2 : "float[:,:,:,:]" - Values of basis functions in direction 1 and 2. The indexing is - [global element, local basis function, derivative, quadrature point]. - mat_fun : "float[:,:]" - Function under the integral evaluated at quadrature points (flattened in each direction). - The indexing is [flattened quadrature point in direction 1, flattened quadrature point in direction 2]. - data : "float[:,:]" - _data array of StencilVector to store the results. - """ + """Apply a 2D mass operator.""" ne1 = spans1.size ne2 = spans2.size - nq1 = shape(w1)[1] - nq2 = shape(w2)[1] + nq1 = w1.shape[1] + nq2 = w2.shape[1] for iel1 in range(ne1): for iel2 in range(ne2): + for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): - # global spline indices + i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 value = 0.0 for q1 in range(nq1): - for q2 in range(nq2): - wvol = w1[iel1, q1] * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] + bi_1 = bi1[iel1, il1, 0, q1] + w_1 = w1[iel1, q1] - value += wvol * bi1[iel1, il1, 0, q1] * bi2[iel2, il2, 0, q2] + for q2 in range(nq2): + value += ( + w_1 + * w2[iel2, q2] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2 + ] + * bi_1 + * bi2[iel2, il2, 0, q2] + ) - data[pads1 + i_local1, pads2 + i_local2] += value + data[ + pads1 + i_local1, + pads2 + i_local2 + ] += value def kernel_2d_eval( @@ -356,63 +270,50 @@ def kernel_2d_eval( coeffs_data: "float[:,:]", values: "float[:,:]", ): - """ - Evaluates sum_(i1, i2) [ coeffs_{i1,i2} * Lambda_{i1, i2}(quad_eta1, quad_eta2) ] for all quadrature points on the calling process. - - The results are written into values. - - Parameters - ---------- - spans1, spans2 : array[int] - Arrays of span indices in direction 1 and 2; the span is the index of the last non-vanishing spline - on each grid element (cell). The length of each array is the number of elements (cells) in that direction. - pi1, pi2 : int - Degree of the basis functions in direction 1 and 2. - starts1, starts2 : int - Starting index on the current rank, in direction 1 and 2. - pads1, pads2 : int - Padding (=spline degree) for ghost regions in coeffs_data, in direction 1 and 2. - bi1, bi2 : "float[:,:,:,:]" - Values of basis functions in direction 1 and 2. The indexing is - [global element, local basis function, derivative, quadrature point]. - coeffs_data : "float[:,:]" - _data array of StencilVector holding the spline coefficients of the function to be evaluated. - values : "float[:,:]" - Output array (flattened over elements and quadrature points in each direction) holding the evaluated - function values; it is set to zero at the start of the kernel, i.e. it is overwritten, not added to. - """ + """Evaluate a 2D spline function at quadrature points.""" values[:, :] = 0.0 ne1 = spans1.size ne2 = spans2.size - nq1 = shape(bi1)[3] - nq2 = shape(bi2)[3] + nq1 = bi1.shape[3] + nq2 = bi2.shape[3] for iel1 in range(ne1): for iel2 in range(ne2): + for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): - # global spline indices + i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 + coeff = coeffs_data[ + pads1 + i_local1, + pads2 + i_local2 + ] + for q1 in range(nq1): + bi_1 = bi1[iel1, il1, 0, q1] + for q2 in range(nq2): - values[iel1 * nq1 + q1, iel2 * nq2 + q2] += ( - coeffs_data[pads1 + i_local1, pads2 + i_local2] - * bi1[iel1, il1, 0, q1] + values[ + iel1 * nq1 + q1, + iel2 * nq2 + q2 + ] += ( + coeff + * bi_1 * bi2[iel2, il2, 0, q2] ) -# ================= 3d ================================= - +# ====================================================================== +# 3D +# ====================================================================== def kernel_3d_mat( spans1: "int[:]", @@ -442,118 +343,93 @@ def kernel_3d_mat( mat_fun: "float[:,:,:]", data: "float[:,:,:,:,:,:]", ): - """ - Performs the integration of Lambda_(i1,i2,i3) * mat_fun(eta1, eta2, eta3) * Lambda_(j1,j2,j3) for the basis functions (i1,i2,i3, j1,j2,j3) available on the calling process. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1, spans2, spans3 : array[int] - Arrays of span indices in direction 1, 2 and 3; the span is the index of the last non-vanishing spline - on each grid element (cell). The length of each array is the number of elements (cells) in that direction. - pi1, pi2, pi3 : int - Degree of the codomain basis functions in direction 1, 2 and 3. - pj1, pj2, pj3 : int - Degree of the domain basis functions in direction 1, 2 and 3. - starts1, starts2, starts3 : int - Starting index on the current rank, in direction 1, 2 and 3. - pads1, pads2, pads3 : int - Padding (=spline degree) for ghost regions in data, in direction 1, 2 and 3. - w1, w2, w3 : "float[:,:]" - Quadrature weights in direction 1, 2 and 3. The indexing is [global element, quadrature point]. - bi1, bi2, bi3 : "float[:,:,:,:]" - Values of codomain basis functions in direction 1, 2 and 3. The indexing is - [global element, local basis function, derivative, quadrature point]. - bj1, bj2, bj3 : "float[:,:,:,:]" - Values of domain basis functions in direction 1, 2 and 3, same indexing convention as bi1, bi2, bi3. - mat_fun : "float[:,:,:]" - Function under the integral evaluated at quadrature points (flattened in each direction). - The indexing is [flattened quad. point dir. 1, flattened quad. point dir. 2, flattened quad. point dir. 3]. - data : "float[:,:,:,:,:,:]" - _data array of StencilMatrix to store the results. - """ + """Assemble a 3D mass matrix.""" ne1 = spans1.size ne2 = spans2.size ne3 = spans3.size - nq1 = shape(w1)[1] - nq2 = shape(w2)[1] - nq3 = shape(w3)[1] - - tmp_bi1 = np.zeros(nq1) - tmp_bi2 = np.zeros(nq2) - tmp_bi3 = np.zeros(nq3) - - tmp_bj1 = np.zeros(nq1) - tmp_bj2 = np.zeros(nq2) - tmp_bj3 = np.zeros(nq3) - - tmp_w1 = np.zeros(nq1) - tmp_w2 = np.zeros(nq2) - tmp_w3 = np.zeros(nq3) - - tmp_mat_fun = np.zeros((nq1, nq2, nq3)) + nq1 = w1.shape[1] + nq2 = w2.shape[1] + nq3 = w3.shape[1] for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - tmp_mat_fun[:, :, :] = mat_fun[ - iel1 * nq1 : (iel1 + 1) * nq1, - iel2 * nq2 : (iel2 + 1) * nq2, - iel3 * nq3 : (iel3 + 1) * nq3, - ] - - tmp_w1[:] = w1[iel1, :] - tmp_w2[:] = w2[iel2, :] - tmp_w3[:] = w3[iel3, :] for il1 in range(pi1 + 1): + i_global1 = spans1[iel1] - pi1 + il1 + i_local1 = i_global1 - starts1 + for il2 in range(pi2 + 1): - for il3 in range(pi3 + 1): - tmp_bi1[:] = bi1[iel1, il1, 0, :] - tmp_bi2[:] = bi2[iel2, il2, 0, :] - tmp_bi3[:] = bi3[iel3, il3, 0, :] + i_global2 = spans2[iel2] - pi2 + il2 + i_local2 = i_global2 - starts2 - # global spline indices - i_global1 = spans1[iel1] - pi1 + il1 - i_global2 = spans2[iel2] - pi2 + il2 + for il3 in range(pi3 + 1): i_global3 = spans3[iel3] - pi3 + il3 - - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) - i_local1 = i_global1 - starts1 - i_local2 = i_global2 - starts2 i_local3 = i_global3 - starts3 for jl1 in range(pj1 + 1): for jl2 in range(pj2 + 1): for jl3 in range(pj3 + 1): - tmp_bj1[:] = bj1[iel1, jl1, 0, :] - tmp_bj2[:] = bj2[iel2, jl2, 0, :] - tmp_bj3[:] = bj3[iel3, jl3, 0, :] value = 0.0 for q1 in range(nq1): + bi_1 = bi1[ + iel1, il1, 0, q1 + ] + bj_1 = bj1[ + iel1, jl1, 0, q1 + ] + w_1 = w1[iel1, q1] + for q2 in range(nq2): + bi_12 = ( + bi_1 + * bi2[ + iel2, il2, 0, q2 + ] + ) + bj_12 = ( + bj_1 + * bj2[ + iel2, jl2, 0, q2 + ] + ) + w_12 = ( + w_1 + * w2[iel2, q2] + ) + for q3 in range(nq3): - wvol = ( - tmp_w1[q1] * tmp_w2[q2] * tmp_w3[q3] * tmp_mat_fun[q1, q2, q3] + value += ( + w_12 + * w3[ + iel3, q3 + ] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2, + iel3 * nq3 + q3 + ] + * bi_12 + * bi3[ + iel3, il3, 0, q3 + ] + * bj_12 + * bj3[ + iel3, jl3, 0, q3 + ] ) - bi = tmp_bi1[q1] * tmp_bi2[q2] * tmp_bi3[q3] - bj = tmp_bj1[q1] * tmp_bj2[q2] * tmp_bj3[q3] - - value += wvol * bi * bj - data[ pads1 + i_local1, pads2 + i_local2, pads3 + i_local3, pads1 + jl1 - il1, pads2 + jl2 - il2, - pads3 + jl3 - il3, + pads3 + jl3 - il3 ] += value @@ -579,75 +455,68 @@ def kernel_3d_vec( mat_fun: "float[:,:,:]", data: "float[:,:,:]", ): - """ - Performs the integration of Lambda_(i1,i2,i3) * mat_fun(eta1, eta2, eta3) for the basis functions (i1,i2,i3) available on the calling process. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1, spans2, spans3 : array[int] - Arrays of span indices in direction 1, 2 and 3; the span is the index of the last non-vanishing spline - on each grid element (cell). The length of each array is the number of elements (cells) in that direction. - pi1, pi2, pi3 : int - Degree of the basis functions in direction 1, 2 and 3. - starts1, starts2, starts3 : int - Starting index on the current rank, in direction 1, 2 and 3. - pads1, pads2, pads3 : int - Padding (=spline degree) for ghost regions in data, in direction 1, 2 and 3. - w1, w2, w3 : "float[:,:]" - Quadrature weights in direction 1, 2 and 3. The indexing is [global element, quadrature point]. - bi1, bi2, bi3 : "float[:,:,:,:]" - Values of basis functions in direction 1, 2 and 3. The indexing is - [global element, local basis function, derivative, quadrature point]. - mat_fun : "float[:,:,:]" - Function under the integral evaluated at quadrature points (flattened in each direction). - The indexing is [flattened quad. point dir. 1, flattened quad. point dir. 2, flattened quad. point dir. 3]. - data : "float[:,:,:]" - _data array of StencilVector to store the results. - """ + """Apply a 3D mass operator.""" ne1 = spans1.size ne2 = spans2.size ne3 = spans3.size - nq1 = shape(w1)[1] - nq2 = shape(w2)[1] - nq3 = shape(w3)[1] + nq1 = w1.shape[1] + nq2 = w2.shape[1] + nq3 = w3.shape[1] for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): + for il1 in range(pi1 + 1): + i_global1 = spans1[iel1] - pi1 + il1 + i_local1 = i_global1 - starts1 + for il2 in range(pi2 + 1): + i_global2 = spans2[iel2] - pi2 + il2 + i_local2 = i_global2 - starts2 + for il3 in range(pi3 + 1): - # global spline indices - i_global1 = spans1[iel1] - pi1 + il1 - i_global2 = spans2[iel2] - pi2 + il2 i_global3 = spans3[iel3] - pi3 + il3 - - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) - i_local1 = i_global1 - starts1 - i_local2 = i_global2 - starts2 i_local3 = i_global3 - starts3 value = 0.0 for q1 in range(nq1): + bi_1 = bi1[iel1, il1, 0, q1] + w_1 = w1[iel1, q1] + for q2 in range(nq2): - for q3 in range(nq3): - wvol = ( - w1[iel1, q1] - * w2[iel2, q2] - * w3[iel3, q3] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] - ) + bi_12 = ( + bi_1 + * bi2[iel2, il2, 0, q2] + ) + w_12 = ( + w_1 + * w2[iel2, q2] + ) + for q3 in range(nq3): value += ( - wvol * bi1[iel1, il1, 0, q1] * bi2[iel2, il2, 0, q2] * bi3[iel3, il3, 0, q3] + w_12 + * w3[iel3, q3] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2, + iel3 * nq3 + q3 + ] + * bi_12 + * bi3[ + iel3, il3, 0, q3 + ] ) - data[pads1 + i_local1, pads2 + i_local2, pads3 + i_local3] += value + data[ + pads1 + i_local1, + pads2 + i_local2, + pads3 + i_local3 + ] += value def kernel_3d_eval( @@ -669,31 +538,7 @@ def kernel_3d_eval( coeffs_data: "float[:,:,:]", values: "float[:,:,:]", ): - """ - Evaluates sum_(i1,i2,i3) [ coeffs_{i1,i2,i3} * Lambda_{i1,i2,i3}(quad_eta1, quad_eta2, quad_eta3) ] for all quadrature points on the calling process. - - The results are written into values. - - Parameters - ---------- - spans1, spans2, spans3 : array[int] - Arrays of span indices in direction 1, 2 and 3; the span is the index of the last non-vanishing spline - on each grid element (cell). The length of each array is the number of elements (cells) in that direction. - pi1, pi2, pi3 : int - Degree of the basis functions in direction 1, 2 and 3. - starts1, starts2, starts3 : int - Starting index on the current rank, in direction 1, 2 and 3. - pads1, pads2, pads3 : int - Padding (=spline degree) for ghost regions in coeffs_data, in direction 1, 2 and 3. - bi1, bi2, bi3 : "float[:,:,:,:]" - Values of basis functions in direction 1, 2 and 3. The indexing is - [global element, local basis function, derivative, quadrature point]. - coeffs_data : "float[:,:,:]" - _data array of StencilVector holding the spline coefficients of the function to be evaluated. - values : "float[:,:,:]" - Output array (flattened over elements and quadrature points in each direction) holding the evaluated - function values; it is set to zero at the start of the kernel, i.e. it is overwritten, not added to. - """ + """Evaluate a 3D spline function at quadrature points.""" values[:, :, :] = 0.0 @@ -701,34 +546,56 @@ def kernel_3d_eval( ne2 = spans2.size ne3 = spans3.size - nq1 = shape(bi1)[3] - nq2 = shape(bi2)[3] - nq3 = shape(bi3)[3] + nq1 = bi1.shape[3] + nq2 = bi2.shape[3] + nq3 = bi3.shape[3] for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): + for il1 in range(pi1 + 1): + i_global1 = spans1[iel1] - pi1 + il1 + i_local1 = i_global1 - starts1 + for il2 in range(pi2 + 1): + i_global2 = spans2[iel2] - pi2 + il2 + i_local2 = i_global2 - starts2 + for il3 in range(pi3 + 1): - # global spline indices - i_global1 = spans1[iel1] - pi1 + il1 - i_global2 = spans2[iel2] - pi2 + il2 i_global3 = spans3[iel3] - pi3 + il3 - - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) - i_local1 = i_global1 - starts1 - i_local2 = i_global2 - starts2 i_local3 = i_global3 - starts3 + coeff = coeffs_data[ + pads1 + i_local1, + pads2 + i_local2, + pads3 + i_local3 + ] + for q1 in range(nq1): + bi_1 = bi1[ + iel1, il1, 0, q1 + ] + for q2 in range(nq2): + bi_12 = ( + bi_1 + * bi2[ + iel2, il2, 0, q2 + ] + ) + for q3 in range(nq3): - values[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] += ( - coeffs_data[pads1 + i_local1, pads2 + i_local2, pads3 + i_local3] - * bi1[iel1, il1, 0, q1] - * bi2[iel2, il2, 0, q2] - * bi3[iel3, il3, 0, q3] + values[ + iel1 * nq1 + q1, + iel2 * nq2 + q2, + iel3 * nq3 + q3 + ] += ( + coeff + * bi_12 + * bi3[ + iel3, il3, 0, q3 + ] ) @@ -770,135 +637,143 @@ def kernel_3d_matrixfree( data_out: "float[:,:,:]", data_in: "float[:,:,:]", ): - """ - Performs the integration of Lambda_(i1, i2, i3) * mat_fun(eta1, eta2, eta3) * f(eta1, eta2, eta3) for the basis functions (i1, i2, i3) available on the calling process, - where f is the spline function represented by the coefficients in data_in. - - The results are written into data_out (attention: data_out is NOT set to zero first, but the results are added to data_out). - This computes the action of the mass matrix on a vector without ever assembling the matrix itself. - - Parameters - ---------- - spansi1, spansi2, spansi3 : array[int] - Arrays of span indices in direction 1, 2 and 3 for the codomain ("i") basis functions; the span is the - index of the last non-vanishing spline on each grid element (cell). - spansj1, spansj2, spansj3 : array[int] - Arrays of span indices in direction 1, 2 and 3 for the domain ("j") basis functions. - pi1, pi2, pi3 : int - Degree of the codomain basis functions in direction 1, 2 and 3. - pj1, pj2, pj3 : int - Degree of the domain basis functions in direction 1, 2 and 3. - startsi1, startsi2, startsi3 : int - Starting index on the current rank for the codomain basis functions, in direction 1, 2 and 3. - startsj1, startsj2, startsj3 : int - Starting index on the current rank for the domain basis functions, in direction 1, 2 and 3. - padsi1, padsi2, padsi3 : int - Padding (=spline degree) for ghost regions in data_out, in direction 1, 2 and 3. - padsj1, padsj2, padsj3 : int - Padding (=spline degree) for ghost regions in data_in, in direction 1, 2 and 3. - w1, w2, w3 : "float[:,:]" - Quadrature weights in direction 1, 2 and 3. The indexing is [global element, quadrature point]. - bi1, bi2, bi3 : "float[:,:,:,:]" - Values of codomain basis functions in direction 1, 2 and 3. The indexing is - [global element, local basis function, derivative, quadrature point]. - bj1, bj2, bj3 : "float[:,:,:,:]" - Values of domain basis functions in direction 1, 2 and 3, same indexing convention as bi1, bi2, bi3. - mat_fun : "float[:,:,:]" - Function under the integral evaluated at quadrature points (flattened in each direction). - The indexing is [flattened quad. point dir. 1, flattened quad. point dir. 2, flattened quad. point dir. 3]. - data_out : "float[:,:,:]" - _data array of StencilVector to store the results of the matrix-vector product. - data_in : "float[:,:,:]" - _data array of StencilVector holding the spline coefficients of the input function f. - """ + """Apply a 3D mass matrix without assembling it.""" ne1 = spansi1.size ne2 = spansi2.size ne3 = spansi3.size - nq1 = shape(w1)[1] - nq2 = shape(w2)[1] - nq3 = shape(w3)[1] - - tmp_w1 = np.zeros(nq1) - tmp_w2 = np.zeros(nq2) - tmp_w3 = np.zeros(nq3) - - tmp_bi1 = np.zeros(pi1 + 1) - tmp_bi2 = np.zeros(pi2 + 1) - tmp_bi3 = np.zeros(pi3 + 1) - - tmp_bj1 = np.zeros(pj1 + 1) - tmp_bj2 = np.zeros(pj2 + 1) - tmp_bj3 = np.zeros(pj3 + 1) - - tmp_mat_fun = np.zeros((nq1, nq2, nq3)) + nq1 = w1.shape[1] + nq2 = w2.shape[1] + nq3 = w3.shape[1] for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - tmp_mat_fun[:, :, :] = mat_fun[ - iel1 * nq1 : (iel1 + 1) * nq1, - iel2 * nq2 : (iel2 + 1) * nq2, - iel3 * nq3 : (iel3 + 1) * nq3, - ] - - tmp_w1[:] = w1[iel1, :] - tmp_w2[:] = w2[iel2, :] - tmp_w3[:] = w3[iel3, :] for q1 in range(nq1): for q2 in range(nq2): for q3 in range(nq3): - tmp_bi1[:] = bi1[iel1, :, 0, q1] - tmp_bi2[:] = bi2[iel2, :, 0, q2] - tmp_bi3[:] = bi3[iel3, :, 0, q3] - - tmp_bj1[:] = bj1[iel1, :, 0, q1] - tmp_bj2[:] = bj2[iel2, :, 0, q2] - tmp_bj3[:] = bj3[iel3, :, 0, q3] bj = 0.0 + for jl1 in range(pj1 + 1): + j_global1 = ( + spansj1[iel1] - pj1 + jl1 + ) + j_local1 = ( + j_global1 + - startsj1 + + padsj1 + ) + + bj_1 = bj1[ + iel1, jl1, 0, q1 + ] + for jl2 in range(pj2 + 1): - for jl3 in range(pj3 + 1): - # global spline indices - j_global1 = spansj1[iel1] - pj1 + jl1 - j_global2 = spansj2[iel2] - pj2 + jl2 - j_global3 = spansj3[iel3] - pj3 + jl3 + j_global2 = ( + spansj2[iel2] - pj2 + jl2 + ) + j_local2 = ( + j_global2 + - startsj2 + + padsj2 + ) + + bj_12 = ( + bj_1 + * bj2[ + iel2, jl2, 0, q2 + ] + ) - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) - j_local1 = j_global1 - startsj1 + padsj1 - j_local2 = j_global2 - startsj2 + padsj2 - j_local3 = j_global3 - startsj3 + padsj3 + for jl3 in range(pj3 + 1): + j_global3 = ( + spansj3[iel3] - pj3 + jl3 + ) + j_local3 = ( + j_global3 + - startsj3 + + padsj3 + ) bj += ( - tmp_bj1[jl1] - * tmp_bj2[jl2] - * tmp_bj3[jl3] - * data_in[j_local1, j_local2, j_local3] + bj_12 + * bj3[ + iel3, jl3, 0, q3 + ] + * data_in[ + j_local1, + j_local2, + j_local3 + ] ) - for il1 in range(pi1 + 1): - for il2 in range(pi2 + 1): - for il3 in range(pi3 + 1): - # global spline indices - i_global1 = spansi1[iel1] - pi1 + il1 - i_global2 = spansi2[iel2] - pi2 + il2 - i_global3 = spansi3[iel3] - pi3 + il3 + wvol = ( + w1[iel1, q1] + * w2[iel2, q2] + * w3[iel3, q3] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2, + iel3 * nq3 + q3 + ] + ) - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) - i_local1 = i_global1 - startsi1 + padsi1 - i_local2 = i_global2 - startsi2 + padsi2 - i_local3 = i_global3 - startsi3 + padsi3 + for il1 in range(pi1 + 1): + i_global1 = ( + spansi1[iel1] - pi1 + il1 + ) + i_local1 = ( + i_global1 + - startsi1 + + padsi1 + ) + + bi_1 = bi1[ + iel1, il1, 0, q1 + ] - wvol = tmp_w1[q1] * tmp_w2[q2] * tmp_w3[q3] * tmp_mat_fun[q1, q2, q3] + for il2 in range(pi2 + 1): + i_global2 = ( + spansi2[iel2] - pi2 + il2 + ) + i_local2 = ( + i_global2 + - startsi2 + + padsi2 + ) - bi = tmp_bi1[il1] * tmp_bi2[il2] * tmp_bi3[il3] + bi_12 = ( + bi_1 + * bi2[ + iel2, il2, 0, q2 + ] + ) - value = wvol * bi * bj + for il3 in range(pi3 + 1): + i_global3 = ( + spansi3[iel3] - pi3 + il3 + ) + i_local3 = ( + i_global3 + - startsi3 + + padsi3 + ) - data_out[i_local1, i_local2, i_local3] += value + data_out[ + i_local1, + i_local2, + i_local3 + ] += ( + wvol + * bi_12 + * bi3[ + iel3, il3, 0, q3 + ] + * bj + ) def kernel_3d_diag( @@ -923,92 +798,41 @@ def kernel_3d_diag( mat_fun: "float[:,:,:]", data: "float[:,:,:]", ): - """ - Computes the diagonal of a mass matrix, assuming that the domain and the codomain are the same. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1, spans2, spans3 : array[int] - Arrays of span indices in direction 1, 2 and 3; the span is the index of the last non-vanishing spline - on each grid element (cell). The length of each array is the number of elements (cells) in that direction. - pi1, pi2, pi3 : int - Degree of the basis functions in direction 1, 2 and 3. - starts1, starts2, starts3 : int - Starting index on the current rank, in direction 1, 2 and 3. - pads1, pads2, pads3 : int - Padding (=spline degree) for ghost regions, in direction 1, 2 and 3 (unused for data, which is a - StencilDiagonalMatrix and therefore has no padding, but kept for a uniform kernel signature). - w1, w2, w3 : "float[:,:]" - Quadrature weights in direction 1, 2 and 3. The indexing is [global element, quadrature point]. - bi1, bi2, bi3 : "float[:,:,:,:]" - Values of basis functions in direction 1, 2 and 3. The indexing is - [global element, local basis function, derivative, quadrature point]. - mat_fun : "float[:,:,:]" - Function under the integral evaluated at quadrature points (flattened in each direction). - The indexing is [flattened quad. point dir. 1, flattened quad. point dir. 2, flattened quad. point dir. 3]. - data : "float[:,:,:]" - _data array of StencilDiagonalMatrix to store the results. Periodic wrap-around (index -= nb) is applied - when a local index runs beyond the array bounds, since there are no ghost regions on this matrix type. - """ + """Compute the diagonal of a 3D mass matrix.""" ne1 = spans1.size ne2 = spans2.size ne3 = spans3.size - nq1 = shape(w1)[1] - nq2 = shape(w2)[1] - nq3 = shape(w3)[1] - - nb1, nb2, nb3 = data.shape - - tmp_bi1 = np.zeros(nq1) - tmp_bi2 = np.zeros(nq2) - tmp_bi3 = np.zeros(nq3) + nq1 = w1.shape[1] + nq2 = w2.shape[1] + nq3 = w3.shape[1] - tmp_w1 = np.zeros(nq1) - tmp_w2 = np.zeros(nq2) - tmp_w3 = np.zeros(nq3) - - tmp_mat_fun = np.zeros((nq1, nq2, nq3)) + nb1 = data.shape[0] + nb2 = data.shape[1] + nb3 = data.shape[2] for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - tmp_mat_fun[:, :, :] = mat_fun[ - iel1 * nq1 : (iel1 + 1) * nq1, - iel2 * nq2 : (iel2 + 1) * nq2, - iel3 * nq3 : (iel3 + 1) * nq3, - ] - - tmp_w1[:] = w1[iel1, :] - tmp_w2[:] = w2[iel2, :] - tmp_w3[:] = w3[iel3, :] for il1 in range(pi1 + 1): - for il2 in range(pi2 + 1): - for il3 in range(pi3 + 1): - tmp_bi1[:] = bi1[iel1, il1, 0, :] - tmp_bi2[:] = bi2[iel2, il2, 0, :] - tmp_bi3[:] = bi3[iel3, il3, 0, :] + i_global1 = spans1[iel1] - pi1 + il1 + i_local1 = i_global1 - starts1 - # global spline indices - i_global1 = spans1[iel1] - pi1 + il1 - i_global2 = spans2[iel2] - pi2 + il2 - i_global3 = spans3[iel3] - pi3 + il3 + if i_local1 >= nb1: + i_local1 -= nb1 - # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) - i_local1 = i_global1 - starts1 - i_local2 = i_global2 - starts2 - i_local3 = i_global3 - starts3 + for il2 in range(pi2 + 1): + i_global2 = spans2[iel2] - pi2 + il2 + i_local2 = i_global2 - starts2 - # Periodic case : last basis function are the first ones (no ghost regions on DiagonalStencilMatrix) - if i_local1 >= nb1: - i_local1 -= nb1 + if i_local2 >= nb2: + i_local2 -= nb2 - if i_local2 >= nb2: - i_local2 -= nb2 + for il3 in range(pi3 + 1): + i_global3 = spans3[iel3] - pi3 + il3 + i_local3 = i_global3 - starts3 if i_local3 >= nb3: i_local3 -= nb3 @@ -1016,17 +840,56 @@ def kernel_3d_diag( value = 0.0 for q1 in range(nq1): + bi_1 = bi1[ + iel1, il1, 0, q1 + ] + for q2 in range(nq2): - for q3 in range(nq3): - wvol = tmp_w1[q1] * tmp_w2[q2] * tmp_w3[q3] * tmp_mat_fun[q1, q2, q3] + bi_12 = ( + bi_1 + * bi2[ + iel2, il2, 0, q2 + ] + ) - bi = tmp_bi1[q1] * tmp_bi2[q2] * tmp_bi3[q3] + for q3 in range(nq3): + value += ( + w1[iel1, q1] + * w2[iel2, q2] + * w3[iel3, q3] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2, + iel3 * nq3 + q3 + ] + * bi_12 + * bi3[ + iel3, il3, 0, q3 + ] + * bi_12 + * bi3[ + iel3, il3, 0, q3 + ] + / ( + bi2[ + iel2, il2, 0, q2 + ] + * bi3[ + iel3, il3, 0, q3 + ] + ) + ) - value += wvol * bi * bi + data[ + i_local1, + i_local2, + i_local3 + ] += value - # No padding on StencilDiagonalMatrix - data[i_local1, i_local2, i_local3] += value +# ====================================================================== +# 3D surface kernels +# ====================================================================== def surface_kernel_3d_vec( spans1: "int[:]", @@ -1048,72 +911,49 @@ def surface_kernel_3d_vec( mat_fun: "float[:,:]", data: "float[:,:,:]", ): - """ - Performs the integration of Lambda_0ij * mat_fun(eta1, eta2) over the boundary surface at the fixed - (normal-direction) global index boundary_index, for the basis functions (ij) available on the calling - process in the two surface (tangential) directions. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1, spans2 : array[int] - Arrays of span indices in the two surface (tangential) directions; the span is the index of the last - non-vanishing spline on each grid element (cell) in that direction. - pi0 : int - Degree of the basis function in the normal direction (kept for a uniform kernel signature; not used - directly since the normal index is fixed to boundary_index). - pi1, pi2 : int - Degree of the basis functions in the two surface directions. - starts0 : int - Starting index on the current rank in the normal direction. - starts1, starts2 : int - Starting index on the current rank in the two surface directions. - pads0 : int - Padding (=spline degree) for ghost regions in data, in the normal direction. - pads1, pads2 : int - Padding (=spline degree) for ghost regions in data, in the two surface directions. - w1, w2 : "float[:,:]" - Quadrature weights in the two surface directions. The indexing is [global element, quadrature point]. - bi1, bi2 : "float[:,:,:,:]" - Values of basis functions in the two surface directions. The indexing is - [global element, local basis function, derivative, quadrature point]. - boundary_index : int - Global index in the normal direction at which the boundary surface is located. - mat_fun : "float[:,:]" - Function under the integral evaluated at surface quadrature points (flattened in each surface direction). - data : "float[:,:,:]" - _data array of StencilVector to store the results; only the slice at the fixed normal index - (pads0 + i_local0) is written. - """ + """Integrate a scalar function over a fixed 3D boundary surface.""" ne1 = spans1.size ne2 = spans2.size - nq1 = shape(w1)[1] - nq2 = shape(w2)[1] + nq1 = w1.shape[1] + nq2 = w2.shape[1] i_local0 = boundary_index - starts0 for iel1 in range(ne1): for iel2 in range(ne2): + for il1 in range(pi1 + 1): + i_global1 = spans1[iel1] - pi1 + il1 + i_local1 = i_global1 - starts1 + for il2 in range(pi2 + 1): - i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 - - i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 value = 0.0 for q1 in range(nq1): - for q2 in range(nq2): - wvol = w1[iel1, q1] * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] + bi_1 = bi1[iel1, il1, 0, q1] - value += wvol * bi1[iel1, il1, 0, q1] * bi2[iel2, il2, 0, q2] + for q2 in range(nq2): + value += ( + w1[iel1, q1] + * w2[iel2, q2] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2 + ] + * bi_1 + * bi2[iel2, il2, 0, q2] + ) - data[pads0 + i_local0, pads1 + i_local1, pads2 + i_local2] += value + data[ + pads0 + i_local0, + pads1 + i_local1, + pads2 + i_local2 + ] += value def surface_kernel_3d_mat( @@ -1143,126 +983,181 @@ def surface_kernel_3d_mat( data: "float[:,:,:,:,:,:]", ): """ - Assembles a boundary (surface) mass matrix: the integration of Lambda_i * mat_fun(eta_s1, eta_s2) * Lambda_j - over the boundary surface at the fixed global index boundary_index in the normal_dir direction, for the - codomain ("i") and domain ("j") basis functions available on the calling process in the two tangential - directions orthogonal to normal_dir. - - The results are written into data (attention: data is NOT set to zero first, but the results are added to data). - - Parameters - ---------- - spans1, spans2 : array[int] - Arrays of span indices in the two tangential grid directions used for the surface quadrature (as - determined by normal_dir); the span is the index of the last non-vanishing spline on each grid element. - pi0, pi1, pi2 : int - Degree of the codomain basis functions along logical axes 0, 1 and 2. - pj0, pj1, pj2 : int - Degree of the domain basis functions along logical axes 0, 1 and 2. - starts0, starts1, starts2 : int - Starting index on the current rank along logical axes 0, 1 and 2. - pads0, pads1, pads2 : int - Padding (=spline degree) for ghost regions in data, along logical axes 0, 1 and 2. - w1, w2 : "float[:,:]" - Quadrature weights in the two tangential directions. The indexing is [global element, quadrature point]. - bi1, bi2 : "float[:,:,:,:]" - Values of codomain basis functions in the two tangential directions. The indexing is - [global element, local basis function, derivative, quadrature point]. - bj1, bj2 : "float[:,:,:,:]" - Values of domain basis functions in the two tangential directions, same indexing convention as bi1, bi2. - boundary_index : int - Global index along normal_dir at which the boundary surface is located. - normal_dir : int - Logical direction (0, 1 or 2) normal to the surface; the remaining two directions are the tangential - directions used for the surface integration. - mat_fun : "float[:,:]" - Function under the integral evaluated at surface quadrature points (flattened in each tangential direction). - data : "float[:,:,:,:,:,:]" - _data array of StencilMatrix to store the results. + Assemble a surface mass matrix. + + No Python lists, tuples, comprehensions, dictionaries, sets, or other + container objects are used here. The normal direction is handled + explicitly to avoid generating gFTL container dependencies. """ ne1 = spans1.size ne2 = spans2.size - nq1 = shape(w1)[1] - nq2 = shape(w2)[1] + nq1 = w1.shape[1] + nq2 = w2.shape[1] - starts = [starts0, starts1, starts2] - pads = [pads0, pads1, pads2] - pi = [pi0, pi1, pi2] - pj = [pj0, pj1, pj2] + if normal_dir == 0: - surf_dirs = [d for d in range(3) if d != normal_dir] + i_local_n = boundary_index - starts0 - pi_s1 = pi[surf_dirs[0]] - pi_s2 = pi[surf_dirs[1]] + for iel1 in range(ne1): + for iel2 in range(ne2): - pj_s1 = pj[surf_dirs[0]] - pj_s2 = pj[surf_dirs[1]] + for il1 in range(pi1 + 1): + i_global1 = spans1[iel1] - pi1 + il1 + i_local1 = i_global1 - starts1 - starts_n = starts[normal_dir] - starts_s1 = starts[surf_dirs[0]] - starts_s2 = starts[surf_dirs[1]] + for il2 in range(pi2 + 1): + i_global2 = spans2[iel2] - pi2 + il2 + i_local2 = i_global2 - starts2 - pads_n = pads[normal_dir] - pads_s1 = pads[surf_dirs[0]] - pads_s2 = pads[surf_dirs[1]] + for jl1 in range(pj1 + 1): + for jl2 in range(pj2 + 1): - i_local_n = boundary_index - starts_n + value = 0.0 - for iel1 in range(ne1): - for iel2 in range(ne2): - for il1 in range(pi_s1 + 1): - for il2 in range(pi_s2 + 1): - i_global1 = spans1[iel1] - pi_s1 + il1 - i_global2 = spans2[iel2] - pi_s2 + il2 + for q1 in range(nq1): + bi_1 = bi1[ + iel1, il1, 0, q1 + ] + bj_1 = bj1[ + iel1, jl1, 0, q1 + ] - i_local1 = i_global1 - starts_s1 - i_local2 = i_global2 - starts_s2 + for q2 in range(nq2): + value += ( + w1[iel1, q1] + * w2[iel2, q2] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2 + ] + * bi_1 + * bi2[ + iel2, il2, 0, q2 + ] + * bj_1 + * bj2[ + iel2, jl2, 0, q2 + ] + ) - for jl1 in range(pj_s1 + 1): - for jl2 in range(pj_s2 + 1): - j_local1 = jl1 - il1 - j_local2 = jl2 - il2 + data[ + pads0 + i_local_n, + pads1 + i_local1, + pads2 + i_local2, + pads0, + pads1 + jl1 - il1, + pads2 + jl2 - il2 + ] += value - value = 0.0 + elif normal_dir == 1: - for q1 in range(nq1): - for q2 in range(nq2): - wvol = w1[iel1, q1] * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] + i_local_n = boundary_index - starts1 - value += ( - wvol - * bi1[iel1, il1, 0, q1] - * bi2[iel2, il2, 0, q2] - * bj1[iel1, jl1, 0, q1] - * bj2[iel2, jl2, 0, q2] - ) + for iel1 in range(ne1): + for iel2 in range(ne2): + + for il1 in range(pi0 + 1): + i_global1 = spans1[iel1] - pi0 + il1 + i_local1 = i_global1 - starts0 + + for il2 in range(pi2 + 1): + i_global2 = spans2[iel2] - pi2 + il2 + i_local2 = i_global2 - starts2 + + for jl1 in range(pj0 + 1): + for jl2 in range(pj2 + 1): + + value = 0.0 + + for q1 in range(nq1): + bi_1 = bi1[ + iel1, il1, 0, q1 + ] + bj_1 = bj1[ + iel1, jl1, 0, q1 + ] + + for q2 in range(nq2): + value += ( + w1[iel1, q1] + * w2[iel2, q2] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2 + ] + * bi_1 + * bi2[ + iel2, il2, 0, q2 + ] + * bj_1 + * bj2[ + iel2, jl2, 0, q2 + ] + ) - if normal_dir == 0: - data[ - pads_n + i_local_n, - pads_s1 + i_local1, - pads_s2 + i_local2, - pads_n, - pads_s1 + j_local1, - pads_s2 + j_local2, - ] += value - elif normal_dir == 1: data[ - pads_s1 + i_local1, - pads_n + i_local_n, - pads_s2 + i_local2, - pads_s1 + j_local1, - pads_n, - pads_s2 + j_local2, + pads0 + i_local1, + pads1 + i_local_n, + pads2 + i_local2, + pads0 + jl1 - il1, + pads1, + pads2 + jl2 - il2 ] += value - else: + + else: + + i_local_n = boundary_index - starts2 + + for iel1 in range(ne1): + for iel2 in range(ne2): + + for il1 in range(pi0 + 1): + i_global1 = spans1[iel1] - pi0 + il1 + i_local1 = i_global1 - starts0 + + for il2 in range(pi1 + 1): + i_global2 = spans2[iel2] - pi1 + il2 + i_local2 = i_global2 - starts1 + + for jl1 in range(pj0 + 1): + for jl2 in range(pj1 + 1): + + value = 0.0 + + for q1 in range(nq1): + bi_1 = bi1[ + iel1, il1, 0, q1 + ] + bj_1 = bj1[ + iel1, jl1, 0, q1 + ] + + for q2 in range(nq2): + value += ( + w1[iel1, q1] + * w2[iel2, q2] + * mat_fun[ + iel1 * nq1 + q1, + iel2 * nq2 + q2 + ] + * bi_1 + * bi2[ + iel2, il2, 0, q2 + ] + * bj_1 + * bj2[ + iel2, jl2, 0, q2 + ] + ) + data[ - pads_s1 + i_local1, - pads_s2 + i_local2, - pads_n + i_local_n, - pads_s1 + j_local1, - pads_s2 + j_local2, - pads_n, + pads0 + i_local1, + pads1 + i_local2, + pads2 + i_local_n, + pads0 + jl1 - il1, + pads1 + jl2 - il2, + pads2 ] += value + diff --git a/src/struphy/feec/preconditioner.py b/src/struphy/feec/preconditioner.py index 5bf9e957c..c45dcbfe2 100644 --- a/src/struphy/feec/preconditioner.py +++ b/src/struphy/feec/preconditioner.py @@ -1,6 +1,9 @@ import logging +import numpy as np + import cunumpy as xp +from cunumpy.xp import to_cunumpy, to_numpy from feectools.api.essential_bc import apply_essential_bc_stencil from feectools.ddm.cart import CartDecomposition, DomainDecomposition from feectools.ddm.mpi import MockComm @@ -260,7 +263,7 @@ def fun(e): M_local = StencilMatrix(V_local, V_local) - row_indices, col_indices = xp.nonzero(M_arr) + row_indices, col_indices = np.nonzero(M_arr) # M_arr is always a host array (StencilMatrix.toarray()) for row_i, col_i in zip(row_indices, col_indices): # only consider row indices on process @@ -273,7 +276,7 @@ def fun(e): ] = M_arr[row_i, col_i] # check if stencil matrix was built correctly - assert xp.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) + assert np.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) # both sides are host arrays matrixcells += [M_local.copy()] # ======================================================================================================= @@ -625,7 +628,7 @@ def __init__(self, mass_operator, apply_bc=True): M_local = StencilMatrix(V_local, V_local) - row_indices, col_indices = xp.nonzero(M_arr) + row_indices, col_indices = np.nonzero(M_arr) # M_arr is always a host array (StencilMatrix.toarray()) for row_i, col_i in zip(row_indices, col_indices): # only consider row indices on process @@ -638,7 +641,7 @@ def __init__(self, mass_operator, apply_bc=True): ] = M_arr[row_i, col_i] # check if stencil matrix was built correctly - assert xp.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) + assert np.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) # both sides are host arrays matrixcells += [M_local.copy()] # ======================================================================================================= @@ -911,7 +914,10 @@ class FFTSolver(BandedSolver): """ def __init__(self, circmat): - assert isinstance(circmat, xp.ndarray) + # circmat comes from StencilMatrix.toarray(), which always returns a + # host (NumPy) array; scipy.linalg.solve_circulant (used in solve()) + # is CPU-only regardless of the active cunumpy backend. + assert isinstance(circmat, np.ndarray) assert is_circulant(circmat) self._space = xp.ndarray @@ -946,20 +952,30 @@ def solve(self, rhs, out=None, transposed=False): assert rhs.T.shape[0] == self._column.size + # scipy.linalg.solve_circulant only understands NumPy; rhs may be a + # CuPy array (e.g. a view into a device-resident StencilVector), so + # convert at this CPU-solver boundary and copy the result back. + rhs_np = to_numpy(rhs) + if out is None: - out = solve_circulant(self._column, rhs.T).T + out = to_cunumpy(solve_circulant(self._column, rhs_np.T).T) else: assert out.shape == rhs.shape assert out.dtype == rhs.dtype try: - out[:] = solve_circulant(self._column, rhs.T).T - except xp.linalg.LinAlgError: + result_np = solve_circulant(self._column, rhs_np.T).T + except np.linalg.LinAlgError: eps = 1e-4 logger.info(f"Stabilizing singular preconditioning FFTSolver with {eps =}:") self._column[0] *= 1.0 + eps - out[:] = solve_circulant(self._column, rhs.T).T + result_np = solve_circulant(self._column, rhs_np.T).T + # cupy's __setitem__ can mishandle a NumPy RHS against a strided + # view (raises "non-scalar numpy.ndarray cannot be used for + # fill"); converting explicitly to the active backend first + # sidesteps that. + out[:] = to_cunumpy(result_np) return out @@ -979,13 +995,15 @@ def is_circulant(mat): Whether the matrix is circulant (=True) or not (=False). """ - assert isinstance(mat, xp.ndarray) + # mat is always a host (NumPy) array in practice: the only callers pass + # StencilMatrix.toarray() output, which feectools always returns on the host. + assert isinstance(mat, np.ndarray) assert len(mat.shape) == 2 assert mat.shape[0] == mat.shape[1] if mat.shape[0] > 1: for i in range(mat.shape[0] - 1): - circulant = xp.allclose(mat[i, :], xp.roll(mat[i + 1, :], -1)) + circulant = np.allclose(mat[i, :], np.roll(mat[i + 1, :], -1)) if not circulant: return circulant else: diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index 101bcfd12..2372a3c58 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -26,8 +26,17 @@ from feectools.linalg.block import BlockVector, BlockVectorSpace from feectools.linalg.stencil import StencilVector, StencilVectorSpace +from cunumpy import PyccelKernel + from struphy.bsplines import evaluation_kernels_3d as eval_3d from struphy.bsplines.evaluation_kernels_3d import eval_spline_mpi_tensor_product_fixed + +# Pyccel kernels only understand NumPy arrays; wrap the ones called directly +# in this module so they also work with CuPy arrays (see cunumpy.kernel). +eval_3d.eval_spline_mpi_sparse_meshgrid = PyccelKernel(eval_3d.eval_spline_mpi_sparse_meshgrid) +eval_3d.eval_spline_mpi_markers = PyccelKernel(eval_3d.eval_spline_mpi_markers) +eval_3d.eval_spline_mpi_matrix = PyccelKernel(eval_3d.eval_spline_mpi_matrix) +eval_spline_mpi_tensor_product_fixed = PyccelKernel(eval_spline_mpi_tensor_product_fixed) from struphy.feec.linear_operators import BoundaryOperator from struphy.feec.local_projectors_kernels import get_local_problem_size, select_quasi_points from struphy.feec.projectors import CommutingProjector, CommutingProjectorLocal @@ -2155,21 +2164,26 @@ def _get_span_and_basis_for_eval_mpi(self, etas, Nspace, end): 2d array of pn values of D-splines indexed by (eta, spline value). """ + from cunumpy.xp import to_cunumpy, to_numpy + from struphy.bsplines import bsplines_kernels - # Extract knot vectors, degree and kind of basis - Tn = Nspace.knots + # bsplines_kernels.find_span/b_d_splines_slim are Pyccel-compiled and + # only understand NumPy; this is a per-point Python loop, so convert + # once up front rather than wrapping every individual kernel call. + Tn = to_numpy(Nspace.knots) pn = Nspace.degree + etas_np = to_numpy(etas) - spans = xp.zeros(etas.size, dtype=int) - bns = xp.zeros((etas.size, pn + 1), dtype=float) - bds = xp.zeros((etas.size, pn), dtype=float) - bn = xp.zeros(pn + 1, dtype=float) - bd = xp.zeros(pn, dtype=float) + spans = np.zeros(etas_np.size, dtype=int) + bns = np.zeros((etas_np.size, pn + 1), dtype=float) + bds = np.zeros((etas_np.size, pn), dtype=float) + bn = np.zeros(pn + 1, dtype=float) + bd = np.zeros(pn, dtype=float) - for n in range(etas.size): + for n in range(etas_np.size): # avoid 1. --> 0. for clamped interpolation - eta = etas[n] % (1.0 + 1e-14) + eta = etas_np[n] % (1.0 + 1e-14) span = bsplines_kernels.find_span(Tn, pn, eta) bsplines_kernels.b_d_splines_slim(Tn, pn, eta, span, bn, bd) # correct span for mpi spline eval @@ -2179,7 +2193,7 @@ def _get_span_and_basis_for_eval_mpi(self, etas, Nspace, end): bns[n] = bn bds[n] = bd - return spans, bns, bds + return to_cunumpy(spans), to_cunumpy(bns), to_cunumpy(bds) class SplineFunction: diff --git a/src/struphy/feec/tests/test_l2_projectors.py b/src/struphy/feec/tests/test_l2_projectors.py index 7495c86d6..92b461869 100644 --- a/src/struphy/feec/tests/test_l2_projectors.py +++ b/src/struphy/feec/tests/test_l2_projectors.py @@ -49,7 +49,7 @@ def test_l2_projectors_mappings( # evaluation points e1 = xp.linspace(0.0, 1.0, 30) e2 = xp.linspace(0.0, 1.0, 40) - e3 = 0.0 + e3 = xp.array([0.0]) ee1, ee2, ee3 = xp.meshgrid(e1, e2, e3, indexing="ij") @@ -112,7 +112,9 @@ def test_l2_projectors_mappings( err = xp.max(xp.abs(f_analytic(ee1, ee2, ee3) - field_vals)) f_plot = field_vals else: - err = [xp.max(xp.abs(exact(ee1, ee2, ee3) - field_v)) for exact, field_v in zip(f_analytic, field_vals)] + err = xp.array( + [xp.max(xp.abs(exact(ee1, ee2, ee3) - field_v)) for exact, field_v in zip(f_analytic, field_vals)] + ) f_plot = field_vals[0] logger.info(f"{sp_id =}, {xp.max(err) =}") @@ -235,7 +237,9 @@ def f(x, y, z): err = xp.max(xp.abs(f_analytic(e1, e2, e3) - field_vals)) f_plot = field_vals else: - err = [xp.max(xp.abs(exact(e1, e2, e3) - field_v)) for exact, field_v in zip(f_analytic, field_vals)] + err = xp.array( + [xp.max(xp.abs(exact(e1, e2, e3) - field_v)) for exact, field_v in zip(f_analytic, field_vals)] + ) f_plot = field_vals[0] errors[sp_id] += [xp.max(err)] @@ -259,7 +263,7 @@ def f(x, y, z): line_for_rate_p1 = [Ne ** (-rate_p1) * errors[sp_id][0] / Nels[0] ** (-rate_p1) for Ne in Nels] line_for_rate_p0 = [Ne ** (-rate_p0) * errors[sp_id][0] / Nels[0] ** (-rate_p0) for Ne in Nels] - m, _ = xp.polyfit(xp.log(Nels), xp.log(errors[sp_id]), deg=1) + m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors[sp_id])), deg=1) logger.info(f"{sp_id =}, fitted convergence rate = {-m}, degree = {pi}") if sp_id in ("H1", "H1vec"): assert -m > (pi + 1 - 0.05) diff --git a/src/struphy/pic/tests/test_pushers.py b/src/struphy/pic/tests/test_pushers.py index fb139de89..3d3df447a 100644 --- a/src/struphy/pic/tests/test_pushers.py +++ b/src/struphy/pic/tests/test_pushers.py @@ -699,12 +699,18 @@ def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): pusher_psy(dt) - n_mks_load = xp.zeros(size, dtype=int) + # MPI communication buffers/counts must be host (NumPy) arrays regardless + # of the active cunumpy backend. + import numpy as np - comm.Allgather(xp.array(xp.shape(particles.markers)[0]), n_mks_load) + from cunumpy.xp import to_numpy - sendcounts = xp.zeros(size, dtype=int) - displacements = xp.zeros(size, dtype=int) + n_mks_load = np.zeros(size, dtype=int) + + comm.Allgather(np.array(xp.shape(particles.markers)[0]), n_mks_load) + + sendcounts = np.zeros(size, dtype=int) + displacements = np.zeros(size, dtype=int) accum_sendcounts = 0.0 for i in range(size): @@ -712,10 +718,10 @@ def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): displacements[i] = accum_sendcounts accum_sendcounts += sendcounts[i] - all_particles_psy = xp.zeros((int(accum_sendcounts) * 3,), dtype=float) + all_particles_psy = np.zeros((int(accum_sendcounts) * 3,), dtype=float) comm.Barrier() - comm.Allgatherv(xp.array(particles.markers[:, :3]), [all_particles_psy, sendcounts, displacements, MPI.DOUBLE]) + comm.Allgatherv(to_numpy(particles.markers[:, :3]), [all_particles_psy, sendcounts, displacements, MPI.DOUBLE]) comm.Barrier() diff --git a/src/struphy/propagators/tests/test_curl_curl.py b/src/struphy/propagators/tests/test_curl_curl.py index b3205cdcc..f0c58833d 100644 --- a/src/struphy/propagators/tests/test_curl_curl.py +++ b/src/struphy/propagators/tests/test_curl_curl.py @@ -189,7 +189,7 @@ def test_convergence_1d( h = 1 / Nel h_vec.append(h) - m, _ = xp.polyfit(xp.log(Nels), xp.log(errors), deg=1) + m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors)), deg=1) logger.info(f"For {p =}, solution converges with rate {-m =} ") if show_plot: @@ -393,7 +393,7 @@ def scalar_current(a, b): h = 1 / Nel h_vec.append(h) - m, _ = xp.polyfit(xp.log(Nels), xp.log(errors), deg=1) + m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors)), deg=1) logger.info(f"For {p =}, solution converges with rate {-m =} ") if show_plot: diff --git a/src/struphy/propagators/tests/test_gyrokinetic_poisson.py b/src/struphy/propagators/tests/test_gyrokinetic_poisson.py index a67ec2406..159a4ba71 100644 --- a/src/struphy/propagators/tests/test_gyrokinetic_poisson.py +++ b/src/struphy/propagators/tests/test_gyrokinetic_poisson.py @@ -206,7 +206,7 @@ def rho_pulled(e1, e2, e3): h = 1 / (Neli) h_vec.append(h) - m, _ = xp.polyfit(xp.log(Nels), xp.log(errors), deg=1) + m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors)), deg=1) logger.info(f"For {pi =}, solution converges in {direction=} with rate {-m =} ") assert -m > (pi + 1 - 0.07) diff --git a/src/struphy/propagators/tests/test_poisson.py b/src/struphy/propagators/tests/test_poisson.py index 55ca649fd..d73217226 100644 --- a/src/struphy/propagators/tests/test_poisson.py +++ b/src/struphy/propagators/tests/test_poisson.py @@ -244,7 +244,7 @@ def rho_pulled(e1, e2, e3): h = 1 / (Neli) h_vec.append(h) - m, _ = xp.polyfit(xp.log(Nels), xp.log(errors), deg=1) + m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors)), deg=1) logger.info(f"For {pi =}, solution converges in {direction=} with rate {-m =} ") assert -m > (pi + 1 - 0.07) From aba7b3635588508dbf2d4d7389563466b2c1e4bc Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 11 Aug 2026 11:13:44 +0200 Subject: [PATCH 002/156] _to_numpy_for_kernel(self.params_numpy) --- feectools | 2 +- params_LinearMHDDriftkineticCC.py | 208 ++++++++++++++++++------------ src/struphy/geometry/base.py | 2 +- 3 files changed, 130 insertions(+), 82 deletions(-) diff --git a/feectools b/feectools index 3d30f8c80..dd72e6def 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 3d30f8c80f0ebdecf83744bed8cb48b182ebc318 +Subproject commit dd72e6def3dd69aed8b86bfca5b36723df41f62a diff --git a/params_LinearMHDDriftkineticCC.py b/params_LinearMHDDriftkineticCC.py index f9f5ecd72..ea26465a8 100644 --- a/params_LinearMHDDriftkineticCC.py +++ b/params_LinearMHDDriftkineticCC.py @@ -1,116 +1,164 @@ -# import model, set verbosity -from struphy.models.hybrid import LinearMHDDriftkineticCC - -from struphy import main -from struphy.fields_background import equils -from struphy.geometry import domains -from struphy.initial import perturbations -from struphy.io.options import BaseUnits, DerhamOptions, EnvironmentOptions, FieldsBackground, Time -from struphy.kinetic_background import maxwellians -from struphy.pic.utilities import ( +# ----------------------------- +# Description of the simulation +# ----------------------------- +# Please fill in a verbal description of the simulation. +# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. + +name = "Default LinearMHDDriftkineticCC" +description = """ +This is the default simulation for the model LinearMHDDriftkineticCC. +It is meant to be a template for users to set up their own simulations with this model. +It contains all the necessary components of a Struphy simulation, including the model, +the environment options, the time stepping options, the geometry, the equilibrium, +the grid, the Derham options, and the initial conditions. +Users can modify this file to set up their own simulations with different parameters and initial conditions. +""" + +import logging +from struphy import set_logging_level +set_logging_level(logging.WARNING) + +# ------------------ +# Import Struphy API +# ------------------ + +from struphy import ( + BaseUnits, + DerhamOptions, + EnvironmentOptions, + FieldsBackground, + Simulation, + Time, + domains, + equils, + grids, + perturbations, +) + +# For particles: +from struphy import ( BinningPlot, BoundaryParameters, KernelDensityPlot, LoadingParameters, WeightsParameters, + SortingParameters, + SavingParameters, + maxwellians, ) -from struphy.topology import grids -# environment options -env = EnvironmentOptions() +# --------------------- +# Instance of the model +# --------------------- -# units +from struphy.models import LinearMHDDriftkineticCC + +# Units base_units = BaseUnits() -# time stepping +# Model instance +model = LinearMHDDriftkineticCC(base_units=base_units) + +# List all variables and decide whether to save their data +model.em_fields.b_field.save_data = True +model.mhd.density.save_data = True +model.mhd.pressure.save_data = True +model.mhd.velocity.save_data = True +model.energetic_ions.var.save_data = True + +# -------------------------- +# Instance of the simulation +# -------------------------- + +# Environment options +env = EnvironmentOptions() + +# Time stepping time_opts = Time() -# geometry +# Geometry domain = domains.Cuboid() -# fluid equilibrium (can be used as part of initial conditions) +# Fluid equilibrium (can be used as part of initial conditions) equil = equils.HomogenSlab() -# grid -grid = grids.TensorProductGrid(Nel=(16, 16, 16)) +# Grid +grid = grids.TensorProductGrid() -# derham options +# Derham options derham_opts = DerhamOptions() -# light-weight model instance -model = LinearMHDDriftkineticCC() +# Simulation object +sim = Simulation( + model=model, + name=name, + description=description, + params_path=__file__, + env=env, + time_opts=time_opts, + domain=domain, + equil=equil, + grid=grid, + derham_opts=derham_opts, +) -# species parameters -model.mhd.set_phys_params() -model.energetic_ions.set_phys_params() +# ------------------- +# Particle parameters +# ------------------- -loading_params = LoadingParameters(ppc=1000) +loading_params = LoadingParameters() weights_params = WeightsParameters() boundary_params = BoundaryParameters() -model.energetic_ions.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, -) -model.energetic_ions.set_sorting_boxes() -model.energetic_ions.set_save_data() +sorting_params = SortingParameters() +saving_params = SavingParameters() +model.energetic_ions.set_markers(loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, + ) + +# ------------------ +# Propagator options +# ------------------ + +model.propagators.push_bxe.options = model.propagators.push_bxe.Options() +model.propagators.push_parallel.options = model.propagators.push_parallel.Options() +model.propagators.shearalfen_cc5d.options = model.propagators.shearalfen_cc5d.Options() +model.propagators.magnetosonic.options = model.propagators.magnetosonic.Options() +model.propagators.cc5d_density.options = model.propagators.cc5d_density.Options() +model.propagators.cc5d_gradb.options = model.propagators.cc5d_gradb.Options() +model.propagators.cc5d_curlb.options = model.propagators.cc5d_curlb.Options() + +# ------------------ +# Initial conditions +# ------------------ +# Initial conditions are the sum of the background(s) and the perturbation(s). +# If backgrounds or perturbations are not specified, they are assumed to be zero. + +# Background for (some) FEEC variables +model.mhd.velocity.add_background(FieldsBackground()) -# propagator options -model.propagators.push_bxe.options = model.propagators.push_bxe.Options( - b_tilde=model.em_fields.b_field, -) -model.propagators.push_parallel.options = model.propagators.push_parallel.Options( - b_tilde=model.em_fields.b_field, -) -model.propagators.shearalfen_cc5d.options = model.propagators.shearalfen_cc5d.Options( - energetic_ions=model.energetic_ions.var, -) -model.propagators.magnetosonic.options = model.propagators.magnetosonic.Options( - b_field=model.em_fields.b_field, -) -model.propagators.cc5d_density.options = model.propagators.cc5d_density.Options( - energetic_ions=model.energetic_ions.var, - b_tilde=model.em_fields.b_field, -) -model.propagators.cc5d_gradb.options = model.propagators.cc5d_gradb.Options( - b_tilde=model.em_fields.b_field, -) -model.propagators.cc5d_curlb.options = model.propagators.cc5d_curlb.Options( - b_tilde=model.em_fields.b_field, -) +# Perturbations for (some) FEEC variables +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis='v', comp=0)) +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis='v', comp=1)) +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis='v', comp=2)) -# background, perturbations and initial conditions -model.mhd.velocity.add_background(FieldsBackground()) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=0)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=1)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=2)) +# For kinetic species the background is mandatory. +# For kinetic species, if add_initial_condition() is not called, the background is taken as the kinetic initial condition. +# For kinetic species the perturbations are added to the moments of the distribution function (defined as tuples). + +# Background for kinetic species maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None), equil=equil) maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None), equil=equil) background = maxwellian_1 + maxwellian_2 model.energetic_ions.var.add_background(background) -# if .add_initial_condition is not called, the background is the kinetic initial condition +# Perturbations for (some) kinetic species perturbation = perturbations.TorusModesCos() maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation), equil=equil) init = maxwellian_1pt + maxwellian_2 model.energetic_ions.var.add_initial_condition(init) -# optional: exclude variables from saving -# model.energetic_ions.var.save_data = False - if __name__ == "__main__": - # start run - verbose = True - - main.run( - model, - params_path=__file__, - env=env, - base_units=base_units, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, - verbose=verbose, - ) + sim.run() \ No newline at end of file diff --git a/src/struphy/geometry/base.py b/src/struphy/geometry/base.py index 6add7e504..b8a15e29b 100644 --- a/src/struphy/geometry/base.py +++ b/src/struphy/geometry/base.py @@ -235,7 +235,7 @@ def _build_args_domain(self): """Build runtime mapping arguments used by compiled evaluation kernels.""" return DomainArguments( self.kind_map, - self.params_numpy, + _to_numpy_for_kernel(self.params_numpy), _to_numpy_for_kernel(xp.array(self.degree)), _to_numpy_for_kernel(self.T[0]), _to_numpy_for_kernel(self.T[1]), From 0bec00a70666b5456cd88390647dcf35b00e56cc Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 11 Aug 2026 11:14:00 +0200 Subject: [PATCH 003/156] Set feectools<=0.1.10 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 11f2af617..b2ed2b434 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,7 +26,7 @@ dependencies = [ "numpy<=2.5.0", "cunumpy>=0.1.4, <=0.1.5", "pyccel>=2.2.0, <=2.2.3", - "feectools<=0.1.8", + "feectools<=0.1.10", "scipy<=1.18.0", "h5py<=3.16.0", "matplotlib<=3.11.0", From 742c0e8639ad260032d8ec07810cf66d6150e707 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 11 Aug 2026 11:25:35 +0200 Subject: [PATCH 004/156] Made sure params_LinearMHDDriftkineticCC.py works with cupy --- feectools | 2 +- src/struphy/feec/basis_projection_ops.py | 2 +- src/struphy/physics/physics.py | 2 +- src/struphy/pic/particles.py | 17 +++++++++-------- src/struphy/propagators/base.py | 5 +++-- .../propagators/current_coupling_5d_gradb.py | 14 +++++++------- 6 files changed, 22 insertions(+), 20 deletions(-) diff --git a/feectools b/feectools index dd72e6def..77c92a6e7 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit dd72e6def3dd69aed8b86bfca5b36723df41f62a +Subproject commit 77c92a6e708f9e5c594d891bb12898c1a4fce045 diff --git a/src/struphy/feec/basis_projection_ops.py b/src/struphy/feec/basis_projection_ops.py index cf843751e..6e2497ec3 100644 --- a/src/struphy/feec/basis_projection_ops.py +++ b/src/struphy/feec/basis_projection_ops.py @@ -1993,7 +1993,7 @@ def assemble(self, weights=None): polar_shift, ) - _ptsG = [pts.flatten() for pts in _ptsG] + _ptsG = [xp.asarray(pts.flatten()) for pts in _ptsG] _Vnbases = [int(space.nbasis) for space in V1d] _Wnbases = [int(space.nbasis) for space in W1d] diff --git a/src/struphy/physics/physics.py b/src/struphy/physics/physics.py index 190b5fdca..c0a5ee07d 100644 --- a/src/struphy/physics/physics.py +++ b/src/struphy/physics/physics.py @@ -112,7 +112,7 @@ def derive_units(self, velocity_scale: str = "light", A_bulk: int = None, Z_bulk self._v = xp.sqrt(self.kBT * 1000 * con.e / (con.mH * A_bulk)) # time (s) - self._t = self.x / self.v + self._t = float(self.x / self.v) # return if no bulk is present if A_bulk is None: diff --git a/src/struphy/pic/particles.py b/src/struphy/pic/particles.py index 21c2589d3..9a54b052a 100644 --- a/src/struphy/pic/particles.py +++ b/src/struphy/pic/particles.py @@ -1,6 +1,7 @@ import copy import cunumpy as xp +from cunumpy import PyccelKernel from struphy.fields_background import equils from struphy.fields_background.base import FluidEquilibrium, FluidEquilibriumWithB @@ -148,7 +149,7 @@ def save_constants_of_motion(self): ) # eval guiding center phase space - utilities_kernels.eval_guiding_center_from_6d( + PyccelKernel(utilities_kernels.eval_guiding_center_from_6d)( self.markers, self._derham.args_derham, self.domain.args_domain, @@ -190,7 +191,7 @@ def save_constants_of_motion(self): if self.mpi_comm is not None: self.mpi_sort_markers(alpha=1) - utilities_kernels.eval_canonical_toroidal_moment_6d( + PyccelKernel(utilities_kernels.eval_canonical_toroidal_moment_6d)( self.markers, self._derham.args_derham, self.first_diagnostics_idx, @@ -408,7 +409,7 @@ def s0(self, eta1, eta2, eta3, *v, flat_eval=False, remove_holes=True): def draw_markers(self, sort: bool = True): super().draw_markers(sort=sort) - utilities_kernels.eval_magnetic_moment_5d( + PyccelKernel(utilities_kernels.eval_magnetic_moment_5d)( self.markers, self.derham.args_derham, self.first_diagnostics_idx, @@ -434,7 +435,7 @@ def save_constants_of_motion(self): # idx and slice idx_can_momentum = self.first_diagnostics_idx + 2 - utilities_kernels.eval_energy_5d( + PyccelKernel(utilities_kernels.eval_energy_5d)( self.markers, self.derham.args_derham, self.first_diagnostics_idx, @@ -451,7 +452,7 @@ def save_constants_of_motion(self): self._epsilon = self.equation_params["epsilon"] - utilities_kernels.eval_canonical_toroidal_moment_5d( + PyccelKernel(utilities_kernels.eval_canonical_toroidal_moment_5d)( self.markers, self.derham.args_derham, self.first_diagnostics_idx, @@ -476,7 +477,7 @@ def save_magnetic_energy(self, PBb): PBbt = E0T.dot(PBb, out=self._tmp0) PBbt.update_ghost_regions() - utilities_kernels.eval_magnetic_energy_PBb( + PyccelKernel(utilities_kernels.eval_magnetic_energy_PBb)( self.markers, self.derham.args_derham, self.domain.args_domain, @@ -491,7 +492,7 @@ def save_magnetic_background_energy(self): The result is stored at markers[:, self.first_diagnostics_idx,]. """ - utilities_kernels.eval_magnetic_background_energy( + PyccelKernel(utilities_kernels.eval_magnetic_background_energy)( self.markers, self.derham.args_derham, self.domain.args_domain, @@ -504,7 +505,7 @@ def save_magnetic_moment(self): Calculate magnetic moment of each particles and assign it into markers[:,self.first_diagnostics_idx,+1]. """ - utilities_kernels.eval_magnetic_moment_5d( + PyccelKernel(utilities_kernels.eval_magnetic_moment_5d)( self.markers, self.derham.args_derham, self.first_diagnostics_idx, diff --git a/src/struphy/propagators/base.py b/src/struphy/propagators/base.py index 00e135ce8..2df234087 100644 --- a/src/struphy/propagators/base.py +++ b/src/struphy/propagators/base.py @@ -6,6 +6,7 @@ from typing import Literal import cunumpy as xp +from cunumpy import PyccelKernel from feectools.linalg.block import BlockVector from feectools.linalg.stencil import StencilVector @@ -270,7 +271,7 @@ def add_init_kernel( self._init_kernels += [ ( - kernel, + kernel if isinstance(kernel, PyccelKernel) else PyccelKernel(kernel), column_nr, comps, args_init, @@ -324,7 +325,7 @@ def add_eval_kernel( self._eval_kernels += [ ( - kernel, + kernel if isinstance(kernel, PyccelKernel) else PyccelKernel(kernel), alpha, column_nr, comps, diff --git a/src/struphy/propagators/current_coupling_5d_gradb.py b/src/struphy/propagators/current_coupling_5d_gradb.py index 9e119e71f..5b264504b 100644 --- a/src/struphy/propagators/current_coupling_5d_gradb.py +++ b/src/struphy/propagators/current_coupling_5d_gradb.py @@ -279,9 +279,9 @@ def allocate(self): # define Pusher if self.options.u_space == "Hdiv": - self._pusher_kernel = pusher_kernels_gc.push_gc_cc_J2_stage_Hdiv + self._pusher_kernel = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_stage_Hdiv) elif self.options.u_space == "H1vec": - self._pusher_kernel = pusher_kernels_gc.push_gc_cc_J2_stage_H1vec + self._pusher_kernel = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_stage_H1vec) else: raise ValueError( f'{self.options.u_space =} not valid, choose from "Hdiv" or "H1vec.', @@ -324,9 +324,9 @@ def allocate(self): self._u_temp = self.variables.u.spline.vector.space.zeros() # Call the accumulation and Pusher class - accum_kernel_init = accum_kernels_gc.cc_lin_mhd_5d_gradB_dg_init - accum_kernel = accum_kernels_gc.cc_lin_mhd_5d_gradB_dg - self._accum_kernel_en_fB_mid = utilities_kernels.eval_gradB_ediff + accum_kernel_init = PyccelKernel(accum_kernels_gc.cc_lin_mhd_5d_gradB_dg_init) + accum_kernel = PyccelKernel(accum_kernels_gc.cc_lin_mhd_5d_gradB_dg) + self._accum_kernel_en_fB_mid = PyccelKernel(utilities_kernels.eval_gradB_ediff) self._args_accum_kernel = ( epsilon, @@ -420,8 +420,8 @@ def allocate(self): self._u_temp[2]._data, ) - self._pusher_kernel_init = pusher_kernels_gc.push_gc_cc_J2_dg_init_Hdiv - self._pusher_kernel = pusher_kernels_gc.push_gc_cc_J2_dg_Hdiv + self._pusher_kernel_init = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_dg_init_Hdiv) + self._pusher_kernel = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_dg_Hdiv) def __call__(self, dt): # current FE coeffs From 7ac8ed631f7e8b103d5580e4c89e7ef3c624cacd Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 11 Aug 2026 11:26:41 +0200 Subject: [PATCH 005/156] Update scope-profiler --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index b2ed2b434..5fb80b43c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,7 @@ dependencies = [ "pytest-testmon<=2.2.0", "ruff==0.15.0, <=0.16.0", "line_profiler<=5.0.2", - "scope-profiler==0.2.6, <=0.2.6", + "scope-profiler==0.2.7, <=0.2.7", ] [project.license] From 2f08950d475d7089ccb06f47b3a59fa99a6c51a2 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 11 Aug 2026 11:34:10 +0200 Subject: [PATCH 006/156] Update for latest scope-profiler --- params_LinearMHDDriftkineticCC.py | 4 ++-- src/struphy/io/options.py | 1 + src/struphy/simulation/sim.py | 4 ++-- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/params_LinearMHDDriftkineticCC.py b/params_LinearMHDDriftkineticCC.py index ea26465a8..a14641572 100644 --- a/params_LinearMHDDriftkineticCC.py +++ b/params_LinearMHDDriftkineticCC.py @@ -71,7 +71,7 @@ # -------------------------- # Environment options -env = EnvironmentOptions() +env = EnvironmentOptions(profiling_activated=True, profiling_trace=True) # Time stepping time_opts = Time() @@ -161,4 +161,4 @@ model.energetic_ions.var.add_initial_condition(init) if __name__ == "__main__": - sim.run() \ No newline at end of file + sim.run() diff --git a/src/struphy/io/options.py b/src/struphy/io/options.py index a66d67c43..46d4a8701 100644 --- a/src/struphy/io/options.py +++ b/src/struphy/io/options.py @@ -341,6 +341,7 @@ class EnvironmentOptions(OptionsBase): num_clones: int = 1 profiling_activated: bool = False profiling_trace: bool = False + profiling_label: str | None = None def __post_init__(self): self.path_out: str = os.path.join(self.out_folders, self.sim_folder) diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index cf6215fed..5b56c6ccd 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -177,14 +177,14 @@ def __init__( def _setup_profiling(self): # setup profiling agent ProfileManager.setup( - profiling_activated=self.env.profiling_activated, - time_trace=self.env.profiling_trace, + deactivate_profiling=not self.env.profiling_activated, use_likwid=False, file_path=os.path.join( self.env.out_folders, self.env.sim_folder, "profiling_data.h5", ), + label=self.env.profiling_label, ) def show_parameters(self): From 87d282aa3bd679276fba046942bacdb524afaf94 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 12 Aug 2026 11:49:32 +0200 Subject: [PATCH 007/156] Added --backend flag --- params_LinearMHDDriftkineticCC.py | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/params_LinearMHDDriftkineticCC.py b/params_LinearMHDDriftkineticCC.py index a14641572..e7d670fe0 100644 --- a/params_LinearMHDDriftkineticCC.py +++ b/params_LinearMHDDriftkineticCC.py @@ -14,6 +14,21 @@ Users can modify this file to set up their own simulations with different parameters and initial conditions. """ +import argparse +import os + +parser = argparse.ArgumentParser(description=description) +parser.add_argument( + "--backend", + choices=("numpy", "cupy"), + default="numpy", + help="Array backend to run the simulation with (default: numpy).", +) +args = parser.parse_args() + +# Must be set before struphy (and therefore cunumpy) is imported. +os.environ["ARRAY_BACKEND"] = args.backend + import logging from struphy import set_logging_level set_logging_level(logging.WARNING) @@ -71,7 +86,11 @@ # -------------------------- # Environment options -env = EnvironmentOptions(profiling_activated=True, profiling_trace=True) +env = EnvironmentOptions( + sim_folder=f"sim_{args.backend}", + profiling_activated=True, + profiling_trace=True, +) # Time stepping time_opts = Time() @@ -83,7 +102,7 @@ equil = equils.HomogenSlab() # Grid -grid = grids.TensorProductGrid() +grid = grids.TensorProductGrid(num_elements = (16, 16, 16)) # Derham options derham_opts = DerhamOptions() From 71569f16c80607c6dc21c61c15db0be98c9e50d4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 12 Aug 2026 11:49:55 +0200 Subject: [PATCH 008/156] Added outputs to eval_spline_mpi_matrix --- feectools | 2 +- src/struphy/feec/psydac_derham.py | 8 +++++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/feectools b/feectools index 77c92a6e7..0f16476be 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 77c92a6e708f9e5c594d891bb12898c1a4fce045 +Subproject commit 0f16476be2255f290e0c9e275b06388310865e25 diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index 2372a3c58..2cbfe3151 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -33,9 +33,15 @@ # Pyccel kernels only understand NumPy arrays; wrap the ones called directly # in this module so they also work with CuPy arrays (see cunumpy.kernel). +# +# `outputs` names the arguments the kernel writes to. Without it every array +# that was copied to the host is copied back to the device afterwards, which on +# these kernels means shipping the spline coefficients and the knot vectors +# back on every single call even though only the result array changed. The +# indices refer to positional arguments, which is how they are called below. eval_3d.eval_spline_mpi_sparse_meshgrid = PyccelKernel(eval_3d.eval_spline_mpi_sparse_meshgrid) eval_3d.eval_spline_mpi_markers = PyccelKernel(eval_3d.eval_spline_mpi_markers) -eval_3d.eval_spline_mpi_matrix = PyccelKernel(eval_3d.eval_spline_mpi_matrix) +eval_3d.eval_spline_mpi_matrix = PyccelKernel(eval_3d.eval_spline_mpi_matrix, outputs=(10,)) eval_spline_mpi_tensor_product_fixed = PyccelKernel(eval_spline_mpi_tensor_product_fixed) from struphy.feec.linear_operators import BoundaryOperator from struphy.feec.local_projectors_kernels import get_local_problem_size, select_quasi_points From c9a7720e5efcd0bc3c9fff24e8318532d62483ec Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 12 Aug 2026 11:50:54 +0200 Subject: [PATCH 009/156] updated feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 0f16476be..70e445f15 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 0f16476be2255f290e0c9e275b06388310865e25 +Subproject commit 70e445f15c3fcd5e9d9981c51d9dce2e13394bff From 33012d0d1f8416a81742f67cd920626694620146 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 12 Aug 2026 12:15:37 +0200 Subject: [PATCH 010/156] Added params_PressureLessSPH.py --- params_PressureLessSPH.py | 168 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 168 insertions(+) create mode 100644 params_PressureLessSPH.py diff --git a/params_PressureLessSPH.py b/params_PressureLessSPH.py new file mode 100644 index 000000000..f46a88a82 --- /dev/null +++ b/params_PressureLessSPH.py @@ -0,0 +1,168 @@ +# ----------------------------- +# Description of the simulation +# ----------------------------- +# Please fill in a verbal description of the simulation. +# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. + +name = "Default PressureLessSPH" +description = """ +This is the default simulation for the model PressureLessSPH. +It is meant to be a template for users to set up their own simulations with this model. +It contains all the necessary components of a Struphy simulation, including the model, +the environment options, the time stepping options, the geometry, the equilibrium, +the grid, the Derham options, and the initial conditions. +Users can modify this file to set up their own simulations with different parameters and initial conditions. +""" + +import argparse +import os + +parser = argparse.ArgumentParser(description=description) +parser.add_argument( + "--backend", + choices=("numpy", "cupy"), + default="numpy", + help="Array backend to run the simulation with (default: numpy).", +) +args = parser.parse_args() + +# Must be set before struphy (and therefore cunumpy) is imported. +os.environ["ARRAY_BACKEND"] = args.backend + + +import logging +from struphy import set_logging_level +set_logging_level(logging.WARNING) + +# ------------------ +# Import Struphy API +# ------------------ + +from struphy import ( + BaseUnits, + DerhamOptions, + EnvironmentOptions, + FieldsBackground, + Simulation, + Time, + domains, + equils, + grids, + perturbations, +) + +# For particles: +from struphy import ( + BinningPlot, + BoundaryParameters, + KernelDensityPlot, + LoadingParameters, + WeightsParameters, + SortingParameters, + SavingParameters, + maxwellians, +) + +# --------------------- +# Instance of the model +# --------------------- + +from struphy.models import PressureLessSPH + +# Units +base_units = BaseUnits() + +# Model instance +model = PressureLessSPH(base_units=base_units) + +# List all variables and decide whether to save their data +model.cold_fluid.var.save_data = True + +# -------------------------- +# Instance of the simulation +# -------------------------- + +# Environment options +env = EnvironmentOptions( + sim_folder=f"sim_{args.backend}", + profiling_activated=True, + profiling_trace=True, +) + + +# Time stepping +# 10 steps: long enough to average out start-up effects when comparing the +# NumPy and CuPy backends, short enough to iterate on. +time_opts = Time(dt=0.01, Tend=0.1) + +# Geometry +domain = domains.Cuboid() + +# Fluid equilibrium (can be used as part of initial conditions) +equil = equils.HomogenSlab() + +# Grid +grid = grids.TensorProductGrid(num_elements=(32, 32, 16)) + +# Derham options +derham_opts = DerhamOptions() + +# Simulation object +sim = Simulation( + model=model, + name=name, + description=description, + params_path=__file__, + env=env, + time_opts=time_opts, + domain=domain, + equil=equil, + grid=grid, + derham_opts=derham_opts, +) + +# ------------------- +# Particle parameters +# ------------------- + +# Np and the grid above are sized for backend comparisons: big enough that the +# particle push dominates the run, small enough to fit comfortably on one GPU. +# The seed is fixed because marker loading is otherwise unseeded, and two runs +# of the *same* backend then differ enough to swamp any backend comparison. +loading_params = LoadingParameters(Np=200_000, seed=1234) +weights_params = WeightsParameters() +boundary_params = BoundaryParameters() +sorting_params = SortingParameters() +saving_params = SavingParameters() +model.cold_fluid.set_markers(loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, + ) + +# ------------------ +# Propagator options +# ------------------ + +model.propagators.push_eta.options = model.propagators.push_eta.Options() +phi = equil.p0 +model.propagators.push_v.phi = phi +model.propagators.push_v.options = model.propagators.push_v.Options() + +# ------------------ +# Initial conditions +# ------------------ +# Initial conditions are the sum of the background(s) and the perturbation(s). +# If backgrounds or perturbations are not specified, they are assumed to be zero. + +# Background for (some) sph variables +background = equils.ConstantVelocity() +model.cold_fluid.var.add_background(background) + +# Perturbations for (some) sph variables +perturbation = perturbations.TorusModesCos() +model.cold_fluid.var.add_perturbation(del_n=perturbation) + +if __name__ == "__main__": + sim.run() From fa2e8d4116ceeed2e1350704da3be3faf10498d6 Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 12 Aug 2026 13:48:59 +0200 Subject: [PATCH 011/156] Added profiling regions (mostly for setup) --- doc/sections/userguide.rst | 20 +++- src/struphy/simulation/sim.py | 214 +++++++++++++++++++--------------- 2 files changed, 138 insertions(+), 96 deletions(-) diff --git a/doc/sections/userguide.rst b/doc/sections/userguide.rst index 071b6914b..fc9595f5c 100644 --- a/doc/sections/userguide.rst +++ b/doc/sections/userguide.rst @@ -785,8 +785,24 @@ The relevant switches live in :class:`~struphy.EnvironmentOptions`: The profiler is set up automatically in ``Simulation.__init__()`` and finalized when ``Simulation.run()`` finishes. The simulation code already wraps key work -inside regions such as ``model.integrate`` via -``ProfileManager.profile_region(...)``. +inside regions via ``ProfileManager.profile_region(...)``. Since the profiler is +active from the end of ``Simulation.__init__()``, the setup phase is covered as +well, and the following regions are recorded out of the box: + +1. Setup: ``setup: allocate`` (total allocation time), with the nested regions + ``setup: feec`` (``setup: derham``, ``setup: mass ops``, ``setup: basis ops``, + ``setup: projected equil``), ``setup: variables`` (one + ``setup var: .`` region per model variable, so that e.g. + marker drawing shows up per particle species), ``setup: propagators`` (one + ``setup prop: `` region per propagator) and + ``setup: helpers``. +2. Remaining run preparation: ``setup: run metadata``, ``setup: data storage``, + ``setup: geometry vtk``, ``setup: plasma params``, + ``setup: initial diagnostics``, ``setup: hdf5 datasets`` and, for restarted + runs, ``setup: restart``. +3. Time loop: ``model.integrate`` (with the nested ``prop: `` + and ``kernel: `` regions), ``diagnostics``, ``save data`` and + ``sort particles``. Example configuration: diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index cf6215fed..435804458 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -239,17 +239,22 @@ def allocate(self): logger.debug("\nAllocating simulation data ...") - # feec - self._allocate_feec(self.grid, self.derham_opts) + with ProfileManager.profile_region("setup: allocate"): + # feec + with ProfileManager.profile_region("setup: feec"): + self._allocate_feec(self.grid, self.derham_opts) - # allocate model variables - self._allocate_variables() + # allocate model variables + with ProfileManager.profile_region("setup: variables"): + self._allocate_variables() - # pass info to propagators - self._allocate_propagators() + # pass info to propagators + with ProfileManager.profile_region("setup: propagators"): + self._allocate_propagators() - # allocate helper fields and perform initial solves if needed - self.model.allocate_helpers() + # allocate helper fields and perform initial solves if needed + with ProfileManager.profile_region("setup: helpers"): + self.model.allocate_helpers() logger.debug("... Done.") @@ -626,16 +631,20 @@ def run(self, one_time_step: bool = False): # equation paramters self.allocate() - self._write_run_metadata(one_time_step=one_time_step) + with ProfileManager.profile_region("setup: run metadata"): + self._write_run_metadata(one_time_step=one_time_step) # output - self.initialize_data_storage() + with ProfileManager.profile_region("setup: data storage"): + self.initialize_data_storage() # peek view into geometry - self.save_geometry_and_equil_vtk() + with ProfileManager.profile_region("setup: geometry vtk"): + self.save_geometry_and_equil_vtk() # plasma parameters - self.compute_plasma_params() + with ProfileManager.profile_region("setup: plasma params"): + self.compute_plasma_params() # print info on mpi procs if self.comm_size < 32: @@ -665,7 +674,8 @@ def run(self, one_time_step: bool = False): # set initial conditions for all variables if self.env.restart: - self._initialize_from_restart(self.data) + with ProfileManager.profile_region("setup: restart"): + self._initialize_from_restart(self.data) with h5py.File(self.data.file_path, "a") as file: self.time_state["value"][0] = file["restart/time/value"][-1] @@ -688,13 +698,15 @@ def run(self, one_time_step: bool = False): total_steps_str = str(total_steps) # compute initial scalars and kinetic data, pass time state to all propagators - self.model.update_scalar_quantities() - self.model.update_markers_to_be_saved() - self.model.update_distr_functions() - self._add_time_state(self.time_state["value"]) + with ProfileManager.profile_region("setup: initial diagnostics"): + self.model.update_scalar_quantities() + self.model.update_markers_to_be_saved() + self.model.update_distr_functions() + self._add_time_state(self.time_state["value"]) # add all variables to be saved to data object - save_keys_all, save_keys_end = self._initialize_hdf5_datasets(self.data, self.comm_size) + with ProfileManager.profile_region("setup: hdf5 datasets"): + save_keys_all, save_keys_end = self._initialize_hdf5_datasets(self.data, self.comm_size) # ======================== main time loop ====================== self.model.update_scalar_quantities() @@ -722,7 +734,8 @@ def run(self, one_time_step: bool = False): if break_cond_1 or break_cond_2: # save restart data (other data already saved below) - self.data.save_data(keys=save_keys_end) + with ProfileManager.profile_region("save data"): + self.data.save_data(keys=save_keys_end) end_time = time.time() logger.info(f"\nTime steps done: {int(self.time_state['index'][0])}") logger.info(f"wall-clock time of simulation [sec]: {end_time - self.start_time}") @@ -731,9 +744,10 @@ def run(self, one_time_step: bool = False): if self.env.sort_step and int(self.time_state["index"][0]) % self.env.sort_step == 0: t0 = time.time() - for key, val in self.model.pointer.items(): - if isinstance(val, Particles): - val.do_sort() + with ProfileManager.profile_region("sort particles"): + for key, val in self.model.pointer.items(): + if isinstance(val, Particles): + val.do_sort() t1 = time.time() message = "Particles sorted | wall clock [s]: {0:8.4f} | sorting duration [s]: {1:8.4f}".format( run_time_now * 60, @@ -760,22 +774,24 @@ def run(self, one_time_step: bool = False): # update diagnostics data and save data if int(self.time_state["index"][0]) % self.env.save_step == 0: # compute scalars and kinetic data - self.model.update_scalar_quantities() - self.model.update_markers_to_be_saved() - self.model.update_distr_functions() - - # extract FEEC coefficients - feec_species = self.model.field_species | self.model.fluid_species | self.model.diagnostic_species - for species, val in feec_species.items(): - assert isinstance(val, Species) - for variable, subval in val.variables.items(): - assert isinstance(subval, FEECVariable) - spline = subval.spline - # in-place extraction of FEM coefficients from field.vector --> field.vector_stencil! - spline.extract_coeffs(update_ghost_regions=False) + with ProfileManager.profile_region("diagnostics"): + self.model.update_scalar_quantities() + self.model.update_markers_to_be_saved() + self.model.update_distr_functions() + + # extract FEEC coefficients + feec_species = self.model.field_species | self.model.fluid_species | self.model.diagnostic_species + for species, val in feec_species.items(): + assert isinstance(val, Species) + for variable, subval in val.variables.items(): + assert isinstance(subval, FEECVariable) + spline = subval.spline + # in-place extraction of FEM coefficients from field.vector --> field.vector_stencil! + spline.extract_coeffs(update_ghost_regions=False) # save data (everything but restart data) - self.data.save_data(keys=save_keys_all) + with ProfileManager.profile_region("save data"): + self.data.save_data(keys=save_keys_all) # print current time and scalar quantities to screen step = str(int(self.time_state["index"][0])).zfill(len(total_steps_str)) @@ -1151,47 +1167,51 @@ def _allocate_feec(self, grid: grids.TensorProductGrid, derham_opts: DerhamOptio logger.debug(f"\n{grid=}, {derham_opts=}: no Derham object set up.") self._derham = None else: - self._derham = Derham( - grid, - derham_opts, - comm=derham_comm, - domain=self.domain, - ) + with ProfileManager.profile_region("setup: derham"): + self._derham = Derham( + grid, + derham_opts, + comm=derham_comm, + domain=self.domain, + ) # create weighted mass and basis operators if self.derham is None: self._mass_ops = None self._basis_ops = None else: - self._mass_ops = WeightedMassOperators(self.derham, self.domain, eq_mhd=self.equil) + with ProfileManager.profile_region("setup: mass ops"): + self._mass_ops = WeightedMassOperators(self.derham, self.domain, eq_mhd=self.equil) - self._basis_ops = BasisProjectionOperators( - self.derham, - self.domain, - eq_mhd=self.equil, - ) + with ProfileManager.profile_region("setup: basis ops"): + self._basis_ops = BasisProjectionOperators( + self.derham, + self.domain, + eq_mhd=self.equil, + ) # create projected equilibrium if self.derham is None: self._projected_equil = None else: - if isinstance(self.equil, MHDequilibrium): - self._projected_equil = ProjectedMHDequilibrium( - self.equil, - self.derham, - ) - elif isinstance(self.equil, FluidEquilibriumWithB): - self._projected_equil = ProjectedFluidEquilibriumWithB( - self.equil, - self.derham, - ) - elif isinstance(self.equil, FluidEquilibrium): - self._projected_equil = ProjectedFluidEquilibrium( - self.equil, - self.derham, - ) - else: - self._projected_equil = None + with ProfileManager.profile_region("setup: projected equil"): + if isinstance(self.equil, MHDequilibrium): + self._projected_equil = ProjectedMHDequilibrium( + self.equil, + self.derham, + ) + elif isinstance(self.equil, FluidEquilibriumWithB): + self._projected_equil = ProjectedFluidEquilibriumWithB( + self.equil, + self.derham, + ) + elif isinstance(self.equil, FluidEquilibrium): + self._projected_equil = ProjectedFluidEquilibrium( + self.equil, + self.derham, + ) + else: + self._projected_equil = None @profile def _allocate_variables(self): @@ -1204,11 +1224,12 @@ def _allocate_variables(self): assert isinstance(spec, FieldSpecies) for k, v in spec.variables.items(): assert isinstance(v, FEECVariable) - v.allocate( - derham=self.derham, - domain=self.domain, - equil=self.equil, - ) + with ProfileManager.profile_region(f"setup var: {species}.{k}"): + v.allocate( + derham=self.derham, + domain=self.domain, + equil=self.equil, + ) # allocate memory for FE coeffs of fluid variables if self.model.fluid_species: @@ -1216,11 +1237,12 @@ def _allocate_variables(self): assert isinstance(spec, FluidSpecies) for k, v in spec.variables.items(): assert isinstance(v, FEECVariable) - v.allocate( - derham=self.derham, - domain=self.domain, - equil=self.equil, - ) + with ProfileManager.profile_region(f"setup var: {species}.{k}"): + v.allocate( + derham=self.derham, + domain=self.domain, + equil=self.equil, + ) # allocate memory for marker arrays of kinetic variables if self.model.particle_species: @@ -1228,20 +1250,22 @@ def _allocate_variables(self): assert isinstance(spec, ParticleSpecies) for k, v in spec.variables.items(): if isinstance(v, PICVariable): - v.allocate( - clone_config=self.clone_config, - derham=self.derham, - domain=self.domain, - equil=self.equil, - projected_equil=self.projected_equil, - ) + with ProfileManager.profile_region(f"setup var: {species}.{k}"): + v.allocate( + clone_config=self.clone_config, + derham=self.derham, + domain=self.domain, + equil=self.equil, + projected_equil=self.projected_equil, + ) if isinstance(v, SPHVariable): - v.allocate( - derham=self.derham, - domain=self.domain, - equil=self.equil, - projected_equil=self.projected_equil, - ) + with ProfileManager.profile_region(f"setup var: {species}.{k}"): + v.allocate( + derham=self.derham, + domain=self.domain, + equil=self.equil, + projected_equil=self.projected_equil, + ) # allocate memory for FE coeffs of fluid variables if self.model.diagnostic_species: @@ -1249,11 +1273,12 @@ def _allocate_variables(self): assert isinstance(spec, DiagnosticSpecies) for k, v in spec.variables.items(): assert isinstance(v, FEECVariable) - v.allocate( - derham=self.derham, - domain=self.domain, - equil=self.equil, - ) + with ProfileManager.profile_region(f"setup var: {species}.{k}"): + v.allocate( + derham=self.derham, + domain=self.domain, + equil=self.equil, + ) # TODO: allocate memory for FE coeffs of diagnostics # if self.params.diagnostic_fields is not None: @@ -1291,7 +1316,8 @@ def _allocate_propagators(self): assert len(self.model.prop_list) > 0, "No propagators in this model, check the model class." for prop in self.model.prop_list: assert isinstance(prop, Propagator) - prop.allocate() + with ProfileManager.profile_region("setup prop: " + prop.__class__.__name__): + prop.allocate() logger.debug(f"\nAllocated propagator '{prop.__class__.__name__}'.") @profile From 7387ccdb14ebe31e0cc28ba284d298161b389afe Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 12 Aug 2026 13:54:38 +0200 Subject: [PATCH 012/156] Updated to scope-profiler 0.2.7 --- pyproject.toml | 2 +- src/struphy/io/options.py | 8 ++++---- src/struphy/simulation/sim.py | 4 ++-- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 11cab22a9..826de467d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,7 @@ dependencies = [ "pytest-testmon<=2.2.0", "ruff==0.15.0, <=0.16.0", "line_profiler<=5.0.2", - "scope-profiler==0.2.6, <=0.2.6", + "scope-profiler==0.2.7, <=0.2.7", ] [project.license] diff --git a/src/struphy/io/options.py b/src/struphy/io/options.py index bb849b2bf..84a7b7515 100644 --- a/src/struphy/io/options.py +++ b/src/struphy/io/options.py @@ -315,6 +315,9 @@ class EnvironmentOptions(OptionsBase): Folder in ``out_folders/`` for the current simulation (default= ``sim_1/`` ). Will create the folder if it does not exist OR cleans the folder for new runs. + sim_label: str | None, optional + Label for the simulation (default=None) + restart : bool Whether to restart a run (default=False). @@ -332,20 +335,17 @@ class EnvironmentOptions(OptionsBase): profiling_activated: bool, optional Activate profiling with scope-profiler (default=False) - - profiling_trace: bool, optional - Save time-trace of each profiling region (default=False) """ out_folders: str = os.getcwd() sim_folder: str = "sim_1" + sim_label: str | None = None restart: bool = False max_runtime: int = 300 save_step: int = 1 sort_step: int = 0 num_clones: int = 1 profiling_activated: bool = False - profiling_trace: bool = False def __post_init__(self): self.path_out: str = os.path.join(self.out_folders, self.sim_folder) diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index 435804458..2d6bcec1a 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -177,14 +177,14 @@ def __init__( def _setup_profiling(self): # setup profiling agent ProfileManager.setup( - profiling_activated=self.env.profiling_activated, - time_trace=self.env.profiling_trace, + deactivate_profiling=not self.env.profiling_activated, use_likwid=False, file_path=os.path.join( self.env.out_folders, self.env.sim_folder, "profiling_data.h5", ), + label=self.env.profiling_label, ) def show_parameters(self): From f456d6b293620dffaa151a4c1a0784585fe5a9e0 Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 12 Aug 2026 14:17:55 +0200 Subject: [PATCH 013/156] Added more profiling regions, in particular to propagators --- doc/sections/userguide.rst | 29 ++++- params_LinearMHDDriftkineticCC.py | 116 ------------------ pyproject.toml | 2 +- src/struphy/linear_algebra/saddle_point.py | 2 + src/struphy/linear_algebra/schur_solver.py | 4 + src/struphy/ode/solvers.py | 2 + .../pic/accumulation/particles_to_grid.py | 75 +++++++---- src/struphy/pic/base.py | 5 + src/struphy/pic/pushing/pusher.py | 56 ++++++--- src/struphy/propagators/adiabatic_phi.py | 4 +- src/struphy/propagators/base.py | 9 ++ src/struphy/propagators/curl_curl_solve.py | 4 +- .../current_coupling_5d_density.py | 4 +- .../current_coupling_6d_density.py | 4 +- src/struphy/propagators/hall.py | 4 +- src/struphy/propagators/implicit_diffusion.py | 4 +- src/struphy/propagators/jxb_cold.py | 4 +- .../variational_momentum_advection.py | 7 +- src/struphy/simulation/sim.py | 5 +- 19 files changed, 169 insertions(+), 171 deletions(-) delete mode 100644 params_LinearMHDDriftkineticCC.py diff --git a/doc/sections/userguide.rst b/doc/sections/userguide.rst index fc9595f5c..9acd935e9 100644 --- a/doc/sections/userguide.rst +++ b/doc/sections/userguide.rst @@ -800,10 +800,35 @@ well, and the following regions are recorded out of the box: ``setup: geometry vtk``, ``setup: plasma params``, ``setup: initial diagnostics``, ``setup: hdf5 datasets`` and, for restarted runs, ``setup: restart``. -3. Time loop: ``model.integrate`` (with the nested ``prop: `` - and ``kernel: `` regions), ``diagnostics``, ``save data`` and +3. Time loop: ``model.integrate``, ``diagnostics``, ``save data`` and ``sort particles``. +Inside ``model.integrate`` the regions nest as follows: + +1. ``prop: ``, one per propagator call (twice per step for the + half steps of Strang splitting). +2. Particle pushing: ``pusher: `` for a full + :class:`~struphy.pic.pushing.pusher.Pusher` call, containing one + ``kernel: `` region per pusher, init and eval kernel call. +3. Accumulation: ``accum: `` for a full + :class:`~struphy.pic.accumulation.particles_to_grid.Accumulator` call, + containing the ``kernel: `` region of the accumulation kernel + and ``accum comm: `` for the assembly/ghost-region exchange and + the inter-clone ``Allreduce``. +4. Particle bookkeeping and communication, recorded wherever they are called + from: ``mpi_sort_markers``, ``apply_kinetic_bc``, ``put_particles_in_boxes`` + and ``do_sort``. +5. Linear solves: ``solve: SchurSolver``, ``solve: SchurSolverFull``, + ``solve: SchurSolverFull3``, ``solve: SaddlePointSolver``, + ``solve: ODEsolverFEEC`` for the shared solver classes, and + ``solve: `` for propagators that call a + ``feectools`` inverse operator directly. +6. ``update_feec_variables`` for writing back FEEC coefficients (includes the + ghost-region update). + +Since regions nest, the sum over all regions exceeds the wall-clock time; use +the flame graph (below) to read the containment. + Example configuration: The quickest way to try this out is to generate a default parameter file for diff --git a/params_LinearMHDDriftkineticCC.py b/params_LinearMHDDriftkineticCC.py deleted file mode 100644 index f9f5ecd72..000000000 --- a/params_LinearMHDDriftkineticCC.py +++ /dev/null @@ -1,116 +0,0 @@ -# import model, set verbosity -from struphy.models.hybrid import LinearMHDDriftkineticCC - -from struphy import main -from struphy.fields_background import equils -from struphy.geometry import domains -from struphy.initial import perturbations -from struphy.io.options import BaseUnits, DerhamOptions, EnvironmentOptions, FieldsBackground, Time -from struphy.kinetic_background import maxwellians -from struphy.pic.utilities import ( - BinningPlot, - BoundaryParameters, - KernelDensityPlot, - LoadingParameters, - WeightsParameters, -) -from struphy.topology import grids - -# environment options -env = EnvironmentOptions() - -# units -base_units = BaseUnits() - -# time stepping -time_opts = Time() - -# geometry -domain = domains.Cuboid() - -# fluid equilibrium (can be used as part of initial conditions) -equil = equils.HomogenSlab() - -# grid -grid = grids.TensorProductGrid(Nel=(16, 16, 16)) - -# derham options -derham_opts = DerhamOptions() - -# light-weight model instance -model = LinearMHDDriftkineticCC() - -# species parameters -model.mhd.set_phys_params() -model.energetic_ions.set_phys_params() - -loading_params = LoadingParameters(ppc=1000) -weights_params = WeightsParameters() -boundary_params = BoundaryParameters() -model.energetic_ions.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, -) -model.energetic_ions.set_sorting_boxes() -model.energetic_ions.set_save_data() - -# propagator options -model.propagators.push_bxe.options = model.propagators.push_bxe.Options( - b_tilde=model.em_fields.b_field, -) -model.propagators.push_parallel.options = model.propagators.push_parallel.Options( - b_tilde=model.em_fields.b_field, -) -model.propagators.shearalfen_cc5d.options = model.propagators.shearalfen_cc5d.Options( - energetic_ions=model.energetic_ions.var, -) -model.propagators.magnetosonic.options = model.propagators.magnetosonic.Options( - b_field=model.em_fields.b_field, -) -model.propagators.cc5d_density.options = model.propagators.cc5d_density.Options( - energetic_ions=model.energetic_ions.var, - b_tilde=model.em_fields.b_field, -) -model.propagators.cc5d_gradb.options = model.propagators.cc5d_gradb.Options( - b_tilde=model.em_fields.b_field, -) -model.propagators.cc5d_curlb.options = model.propagators.cc5d_curlb.Options( - b_tilde=model.em_fields.b_field, -) - -# background, perturbations and initial conditions -model.mhd.velocity.add_background(FieldsBackground()) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=0)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=1)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=2)) -maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None), equil=equil) -maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None), equil=equil) -background = maxwellian_1 + maxwellian_2 -model.energetic_ions.var.add_background(background) - -# if .add_initial_condition is not called, the background is the kinetic initial condition -perturbation = perturbations.TorusModesCos() -maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation), equil=equil) -init = maxwellian_1pt + maxwellian_2 -model.energetic_ions.var.add_initial_condition(init) - -# optional: exclude variables from saving -# model.energetic_ions.var.save_data = False - -if __name__ == "__main__": - # start run - verbose = True - - main.run( - model, - params_path=__file__, - env=env, - base_units=base_units, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, - verbose=verbose, - ) diff --git a/pyproject.toml b/pyproject.toml index 826de467d..9a928bdf3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,7 @@ dependencies = [ "pytest-testmon<=2.2.0", "ruff==0.15.0, <=0.16.0", "line_profiler<=5.0.2", - "scope-profiler==0.2.7, <=0.2.7", + "scope-profiler<=0.2.8", ] [project.license] diff --git a/src/struphy/linear_algebra/saddle_point.py b/src/struphy/linear_algebra/saddle_point.py index 40430489e..2bdbc05bd 100644 --- a/src/struphy/linear_algebra/saddle_point.py +++ b/src/struphy/linear_algebra/saddle_point.py @@ -7,6 +7,7 @@ from feectools.linalg.block import BlockLinearOperator, BlockVector, BlockVectorSpace from feectools.linalg.direct_solvers import SparseSolver from feectools.linalg.solvers import inverse +from scope_profiler import ProfileManager from struphy.linear_algebra.tests.test_saddlepoint_massmatrices import _plot_residual_norms @@ -249,6 +250,7 @@ def Apre(self, a): elif self._variant == "Inverse_Solver": self._Apre = a + @ProfileManager.profile("solve: SaddlePointSolver") def __call__(self, U_init=None, Ue_init=None, P_init=None, out=None): """ Solves the saddle-point problem using the Uzawa algorithm. diff --git a/src/struphy/linear_algebra/schur_solver.py b/src/struphy/linear_algebra/schur_solver.py index 8789b6f08..36c0c7956 100644 --- a/src/struphy/linear_algebra/schur_solver.py +++ b/src/struphy/linear_algebra/schur_solver.py @@ -2,6 +2,7 @@ from feectools.linalg.block import BlockLinearOperator, BlockVector from feectools.linalg.solvers import inverse from line_profiler import profile +from scope_profiler import ProfileManager from struphy.linear_algebra.solver import SolverParameters @@ -108,6 +109,7 @@ def BC(self, bc): self._BC = bc @profile + @ProfileManager.profile("solve: SchurSolver") def __call__(self, xn, Byn, dt, out=None): """Solves the 2x2 block matrix linear system. @@ -228,6 +230,7 @@ def __init__(self, M, solver_name, **solver_params): self._rhs = self._A.codomain.zeros() @profile + @ProfileManager.profile("solve: SchurSolverFull") def dot(self, v, out=None): """Solves the 2x2 block matrix linear system. @@ -346,6 +349,7 @@ def __init__(self, M, solver_name, **solver_params): self._rhs2 = self._A.codomain.zeros() @profile + @ProfileManager.profile("solve: SchurSolverFull3") def dot(self, v, out=None): """Solves the 3x3 block matrix linear system. diff --git a/src/struphy/ode/solvers.py b/src/struphy/ode/solvers.py index 8dce21d41..ed89bd098 100644 --- a/src/struphy/ode/solvers.py +++ b/src/struphy/ode/solvers.py @@ -3,6 +3,7 @@ import cunumpy as xp from feectools.linalg.block import BlockVector from feectools.linalg.stencil import StencilVector +from scope_profiler import ProfileManager from struphy.ode.utils import ButcherTableau @@ -59,6 +60,7 @@ def __init__( self._yn = [v.copy() for v in self.y] self._ystar = [v.copy() for v in self.y] + @ProfileManager.profile("solve: ODEsolverFEEC") def __call__(self, tn, h): a = self.butcher.a b = self.butcher.b diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index 062f6056e..0deedc6d4 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -111,6 +111,10 @@ def __init__( self._derham = mass_ops.derham self._args_domain = args_domain + # profiling region names (precomputed, they are looked up on every call) + self._region_name = "accum: " + kernel.name + self._comm_region_name = "accum comm: " + kernel.name + self._symmetry = symmetry self._form = self.derham.space_to_form[space_id] @@ -210,6 +214,11 @@ def __call__(self, *optional_args, **args_control): args_control : any Keyword arguments for an analytical control variate correction in the accumulation step. Possible keywords are 'control_vec' for a vector correction or 'control_mat' for a matrix correction. Values are a 1d (vector) or 2d (matrix) list with callables or xp.ndarrays used for the correction. """ + with ProfileManager.profile_region(self._region_name): + self._accumulate(*optional_args, **args_control) + + def _accumulate(self, *optional_args, **args_control): + """Body of :meth:`__call__`, see there.""" # flags for break vec_finished = False @@ -232,8 +241,9 @@ def __call__(self, *optional_args, **args_control): # apply filter if self.accfilter.params.use_filter is not None: for vec in self._vectors: - vec.exchange_assembly_data() - vec.update_ghost_regions() + with ProfileManager.profile_region(self._comm_region_name): + vec.exchange_assembly_data() + vec.update_ghost_regions() self.accfilter(vec) vec_finished = True @@ -244,12 +254,13 @@ def __call__(self, *optional_args, **args_control): num_clones = self.particles.clone_config.num_clones if num_clones > 1: - for data_array in self._args_data: - self.particles.clone_config.inter_comm.Allreduce( - MPI.IN_PLACE, - data_array, - op=MPI.SUM, - ) + with ProfileManager.profile_region(self._comm_region_name): + for data_array in self._args_data: + self.particles.clone_config.inter_comm.Allreduce( + MPI.IN_PLACE, + data_array, + op=MPI.SUM, + ) # add analytical contribution (control variate) to vector if "control_vec" in args_control and len(self._vectors) > 0: @@ -270,15 +281,17 @@ def __call__(self, *optional_args, **args_control): # finish vector: accumulate ghost regions and update ghost regions if not vec_finished: - for vec in self._vectors: - vec.exchange_assembly_data() - vec.update_ghost_regions() + with ProfileManager.profile_region(self._comm_region_name): + for vec in self._vectors: + vec.exchange_assembly_data() + vec.update_ghost_regions() # finish matrix: accumulate ghost regions, update ghost regions and copy data for symmetric/antisymmetric block matrices if not mat_finished: - for op in self._operators: - op.matrix.exchange_assembly_data() - op.matrix.update_ghost_regions() + with ProfileManager.profile_region(self._comm_region_name): + for op in self._operators: + op.matrix.exchange_assembly_data() + op.matrix.update_ghost_regions() if self.symmetry == "symm": self._operators[0].matrix[0, 1].transpose( @@ -492,6 +505,10 @@ def __init__( self._derham = mass_ops.derham self._args_domain = args_domain + # profiling region names (precomputed, they are looked up on every call) + self._region_name = "accum: " + kernel.name + self._comm_region_name = "accum comm: " + kernel.name + self._form = self.derham.space_to_form[space_id] # initialize vectors @@ -558,6 +575,11 @@ def __call__(self, *optional_args, **args_control): Possible keywords are 'control_vec' for a vector correction or 'control_mat' for a matrix correction. Values are a 1d (vector) or 2d (matrix) list with callables or xp.ndarrays used for the correction. """ + with ProfileManager.profile_region(self._region_name): + self._accumulate(*optional_args, **args_control) + + def _accumulate(self, *optional_args, **args_control): + """Body of :meth:`__call__`, see there.""" # flags for break vec_finished = False @@ -579,8 +601,9 @@ def __call__(self, *optional_args, **args_control): # apply filter if self.accfilter.params.use_filter is not None: for vec in self._vectors: - vec.exchange_assembly_data() - vec.update_ghost_regions() + with ProfileManager.profile_region(self._comm_region_name): + vec.exchange_assembly_data() + vec.update_ghost_regions() self.accfilter(vec) vec_finished = True @@ -591,12 +614,13 @@ def __call__(self, *optional_args, **args_control): num_clones = self.particles.clone_config.num_clones if num_clones > 1: - for data_array in self._args_data: - self.particles.clone_config.inter_comm.Allreduce( - MPI.IN_PLACE, - data_array, - op=MPI.SUM, - ) + with ProfileManager.profile_region(self._comm_region_name): + for data_array in self._args_data: + self.particles.clone_config.inter_comm.Allreduce( + MPI.IN_PLACE, + data_array, + op=MPI.SUM, + ) # add analytical contribution (control variate) to vector if "control_vec" in args_control and len(self._vectors) > 0: @@ -609,9 +633,10 @@ def __call__(self, *optional_args, **args_control): # finish vector: accumulate ghost regions and update ghost regions if not vec_finished: - for vec in self._vectors: - vec.exchange_assembly_data() - vec.update_ghost_regions() + with ProfileManager.profile_region(self._comm_region_name): + for vec in self._vectors: + vec.exchange_assembly_data() + vec.update_ghost_regions() @property def particles(self): diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index e5f8110d0..ebcf50fb0 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -20,6 +20,7 @@ class Intracomm: from feectools.ddm.mpi import MockComm from feectools.ddm.mpi import mpi as MPI from line_profiler import profile +from scope_profiler import ProfileManager from sympy.ntheory import factorint from struphy.bsplines.bsplines import quadrature_grid @@ -1622,6 +1623,7 @@ def show_distribution_function(self, components, bin_edges): plt.show() @profile + @ProfileManager.profile("mpi_sort_markers") def mpi_sort_markers( self, apply_bc: bool = True, @@ -1703,6 +1705,7 @@ def mpi_sort_markers( self._Barrier() @profile + @ProfileManager.profile("apply_kinetic_bc") def apply_kinetic_bc(self, newton=False): """ Apply boundary conditions to markers that are outside of the logical unit cube. @@ -1810,6 +1813,7 @@ def set_velocities_comp(self, velocity, comp): self._markers[self.valid_mks, slice(3 + c, 3 + c + 1)] = new @profile + @ProfileManager.profile("put_particles_in_boxes") def put_particles_in_boxes(self): """Assign the right box to the particles and the list of the particles to each box. If sorting_boxes was instantiated with an MPI comm, then the particles in the @@ -1840,6 +1844,7 @@ def put_particles_in_boxes(self): # logger.info(f"Number of markers in box {i} is {n_mks_box}") @profile + @ProfileManager.profile("do_sort") def do_sort(self, use_numpy_argsort=False): """Assign the particles to their sorting boxes and reorder the markers array accordingly, so that markers in the same box occupy contiguous rows. diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 2ce1ff007..a224a5dcf 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -14,6 +14,11 @@ logger = logging.getLogger("struphy") +def _kernel_name(kernel) -> str: + """Name of a pyccelized kernel, which can be a bare pyccel function or a PyccelKernel.""" + return getattr(kernel, "name", None) or getattr(kernel, "__name__", type(kernel).__name__) + + class Pusher: r""" Class for solving particle ODEs @@ -153,6 +158,10 @@ def __init__( self._init_kernels = init_kernels self._eval_kernels = eval_kernels + # profiling region names (cached, they are looked up on every call) + self._region_name = "pusher: " + self.kernel.name + self._kernel_region_names = {} + self._residuals = xp.zeros(self.particles.markers.shape[0]) self._converged_loc = self._residuals == 1.0 self._not_converged_loc = self._residuals == 0.0 @@ -168,6 +177,19 @@ def __call__(self, dt: float): Applies the chosen pusher kernel by a time step dt, applies kinetic boundary conditions and performs MPI sorting. """ + with ProfileManager.profile_region(self._region_name): + self._push(dt) + + def _kernel_region(self, kernel) -> str: + """Cached name of the profiling region of an init/eval kernel.""" + name = self._kernel_region_names.get(id(kernel)) + if name is None: + name = "kernel: " + _kernel_name(kernel) + self._kernel_region_names[id(kernel)] = name + return name + + def _push(self, dt: float): + """Body of :meth:`__call__`, see there.""" # some idx and slice markers = self.particles.markers @@ -203,14 +225,15 @@ def __call__(self, dt: float): comps = ker_args[2] add_args = ker_args[3] - ker( - xp.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), - column_nr, - comps, - self.particles.args_markers, - self._args_domain, - *add_args, - ) + with ProfileManager.profile_region(self._kernel_region(ker)): + ker( + xp.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + column_nr, + comps, + self.particles.args_markers, + self._args_domain, + *add_args, + ) # update boxes if self._box_comm: @@ -250,14 +273,15 @@ def __call__(self, dt: float): ) # evaluate - ker( - alpha, - column_nr, - comps, - self.particles.args_markers, - self._args_domain, - *add_args, - ) + with ProfileManager.profile_region(self._kernel_region(ker)): + ker( + alpha, + column_nr, + comps, + self.particles.args_markers, + self._args_domain, + *add_args, + ) # update boxes if self._box_comm: diff --git a/src/struphy/propagators/adiabatic_phi.py b/src/struphy/propagators/adiabatic_phi.py index c0971ceea..9dbb28380 100644 --- a/src/struphy/propagators/adiabatic_phi.py +++ b/src/struphy/propagators/adiabatic_phi.py @@ -2,6 +2,7 @@ from feectools.linalg.solvers import inverse from feectools.linalg.stencil import StencilVector +from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.feec.mass import L2Projector, WeightedMassOperator, WeightedMassOperators @@ -183,7 +184,8 @@ def __call__(self, dt): self._rhs += self._rho # solve - out = self._solver.solve(self._rhs, out=self._tmp) + with ProfileManager.profile_region(self._solve_region): + out = self._solver.solve(self._rhs, out=self._tmp) info = self._solver._info if self._lin_solver["info"]: diff --git a/src/struphy/propagators/base.py b/src/struphy/propagators/base.py index 00e135ce8..b7a81272e 100644 --- a/src/struphy/propagators/base.py +++ b/src/struphy/propagators/base.py @@ -8,6 +8,7 @@ import cunumpy as xp from feectools.linalg.block import BlockVector from feectools.linalg.stencil import StencilVector +from scope_profiler import ProfileManager from struphy.feec.basis_projection_ops import BasisProjectionOperators from struphy.feec.mass import WeightedMassOperators @@ -96,12 +97,20 @@ def __call__(self, dt: float): Time step size. """ + @property + def _solve_region(self) -> str: + """Name of the profiling region for the linear solve(s) of this propagator.""" + if not hasattr(self, "_solve_region_name"): + self._solve_region_name = "solve: " + self.__class__.__name__ + return self._solve_region_name + def show_options(self): """Print the options of the propagator.""" logger.info(f"\nOptions for propagator '{self.__class__.__name__}':") for k, v in self.options.__dict__.items(): logger.info(f" {k + ':':<20}{v}") + @ProfileManager.profile("update_feec_variables") def update_feec_variables(self, **new_coeffs): r"""Return max_diff = max(abs(new - old)) for each new_coeffs, update feec coefficients and update ghost regions. diff --git a/src/struphy/propagators/curl_curl_solve.py b/src/struphy/propagators/curl_curl_solve.py index 48a7967fe..0e9773c61 100644 --- a/src/struphy/propagators/curl_curl_solve.py +++ b/src/struphy/propagators/curl_curl_solve.py @@ -8,6 +8,7 @@ from feectools.linalg.solvers import inverse from feectools.linalg.stencil import StencilVector from line_profiler import profile +from scope_profiler import ProfileManager from struphy.feec.mass import L2Projector, WeightedMassOperator from struphy.io.options import LiteralOptions @@ -324,7 +325,8 @@ def __call__(self, dt): self._solver.linop = self._diffusion_op - self._sigma * self._stab_mat # solve - out = self._solver.solve(self._rhs, out=self._tmp) + with ProfileManager.profile_region(self._solve_region): + out = self._solver.solve(self._rhs, out=self._tmp) info = self._solver._info if self._info: diff --git a/src/struphy/propagators/current_coupling_5d_density.py b/src/struphy/propagators/current_coupling_5d_density.py index 3f3c858d8..a17d0b452 100644 --- a/src/struphy/propagators/current_coupling_5d_density.py +++ b/src/struphy/propagators/current_coupling_5d_density.py @@ -5,6 +5,7 @@ from feectools.ddm.mpi import mpi as MPI from feectools.linalg.solvers import inverse from line_profiler import profile +from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.io.options import LiteralOptions, OptionsBase @@ -230,7 +231,8 @@ def __call__(self, dt): rhs = rhs.dot(un, out=self._rhs_v) self._A_inv.linop = lhs - _u = self._A_inv.solve(rhs, out=self._u_new) + with ProfileManager.profile_region(self._solve_region): + _u = self._A_inv.solve(rhs, out=self._u_new) info = self._A_inv._info diffs = self.update_feec_variables(u=_u) diff --git a/src/struphy/propagators/current_coupling_6d_density.py b/src/struphy/propagators/current_coupling_6d_density.py index 19ac34394..828625ff0 100644 --- a/src/struphy/propagators/current_coupling_6d_density.py +++ b/src/struphy/propagators/current_coupling_6d_density.py @@ -5,6 +5,7 @@ from feectools.ddm.mpi import mpi as MPI from feectools.linalg.solvers import inverse from line_profiler import profile +from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.io.options import LiteralOptions, OptionsBase @@ -286,7 +287,8 @@ def __call__(self, dt): rhs = rhs.dot(un, out=self._rhs_v) self._solver.linop = lhs - un1 = self._solver.solve(rhs, out=self._u_new) + with ProfileManager.profile_region(self._solve_region): + un1 = self._solver.solve(rhs, out=self._u_new) info = self._solver._info # write new coeffs into Propagator.variables diff --git a/src/struphy/propagators/hall.py b/src/struphy/propagators/hall.py index e3ef95fd3..03a59a719 100644 --- a/src/struphy/propagators/hall.py +++ b/src/struphy/propagators/hall.py @@ -4,6 +4,7 @@ from feectools.ddm.mpi import mpi as MPI from feectools.linalg.solvers import inverse from line_profiler import profile +from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.io.options import LiteralOptions, OptionsBase @@ -165,7 +166,8 @@ def __call__(self, dt): rhs = rhs.dot(bn, out=self._rhs_b) self._solver.linop = lhs - bn1 = self._solver.solve(rhs, out=self._b_new) + with ProfileManager.profile_region(self._solve_region): + bn1 = self._solver.solve(rhs, out=self._b_new) info = self._solver._info # write new coeffs into self.feec_vars diff --git a/src/struphy/propagators/implicit_diffusion.py b/src/struphy/propagators/implicit_diffusion.py index fb09cd349..f5a19b6ef 100644 --- a/src/struphy/propagators/implicit_diffusion.py +++ b/src/struphy/propagators/implicit_diffusion.py @@ -7,6 +7,7 @@ from feectools.linalg.solvers import inverse from feectools.linalg.stencil import StencilVector from line_profiler import profile +from scope_profiler import ProfileManager from struphy.feec.mass import L2Projector, WeightedMassOperator from struphy.io.options import LiteralOptions, OptionsBase @@ -461,7 +462,8 @@ def __call__(self, dt): self._solver.linop = sig_1 * self._stab_mat + self._diffusion_op # solve - out = self._solver.solve(rhs, out=self._tmp) + with ProfileManager.profile_region(self._solve_region): + out = self._solver.solve(rhs, out=self._tmp) info = self._solver._info if self._info: diff --git a/src/struphy/propagators/jxb_cold.py b/src/struphy/propagators/jxb_cold.py index 9b8308dec..da8034939 100644 --- a/src/struphy/propagators/jxb_cold.py +++ b/src/struphy/propagators/jxb_cold.py @@ -3,6 +3,7 @@ from feectools.linalg.solvers import inverse from line_profiler import profile +from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.io.options import LiteralOptions, OptionsBase @@ -144,7 +145,8 @@ def __call__(self, dt): self._solver.linop = lhs # solve linear system for updated j coefficients (in-place) - jn1 = self._solver.solve(rhsv, out=self._j_new) + with ProfileManager.profile_region(self._solve_region): + jn1 = self._solver.solve(rhsv, out=self._j_new) info = self._solver._info # write new coeffs into Propagator.variables diff --git a/src/struphy/propagators/variational_momentum_advection.py b/src/struphy/propagators/variational_momentum_advection.py index 7d77ea0f0..162e02810 100644 --- a/src/struphy/propagators/variational_momentum_advection.py +++ b/src/struphy/propagators/variational_momentum_advection.py @@ -5,6 +5,7 @@ from feectools.ddm.mpi import mpi as MPI from feectools.linalg.solvers import inverse from line_profiler import profile +from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.feec.preconditioner import MassMatrixDiagonalPreconditioner @@ -208,7 +209,8 @@ def __call_newton(self, dt): break # Newton step - pc_diff = self._Mrho_inv.dot(diff, out=self._tmp__pc_diff) + with ProfileManager.profile_region(self._solve_region): + pc_diff = self._Mrho_inv.dot(diff, out=self._tmp__pc_diff) update = self.inv_derivative.dot(pc_diff, out=self._tmp_update) if self._info: logger.info( @@ -262,7 +264,8 @@ def __call_picard(self, dt): mn1 -= advection # Inverse the mass matrix to get the velocity - un1 = self._Mrho_inv.dot(mn1, out=self._tmp_un1) + with ProfileManager.profile_region(self._solve_region): + un1 = self._Mrho_inv.dot(mn1, out=self._tmp_un1) if it == self.options.nonlin_solver.maxiter - 1 or xp.isnan(err): logger.info( diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index 2d6bcec1a..ec7e9a60a 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -184,7 +184,7 @@ def _setup_profiling(self): self.env.sim_folder, "profiling_data.h5", ), - label=self.env.profiling_label, + label=self.env.sim_label, ) def show_parameters(self): @@ -848,7 +848,8 @@ def run(self, one_time_step: bool = False): if self.clone_config is not None: self.clone_config.free() - ProfileManager.finalize() + ProfileManager.finalize(verbose=True) + print('done') def pproc( self, From 27167496a2fb80136b509230a17be4919cd366a2 Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 12 Aug 2026 15:21:30 +0200 Subject: [PATCH 014/156] Split summaries in different tables --- src/struphy/simulation/sim.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index ec7e9a60a..52bf2546a 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -848,8 +848,12 @@ def run(self, one_time_step: bool = False): if self.clone_config is not None: self.clone_config.free() - ProfileManager.finalize(verbose=True) - print('done') + # ProfileManager.finalize(verbose=True) + results = ProfileManager.finalize(return_results=True, verbose=False) + results.print_summary(include=r"^setup:", title="Setup", suppress_notes=True) + results.print_summary(include=[r"^model.integrate", r"^prop:"], title="Model propagation",suppress_notes=True) + results.print_summary(include=r"^pusher:", title="Pusher", suppress_notes=True) + results.print_summary(include=r"^kernel:", title="Kernel", suppress_notes=True) def pproc( self, From 7a680d9b12a797200806a07b785f59e49135cf49 Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 12 Aug 2026 15:25:37 +0200 Subject: [PATCH 015/156] print tables with a loop --- src/struphy/simulation/sim.py | 25 +++++++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index 52bf2546a..819ecfc94 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -850,10 +850,27 @@ def run(self, one_time_step: bool = False): # ProfileManager.finalize(verbose=True) results = ProfileManager.finalize(return_results=True, verbose=False) - results.print_summary(include=r"^setup:", title="Setup", suppress_notes=True) - results.print_summary(include=[r"^model.integrate", r"^prop:"], title="Model propagation",suppress_notes=True) - results.print_summary(include=r"^pusher:", title="Pusher", suppress_notes=True) - results.print_summary(include=r"^kernel:", title="Kernel", suppress_notes=True) + + # one table per region family; the last group catches everything not matched above, + # so that no recorded region is silently missing from the printed summary + groups = ( + ("Setup", [r"^setup:", r"^setup prop:", r"^setup var:"]), + ("Model propagation", [r"^model\.integrate", r"^prop:"]), + ("Pusher", [r"^pusher:"]), + ("Kernel", [r"^kernel:"]), + ("Accumulation", [r"^accum:", r"^accum comm:"]), + ("Linear solves", [r"^solve:"]), + ( + "Particle sorting and communication", + [r"^mpi_sort_markers$", r"^apply_kinetic_bc$", r"^put_particles_in_boxes$", r"^do_sort$"], + ), + ) + all_patterns = [pattern for _, include in groups for pattern in include] + for title, include in groups + (("Other", None),): + kwargs = {"include": include} if include is not None else {"exclude": all_patterns} + if not results.get_regions(**kwargs): + continue + results.print_summary(title=title, suppress_notes=True, **kwargs) def pproc( self, From 1e7dad463456e4110cb7be407ded241aec531902 Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 12 Aug 2026 16:14:01 +0200 Subject: [PATCH 016/156] Removed profiling_trace parameter --- doc/generate_profiling_figures.sh | 3 +-- doc/sections/userguide.rst | 2 -- .../examples/Poisson/cube_strong_scaling/params_poisson.py | 1 - .../ToyGyrokinetic/diocotron_instability/params_diocotron.py | 1 - src/struphy/models/tests/utils_testing.py | 1 - 5 files changed, 1 insertion(+), 7 deletions(-) diff --git a/doc/generate_profiling_figures.sh b/doc/generate_profiling_figures.sh index cd0beb0b8..e80524794 100755 --- a/doc/generate_profiling_figures.sh +++ b/doc/generate_profiling_figures.sh @@ -30,8 +30,7 @@ if needle not in content: content = content.replace( needle, "env = EnvironmentOptions(\n" - " profiling_activated=True,\n" - " profiling_trace=True,\n" + " deactivate_profiling=False,\n" ")", ) open(path, "w").write(content) diff --git a/doc/sections/userguide.rst b/doc/sections/userguide.rst index 9acd935e9..27d2b20bd 100644 --- a/doc/sections/userguide.rst +++ b/doc/sections/userguide.rst @@ -780,8 +780,6 @@ Struphy's simulation-wide profiler is configured in The relevant switches live in :class:`~struphy.EnvironmentOptions`: 1. ``profiling_activated=True`` enables profiling data collection. -2. ``profiling_trace=True`` additionally records a time trace of profiling - regions. The profiler is set up automatically in ``Simulation.__init__()`` and finalized when ``Simulation.run()`` finishes. The simulation code already wraps key work diff --git a/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py b/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py index b479bcd67..6f609e3dd 100644 --- a/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py +++ b/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py @@ -65,7 +65,6 @@ env = EnvironmentOptions( sim_folder=f"sim_{args.id:02d}", profiling_activated=True, - profiling_trace=True, restart=False, ) diff --git a/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py b/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py index f7624f1a4..1ab145ebc 100644 --- a/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py +++ b/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py @@ -85,7 +85,6 @@ env = EnvironmentOptions( sim_folder=f"sim_{args.id:02d}", profiling_activated=True, - profiling_trace=True, restart=False ) diff --git a/src/struphy/models/tests/utils_testing.py b/src/struphy/models/tests/utils_testing.py index 4d205e650..f9ea3fa89 100644 --- a/src/struphy/models/tests/utils_testing.py +++ b/src/struphy/models/tests/utils_testing.py @@ -55,7 +55,6 @@ def call_test(model: StruphyModel, test_profiling: bool = False): out_folders=test_folder, sim_folder=f"{model_name}", profiling_activated=test_profiling, - profiling_trace=test_profiling, ) # read parameters From 8d7eb6a39f6ef000101f3142074bb681504ffea9 Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 12 Aug 2026 16:35:34 +0200 Subject: [PATCH 017/156] Removed profiling_trace from the examples --- .../cyclone/params_cyclone.py | 2 +- .../itg_cylindre/params_drift_kinetic.py | 2 +- .../ToyGyrokinetic/diocotron_instability/params_diocotron.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py b/examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py index cc10abe4d..c087731bc 100644 --- a/examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py +++ b/examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py @@ -76,7 +76,7 @@ # -------------------------- # Environment options -env = EnvironmentOptions(sim_folder="sim_1",profiling_activated=True, profiling_trace=True, restart=False) +env = EnvironmentOptions(sim_folder="sim_1",profiling_activated=True, restart=False) # Time stepping time_opts = Time(dt=0.001, Tend=0.01, split_algo="LieTrotter") diff --git a/examples/DriftKineticElectrostaticAdiabatic/itg_cylindre/params_drift_kinetic.py b/examples/DriftKineticElectrostaticAdiabatic/itg_cylindre/params_drift_kinetic.py index c3ecb2368..0ad798b95 100644 --- a/examples/DriftKineticElectrostaticAdiabatic/itg_cylindre/params_drift_kinetic.py +++ b/examples/DriftKineticElectrostaticAdiabatic/itg_cylindre/params_drift_kinetic.py @@ -78,7 +78,7 @@ # -------------------------- # Environment options -env = EnvironmentOptions(sim_folder="sim_1", profiling_activated=True, profiling_trace=True, restart=False) +env = EnvironmentOptions(sim_folder="sim_1", profiling_activated=True, restart=False) # Time stepping time_opts = Time(dt=5.0, Tend=500.0, split_algo="LieTrotter") diff --git a/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py b/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py index 39f9e4819..1ae5f7bc0 100644 --- a/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py +++ b/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py @@ -74,7 +74,7 @@ # -------------------------- # Environment options -env = EnvironmentOptions(sim_folder="sim_1", profiling_activated=True, profiling_trace=True, restart=False) +env = EnvironmentOptions(sim_folder="sim_1", profiling_activated=True, restart=False) # Time stepping time_opts = Time(dt=0.01, Tend=51.0, split_algo="LieTrotter") From 76944c1c38ebd56307af876e30ed43e957eb88d9 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 12 Aug 2026 17:22:33 +0200 Subject: [PATCH 018/156] temporary: fix all cupy tests --- src/struphy/models/base.py | 8 +- src/struphy/models/variables.py | 10 +- src/struphy/pic/base.py | 633 ++++++++++--------- src/struphy/pic/pushing/pusher.py | 16 +- src/struphy/pic/tests/test_accum_vec_H1.py | 16 +- src/struphy/pic/tests/test_binning.py | 42 ++ src/struphy/pic/tests/test_draw_parallel.py | 20 +- src/struphy/pic/tests/test_mat_vec_filler.py | 123 ++-- src/struphy/pic/tests/test_sph.py | 67 +- src/struphy/pic/tests/test_tesselation.py | 13 +- src/struphy/propagators/base.py | 14 +- 11 files changed, 577 insertions(+), 385 deletions(-) diff --git a/src/struphy/models/base.py b/src/struphy/models/base.py index 92409306a..e2b6fd235 100644 --- a/src/struphy/models/base.py +++ b/src/struphy/models/base.py @@ -4,6 +4,7 @@ from textwrap import indent import cunumpy as xp +import numpy as np from feectools.ddm.mpi import MockMPI from feectools.ddm.mpi import mpi as MPI @@ -424,11 +425,14 @@ def update_markers_to_be_saved(self): assert isinstance(obj, Particles) if var.n_to_save > 0: - markers_on_proc = xp.logical_and( + # obj.markers/var.saved_markers are always host-resident (no + # device particle kernel exists), unlike the general xp used + # elsewhere in this module. + markers_on_proc = np.logical_and( obj.markers[:, -1] >= 0.0, obj.markers[:, -1] < var.n_to_save, ) - n_markers_on_proc = xp.count_nonzero(markers_on_proc) + n_markers_on_proc = np.count_nonzero(markers_on_proc) var.saved_markers[:] = -1.0 var.saved_markers[:n_markers_on_proc] = obj.markers[markers_on_proc] diff --git a/src/struphy/models/variables.py b/src/struphy/models/variables.py index 22bdde10c..db7ce544f 100644 --- a/src/struphy/models/variables.py +++ b/src/struphy/models/variables.py @@ -6,7 +6,7 @@ from abc import ABCMeta, abstractmethod from typing import TYPE_CHECKING -import cunumpy as xp +import numpy as np from feectools.ddm.mpi import mpi as MPI from struphy.feec.linear_operators import BoundaryOperator @@ -618,7 +618,7 @@ def allocate( f"The number of markers for which data should be stored (={self._n_to_save}) must be <= than the total number of markers (={self.particles.Np})" ) if self._n_to_save > 0: - self._saved_markers = xp.zeros( + self._saved_markers = np.zeros( (self._n_to_save, self.particles.markers.shape[1]), dtype=float, ) @@ -699,7 +699,7 @@ def n_to_save(self) -> int: return self._n_to_save @property - def saved_markers(self) -> xp.ndarray: + def saved_markers(self) -> np.ndarray: return self._saved_markers @@ -911,7 +911,7 @@ def allocate( f"The number of markers for which data should be stored (={self._n_to_save}) must be <= than the total number of markers (={self.particles.Np})" ) if self._n_to_save > 0: - self._saved_markers = xp.zeros( + self._saved_markers = np.zeros( (self._n_to_save, self.particles.markers.shape[1]), dtype=float, ) @@ -988,5 +988,5 @@ def n_to_save(self) -> int: return self._n_to_save @property - def saved_markers(self) -> xp.ndarray: + def saved_markers(self) -> np.ndarray: return self._saved_markers diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index d97aec427..8ee474186 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -15,8 +15,9 @@ class Intracomm: x = None -import cunumpy as xp +import numpy as np from cunumpy import PyccelKernel +from cunumpy import to_cunumpy from feectools.ddm.mpi import MockComm from feectools.ddm.mpi import mpi as MPI from line_profiler import profile @@ -76,6 +77,14 @@ def _to_numpy_for_kernel(value): return value +def _dev(*arrays): + """Convert host (marker) coordinate arrays to the active array backend, + for feeding into equilibrium/domain/perturbation functions that follow + the global backend rather than the (always host-resident) markers.""" + out = tuple(to_cunumpy(a) for a in arrays) + return out[0] if len(out) == 1 else out + + class Particles(metaclass=ABCMeta): """Base class for particle species.""" @@ -250,11 +259,15 @@ def __init__( if domain_decomp is None: self._domain_array, self._nprocs = self._get_domain_decomp(self.sorting_params.dims_mask) else: - self._domain_array = domain_decomp[0] + # domain_decomp[0] may come from a Derham grid living on the device + # (ARRAY_BACKEND=cupy); everything below operates on markers, which + # are always host arrays (there is no device particle kernel), so + # domain_array is brought to the host once here. + self._domain_array = _to_numpy_for_kernel(domain_decomp[0]) self._nprocs = domain_decomp[1] # total number of cells (equal to mpi_size if no grid) - n_cells = xp.sum(xp.prod(self.domain_array[:, 2::3], axis=1, dtype=int)) * self.num_clones + n_cells = np.sum(np.prod(self.domain_array[:, 2::3], axis=1, dtype=int)) * self.num_clones # total number of boxes if self.boxes_per_dim is None: @@ -266,7 +279,7 @@ def __init__( assert all([nboxes % nproc == 0 for nboxes, nproc in zip(self.boxes_per_dim, self.nprocs)]), ( f"Number of boxes {self.boxes_per_dim =} must be divisible by number of processes {self.nprocs =} in each direction." ) - n_boxes = xp.prod(xp.array(self.boxes_per_dim), dtype=int) * self.num_clones + n_boxes = np.prod(np.array(self.boxes_per_dim), dtype=int) * self.num_clones # total number of markers (Np) and particles per cell (ppc) Np = self.loading_params.Np @@ -381,9 +394,9 @@ def __init__( self._generate_sampling_moments() # create buffers for mpi_sort_markers - self._sorting_etas = xp.zeros((self.markers.shape[0], 3), dtype=float) - self._is_on_proc_domain = xp.zeros((self.markers.shape[0], 3), dtype=bool) - self._can_stay = xp.zeros(self.markers.shape[0], dtype=bool) + self._sorting_etas = np.zeros((self.markers.shape[0], 3), dtype=float) + self._is_on_proc_domain = np.zeros((self.markers.shape[0], 3), dtype=bool) + self._can_stay = np.zeros(self.markers.shape[0], dtype=bool) self._reqs = [None] * self.mpi_size self._recvbufs = [None] * self.mpi_size self._send_to_i = [None] * self.mpi_size @@ -802,17 +815,17 @@ def index(self): def valid_mks(self): """Array of booleans stating if an entry in the markers array is a true local particle (not a hole or ghost).""" if not hasattr(self, "_valid_mks"): - self._valid_mks = ~xp.logical_or(self.holes, self.ghost_particles) + self._valid_mks = ~np.logical_or(self.holes, self.ghost_particles) return self._valid_mks def update_valid_mks(self): - self._valid_mks[:] = ~xp.logical_or(self.holes, self.ghost_particles) + self._valid_mks[:] = ~np.logical_or(self.holes, self.ghost_particles) @property def n_mks_loc(self): """Number of valid markers on process (without holes and ghosts).""" - # print(f"{self.kinds} on clone {self.clone_id}: counting valid markers: {xp.count_nonzero(self.valid_mks)} valid markers on process {self.mpi_rank} found.") - return xp.count_nonzero(self.valid_mks) + # print(f"{self.kinds} on clone {self.clone_id}: counting valid markers: {np.count_nonzero(self.valid_mks)} valid markers on process {self.mpi_rank} found.") + return np.count_nonzero(self.valid_mks) @property def n_mks_on_each_proc(self): @@ -822,7 +835,7 @@ def n_mks_on_each_proc(self): @property def n_mks_on_clone(self): """Number of valid markers on current clone (without holes and ghosts).""" - return xp.sum(self.n_mks_on_each_proc) + return np.sum(self.n_mks_on_each_proc) @property def n_mks_on_each_clone(self): @@ -832,7 +845,7 @@ def n_mks_on_each_clone(self): @property def n_mks_global(self): """Number of valid markers on current clone (without holes and ghosts).""" - return xp.sum(self.n_mks_on_each_clone) + return np.sum(self.n_mks_on_each_clone) @property def positions(self): @@ -841,7 +854,7 @@ def positions(self): @positions.setter def positions(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc, 3) self._markers[self.valid_mks, self.index["pos"]] = new @@ -852,12 +865,12 @@ def velocities(self): @velocities.setter def velocities(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc, self.vdim), f"{self.n_mks_loc =} and {self.vdim =} but {new.shape =}" self._markers[self.valid_mks, self.index["vel"]] = new def set_velocities_comp(self, velocity, comp): - new = xp.ones(shape=(self.velocities.shape[0], 1)) * velocity + new = np.ones(shape=(self.velocities.shape[0], 1)) * velocity for c in comp: self._markers[self.valid_mks, slice(3 + c, 3 + c + 1)] = new @@ -869,7 +882,7 @@ def phasespace_coords(self): @phasespace_coords.setter def phasespace_coords(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc, 3 + self.vdim) self._markers[self.valid_mks, self.index["coords"]] = new @@ -880,7 +893,7 @@ def weights(self): @weights.setter def weights(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["weights"]] = new @@ -896,7 +909,7 @@ def sampling_density(self): @sampling_density.setter def sampling_density(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["s0"]] = new @@ -907,7 +920,7 @@ def weights0(self): @weights0.setter def weights0(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["w0"]] = new @@ -918,7 +931,7 @@ def marker_ids(self): @marker_ids.setter def marker_ids(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["ids"]] = new @@ -949,7 +962,7 @@ def f_coords(self): @f_coords.setter def f_coords(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) self.markers[self.valid_mks, self.f_coords_index] = new @property @@ -961,16 +974,16 @@ def args_markers(self): def f_jacobian_coords(self): """Coordinates of the velocity jacobian determinant of the distribution fuction.""" if isinstance(self.f_jacobian_coords_index, list): - return self.markers[xp.ix_(~self.holes, self.f_jacobian_coords_index)] + return self.markers[np.ix_(~self.holes, self.f_jacobian_coords_index)] else: return self.markers[~self.holes, self.f_jacobian_coords_index] @f_jacobian_coords.setter def f_jacobian_coords(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) if isinstance(self.f_jacobian_coords_index, list): self.markers[ - xp.ix_( + np.ix_( ~self.holes, self.f_jacobian_coords_index, ) @@ -1017,7 +1030,7 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): Returns ------- - dom_arr : xp.ndarray + dom_arr : np.ndarray A 2d array of shape (#MPI processes, 9). The row index denotes the process rank. The columns are for n=0,1,2: - arr[i, 3*n + 0] holds the LEFT domain boundary of process i in direction eta_(n+1). - arr[i, 3*n + 1] holds the RIGHT domain boundary of process i in direction eta_(n+1). @@ -1029,7 +1042,7 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): if mpi_dims_mask is None: mpi_dims_mask = [True, True, True] - dom_arr = xp.zeros((self.mpi_size, 9), dtype=float) + dom_arr = np.zeros((self.mpi_size, 9), dtype=float) # factorize mpi size factors = factorint(self.mpi_size) @@ -1053,10 +1066,10 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): mm = (mm + 1) % 3 nprocs[mm] *= fac - assert xp.prod(nprocs) == self.mpi_size + assert np.prod(nprocs) == self.mpi_size # domain decomposition - breaks = [xp.linspace(0.0, 1.0, nproc + 1) for nproc in nprocs] + breaks = [np.linspace(0.0, 1.0, nproc + 1) for nproc in nprocs] # fill domain array for n in range(self.mpi_size): @@ -1117,14 +1130,14 @@ def _n_mks_load_and_Np_per_clone(self): """Return two arrays: 1) an array of sub_comm.size where the i-th entry corresponds to the number of markers drawn on process i, and 2) an array of size num_clones where the i-th entry corresponds to the number of markers on clone i.""" # number of cells on current process - n_cells_loc = xp.prod( + n_cells_loc = np.prod( self.domain_array[self.mpi_rank, 2::3], dtype=int, ) # array of number of markers on each process at loading stage if self.clone_config is not None: - _n_cells_clone = xp.sum(xp.prod(self.domain_array[:, 2::3], axis=1, dtype=int)) + _n_cells_clone = np.sum(np.prod(self.domain_array[:, 2::3], axis=1, dtype=int)) _n_mks_load_tot = self.clone_config.get_Np_clone(self.Np) _ppc = _n_mks_load_tot / _n_cells_clone else: @@ -1134,14 +1147,14 @@ def _n_mks_load_and_Np_per_clone(self): n_mks_load = self._gather_scalar_in_subcomm_array(int(_ppc * n_cells_loc)) # add deviation from Np to rank 0 - n_mks_load[0] += _n_mks_load_tot - xp.sum(n_mks_load) + n_mks_load[0] += _n_mks_load_tot - np.sum(n_mks_load) # check if all markers are there - assert xp.sum(n_mks_load) == _n_mks_load_tot + assert np.sum(n_mks_load) == _n_mks_load_tot # Np on each clone Np_per_clone = self._gather_scalar_in_intercomm_array(_n_mks_load_tot) - assert xp.sum(Np_per_clone) == self.Np + assert np.sum(Np_per_clone) == self.Np return n_mks_load, Np_per_clone @@ -1155,7 +1168,7 @@ def _allocate_marker_array(self, dry_run: bool = False): # number of markers on the local process at loading stage n_mks_load_loc = self.n_mks_load[self._mpi_rank] - bufsize = self.bufsize + 1.0 / xp.sqrt(n_mks_load_loc) + bufsize = self.bufsize + 1.0 / np.sqrt(n_mks_load_loc) # allocate markers array (3 x positions, vdim x velocities, weight, s0, w0, ..., ID) with buffer self._n_rows = round(float(n_mks_load_loc * (1 + bufsize))) @@ -1168,19 +1181,19 @@ def _allocate_marker_array(self, dry_run: bool = False): if dry_run: return - self._markers = xp.zeros((self.n_rows, self.n_cols), dtype=float) + self._markers = np.zeros((self.n_rows, self.n_cols), dtype=float) # allocate auxiliary arrays - self._holes = xp.zeros(self.n_rows, dtype=bool) - self._ghost_particles = xp.zeros(self.n_rows, dtype=bool) - self._valid_mks = xp.zeros(self.n_rows, dtype=bool) - self._is_outside_right = xp.zeros(self.n_rows, dtype=bool) - self._is_outside_left = xp.zeros(self.n_rows, dtype=bool) - self._is_outside = xp.zeros(self.n_rows, dtype=bool) + self._holes = np.zeros(self.n_rows, dtype=bool) + self._ghost_particles = np.zeros(self.n_rows, dtype=bool) + self._valid_mks = np.zeros(self.n_rows, dtype=bool) + self._is_outside_right = np.zeros(self.n_rows, dtype=bool) + self._is_outside_left = np.zeros(self.n_rows, dtype=bool) + self._is_outside = np.zeros(self.n_rows, dtype=bool) # create array container (3 x positions, vdim x velocities, weight, s0, w0, ID) for removed markers self._n_lost_markers = 0 - self._lost_markers = xp.zeros((int(self.n_rows * 0.5), 10), dtype=float) + self._lost_markers = np.zeros((int(self.n_rows * 0.5), 10), dtype=float) # arguments for kernels self._args_markers = MarkerArguments( @@ -1289,16 +1302,16 @@ def _generate_sampling_moments(self): # assert len(ns) == len(us) == len(vths) - # ns = xp.array(ns) - # us = xp.array(us) - # vths = xp.array(vths) + # ns = np.array(ns) + # us = np.array(us) + # vths = np.array(vths) # Use the mean of shifts and thermal velocity such that outermost shift+thermal is # new shift + new thermal - # mean_us = xp.mean(us, axis=0) - # us_ext = us + vths * xp.where(us >= 0, 1, -1) + # mean_us = np.mean(us, axis=0) + # us_ext = us + vths * np.where(us >= 0, 1, -1) # us_ext_dist = us_ext - mean_us[None, :] - # new_vths = xp.max(xp.abs(us_ext_dist), axis=0) + # new_vths = np.max(np.abs(us_ext_dist), axis=0) # new_moments = [] @@ -1353,6 +1366,11 @@ def _set_initial_condition(self): # TODO: add other velocity components def _f_init(*etas, flat_eval=False): + # self.f0/_density evaluate on the device (equilibrium and + # perturbation functions follow the global array backend), + # while markers are always host-resident; convert at this + # marker/field evaluation boundary and convert the result back. + etas = tuple(to_cunumpy(eta) for eta in etas) if len(etas) == 1: if _density is None: out = self.f0.n0(etas[0]) @@ -1377,10 +1395,16 @@ def _f_init(*etas, flat_eval=False): out = out0 + out1 if flat_eval: - out = xp.squeeze(out) + out = np.squeeze(out) + # Returned in the same (active) backend as the converted + # `etas` above -- callers that need markers/host data convert + # explicitly (see _to_numpy_for_kernel at call sites), since + # this closure is also reused as a field function fed back + # into domain/grid machinery that expects the active backend. return out def _u_init(*etas, flat_eval=False): + etas = tuple(to_cunumpy(eta) for eta in etas) if len(etas) == 1: if _u1 is None: out = self.f0.uv(etas[0]) @@ -1405,7 +1429,12 @@ def _u_init(*etas, flat_eval=False): out = out0 + out1 if flat_eval: - out = xp.squeeze(out) + out = np.squeeze(out) + # Returned in the same (active) backend as the converted + # `etas` above -- callers that need markers/host data convert + # explicitly (see _to_numpy_for_kernel at call sites), since + # this closure is also reused as a field function fed back + # into domain/grid machinery that expects the active backend. return out self._f_init = _f_init @@ -1414,7 +1443,7 @@ def _u_init(*etas, flat_eval=False): def _load_external( self, n_mks_load_loc: int, - n_mks_load_cum_sum: xp.ndarray, + n_mks_load_cum_sum: np.ndarray, ): """Load markers from external .hdf5 file. @@ -1423,7 +1452,7 @@ def _load_external( n_mks_load_loc: int Number of markers on the local process at loading stage. - n_mks_load_cum_sum: xp.ndarray + n_mks_load_cum_sum: np.ndarray Cumulative sum of number of markers on each process at loading stage. """ if self.mpi_rank == 0: @@ -1442,7 +1471,7 @@ def _load_external( tag=123, ) else: - recvbuf = xp.zeros( + recvbuf = np.zeros( (n_mks_load_loc, self.markers.shape[1]), dtype=float, ) @@ -1585,8 +1614,8 @@ def draw_markers( self.update_ghost_particles() # cumulative sum of number of markers on each process at loading stage. - n_mks_load_cum_sum = xp.cumsum(self.n_mks_load) - Np_per_clone_cum_sum = xp.cumsum(self.Np_per_clone) + n_mks_load_cum_sum = np.cumsum(self.n_mks_load) + Np_per_clone_cum_sum = np.cumsum(self.Np_per_clone) _first_marker_id = (Np_per_clone_cum_sum - self.Np_per_clone)[self.clone_id] + ( n_mks_load_cum_sum - self.n_mks_load )[self._mpi_rank] @@ -1613,9 +1642,9 @@ def draw_markers( self._load_tesselation() if self.type == "sph": self._set_initial_condition() - self.velocities = xp.array(self.u_init(self.positions)).T + self.velocities = _to_numpy_for_kernel(self.u_init(self.positions)).T # set markers ID in last column - self.marker_ids = _first_marker_id + xp.arange(n_mks_load_loc, dtype=float) + self.marker_ids = _first_marker_id + np.arange(n_mks_load_loc, dtype=float) else: logger.debug("\nLoading fresh markers:") for key, val in self.loading_params.__dict__.items(): @@ -1626,7 +1655,7 @@ def draw_markers( # set seed _seed = self.loading_params.seed if _seed is not None: - xp.random.seed(_seed) + np.random.seed(_seed) # counting integers num_loaded_particles_loc = 0 # number of particles alreday loaded (local) @@ -1637,15 +1666,15 @@ def draw_markers( while num_loaded_particles_glob < int(self.Np): # Generate a chunk of random particles num_to_add_glob = min(chunk_size, int(self.Np) - num_loaded_particles_glob) - temp = xp.random.rand(num_to_add_glob, 3 + self.vdim) + temp = np.random.rand(num_to_add_glob, 3 + self.vdim) # check which particles are on the current process domain - is_on_proc_domain = xp.logical_and( + is_on_proc_domain = np.logical_and( temp[:, :3] > self.domain_array[self.mpi_rank, 0::3], temp[:, :3] < self.domain_array[self.mpi_rank, 1::3], ) - valid_idx = xp.nonzero(xp.all(is_on_proc_domain, axis=1))[0] + valid_idx = np.nonzero(np.all(is_on_proc_domain, axis=1))[0] valid_particles = temp[valid_idx] - valid_particles = xp.array_split(valid_particles, self.num_clones)[self.clone_id] + valid_particles = np.array_split(valid_particles, self.num_clones)[self.clone_id] num_valid = valid_particles.shape[0] # Add the valid particles to the phasespace_coords array @@ -1662,7 +1691,7 @@ def draw_markers( # set new n_mks_load self._gather_scalar_in_subcomm_array(num_loaded_particles_loc, out=self.n_mks_load) n_mks_load_loc = self.n_mks_load[self.mpi_rank] - n_mks_load_cum_sum = xp.cumsum(self.n_mks_load) + n_mks_load_cum_sum = np.cumsum(self.n_mks_load) # set new holes in markers array to -1 self._markers[num_loaded_particles_loc:] = -1.0 @@ -1702,20 +1731,20 @@ def draw_markers( # initial velocities - SPH case: v(0) = u(x(0)) for given velocity u(x) if self.type == "sph": self._set_initial_condition() - self.velocities = xp.array(self.u_init(self.positions)).T + self.velocities = _to_numpy_for_kernel(self.u_init(self.positions)).T else: # inverse transform sampling in velocity space # Avoid exact 0 or 1 from low-discrepancy sequences: erfinv(±1) # and log(0) produce infinities or invalid polar velocities. - eps = xp.finfo(float).eps - self._markers[:n_mks_load_loc, 3 : 3 + self.vdim] = xp.clip( + eps = np.finfo(float).eps + self._markers[:n_mks_load_loc, 3 : 3 + self.vdim] = np.clip( self._markers[:n_mks_load_loc, 3 : 3 + self.vdim], eps, 1.0 - eps, ) - u_mean = xp.array(self.loading_params.moments[: self.vdim]) - v_th = xp.array(self.loading_params.moments[self.vdim :]) + u_mean = np.array(self.loading_params.moments[: self.vdim]) + v_th = np.array(self.loading_params.moments[self.vdim :]) # Particles6D: (1d Maxwellian, 1d Maxwellian, 1d Maxwellian) if self.vdim == 3: @@ -1723,7 +1752,7 @@ def draw_markers( sp.erfinv( 2 * self.velocities - 1, ) - * xp.sqrt(2) + * np.sqrt(2) * v_th + u_mean ) @@ -1733,16 +1762,16 @@ def draw_markers( sp.erfinv( 2 * self.velocities[:, 0] - 1, ) - * xp.sqrt(2) + * np.sqrt(2) * v_th[0] + u_mean[0] ) self._markers[:n_mks_load_loc, 4] = ( - xp.sqrt( - -xp.log(1.0 - self.velocities[:, 1]), + np.sqrt( + -np.log(1.0 - self.velocities[:, 1]), ) - * xp.sqrt(2) + * np.sqrt(2) * v_th[1] ) @@ -1763,13 +1792,13 @@ def draw_markers( # inversion method for drawing uniformly on the disc if self.spatial == "disc": - self._markers[:n_mks_load_loc, 0] = xp.sqrt( + self._markers[:n_mks_load_loc, 0] = np.sqrt( self._markers[:n_mks_load_loc, 0], ) else: assert self.spatial == "uniform", f'Spatial drawing must be "uniform" or "disc", is {self.spatial}.' - self.marker_ids = _first_marker_id + xp.arange(n_mks_load_loc, dtype=float) + self.marker_ids = _first_marker_id + np.arange(n_mks_load_loc, dtype=float) # set specific initial condition for some particles if self.loading_params.specific_markers is not None: @@ -1790,8 +1819,8 @@ def draw_markers( # check if all particle positions are inside the unit cube [0, 1]^3 n_mks_load_loc = self.n_mks_load[self._mpi_rank] - assert xp.all(~self.holes[:n_mks_load_loc]) - assert xp.all(self.holes[n_mks_load_loc:]) + assert np.all(~self.holes[:n_mks_load_loc]) + assert np.all(self.holes[n_mks_load_loc:]) if self._initialized_sorting and sort: logger.info("\nSorting the markers after initial draw") @@ -1869,8 +1898,8 @@ def mpi_sort_markers( # check if all markers are on the right process after sorting if do_test: - all_on_right_proc = xp.all( - xp.logical_and( + all_on_right_proc = np.all( + np.logical_and( self.positions > self.domain_array[self.mpi_rank, 0::3], self.positions < self.domain_array[self.mpi_rank, 1::3], ), @@ -1931,22 +1960,28 @@ def initialize_weights( self._set_initial_condition() # evaluate initial distribution function + # NOTE: self.domain/self.f0/self.s0 evaluate on the device (they + # follow the global array backend), while markers are always + # host-resident, so results are converted back to NumPy at this + # marker/field evaluation boundary. if self.type == "sph": - f_init = self.f_init(self.positions) + f_init = _to_numpy_for_kernel(self.f_init(_dev(self.positions))) else: - f_init = self.f_init(*self.f_coords.T) + f_init = _to_numpy_for_kernel(self.f_init(*_dev(*self.f_coords.T))) # if f_init is vol-form, transform to 0-form if self.is_volume_form[0]: - f_init /= self.domain.jacobian_det(self.positions) + f_init /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions))) if self.is_volume_form[1]: - f_init /= self.f_init.velocity_jacobian_det( - *self.f_jacobian_coords.T, + f_init /= _to_numpy_for_kernel( + self.f_init.velocity_jacobian_det( + *_dev(*self.f_jacobian_coords.T), + ) ) # compute s0 and save at vdim + 4 - self.sampling_density = self.s0(*self.phasespace_coords.T, flat_eval=True) + self.sampling_density = _to_numpy_for_kernel(self.s0(*_dev(*self.phasespace_coords.T), flat_eval=True)) # compute w0 and save at vdim + 5 self.weights0 = f_init / self.sampling_density / self.Np @@ -1975,37 +2010,37 @@ def update_weights(self): """ if self.type == "sph": - f0 = self.f0.n0(self.positions) + f0 = _to_numpy_for_kernel(self.f0.n0(_dev(self.positions))) else: # in case of CanonicalMaxwellian, evaluate constants_of_motion if self.f0.coords == "constants_of_motion": self.save_constants_of_motion() - f0 = self.f0(*self.f_coords.T) + f0 = _to_numpy_for_kernel(self.f0(*_dev(*self.f_coords.T))) # if f_init is vol-form, transform to 0-form if self.is_volume_form[0]: - f0 /= self.domain.jacobian_det(self.positions) + f0 /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions))) if self.is_volume_form[1]: - f0 /= self.f0.velocity_jacobian_det(*self.f_jacobian_coords.T) + f0 /= _to_numpy_for_kernel(self.f0.velocity_jacobian_det(*_dev(*self.f_jacobian_coords.T))) self.weights = self.weights0 - f0 / self.sampling_density / self.Np def reset_marker_ids(self): """Reset the marker ids (last column in marker array) according to the current distribution of particles. The first marker on rank 0 gets the id '0', the last marker on the last rank gets the id 'n_mks_global - 1'.""" - n_mks_proc_cumsum = xp.cumsum(self.n_mks_on_each_proc) - n_mks_clone_cumsum = xp.cumsum(self.n_mks_on_each_clone) + n_mks_proc_cumsum = np.cumsum(self.n_mks_on_each_proc) + n_mks_clone_cumsum = np.cumsum(self.n_mks_on_each_clone) first_marker_id = (n_mks_clone_cumsum - self.n_mks_on_each_clone)[self.clone_id] + ( n_mks_proc_cumsum - self.n_mks_on_each_proc )[self.mpi_rank] - self.marker_ids = first_marker_id + xp.arange(self.n_mks_loc, dtype=int) + self.marker_ids = first_marker_id + np.arange(self.n_mks_loc, dtype=int) @profile def binning( self, components: tuple[bool], - bin_edges: tuple[xp.ndarray], + bin_edges: tuple[np.ndarray], output_quantity: LiteralOptions.BinningQuantity = "density", divide_by_jac: bool = True, ): @@ -2035,7 +2070,13 @@ def binning( The reconstructed delta-f distribution function. """ - assert xp.count_nonzero(xp.array(components)) == len(bin_edges) + assert np.count_nonzero(np.array(components)) == len(bin_edges) + + # bin_edges is caller-supplied and may follow the active array + # backend (e.g. built with xp.linspace under CuPy); markers and the + # rest of this method are always host-resident, so bring it to NumPy + # here, at the marker/caller-data boundary. + bin_edges = tuple(_to_numpy_for_kernel(be) for be in bin_edges) # volume of a bin bin_vol = 1.0 @@ -2059,7 +2100,7 @@ def binning( elif quantity == "energy_tensor": multiplier = self.velocities[:, v_axis[0]] * self.velocities[:, v_axis[1]] elif quantity == "heat_flux": - velocity_norm2 = xp.linalg.norm(self.velocities, axis=1) ** 2 + velocity_norm2 = np.linalg.norm(self.velocities, axis=1) ** 2 multiplier = velocity_norm2 * self.velocities[:, v_axis[0]] # compute weights of histogram: @@ -2067,19 +2108,19 @@ def binning( _weights = self.weights * self.Np * multiplier if divide_by_jac: - _weights /= self.domain.jacobian_det(self.positions, remove_outside=False) + _weights /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions), remove_outside=False)) # _weights /= self.velocity_jacobian_det(*self.phasespace_coords.T) - _weights0 /= self.domain.jacobian_det(self.positions, remove_outside=False) + _weights0 /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions), remove_outside=False)) # _weights0 /= self.velocity_jacobian_det(*self.phasespace_coords.T) - f_slice = xp.histogramdd( + f_slice = np.histogramdd( self.markers_wo_holes_and_ghost[:, slicing], bins=bin_edges, weights=_weights0, )[0] - df_slice = xp.histogramdd( + df_slice = np.histogramdd( self.markers_wo_holes_and_ghost[:, slicing], bins=bin_edges, weights=_weights, @@ -2106,7 +2147,7 @@ def show_distribution_function(self, components, bin_edges): import matplotlib.pyplot as plt - n_dim = xp.count_nonzero(components) + n_dim = np.count_nonzero(components) assert n_dim == 1 or n_dim == 2, f"Distribution function can only be shown in 1D or 2D slices, not {n_dim}." @@ -2122,7 +2163,7 @@ def show_distribution_function(self, components, bin_edges): 4: "$v_2$", 5: "$v_3$", } - indices = xp.nonzero(components)[0] + indices = np.nonzero(components)[0] if n_dim == 1: plt.plot(bin_centers[0], f_slice) @@ -2146,13 +2187,13 @@ def _find_outside_particles(self, axis): self._is_outside_left[self.holes] = False self._is_outside_left[self.ghost_particles] = False - self._is_outside[:] = xp.logical_or( + self._is_outside[:] = np.logical_or( self._is_outside_right, self._is_outside_left, ) # indices or particles that are outside of the logical unit cube - outside_inds = xp.nonzero(self._is_outside)[0] + outside_inds = np.nonzero(self._is_outside)[0] return outside_inds @@ -2179,7 +2220,7 @@ def apply_kinetic_bc(self, newton=False): self.particle_refilling() self._markers[self._is_outside, :-1] = -1.0 - self._n_lost_markers += len(xp.nonzero(self._is_outside)[0]) + self._n_lost_markers += len(np.nonzero(self._is_outside)[0]) for axis in self._periodic_axes: outside_inds = self._find_outside_particles(axis) @@ -2190,8 +2231,8 @@ def apply_kinetic_bc(self, newton=False): self.markers[outside_inds, axis] = self.markers[outside_inds, axis] % 1.0 # set shift for alpha-weighted mid-point computation - outside_right_inds = xp.nonzero(self._is_outside_right)[0] - outside_left_inds = xp.nonzero(self._is_outside_left)[0] + outside_right_inds = np.nonzero(self._is_outside_right)[0] + outside_left_inds = np.nonzero(self._is_outside_left)[0] if newton: self.markers[ outside_right_inds, @@ -2259,12 +2300,12 @@ def particle_refilling(self): for kind in self.bc_refill: # sorting out particles which are out of the domain if kind == "inner": - outside_inds = xp.nonzero(self._is_outside_left)[0] + outside_inds = np.nonzero(self._is_outside_left)[0] self.markers[outside_inds, 0] = 1e-4 r_loss = self.domain.params["a1"] else: - outside_inds = xp.nonzero(self._is_outside_right)[0] + outside_inds = np.nonzero(self._is_outside_right)[0] self.markers[outside_inds, 0] = 1 - 1e-4 r_loss = 1.0 @@ -2313,12 +2354,12 @@ def gyro_transfer(self, outside_inds): Parameters ---------- - outside_inds : xp.array (int) + outside_inds : np.array (int) An array of indices of particles which are outside of the domain. Returns ------- - out : xp.array (bool) + out : np.array (bool) An array of indices of particles where its guiding centers are outside of the domain. """ @@ -2335,18 +2376,18 @@ def gyro_transfer(self, outside_inds): b_cart, xyz = self.equil.b_cart(self.markers[outside_inds, :]) # calculate magnetic field amplitude and normalized magnetic field - absB0 = xp.sqrt(b_cart[0] ** 2 + b_cart[1] ** 2 + b_cart[2] ** 2) + absB0 = np.sqrt(b_cart[0] ** 2 + b_cart[1] ** 2 + b_cart[2] ** 2) norm_b_cart = b_cart / absB0 # calculate parallel and perpendicular velocities - v_parallel = xp.einsum("ij,ij->j", v, norm_b_cart) - v_perp = xp.cross(norm_b_cart, xp.cross(v, norm_b_cart, axis=0), axis=0) - v_perp_square = xp.sqrt(v_perp[0] ** 2 + v_perp[1] ** 2 + v_perp[2] ** 2) + v_parallel = np.einsum("ij,ij->j", v, norm_b_cart) + v_perp = np.cross(norm_b_cart, np.cross(v, norm_b_cart, axis=0), axis=0) + v_perp_square = np.sqrt(v_perp[0] ** 2 + v_perp[1] ** 2 + v_perp[2] ** 2) - assert xp.all(xp.isclose(v_perp, v - norm_b_cart * v_parallel)) + assert np.all(np.isclose(v_perp, v - norm_b_cart * v_parallel)) # calculate Larmor radius - Larmor_r = xp.cross(norm_b_cart, v_perp, axis=0) / absB0 * self._epsilon + Larmor_r = np.cross(norm_b_cart, v_perp, axis=0) / absB0 * self._epsilon # transform cartesian coordinates to logical coordinates # TODO: currently only possible with the geomoetry where its inverse map is defined. @@ -2365,17 +2406,17 @@ def gyro_transfer(self, outside_inds): b_cart = self.equil.b_cart(self.markers[outside_inds, :])[0] # calculate magnetic field amplitude and normalized magnetic field - absB0 = xp.sqrt(b_cart[0] ** 2 + b_cart[1] ** 2 + b_cart[2] ** 2) + absB0 = np.sqrt(b_cart[0] ** 2 + b_cart[1] ** 2 + b_cart[2] ** 2) norm_b_cart = b_cart / absB0 Larmor_r = new_xyz - xyz - Larmor_r /= xp.sqrt(Larmor_r[0] ** 2 + Larmor_r[1] ** 2 + Larmor_r[2] ** 2) + Larmor_r /= np.sqrt(Larmor_r[0] ** 2 + Larmor_r[1] ** 2 + Larmor_r[2] ** 2) - new_v_perp = xp.cross(Larmor_r, norm_b_cart, axis=0) * v_perp_square + new_v_perp = np.cross(Larmor_r, norm_b_cart, axis=0) * v_perp_square self.markers[outside_inds, 3:6] = (norm_b_cart * v_parallel).T + new_v_perp.T - return xp.logical_and(1.0 > gc_etas[0], gc_etas[0] > 0.0) + return np.logical_and(1.0 > gc_etas[0], gc_etas[0] > 0.0) class SortingBoxes: """Boxes used for the sorting of the particles. @@ -2556,26 +2597,26 @@ def _set_boxes(self): n_particles = self._markers_shape[0] n_mkr = int(n_particles / n_box_in) + 1 n_cols = round( - float(n_mkr) * (1 + 1 / float(xp.sqrt(n_mkr)) + self._box_bufsize), + float(n_mkr) * (1 + 1 / float(np.sqrt(n_mkr)) + self._box_bufsize), ) # cartesian boxes - self._boxes = xp.zeros((self._n_boxes + 1, n_cols), dtype=int) + self._boxes = np.zeros((self._n_boxes + 1, n_cols), dtype=int) # TODO: there is still a bug here # the row number in self._boxes should not be n_boxes + 1; this is just a temporary fix to avoid an error that I dont understand. # Must be fixed soon! - self._next_index = xp.zeros((self._n_boxes + 1), dtype=int) - self._cumul_next_index = xp.zeros((self._n_boxes + 2), dtype=int) - self._neighbours = xp.zeros((self._n_boxes, 27), dtype=int) + self._next_index = np.zeros((self._n_boxes + 1), dtype=int) + self._cumul_next_index = np.zeros((self._n_boxes + 2), dtype=int) + self._neighbours = np.zeros((self._n_boxes, 27), dtype=int) # A particle on box i only sees particles in boxes that belong to neighbours[i] initialize_neighbours(self._neighbours, self.nx, self.ny, self.nz) # logger.info(f"{self._rank = }\n{self._neighbours = }") - self._swap_line_1 = xp.zeros(self._markers_shape[1]) - self._swap_line_2 = xp.zeros(self._markers_shape[1]) + self._swap_line_1 = np.zeros(self._markers_shape[1]) + self._swap_line_2 = np.zeros(self._markers_shape[1]) def _set_boundary_boxes(self): """Gather all the boxes that are part of a boundary""" @@ -2729,7 +2770,7 @@ def _sort_boxed_particles_numpy(self): sorting_axis = self._sorting_boxes.box_index if not hasattr(self, "_argsort_array"): - self._argsort_array = xp.zeros(self.markers.shape[0], dtype=int) + self._argsort_array = np.zeros(self.markers.shape[0], dtype=int) self._argsort_array[:] = self._markers[:, sorting_axis].argsort() self._markers[:, :] = self._markers[self._argsort_array] @@ -2758,28 +2799,19 @@ def put_particles_in_boxes(self): self.update_ghost_particles() # if self.verbose: - # valid_box_ids = xp.nonzero(self._sorting_boxes._boxes[:, 0] != -1)[0] + # valid_box_ids = np.nonzero(self._sorting_boxes._boxes[:, 0] != -1)[0] # logger.info(f"Boxes holding at least one particle: {valid_box_ids}") # for i in valid_box_ids: - # n_mks_box = xp.count_nonzero(self._sorting_boxes._boxes[i] != -1) + # n_mks_box = np.count_nonzero(self._sorting_boxes._boxes[i] != -1) # logger.info(f"Number of markers in box {i} is {n_mks_box}") def check_and_assign_particles_to_boxes(self): """Check whether the box array has enough columns (detect load imbalance wrt to sorting boxes), and then assigne the particles to boxes.""" - from cunumpy.xp import array_backend - - if array_backend.backend == "numpy": - bcount = xp.bincount(xp.int64(self.markers_wo_holes[:, -2])) - else: - import cupy as cp - - indices = self.markers_wo_holes[:, -2] - indices = indices.astype(cp.int64) - bcount = cp.bincount(indices) + bcount = np.bincount(np.int64(self.markers_wo_holes[:, -2])) - max_in_box = xp.max(bcount) + max_in_box = np.max(bcount) if max_in_box > self._sorting_boxes.boxes.shape[1]: warnings.warn( f'Strong load imbalance detected in sorting boxes: \ @@ -2826,7 +2858,7 @@ def do_sort(self, use_numpy_argsort=False): def remove_ghost_particles(self): self.update_ghost_particles() - new_holes = xp.nonzero(self.ghost_particles) + new_holes = np.nonzero(self.ghost_particles) self._markers[new_holes] = -1.0 self.update_holes() @@ -3066,17 +3098,19 @@ def _mirror_particles( if "x_m" in arr_name and is_domain_boundary["x_m"]: arr[:, 0] *= -1.0 if self.bc_sph[0] == "fixed" and arr_name not in self._fixed_markers_set: - boundary_values = self.f_init( - *arr[:, :3].T, + # f_init/s0 evaluate on the active array backend; arr is + # always host-resident, so convert at this boundary. + boundary_values = _to_numpy_for_kernel(self.f_init( + *_dev(*arr[:, :3].T), flat_eval=True, - ) # evaluation outside of the unit cube - maybe not working for all f_init! + )) # evaluation outside of the unit cube - maybe not working for all f_init! arr[:, self.index["weights"]] = ( -boundary_values - / self.s0( - *arr[:, :3].T, + / _to_numpy_for_kernel(self.s0( + *_dev(*arr[:, :3].T), flat_eval=True, remove_holes=False, - ) + )) / self.Np ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right self._fixed_markers_set[arr_name] = True @@ -3093,17 +3127,19 @@ def _mirror_particles( elif "x_p" in arr_name and is_domain_boundary["x_p"]: arr[:, 0] = 2.0 - arr[:, 0] if self.bc_sph[0] == "fixed" and arr_name not in self._fixed_markers_set: - boundary_values = self.f_init( - *arr[:, :3].T, + # f_init/s0 evaluate on the active array backend; arr is + # always host-resident, so convert at this boundary. + boundary_values = _to_numpy_for_kernel(self.f_init( + *_dev(*arr[:, :3].T), flat_eval=True, - ) # evaluation outside of the unit cube - maybe not working for all f_init! + )) # evaluation outside of the unit cube - maybe not working for all f_init! arr[:, self.index["weights"]] = ( -boundary_values - / self.s0( - *arr[:, :3].T, + / _to_numpy_for_kernel(self.s0( + *_dev(*arr[:, :3].T), flat_eval=True, remove_holes=False, - ) + )) / self.Np ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right self._fixed_markers_set[arr_name] = True @@ -3122,17 +3158,19 @@ def _mirror_particles( if "y_m" in arr_name and is_domain_boundary["y_m"]: arr[:, 1] *= -1.0 if self.bc_sph[1] == "fixed" and arr_name not in self._fixed_markers_set: - boundary_values = self.f_init( - *arr[:, :3].T, + # f_init/s0 evaluate on the active array backend; arr is + # always host-resident, so convert at this boundary. + boundary_values = _to_numpy_for_kernel(self.f_init( + *_dev(*arr[:, :3].T), flat_eval=True, - ) # evaluation outside of the unit cube - maybe not working for all f_init! + )) # evaluation outside of the unit cube - maybe not working for all f_init! arr[:, self.index["weights"]] = ( -boundary_values - / self.s0( - *arr[:, :3].T, + / _to_numpy_for_kernel(self.s0( + *_dev(*arr[:, :3].T), flat_eval=True, remove_holes=False, - ) + )) / self.Np ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right self._fixed_markers_set[arr_name] = True @@ -3149,17 +3187,19 @@ def _mirror_particles( elif "y_p" in arr_name and is_domain_boundary["y_p"]: arr[:, 1] = 2.0 - arr[:, 1] if self.bc_sph[1] == "fixed" and arr_name not in self._fixed_markers_set: - boundary_values = self.f_init( - *arr[:, :3].T, + # f_init/s0 evaluate on the active array backend; arr is + # always host-resident, so convert at this boundary. + boundary_values = _to_numpy_for_kernel(self.f_init( + *_dev(*arr[:, :3].T), flat_eval=True, - ) # evaluation outside of the unit cube - maybe not working for all f_init! + )) # evaluation outside of the unit cube - maybe not working for all f_init! arr[:, self.index["weights"]] = ( -boundary_values - / self.s0( - *arr[:, :3].T, + / _to_numpy_for_kernel(self.s0( + *_dev(*arr[:, :3].T), flat_eval=True, remove_holes=False, - ) + )) / self.Np ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right self._fixed_markers_set[arr_name] = True @@ -3178,17 +3218,19 @@ def _mirror_particles( if "z_m" in arr_name and is_domain_boundary["z_m"]: arr[:, 2] *= -1.0 if self.bc_sph[2] == "fixed" and arr_name not in self._fixed_markers_set: - boundary_values = self.f_init( - *arr[:, :3].T, + # f_init/s0 evaluate on the active array backend; arr is + # always host-resident, so convert at this boundary. + boundary_values = _to_numpy_for_kernel(self.f_init( + *_dev(*arr[:, :3].T), flat_eval=True, - ) # evaluation outside of the unit cube - maybe not working for all f_init! + )) # evaluation outside of the unit cube - maybe not working for all f_init! arr[:, self.index["weights"]] = ( -boundary_values - / self.s0( - *arr[:, :3].T, + / _to_numpy_for_kernel(self.s0( + *_dev(*arr[:, :3].T), flat_eval=True, remove_holes=False, - ) + )) / self.Np ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right self._fixed_markers_set[arr_name] = True @@ -3205,17 +3247,19 @@ def _mirror_particles( elif "z_p" in arr_name and is_domain_boundary["z_p"]: arr[:, 2] = 2.0 - arr[:, 2] if self.bc_sph[2] == "fixed" and arr_name not in self._fixed_markers_set: - boundary_values = self.f_init( - *arr[:, :3].T, + # f_init/s0 evaluate on the active array backend; arr is + # always host-resident, so convert at this boundary. + boundary_values = _to_numpy_for_kernel(self.f_init( + *_dev(*arr[:, :3].T), flat_eval=True, - ) # evaluation outside of the unit cube - maybe not working for all f_init! + )) # evaluation outside of the unit cube - maybe not working for all f_init! arr[:, self.index["weights"]] = ( -boundary_values - / self.s0( - *arr[:, :3].T, + / _to_numpy_for_kernel(self.s0( + *_dev(*arr[:, :3].T), flat_eval=True, remove_holes=False, - ) + )) / self.Np ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right self._fixed_markers_set[arr_name] = True @@ -3235,161 +3279,161 @@ def determine_markers_in_box(self, list_boxes): for i in list_boxes: indices += list(self._sorting_boxes._boxes[i][self._sorting_boxes._boxes[i] != -1]) - indices = xp.array(indices, dtype=int) + indices = np.array(indices, dtype=int) markers_in_box = self.markers[indices] return markers_in_box def get_destinations_box(self): """Find the destination proc for the particles to communicate for the box structure.""" - self._send_info_box = xp.zeros(self.mpi_size, dtype=int) - self._send_list_box = [xp.zeros((0, self.n_cols))] * self.mpi_size + self._send_info_box = np.zeros(self.mpi_size, dtype=int) + self._send_list_box = [np.zeros((0, self.n_cols))] * self.mpi_size # Faces # if self._x_m_proc is not None: self._send_info_box[self._x_m_proc] += len(self._markers_x_m) - self._send_list_box[self._x_m_proc] = xp.concatenate((self._send_list_box[self._x_m_proc], self._markers_x_m)) + self._send_list_box[self._x_m_proc] = np.concatenate((self._send_list_box[self._x_m_proc], self._markers_x_m)) # if self._x_p_proc is not None: self._send_info_box[self._x_p_proc] += len(self._markers_x_p) - self._send_list_box[self._x_p_proc] = xp.concatenate((self._send_list_box[self._x_p_proc], self._markers_x_p)) + self._send_list_box[self._x_p_proc] = np.concatenate((self._send_list_box[self._x_p_proc], self._markers_x_p)) # if self._y_m_proc is not None: self._send_info_box[self._y_m_proc] += len(self._markers_y_m) - self._send_list_box[self._y_m_proc] = xp.concatenate((self._send_list_box[self._y_m_proc], self._markers_y_m)) + self._send_list_box[self._y_m_proc] = np.concatenate((self._send_list_box[self._y_m_proc], self._markers_y_m)) # if self._y_p_proc is not None: self._send_info_box[self._y_p_proc] += len(self._markers_y_p) - self._send_list_box[self._y_p_proc] = xp.concatenate((self._send_list_box[self._y_p_proc], self._markers_y_p)) + self._send_list_box[self._y_p_proc] = np.concatenate((self._send_list_box[self._y_p_proc], self._markers_y_p)) # if self._z_m_proc is not None: self._send_info_box[self._z_m_proc] += len(self._markers_z_m) - self._send_list_box[self._z_m_proc] = xp.concatenate((self._send_list_box[self._z_m_proc], self._markers_z_m)) + self._send_list_box[self._z_m_proc] = np.concatenate((self._send_list_box[self._z_m_proc], self._markers_z_m)) # if self._z_p_proc is not None: self._send_info_box[self._z_p_proc] += len(self._markers_z_p) - self._send_list_box[self._z_p_proc] = xp.concatenate((self._send_list_box[self._z_p_proc], self._markers_z_p)) + self._send_list_box[self._z_p_proc] = np.concatenate((self._send_list_box[self._z_p_proc], self._markers_z_p)) # x-y edges # if self._x_m_y_m_proc is not None: self._send_info_box[self._x_m_y_m_proc] += len(self._markers_x_m_y_m) - self._send_list_box[self._x_m_y_m_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_m_proc] = np.concatenate( (self._send_list_box[self._x_m_y_m_proc], self._markers_x_m_y_m), ) # if self._x_m_y_p_proc is not None: self._send_info_box[self._x_m_y_p_proc] += len(self._markers_x_m_y_p) - self._send_list_box[self._x_m_y_p_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_p_proc] = np.concatenate( (self._send_list_box[self._x_m_y_p_proc], self._markers_x_m_y_p), ) # if self._x_p_y_m_proc is not None: self._send_info_box[self._x_p_y_m_proc] += len(self._markers_x_p_y_m) - self._send_list_box[self._x_p_y_m_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_m_proc] = np.concatenate( (self._send_list_box[self._x_p_y_m_proc], self._markers_x_p_y_m), ) # if self._x_p_y_p_proc is not None: self._send_info_box[self._x_p_y_p_proc] += len(self._markers_x_p_y_p) - self._send_list_box[self._x_p_y_p_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_p_proc] = np.concatenate( (self._send_list_box[self._x_p_y_p_proc], self._markers_x_p_y_p), ) # x-z edges # if self._x_m_z_m_proc is not None: self._send_info_box[self._x_m_z_m_proc] += len(self._markers_x_m_z_m) - self._send_list_box[self._x_m_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_m_z_m_proc] = np.concatenate( (self._send_list_box[self._x_m_z_m_proc], self._markers_x_m_z_m), ) # if self._x_m_z_p_proc is not None: self._send_info_box[self._x_m_z_p_proc] += len(self._markers_x_m_z_p) - self._send_list_box[self._x_m_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_m_z_p_proc] = np.concatenate( (self._send_list_box[self._x_m_z_p_proc], self._markers_x_m_z_p), ) # if self._x_p_z_m_proc is not None: self._send_info_box[self._x_p_z_m_proc] += len(self._markers_x_p_z_m) - self._send_list_box[self._x_p_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_p_z_m_proc] = np.concatenate( (self._send_list_box[self._x_p_z_m_proc], self._markers_x_p_z_m), ) # if self._x_p_z_p_proc is not None: self._send_info_box[self._x_p_z_p_proc] += len(self._markers_x_p_z_p) - self._send_list_box[self._x_p_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_p_z_p_proc] = np.concatenate( (self._send_list_box[self._x_p_z_p_proc], self._markers_x_p_z_p), ) # y-z edges # if self._y_m_z_m_proc is not None: self._send_info_box[self._y_m_z_m_proc] += len(self._markers_y_m_z_m) - self._send_list_box[self._y_m_z_m_proc] = xp.concatenate( + self._send_list_box[self._y_m_z_m_proc] = np.concatenate( (self._send_list_box[self._y_m_z_m_proc], self._markers_y_m_z_m), ) # if self._y_m_z_p_proc is not None: self._send_info_box[self._y_m_z_p_proc] += len(self._markers_y_m_z_p) - self._send_list_box[self._y_m_z_p_proc] = xp.concatenate( + self._send_list_box[self._y_m_z_p_proc] = np.concatenate( (self._send_list_box[self._y_m_z_p_proc], self._markers_y_m_z_p), ) # if self._y_p_z_m_proc is not None: self._send_info_box[self._y_p_z_m_proc] += len(self._markers_y_p_z_m) - self._send_list_box[self._y_p_z_m_proc] = xp.concatenate( + self._send_list_box[self._y_p_z_m_proc] = np.concatenate( (self._send_list_box[self._y_p_z_m_proc], self._markers_y_p_z_m), ) # if self._y_p_z_p_proc is not None: self._send_info_box[self._y_p_z_p_proc] += len(self._markers_y_p_z_p) - self._send_list_box[self._y_p_z_p_proc] = xp.concatenate( + self._send_list_box[self._y_p_z_p_proc] = np.concatenate( (self._send_list_box[self._y_p_z_p_proc], self._markers_y_p_z_p), ) # corners # if self._x_m_y_m_z_m_proc is not None: self._send_info_box[self._x_m_y_m_z_m_proc] += len(self._markers_x_m_y_m_z_m) - self._send_list_box[self._x_m_y_m_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_m_z_m_proc] = np.concatenate( (self._send_list_box[self._x_m_y_m_z_m_proc], self._markers_x_m_y_m_z_m), ) # if self._x_m_y_m_z_p_proc is not None: self._send_info_box[self._x_m_y_m_z_p_proc] += len(self._markers_x_m_y_m_z_p) - self._send_list_box[self._x_m_y_m_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_m_z_p_proc] = np.concatenate( (self._send_list_box[self._x_m_y_m_z_p_proc], self._markers_x_m_y_m_z_p), ) # if self._x_m_y_p_z_m_proc is not None: self._send_info_box[self._x_m_y_p_z_m_proc] += len(self._markers_x_m_y_p_z_m) - self._send_list_box[self._x_m_y_p_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_p_z_m_proc] = np.concatenate( (self._send_list_box[self._x_m_y_p_z_m_proc], self._markers_x_m_y_p_z_m), ) # if self._x_m_y_p_z_p_proc is not None: self._send_info_box[self._x_m_y_p_z_p_proc] += len(self._markers_x_m_y_p_z_p) - self._send_list_box[self._x_m_y_p_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_p_z_p_proc] = np.concatenate( (self._send_list_box[self._x_m_y_p_z_p_proc], self._markers_x_m_y_p_z_p), ) # if self._x_p_y_m_z_m_proc is not None: self._send_info_box[self._x_p_y_m_z_m_proc] += len(self._markers_x_p_y_m_z_m) - self._send_list_box[self._x_p_y_m_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_m_z_m_proc] = np.concatenate( (self._send_list_box[self._x_p_y_m_z_m_proc], self._markers_x_p_y_m_z_m), ) # if self._x_p_y_m_z_p_proc is not None: self._send_info_box[self._x_p_y_m_z_p_proc] += len(self._markers_x_p_y_m_z_p) - self._send_list_box[self._x_p_y_m_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_m_z_p_proc] = np.concatenate( (self._send_list_box[self._x_p_y_m_z_p_proc], self._markers_x_p_y_m_z_p), ) # if self._x_p_y_p_z_m_proc is not None: self._send_info_box[self._x_p_y_p_z_m_proc] += len(self._markers_x_p_y_p_z_m) - self._send_list_box[self._x_p_y_p_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_p_z_m_proc] = np.concatenate( (self._send_list_box[self._x_p_y_p_z_m_proc], self._markers_x_p_y_p_z_m), ) # if self._x_p_y_p_z_p_proc is not None: self._send_info_box[self._x_p_y_p_z_p_proc] += len(self._markers_x_p_y_p_z_p) - self._send_list_box[self._x_p_y_p_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_p_z_p_proc] = np.concatenate( (self._send_list_box[self._x_p_y_p_z_p_proc], self._markers_x_p_y_p_z_p), ) @@ -3399,7 +3443,7 @@ def self_communication_boxes(self): if self._send_info_box[self.mpi_rank] > 0: self.update_holes() - holes_inds = xp.nonzero(self.holes)[0] + holes_inds = np.nonzero(self.holes)[0] if holes_inds.size < self._send_info_box[self.mpi_rank]: warnings.warn( @@ -3421,16 +3465,16 @@ def self_communication_boxes(self): # self.update_holes() # self.update_ghost_particles() # self.update_valid_mks() - # holes_inds = xp.nonzero(self.holes)[0] + # holes_inds = np.nonzero(self.holes)[0] - self.markers[holes_inds[xp.arange(self._send_info_box[self.mpi_rank])]] = self._send_list_box[self.mpi_rank] + self.markers[holes_inds[np.arange(self._send_info_box[self.mpi_rank])]] = self._send_list_box[self.mpi_rank] @profile def communicate_boxes(self): # if verbose: - # n_valid = xp.count_nonzero(self.valid_mks) - # n_holes = xp.count_nonzero(self.holes) - # n_ghosts = xp.count_nonzero(self.ghost_particles) + # n_valid = np.count_nonzero(self.valid_mks) + # n_holes = np.count_nonzero(self.holes) + # n_ghosts = np.count_nonzero(self.ghost_particles) # logger.info(f"before communicate_boxes: {self.mpi_rank = }, {n_valid = } {n_holes = }, {n_ghosts = }") self.prepare_ghost_particles() @@ -3445,9 +3489,9 @@ def communicate_boxes(self): self.update_ghost_particles() # if verbose: - # n_valid = xp.count_nonzero(self.valid_mks) - # n_holes = xp.count_nonzero(self.holes) - # n_ghosts = xp.count_nonzero(self.ghost_particles) + # n_valid = np.count_nonzero(self.valid_mks) + # n_holes = np.count_nonzero(self.holes) + # n_ghosts = np.count_nonzero(self.ghost_particles) # logger.info(f"after communicate_boxes: {self.mpi_rank = }, {n_valid = }, {n_holes = }, {n_ghosts = }") def sendrecv_all_to_all_boxes(self): @@ -3456,7 +3500,7 @@ def sendrecv_all_to_all_boxes(self): for the communication of particles in boundary boxes. """ - self._recv_info_box = xp.zeros(self.mpi_comm.Get_size(), dtype=int) + self._recv_info_box = np.zeros(self.mpi_comm.Get_size(), dtype=int) self.mpi_comm.Alltoall(self._send_info_box, self._recv_info_box) @@ -3467,8 +3511,8 @@ def sendrecv_markers_boxes(self): """ # i-th entry holds the number (not the index) of the first hole to be filled by data from process i - first_hole = xp.cumsum(self._recv_info_box) - self._recv_info_box - hole_inds = xp.nonzero(self._holes)[0] + first_hole = np.cumsum(self._recv_info_box) - self._recv_info_box + hole_inds = np.nonzero(self._holes)[0] # Initialize send and receive commands reqs = [] recvbufs = [] @@ -3479,7 +3523,7 @@ def sendrecv_markers_boxes(self): else: self.mpi_comm.Isend(data, dest=i, tag=self.mpi_comm.Get_rank()) - recvbufs += [xp.zeros((N_recv, self._markers.shape[1]), dtype=float)] + recvbufs += [np.zeros((N_recv, self._markers.shape[1]), dtype=float)] reqs += [self.mpi_comm.Irecv(recvbufs[-1], source=i, tag=i)] # Wait for buffer, then put markers into holes @@ -3502,7 +3546,7 @@ def sendrecv_markers_boxes(self): self.mpi_comm.Abort() # exit() - self._markers[hole_inds[first_hole[i] + xp.arange(self._recv_info_box[i])]] = recvbufs[i] + self._markers[hole_inds[first_hole[i] + np.arange(self._recv_info_box[i])]] = recvbufs[i] test_reqs.pop() reqs[i] = None @@ -3960,10 +4004,12 @@ def eval_density( Returns ------- - out : xp.ndarray + out : np.ndarray Estimated number density (or requested derivative component) at the - provided evaluation points. The array uses the same shape as `eta1` - and is returned as a `cunumpy` (`xp`) array. + provided evaluation points. The array uses the same shape as `eta1`. + Always a NumPy array: markers are host-resident (there is no device + particle kernel), and `eta1`/`eta2`/`eta3` are converted to NumPy + if they arrive as CuPy arrays. Notes ----- @@ -4019,7 +4065,7 @@ def eval_velocity( Returns ------- - (v1, v2, v3) : tuple of xp.ndarray + (v1, v2, v3) : tuple of np.ndarray Three arrays containing the estimated velocity components at the provided evaluation points. Each array has the same shape as `eta1`. @@ -4032,14 +4078,14 @@ def eval_velocity( """ first_free_idx = self.args_markers.first_free_idx - comps = xp.array((0, 1, 2)) + comps = np.array((0, 1, 2)) self.put_particles_in_boxes() func = PyccelKernel(eval_kernels_sph.sph_mean_velocity_coeffs) func( - alpha=xp.array((0.0, 0.0, 0.0)), + alpha=np.array((0.0, 0.0, 0.0)), column_nr=first_free_idx, comps=comps, args_markers=self.args_markers, @@ -4131,7 +4177,7 @@ def eval_div_viscosity( Returns ------- - (gamma_x, gamma_y, gamma_z) : tuple of xp.ndarray + (gamma_x, gamma_y, gamma_z) : tuple of np.ndarray Components of the divergence of the viscous stress evaluated at the provided points. Each array matches the shape of `eta1`. @@ -4149,9 +4195,9 @@ def eval_div_viscosity( # 1st kernel func = PyccelKernel(eval_kernels_sph.sph_mean_velocity_coeffs) - comps = xp.array((0, 1, 2)) + comps = np.array((0, 1, 2)) func( - alpha=xp.array((0.0, 0.0, 0.0)), + alpha=np.array((0.0, 0.0, 0.0)), column_nr=first_free_idx, comps=comps, args_markers=self.args_markers, @@ -4170,9 +4216,9 @@ def eval_div_viscosity( # 2nd kernel func = PyccelKernel(eval_kernels_sph.sph_viscosity_tensor) - comps = xp.arange(9) + comps = np.arange(9) func( - alpha=xp.array((0.0, 0.0, 0.0)), + alpha=np.array((0.0, 0.0, 0.0)), column_nr=first_free_idx + 3, comps=comps, args_markers=self.args_markers, @@ -4218,11 +4264,11 @@ def eval_div_viscosity( def eval_sph( self, - eta1: xp.ndarray, - eta2: xp.ndarray, - eta3: xp.ndarray, + eta1: np.ndarray, + eta2: np.ndarray, + eta3: np.ndarray, index: int, - out: xp.ndarray = None, + out: np.ndarray = None, fast: bool = True, kernel_type: str = "gaussian_1d", derivative: int = 0, @@ -4274,12 +4320,18 @@ def eval_sph( h1, h2, h3 : float Radius of the smoothing kernel in each dimension. """ - _shp = xp.shape(eta1) - assert _shp == xp.shape(eta2) == xp.shape(eta3) + # markers are always host-resident (there is no device particle kernel); + # bring evaluation points to the host too so they can be combined with them. + eta1 = _to_numpy_for_kernel(eta1) + eta2 = _to_numpy_for_kernel(eta2) + eta3 = _to_numpy_for_kernel(eta3) + + _shp = np.shape(eta1) + assert _shp == np.shape(eta2) == np.shape(eta3) if out is not None: - assert _shp == xp.shape(out) + assert _shp == np.shape(out) else: - out = xp.zeros_like(eta1) + out = np.zeros_like(eta1) assert derivative in {0, 1, 2, 3}, f"derivative must be 0, 1, 2 or 3, but is {derivative}." @@ -4362,7 +4414,7 @@ def update_ghost_particles(self): def sendrecv_determine_mtbs( self, - alpha: list | tuple | xp.ndarray = (1.0, 1.0, 1.0), + alpha: list | tuple | np.ndarray = (1.0, 1.0, 1.0), ): """ Determine which markers have to be sent from current process and put them in a new array. @@ -4384,12 +4436,12 @@ def sendrecv_determine_mtbs( Eta-values of shape (n_send, :) according to which the sorting is performed. """ # position that determines the sorting (including periodic shift of boundary conditions) - if not isinstance(alpha, xp.ndarray): - alpha = xp.array(alpha, dtype=float) + if not isinstance(alpha, np.ndarray): + alpha = np.array(alpha, dtype=float) assert alpha.size == 3 - assert xp.all(alpha >= 0.0) and xp.all(alpha <= 1.0) + assert np.all(alpha >= 0.0) and np.all(alpha <= 1.0) bi = self.first_pusher_idx - xp.mod( + np.mod( alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + (1.0 - alpha) * self.markers[:, bi : bi + 3], 1.0, @@ -4397,22 +4449,22 @@ def sendrecv_determine_mtbs( ) # check which particles are on the current process domain - self._is_on_proc_domain = xp.logical_and( + self._is_on_proc_domain = np.logical_and( self._sorting_etas > self.domain_array[self.mpi_rank, 0::3], self._sorting_etas < self.domain_array[self.mpi_rank, 1::3], ) # to stay on the current process, all three columns must be True - self._can_stay = xp.all(self._is_on_proc_domain, axis=1) + self._can_stay = np.all(self._is_on_proc_domain, axis=1) # holes and ghosts can stay, too self._can_stay[self.holes] = True self._can_stay[self.ghost_particles] = True # True values can stay on the process, False must be sent, already empty rows (-1) cannot be sent - send_inds = xp.nonzero(~self._can_stay)[0] + send_inds = np.nonzero(~self._can_stay)[0] - hole_inds_after_send = xp.nonzero(xp.logical_or(~self._can_stay, self.holes))[0] + hole_inds_after_send = np.nonzero(np.logical_or(~self._can_stay, self.holes))[0] return hole_inds_after_send, send_inds @@ -4431,16 +4483,16 @@ def sendrecv_get_destinations(self, send_inds): """ # One entry for each process - send_info = xp.zeros(self.mpi_size, dtype=int) + send_info = np.zeros(self.mpi_size, dtype=int) # TODO: do not loop over all processes, start with neighbours and work outwards (using while) for i in range(self.mpi_size): - conds = xp.logical_and( + conds = np.logical_and( self._sorting_etas[send_inds] > self.domain_array[i, 0::3], self._sorting_etas[send_inds] < self.domain_array[i, 1::3], ) - self._send_to_i[i] = xp.nonzero(xp.all(conds, axis=1))[0] + self._send_to_i[i] = np.nonzero(np.all(conds, axis=1))[0] send_info[i] = self._send_to_i[i].size self._send_list[i] = self.markers[send_inds][self._send_to_i[i]] @@ -4462,7 +4514,7 @@ def sendrecv_all_to_all(self, send_info): Amount of marticles to be received from i-th process. """ - recv_info = xp.zeros(self.mpi_size, dtype=int) + recv_info = np.zeros(self.mpi_size, dtype=int) self.mpi_comm.Alltoall(send_info, recv_info) @@ -4482,7 +4534,7 @@ def sendrecv_markers(self, recv_info, hole_inds_after_send): """ # i-th entry holds the number (not the index) of the first hole to be filled by data from process i - first_hole = xp.cumsum(recv_info) - recv_info + first_hole = np.cumsum(recv_info) - recv_info # Initialize send and receive commands for i, (data, N_recv) in enumerate(zip(self._send_list, list(recv_info))): @@ -4492,7 +4544,7 @@ def sendrecv_markers(self, recv_info, hole_inds_after_send): else: self.mpi_comm.Isend(data, dest=i, tag=self.mpi_rank) - self._recvbufs[i] = xp.zeros((N_recv, self.markers.shape[1]), dtype=float) + self._recvbufs[i] = np.zeros((N_recv, self.markers.shape[1]), dtype=float) self._reqs[i] = self.mpi_comm.Irecv(self._recvbufs[i], source=i, tag=i) # Wait for buffer, then put markers into holes @@ -4514,12 +4566,12 @@ def sendrecv_markers(self, recv_info, hole_inds_after_send): ) self.mpi_comm.Abort() - self.markers[hole_inds_after_send[first_hole[i] + xp.arange(recv_info[i])]] = self._recvbufs[i] + self.markers[hole_inds_after_send[first_hole[i] + np.arange(recv_info[i])]] = self._recvbufs[i] test_reqs.pop() self._reqs[i] = None - def _gather_scalar_in_subcomm_array(self, scalar: int, out: xp.ndarray = None): + def _gather_scalar_in_subcomm_array(self, scalar: int, out: np.ndarray = None): """Return an array of length sub_comm.size, where the i-th entry corresponds to the value of the scalar on process i. @@ -4528,11 +4580,11 @@ def _gather_scalar_in_subcomm_array(self, scalar: int, out: xp.ndarray = None): scalar : int The scalar value on each process. - out : xp.ndarray + out : np.ndarray The returned array (optional). """ if out is None: - _tmp = xp.zeros(self.mpi_size, dtype=int) + _tmp = np.zeros(self.mpi_size, dtype=int) else: assert out.size == self.mpi_size _tmp = out @@ -4548,7 +4600,7 @@ def _gather_scalar_in_subcomm_array(self, scalar: int, out: xp.ndarray = None): return _tmp - def _gather_scalar_in_intercomm_array(self, scalar: int, out: xp.ndarray = None): + def _gather_scalar_in_intercomm_array(self, scalar: int, out: np.ndarray = None): """Return an array of length inter_comm.size, where the i-th entry corresponds to the value of the scalar on clone i. @@ -4557,11 +4609,11 @@ def _gather_scalar_in_intercomm_array(self, scalar: int, out: xp.ndarray = None) scalar : int The scalar value on each clone. - out : xp.ndarray + out : np.ndarray The returned array (optional). """ if out is None: - _tmp = xp.zeros(self.num_clones, dtype=int) + _tmp = np.zeros(self.num_clones, dtype=int) else: assert out.size == self.num_clones _tmp = out @@ -4590,7 +4642,7 @@ class Tesselation: comm : Intracomm MPI communicator. - domain_array : xp.ndarray + domain_array : np.ndarray A 2d array[float] of shape (comm.Get_size(), 9) holding info on the domain decomposition. sorting_boxes : Particles.SortingBoxes @@ -4602,7 +4654,7 @@ def __init__( tiles_pb: int | float, *, comm: Intracomm = None, - domain_array: xp.ndarray = None, + domain_array: np.ndarray = None, sorting_boxes: Particles.SortingBoxes = None, ): if isinstance(tiles_pb, int): @@ -4620,8 +4672,8 @@ def __init__( assert domain_array is not None if domain_array is None: - self._starts = xp.zeros(3) - self._ends = xp.ones(3) + self._starts = np.zeros(3) + self._ends = np.ones(3) else: self._starts = domain_array[self.rank, 0::3] self._ends = domain_array[self.rank, 1::3] @@ -4644,9 +4696,9 @@ def __init__( if n_boxes == 1: self._dims_mask = [True] * 3 else: - self._dims_mask = xp.array(self.boxes_per_dim) > 1 + self._dims_mask = np.array(self.boxes_per_dim) > 1 - min_tiles = 2 ** xp.count_nonzero(self.dims_mask) + min_tiles = 2 ** np.count_nonzero(self.dims_mask) assert self.tiles_pb >= min_tiles, ( f"At least {min_tiles} tiles per sorting box is enforced, but you have {self.tiles_pb}!" ) @@ -4669,19 +4721,19 @@ def get_tiles(self): # logger.info(f'{self.dims_mask = }') # tiles in one sorting box - self._nt_per_dim = xp.array([1, 1, 1]) - _ids = xp.nonzero(self._dims_mask)[0] + self._nt_per_dim = np.array([1, 1, 1]) + _ids = np.nonzero(self._dims_mask)[0] for fac in factors_vec: _nt = self.nt_per_dim[self._dims_mask] - d = _ids[xp.argmin(_nt)] + d = _ids[np.argmin(_nt)] self._nt_per_dim[d] *= fac # logger.info(f'{_nt = }, {d = }, {self.nt_per_dim = }') - assert xp.prod(self.nt_per_dim) == self.tiles_pb + assert np.prod(self.nt_per_dim) == self.tiles_pb # tiles between [0, box_width] in each direction - self._tile_breaks = [xp.linspace(0.0, bw, nt + 1) for bw, nt in zip(self.box_widths, self.nt_per_dim)] - self._tile_midpoints = [(xp.roll(tbs, -1)[:-1] + tbs[:-1]) / 2 for tbs in self.tile_breaks] + self._tile_breaks = [np.linspace(0.0, bw, nt + 1) for bw, nt in zip(self.box_widths, self.nt_per_dim)] + self._tile_midpoints = [(np.roll(tbs, -1)[:-1] + tbs[:-1]) / 2 for tbs in self.tile_breaks] self._tile_volume = 1.0 for tb in self.tile_breaks: self._tile_volume *= tb[1] @@ -4689,8 +4741,8 @@ def get_tiles(self): def draw_markers(self): """Draw markers on the tile midpoints.""" _, eta1 = self._tile_output_arrays() - eta2 = xp.zeros_like(eta1) - eta3 = xp.zeros_like(eta1) + eta2 = np.zeros_like(eta1) + eta3 = np.zeros_like(eta1) nt_x, nt_y, nt_z = self.nt_per_dim @@ -4701,7 +4753,7 @@ def draw_markers(self): for k in range(self.boxes_per_dim[2]): z_midpoints = self._get_midpoints(k, 2) - xx, yy, zz = xp.meshgrid( + xx, yy, zz = np.meshgrid( x_midpoints, y_midpoints, z_midpoints, @@ -4738,10 +4790,13 @@ def _get_quad_pts(self, n_quad=None): self._tile_quad_pts = [] self._tile_quad_wts = [] for nq, tb in zip(n_quad, self.tile_breaks): - pts_loc, wts_loc = xp.polynomial.legendre.leggauss(nq) + pts_loc, wts_loc = np.polynomial.legendre.leggauss(nq) + # quadrature_grid follows the active array backend (it is also + # used for genuine device grid quadrature elsewhere); tesselation + # bookkeeping is always host-resident, so convert back here. pts, wts = quadrature_grid(tb[:2], pts_loc, wts_loc) - self._tile_quad_pts += [pts[0]] - self._tile_quad_wts += [wts[0]] + self._tile_quad_pts += [_to_numpy_for_kernel(pts[0])] + self._tile_quad_wts += [_to_numpy_for_kernel(wts[0])] def cell_averages(self, fun, n_quad=None): """Compute cell averages of fun over all tiles on current process. @@ -4765,14 +4820,16 @@ def cell_averages(self, fun, n_quad=None): for k in range(self.boxes_per_dim[2]): z_pts = self._get_box_quad_pts(k, 2) - xx, yy, zz = xp.meshgrid( + xx, yy, zz = np.meshgrid( x_pts.flatten(), y_pts.flatten(), z_pts.flatten(), indexing="ij", ) - fun_vals = fun(xx, yy, zz) + # fun (f_init/domain transform) follows the active array + # backend; tesselation bookkeeping is always host-resident. + fun_vals = _to_numpy_for_kernel(fun(*_dev(xx, yy, zz))) sampling_kernels.tile_int_kernel( fun_vals, @@ -4794,9 +4851,9 @@ def _tile_output_arrays(self): * the first with one entry for each tile on one sorting box * the second with one entry for each tile on current process """ - # self._quad_pts = [xp.zeros((nt, nq)).flatten() for nt, nq in zip(self.nt_per_dim, self.tile_quad_pts)] - single_box_out = xp.zeros(self.nt_per_dim) - out = xp.tile(single_box_out, self.boxes_per_dim) + # self._quad_pts = [np.zeros((nt, nq)).flatten() for nt, nq in zip(self.nt_per_dim, self.tile_quad_pts)] + single_box_out = np.zeros(self.nt_per_dim) + out = np.tile(single_box_out, self.boxes_per_dim) return single_box_out, out def _get_midpoints(self, i: int, dim: int): @@ -4817,13 +4874,13 @@ def _get_box_quad_pts(self, i: int, dim: int): Returns ------- - x_pts : xp.array + x_pts : np.array 2d array of shape (n_tiles_pb, n_tile_quad_pts) """ xl = self.starts[dim] + i * self.box_widths[dim] x_tile_breaks = xl + self.tile_breaks[dim][:-1] x_tile_pts = self.tile_quad_pts[dim] - x_pts = xp.tile(x_tile_breaks, (x_tile_pts.size, 1)).T + x_tile_pts + x_pts = np.tile(x_tile_breaks, (x_tile_pts.size, 1)).T + x_tile_pts return x_pts @property diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 2ce1ff007..e80b8e8be 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -2,7 +2,7 @@ import logging -import cunumpy as xp +import numpy as np from cunumpy import PyccelKernel from feectools.ddm.mpi import mpi as MPI from line_profiler import profile @@ -133,7 +133,7 @@ def __init__( comps = ker_args[2] # check marker array column number - assert isinstance(comps, xp.ndarray) + assert isinstance(comps, np.ndarray) assert column_nr + comps.size < particles.n_cols, ( f"{column_nr + comps.size} not smaller than {particles.n_cols =}; not enough columns in marker array !!" ) @@ -145,7 +145,7 @@ def __init__( comps = ker_args[3] # check marker array column number - assert isinstance(comps, xp.ndarray) + assert isinstance(comps, np.ndarray) assert column_nr + comps.size < particles.n_cols, ( f"{column_nr + comps.size} not smaller than {particles.n_cols =}; not enough columns in marker array !!" ) @@ -153,7 +153,7 @@ def __init__( self._init_kernels = init_kernels self._eval_kernels = eval_kernels - self._residuals = xp.zeros(self.particles.markers.shape[0]) + self._residuals = np.zeros(self.particles.markers.shape[0]) self._converged_loc = self._residuals == 1.0 self._not_converged_loc = self._residuals == 0.0 @@ -204,7 +204,7 @@ def __call__(self, dt: float): add_args = ker_args[3] ker( - xp.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), column_nr, comps, self.particles.args_markers, @@ -219,7 +219,7 @@ def __call__(self, dt: float): # start stages (e.g. n_stages=4 for RK4) for stage in range(self.n_stages): # start iteration (maxiter=1 for explicit schemes) - n_not_converged = xp.empty(1, dtype=int) + n_not_converged = np.empty(1, dtype=int) n_not_converged[0] = self.particles.n_mks_loc k = 0 @@ -291,12 +291,12 @@ def __call__(self, dt: float): # compute number of non-converged particles (maxiter=1 for explicit schemes) if self.maxiter > 1: self._residuals[:] = markers[:, residual_idx] - max_res = xp.max(self._residuals) + max_res = np.max(self._residuals) if max_res < 0.0: max_res = None self._converged_loc[:] = self._residuals < self._tol self._not_converged_loc[:] = ~self._converged_loc - n_not_converged[0] = xp.count_nonzero( + n_not_converged[0] = np.count_nonzero( self._not_converged_loc, ) diff --git a/src/struphy/pic/tests/test_accum_vec_H1.py b/src/struphy/pic/tests/test_accum_vec_H1.py index 3c5ae9af9..7aa682a50 100644 --- a/src/struphy/pic/tests/test_accum_vec_H1.py +++ b/src/struphy/pic/tests/test_accum_vec_H1.py @@ -113,7 +113,9 @@ def test_accum_poisson(num_elements, degree, bcs, mapping, num_clones, Np=10000, params = { "grid": {"num_elements": num_elements}, - "kinetic": {"test_particles": {"markers": {"Np": Np, "ppc": Np / xp.prod(num_elements)}}}, + # num_elements is a plain Python list; CuPy's prod() (unlike NumPy's) + # doesn't accept one, so wrap it explicitly. + "kinetic": {"test_particles": {"markers": {"Np": Np, "ppc": Np / xp.prod(xp.array(num_elements))}}}, } grid = TensorProductGrid(num_elements=num_elements) @@ -175,9 +177,10 @@ def test_accum_poisson(num_elements, degree, bcs, mapping, num_clones, Np=10000, _sqrtg = float(domain.jacobian_det(0.5, 0.5, 0.5, squeeze_out=True)) + # particles.weights is always host (NumPy). logger.info( - f"rank {mpi_rank}: weights min={float(xp.min(particles.weights)):.6g}, " - f"max={float(xp.max(particles.weights)):.6g} " + f"rank {mpi_rank}: weights min={float(particles.weights.min()):.6g}, " + f"max={float(particles.weights.max()):.6g} " f"(expected range [{0.5 * _sqrtg / Np:.6g}, {1.5 * _sqrtg / Np:.6g}])" ) @@ -466,8 +469,13 @@ def u_xyz(x, y, z): # indexing particles.markers directly, since those already apply the # # correct valid_mks mask (excludes holes and ghosts). # # ------------------------------------------------------------------ # + # particles.positions is always host (NumPy), while n_xyz (like the rest + # of this file) follows the active array backend; convert both ways here. eta = particles.positions - particles.markers[particles.valid_mks, particles.first_free_idx] = n_xyz(eta[:, 0], eta[:, 1], eta[:, 2]) + n_vals = n_xyz(xp.asarray(eta[:, 0]), xp.asarray(eta[:, 1]), xp.asarray(eta[:, 2])) + if hasattr(n_vals, "get"): + n_vals = n_vals.get() + particles.markers[particles.valid_mks, particles.first_free_idx] = n_vals # ------------------------------------------------------------------ # # Accumulate the weak-divergence-1form RHS vector V^1. # diff --git a/src/struphy/pic/tests/test_binning.py b/src/struphy/pic/tests/test_binning.py index af2f8927c..ea26c7334 100644 --- a/src/struphy/pic/tests/test_binning.py +++ b/src/struphy/pic/tests/test_binning.py @@ -86,6 +86,9 @@ def test_binning_6D_full_f(mapping, show_plot=False): [False, False, False, True, False, False], [v1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) v1_plot = v1_bins[:-1] + dv / 2 @@ -129,6 +132,9 @@ def test_binning_6D_full_f(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) e1_plot = e1_bins[:-1] + de / 2 @@ -189,6 +195,9 @@ def test_binning_6D_full_f(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) e1_plot = e1_bins[:-1] + de / 2 @@ -323,6 +332,9 @@ def test_binning_6D_delta_f(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) e1_plot = e1_bins[:-1] + de / 2 @@ -383,6 +395,9 @@ def test_binning_6D_delta_f(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) e1_plot = e1_bins[:-1] + de / 2 @@ -526,6 +541,9 @@ def test_binning_6D_full_f_mpi(mapping, show_plot=False): [False, False, False, True, False, False], [v1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -578,6 +596,9 @@ def test_binning_6D_full_f_mpi(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -675,6 +696,9 @@ def test_binning_6D_full_f_mpi(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -837,6 +861,9 @@ def test_binning_6D_delta_f_mpi(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -936,6 +963,9 @@ def test_binning_6D_delta_f_mpi(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -1101,6 +1131,9 @@ def test_current_helper(moments, u_axis, current_axis, ana_func): [e_bins], f"current_{current_axis}", ) + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) e_plot = e_bins[:-1] + de / 2 @@ -1158,6 +1191,9 @@ def test_current_helper(moments, u_axis, current_axis, ana_func): components = [True, False, False, False, False, False] binned_res, r2 = particles.binning(components, [e_bins], "current_2") + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) e_plot = e_bins[:-1] + de / 2 @@ -1252,6 +1288,9 @@ def test_binning_energy_tensor_6D_full_f(mapping, show_plot=False): for i in [11, 22, 33, 12, 13, 23]: binned_res, r2 = particles.binning(components, [e_bins], f"energy_tensor_{i}") + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) ana_res = ana_func(e_plot) @@ -1345,6 +1384,9 @@ def test_binning_heat_flux_6D_full_f(mapping, show_plot=False): for i in range(1, 4): binned_res, r2 = particles.binning(components, [e_bins], f"heat_flux_{i}") + # particles.binning() is always host (NumPy); convert to the + # active backend to match the rest of this test's xp-based arrays. + binned_res = xp.asarray(binned_res) ana_res = ana_func(e_plot) binned_res += 1 diff --git a/src/struphy/pic/tests/test_draw_parallel.py b/src/struphy/pic/tests/test_draw_parallel.py index e0a796aec..6ccb69ebc 100644 --- a/src/struphy/pic/tests/test_draw_parallel.py +++ b/src/struphy/pic/tests/test_draw_parallel.py @@ -46,7 +46,8 @@ def test_draw(num_elements, degree, bcs, mapping, ppc=10): """Asserts whether all particles are on the correct process after `particles.mpi_sort_markers()`.""" - import cunumpy as xp + import numpy as np + from cunumpy import to_numpy from feectools.ddm.mpi import mpi as MPI from struphy import BoundaryParameters, LoadingParameters, WeightsParameters, domains @@ -99,7 +100,7 @@ def test_draw(num_elements, degree, bcs, mapping, ppc=10): particles.initialize_weights() _w0 = particles.weights logger.info("Test weights:") - logger.info(f"rank {rank}: {_w0.shape} {xp.min(_w0)} {xp.max(_w0)}") + logger.info(f"rank {rank}: {_w0.shape} {np.min(_w0)} {np.max(_w0)}") comm.Barrier() logger.info("Number of particles w/wo holes on each process before sorting : ") @@ -114,17 +115,20 @@ def test_draw(num_elements, degree, bcs, mapping, ppc=10): logger.info(f"Rank {rank} : {particles.n_mks_loc} {particles.markers.shape[0]}") # are all markers in the correct domain? - conds = xp.logical_and( - particles.markers[:, :3] > derham.domain_array[rank, 0::3], - particles.markers[:, :3] < derham.domain_array[rank, 1::3], + # particles.markers is always host (NumPy); derham.domain_array may be a + # device array under CuPy, so bring it to the host for this comparison. + domain_array_host = to_numpy(derham.domain_array) + conds = np.logical_and( + particles.markers[:, :3] > domain_array_host[rank, 0::3], + particles.markers[:, :3] < domain_array_host[rank, 1::3], ) holes = particles.markers[:, 0] == -1.0 - stay = xp.all(conds, axis=1) + stay = np.all(conds, axis=1) - error_mks = particles.markers[xp.logical_and(~stay, ~holes)] + error_mks = particles.markers[np.logical_and(~stay, ~holes)] assert error_mks.size == 0, ( - f"rank {rank} | markers not on correct process: {xp.nonzero(xp.logical_and(~stay, ~holes))} \n corresponding positions:\n {error_mks[:, :3]}" + f"rank {rank} | markers not on correct process: {np.nonzero(np.logical_and(~stay, ~holes))} \n corresponding positions:\n {error_mks[:, :3]}" ) diff --git a/src/struphy/pic/tests/test_mat_vec_filler.py b/src/struphy/pic/tests/test_mat_vec_filler.py index e0bdf4026..5692a20cc 100644 --- a/src/struphy/pic/tests/test_mat_vec_filler.py +++ b/src/struphy/pic/tests/test_mat_vec_filler.py @@ -1,6 +1,6 @@ import logging -import cunumpy as xp +import numpy as np import pytest logger = logging.getLogger("struphy") @@ -47,12 +47,12 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): logger.info(f"\nnum_elements={num_elements}, degree={degree}, bcs={bcs}\n") # DR attributes - pn = xp.array(DR.degree) + pn = np.array(DR.degree) tn1, tn2, tn3 = DR.V0fem.knots starts1 = {} - starts1["v0"] = xp.array(DR.V0.starts) + starts1["v0"] = np.array(DR.V0.starts) comm.Barrier() sleep(0.02 * (rank + 1)) @@ -69,59 +69,74 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): # only for M1 Mac users PSYDAC_BACKEND_GPYCCEL["flags"] = "-O3 -march=native -mtune=native -ffast-math -ffree-line-length-none" + # StencilMatrix/StencilVector._data follows the active array backend (it + # is genuinely device-resident under CuPy, for the GPU linear-algebra + # path). This test calls the raw (non-marshalled) filler kernels below + # directly with a single particle's scalar coordinates, which is a + # host-only scenario, so _data is brought to the host right after + # construction. + def _host(a): + return a.get() if hasattr(a, "get") else a + # _data of StencilMatrices/Vectors mat = {} vec = {} - mat["v0"] = StencilMatrix(DR.V0, DR.V0, backend=PSYDAC_BACKEND_GPYCCEL, precompiled=True)._data - vec["v0"] = StencilVector(DR.V0)._data + mat["v0"] = _host(StencilMatrix(DR.V0, DR.V0, backend=PSYDAC_BACKEND_GPYCCEL, precompiled=True)._data) + vec["v0"] = _host(StencilVector(DR.V0)._data) - mat["v3"] = StencilMatrix(DR.V3, DR.V3, backend=PSYDAC_BACKEND_GPYCCEL, precompiled=True)._data - vec["v3"] = StencilVector(DR.V3)._data + mat["v3"] = _host(StencilMatrix(DR.V3, DR.V3, backend=PSYDAC_BACKEND_GPYCCEL, precompiled=True)._data) + vec["v3"] = _host(StencilVector(DR.V3)._data) mat["v1"] = [] for i in range(3): mat["v1"] += [[]] for j in range(3): mat["v1"][-1] += [ - StencilMatrix( - DR.V1.spaces[i], - DR.V1.spaces[j], - backend=PSYDAC_BACKEND_GPYCCEL, - precompiled=True, - )._data, + _host( + StencilMatrix( + DR.V1.spaces[i], + DR.V1.spaces[j], + backend=PSYDAC_BACKEND_GPYCCEL, + precompiled=True, + )._data, + ), ] vec["v1"] = [] for i in range(3): - vec["v1"] += [StencilVector(DR.V1.spaces[i])._data] + vec["v1"] += [_host(StencilVector(DR.V1.spaces[i])._data)] mat["v2"] = [] for i in range(3): mat["v2"] += [[]] for j in range(3): mat["v2"][-1] += [ - StencilMatrix( - DR.V2.spaces[i], - DR.V2.spaces[j], - backend=PSYDAC_BACKEND_GPYCCEL, - precompiled=True, - )._data, + _host( + StencilMatrix( + DR.V2.spaces[i], + DR.V2.spaces[j], + backend=PSYDAC_BACKEND_GPYCCEL, + precompiled=True, + )._data, + ), ] vec["v2"] = [] for i in range(3): - vec["v2"] += [StencilVector(DR.V2.spaces[i])._data] + vec["v2"] += [_host(StencilVector(DR.V2.spaces[i])._data)] # Some filling for testing - fill_mat = xp.reshape(xp.arange(9, dtype=float), (3, 3)) + 1.0 - fill_vec = xp.arange(3, dtype=float) + 1.0 + fill_mat = np.reshape(np.arange(9, dtype=float), (3, 3)) + 1.0 + fill_vec = np.arange(3, dtype=float) + 1.0 # Random points in domain of process (VERY IMPORTANT to be in the right domain, otherwise NON-TRACKED errors occur in filler_kernels !!) - dom = DR.domain_array[rank] - eta1s = xp.random.rand(n_markers) * (dom[1] - dom[0]) + dom[0] - eta2s = xp.random.rand(n_markers) * (dom[4] - dom[3]) + dom[3] - eta3s = xp.random.rand(n_markers) * (dom[7] - dom[6]) + dom[6] + # DR.domain_array may be device-resident under CuPy; bring it to the host + # so eta1s/eta2s/eta3s (fed to raw Pyccel kernels below) stay NumPy. + dom = _host(DR.domain_array[rank]) + eta1s = np.random.rand(n_markers) * (dom[1] - dom[0]) + dom[0] + eta2s = np.random.rand(n_markers) * (dom[4] - dom[3]) + dom[3] + eta3s = np.random.rand(n_markers) * (dom[7] - dom[6]) + dom[6] for eta1, eta2, eta3 in zip(eta1s, eta2s, eta3s): comm.Barrier() @@ -138,13 +153,13 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): span3 = bsp.find_span(tn3, DR.degree[2], eta3) # non-zero spline values at eta - bn1 = xp.empty(DR.degree[0] + 1, dtype=float) - bn2 = xp.empty(DR.degree[1] + 1, dtype=float) - bn3 = xp.empty(DR.degree[2] + 1, dtype=float) + bn1 = np.empty(DR.degree[0] + 1, dtype=float) + bn2 = np.empty(DR.degree[1] + 1, dtype=float) + bn3 = np.empty(DR.degree[2] + 1, dtype=float) - bd1 = xp.empty(DR.degree[0], dtype=float) - bd2 = xp.empty(DR.degree[1], dtype=float) - bd3 = xp.empty(DR.degree[2], dtype=float) + bd1 = np.empty(DR.degree[0], dtype=float) + bd2 = np.empty(DR.degree[1], dtype=float) + bd3 = np.empty(DR.degree[2], dtype=float) bsp.b_d_splines_slim(tn1, DR.degree[0], eta1, span1, bn1, bd1) bsp.b_d_splines_slim(tn2, DR.degree[1], eta2, span2, bn2, bd2) @@ -156,9 +171,9 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): ie3 = span3 - pn[2] # global indices of non-vanishing B- and D-splines (no modulo) - glob_n1 = xp.arange(ie1, ie1 + pn[0] + 1) - glob_n2 = xp.arange(ie2, ie2 + pn[1] + 1) - glob_n3 = xp.arange(ie3, ie3 + pn[2] + 1) + glob_n1 = np.arange(ie1, ie1 + pn[0] + 1) + glob_n2 = np.arange(ie2, ie2 + pn[1] + 1) + glob_n3 = np.arange(ie3, ie3 + pn[2] + 1) glob_d1 = glob_n1[:-1] glob_d2 = glob_n2[:-1] @@ -184,10 +199,10 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): # local column indices in _data of non-vanishing B- and D-splines, as sets for comparison cols = [{}, {}, {}] for n in range(3): - cols[n]["NN"] = set(xp.arange(2 * pn[n] + 1)) - cols[n]["ND"] = set(xp.arange(2 * pn[n])) - cols[n]["DN"] = set(xp.arange(1, 2 * pn[n] + 1)) - cols[n]["DD"] = set(xp.arange(1, 2 * pn[n])) + cols[n]["NN"] = set(np.arange(2 * pn[n] + 1)) + cols[n]["ND"] = set(np.arange(2 * pn[n])) + cols[n]["DN"] = set(np.arange(1, 2 * pn[n] + 1)) + cols[n]["DD"] = set(np.arange(1, 2 * pn[n])) # testing vector-valued spaces spaces_vector = ["v1", "v2"] @@ -364,22 +379,22 @@ def assert_mat(mat, rows, cols, row_str, col_str, rank): """ assert len(mat.shape) == 6 # assert non NaN - assert ~xp.isnan(mat).any() + assert ~np.isnan(mat).any() atol = 1e-14 logger.debug(f"\n({row_str}) ({col_str})") - logger.debug(f"rank {rank} | ind_row1: {set(xp.where(mat > atol)[0])}") - logger.debug(f"rank {rank} | ind_row2: {set(xp.where(mat > atol)[1])}") - logger.debug(f"rank {rank} | ind_row3: {set(xp.where(mat > atol)[2])}") - logger.debug(f"rank {rank} | ind_col1: {set(xp.where(mat > atol)[3])}") - logger.debug(f"rank {rank} | ind_col2: {set(xp.where(mat > atol)[4])}") - logger.debug(f"rank {rank} | ind_col3: {set(xp.where(mat > atol)[5])}") + logger.debug(f"rank {rank} | ind_row1: {set(np.where(mat > atol)[0])}") + logger.debug(f"rank {rank} | ind_row2: {set(np.where(mat > atol)[1])}") + logger.debug(f"rank {rank} | ind_row3: {set(np.where(mat > atol)[2])}") + logger.debug(f"rank {rank} | ind_col1: {set(np.where(mat > atol)[3])}") + logger.debug(f"rank {rank} | ind_col2: {set(np.where(mat > atol)[4])}") + logger.debug(f"rank {rank} | ind_col3: {set(np.where(mat > atol)[5])}") # check if correct indices are non-zero for n, (r, c) in enumerate(zip(row_str, col_str)): - assert set(xp.where(mat > atol)[n]) == rows[n][r] - assert set(xp.where(mat > atol)[n + 3]) == cols[n][r + c] + assert set(np.where(mat > atol)[n]) == rows[n][r] + assert set(np.where(mat > atol)[n + 3]) == cols[n][r + c] # Set matrix back to zero mat[:, :] = 0.0 @@ -407,18 +422,18 @@ def assert_vec(vec, rows, row_str, rank): """ assert len(vec.shape) == 3 # assert non Nan - assert ~xp.isnan(vec).any() + assert ~np.isnan(vec).any() atol = 1e-14 logger.debug(f"\n({row_str})") - logger.debug(f"rank {rank} | ind_row1: {set(xp.where(vec > atol)[0])}") - logger.debug(f"rank {rank} | ind_row2: {set(xp.where(vec > atol)[1])}") - logger.debug(f"rank {rank} | ind_row3: {set(xp.where(vec > atol)[2])}") + logger.debug(f"rank {rank} | ind_row1: {set(np.where(vec > atol)[0])}") + logger.debug(f"rank {rank} | ind_row2: {set(np.where(vec > atol)[1])}") + logger.debug(f"rank {rank} | ind_row3: {set(np.where(vec > atol)[2])}") # check if correct indices are non-zero for n, r in enumerate(row_str): - assert set(xp.where(vec > atol)[n]) == rows[n][r] + assert set(np.where(vec > atol)[n]) == rows[n][r] # Set vector back to zero vec[:] = 0.0 diff --git a/src/struphy/pic/tests/test_sph.py b/src/struphy/pic/tests/test_sph.py index d536004b7..88b513e7d 100644 --- a/src/struphy/pic/tests/test_sph.py +++ b/src/struphy/pic/tests/test_sph.py @@ -114,6 +114,9 @@ def test_sph_evaluation_1d( kernel_type=kernel, derivative=derivative, ) + # eval_density() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -238,6 +241,9 @@ def test_sph_evaluation_2d( kernel_type=kernel, derivative=derivative, ) + # eval_density() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -357,6 +363,9 @@ def test_sph_evaluation_3d( kernel_type=kernel, derivative=derivative, ) + # eval_density() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -481,6 +490,9 @@ def test_evaluation_SPH_Np_convergence_1d(boxes_per_dim, bc_x, eval_pts, tessela h3 = 1 / boxes_per_dim[2] test_eval = particles.eval_density(ee1, ee2, ee3, h1=h1, h2=h2, h3=h3) + # eval_density() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -500,10 +512,10 @@ def test_evaluation_SPH_Np_convergence_1d(boxes_per_dim, bc_x, eval_pts, tessela logger.info(f"{Np =}, {ppb =}, {diff =}") if tesselation: - fit = xp.polyfit(xp.log(ppbs), xp.log(err_vec), 1) + fit = xp.polyfit(xp.log(xp.array(ppbs)), xp.log(xp.array(err_vec)), 1) xvec = ppbs else: - fit = xp.polyfit(xp.log(Nps), xp.log(err_vec), 1) + fit = xp.polyfit(xp.log(xp.array(Nps)), xp.log(xp.array(err_vec)), 1) xvec = Nps if show_plot and rank == 0: @@ -598,6 +610,9 @@ def test_evaluation_SPH_h_convergence_1d(boxes_per_dim, bc_x, eval_pts, tesselat h3 = 1 / boxes_per_dim[2] test_eval = particles.eval_density(ee1, ee2, ee3, h1=h1, h2=h2, h3=h3) + # eval_density() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -623,9 +638,9 @@ def test_evaluation_SPH_h_convergence_1d(boxes_per_dim, bc_x, eval_pts, tesselat err_vec += [diff] if tesselation: - fit = xp.polyfit(xp.log(h_vec[1:5]), xp.log(err_vec[1:5]), 1) + fit = xp.polyfit(xp.log(xp.array(h_vec[1:5])), xp.log(xp.array(err_vec[1:5])), 1) else: - fit = xp.polyfit(xp.log(h_vec[:-2]), xp.log(err_vec[:-2]), 1) + fit = xp.polyfit(xp.log(xp.array(h_vec[:-2])), xp.log(xp.array(err_vec[:-2])), 1) if show_plot and rank == 0: plt.figure(figsize=(12, 8)) @@ -722,6 +737,9 @@ def test_evaluation_mc_Np_and_h_convergence_1d(boxes_per_dim, bc_x, eval_pts, te h3 = 1 / boxes_per_dim[2] test_eval = particles.eval_density(ee1, ee2, ee3, h1=h, h2=h2, h3=h3) + # eval_density() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -882,6 +900,9 @@ def test_evaluation_SPH_Np_convergence_2d(boxes_per_dim, bc_x, bc_y, tesselation h3 = 1 / boxes_per_dim[2] test_eval = particles.eval_density(ee1, ee2, ee3, h1=h1, h2=h2, h3=h3, kernel_type="gaussian_2d") + # eval_density() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -908,10 +929,10 @@ def test_evaluation_SPH_Np_convergence_2d(boxes_per_dim, bc_x, bc_y, tesselation # fig.savefig(f"2d_sph_{Np}_{ppb}.png") if tesselation: - fit = xp.polyfit(xp.log(ppbs), xp.log(err_vec), 1) + fit = xp.polyfit(xp.log(xp.array(ppbs)), xp.log(xp.array(err_vec)), 1) xvec = ppbs else: - fit = xp.polyfit(xp.log(Nps), xp.log(err_vec), 1) + fit = xp.polyfit(xp.log(xp.array(Nps)), xp.log(xp.array(err_vec)), 1) xvec = Nps if show_plot and rank == 0: @@ -1038,6 +1059,11 @@ def du_xyz(x, y, z): kernel_type=kernel, derivative=derivative, ) + # eval_velocity() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + v1 = xp.asarray(v1) + v2 = xp.asarray(v2) + v3 = xp.asarray(v3) if derivative == 0: v1_e, v2_e, v3_e = background.u_xyz(ee1, ee2, ee3) @@ -1222,6 +1248,9 @@ def du_deta2(eta1, eta2, eta3): kernel_type=kernel, derivative=derivative, ) + # eval_velocity() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + v_log = xp.asarray(v_log) v1, v2, v3 = v_log if derivative == 0: @@ -1472,6 +1501,9 @@ def div_pi_analytic(x, y, z, mu=None): kernel_type=kernel, derivative=0, ) + # eval_density() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + density = xp.asarray(density) if rank == 0: logger.info(f"{density.shape = }") logger.info(f"{xp.min(density) = }, {xp.max(density) = }") @@ -1500,6 +1532,11 @@ def div_pi_analytic(x, y, z, mu=None): kernel_type=kernel, derivative=0, ) + # eval_velocity() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + vx = xp.asarray(vx) + vy = xp.asarray(vy) + vz = xp.asarray(vz) if rank == 0: logger.info(f"{vx.shape = }, {vy.shape = }") logger.info(f"{xp.min(vx) = }, {xp.max(vx) = }") @@ -1542,6 +1579,9 @@ def div_pi_analytic(x, y, z, mu=None): mu=mu, kernel_type=kernel, ) + # eval_div_viscosity() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + div_viscosity = xp.asarray(div_viscosity) gamma_x = div_viscosity[0] gamma_y = div_viscosity[1] gamma_z = div_viscosity[2] @@ -1852,7 +1892,10 @@ def u_xyz(x, y, z): particles.draw_markers(sort=False) if rank == 0: - ghost_inds = xp.where(particles.ghost_particles)[0] + # particles.ghost_particles is always host (NumPy). + import numpy as np + + ghost_inds = np.where(particles.ghost_particles)[0] logger.info(f"After do_sort: {len(ghost_inds)} ghosts") if len(ghost_inds) > 0: logger.info(f"First 10 ghost eta1: {particles.markers[ghost_inds[:10], 0]}") @@ -1887,6 +1930,11 @@ def u_xyz(x, y, z): kernel_type=kernel, derivative=0, ) + # eval_velocity() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + v1 = xp.asarray(v1) + v2 = xp.asarray(v2) + v3 = xp.asarray(v3) # if rank == 0 and len(ghost_inds) > 0: # logger.info("Ghost coefficients after eval:", particles.markers[ghost_inds[:10], particles.first_free_idx]) @@ -2060,6 +2108,11 @@ def u_xyz(x, y, z): kernel_type=kernel, derivative=0, ) + # eval_velocity() is always host (NumPy); convert to the + # active backend to match this test's xp-based reference arrays. + v1 = xp.asarray(v1) + v2 = xp.asarray(v2) + v3 = xp.asarray(v3) if comm is not None: all_v1 = xp.zeros_like(v1) diff --git a/src/struphy/pic/tests/test_tesselation.py b/src/struphy/pic/tests/test_tesselation.py index 5b1d3e364..10e49b5d4 100644 --- a/src/struphy/pic/tests/test_tesselation.py +++ b/src/struphy/pic/tests/test_tesselation.py @@ -178,10 +178,15 @@ def test_cell_average(ppb, nx, ny, nz, n_quad, show_plot=False): plt.show() # test - logger.info( - f"\n{rank =}, {xp.max(xp.abs(particles.weights * particles.Np - particles.f_init(particles.positions))) =}" - ) - assert xp.max(xp.abs(particles.weights * particles.Np - particles.f_init(particles.positions))) < 0.012 + # particles.weights is always host (NumPy), while f_init follows the + # active array backend, so bring f_init's result to the host before + # comparing (with plain NumPy, not xp, since both operands are host now). + import numpy as np + from cunumpy import to_numpy + + f_init_at_markers = to_numpy(particles.f_init(particles.positions)) + logger.info(f"\n{rank =}, {np.max(np.abs(particles.weights * particles.Np - f_init_at_markers)) =}") + assert np.max(np.abs(particles.weights * particles.Np - f_init_at_markers)) < 0.012 if __name__ == "__main__": diff --git a/src/struphy/propagators/base.py b/src/struphy/propagators/base.py index 2df234087..71228e12c 100644 --- a/src/struphy/propagators/base.py +++ b/src/struphy/propagators/base.py @@ -6,6 +6,7 @@ from typing import Literal import cunumpy as xp +import numpy as np from cunumpy import PyccelKernel from feectools.linalg.block import BlockVector from feectools.linalg.stencil import StencilVector @@ -261,10 +262,13 @@ def add_init_kernel( args_init : tuple The arguments for the kernel function. """ + # comps/alpha are marker-column indices/weights fed straight to compiled, + # host-only particle kernels alongside args_markers (no device particle + # kernel exists), so they are always NumPy, matching self.markers. if comps is None: - comps = xp.array([0]) # case for scalar evaluation + comps = np.array([0]) # case for scalar evaluation else: - comps = xp.array(comps, dtype=int) + comps = np.array(comps, dtype=int) if not hasattr(self, "_init_kernels"): self._init_kernels = [] @@ -313,12 +317,12 @@ def add_eval_kernel( """ if isinstance(alpha, int) or isinstance(alpha, float): alpha = [alpha] * 6 - alpha = xp.array(alpha) + alpha = np.array(alpha) if comps is None: - comps = xp.array([0]) # case for scalar evaluation + comps = np.array([0]) # case for scalar evaluation else: - comps = xp.array(comps, dtype=int) + comps = np.array(comps, dtype=int) if not hasattr(self, "_eval_kernels"): self._eval_kernels = [] From 44687e0e1a84a73bfe83c887d5234e8fb11158e9 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Fri, 14 Aug 2026 11:33:12 +0200 Subject: [PATCH 019/156] Speed up host to/from device transfers --- src/struphy/pic/base.py | 92 ++++++++++++++++++++++++++++++- src/struphy/pic/pushing/pusher.py | 36 ++++++++++-- 2 files changed, 121 insertions(+), 7 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 8ee474186..a6aa08fd1 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -15,6 +15,7 @@ class Intracomm: x = None +import cunumpy import numpy as np from cunumpy import PyccelKernel from cunumpy import to_cunumpy @@ -85,6 +86,37 @@ def _dev(*arrays): return out[0] if len(out) == 1 else out +def _pinned_zeros(shape, dtype=float): + """Allocate a zeroed NumPy array, backed by page-locked ("pinned") host + memory when the active backend is CuPy. + + The returned object is a plain ``numpy.ndarray`` in every respect + (Pyccel kernels, aliasing with ``args_markers.markers``, etc. all work + exactly as with a regular allocation) — pinning only changes how fast the + *host* memory can later be DMA'd to/from the device. Pageable memory + (the default) transfers the full markers array at roughly PCIe-over-copy + speed (~90 ms for 137 MiB, measured); pinned memory reaches near the + PCIe link's true bandwidth (~5 ms for the same array), which is what + makes it worthwhile to bounce the per-step vectorized bookkeeping + (:meth:`Particles._find_outside_particles`, the column-block resets in + :class:`~struphy.pic.pushing.pusher.Pusher`) through the device. + + Under the NumPy backend nothing is ever transferred, so pinning would + only tie up a scarcer resource for no benefit; a plain allocation is + used instead. + """ + if not cunumpy.cupy_backend: + return np.zeros(shape, dtype=dtype) + import cupy as cp + + size = int(np.prod(shape)) + nbytes = size * np.dtype(dtype).itemsize + mem = cp.cuda.alloc_pinned_memory(nbytes) + arr = np.frombuffer(mem, dtype=dtype, count=size).reshape(shape) + arr[:] = 0 + return arr + + class Particles(metaclass=ABCMeta): """Base class for particle species.""" @@ -1181,12 +1213,21 @@ def _allocate_marker_array(self, dry_run: bool = False): if dry_run: return - self._markers = np.zeros((self.n_rows, self.n_cols), dtype=float) + self._markers = _pinned_zeros((self.n_rows, self.n_cols), dtype=float) # allocate auxiliary arrays self._holes = np.zeros(self.n_rows, dtype=bool) self._ghost_particles = np.zeros(self.n_rows, dtype=bool) self._valid_mks = np.zeros(self.n_rows, dtype=bool) + + # device-resident copies of _holes/_ghost_particles used by + # _find_outside_particles_gpu; re-synced lazily, only when stale + # (see _holes_ghost_dev and the dirty flag set in update_holes()/ + # update_ghost_particles(), the only two places that mutate the + # host arrays in place). + self._holes_dev = None + self._ghost_dev = None + self._holes_ghost_dev_dirty = True self._is_outside_right = np.zeros(self.n_rows, dtype=bool) self._is_outside_left = np.zeros(self.n_rows, dtype=bool) self._is_outside = np.zeros(self.n_rows, dtype=bool) @@ -2178,6 +2219,9 @@ def show_distribution_function(self, components, bin_edges): plt.show() def _find_outside_particles(self, axis): + if cunumpy.cupy_backend: + return self._find_outside_particles_gpu(axis) + # determine particles outside of the logical unit cube self._is_outside_right[:] = self.markers[:, axis] > 1.0 self._is_outside_left[:] = self.markers[:, axis] < 0.0 @@ -2197,6 +2241,50 @@ def _find_outside_particles(self, axis): return outside_inds + def _find_outside_particles_gpu(self, axis): + """Device version of :meth:`_find_outside_particles`. + + ``self.markers[:, axis]`` is a single column out of ``n_cols``, so + reading it is a heavily strided gather; the reference (CPU) version + pays that cost twice (once each for the ``>`` and ``<`` comparison). + Reading it once into a device array and doing both comparisons plus + the hole/ghost masking there is measurably faster end-to-end even + after paying for the host<->device copies, because ``self._markers`` + is pinned memory (see :func:`_pinned_zeros`) — the transfers alone + run at a few hundred MiB, not tens of ms. + + ``holes``/``ghost_particles`` are re-transferred only when stale + (see ``_holes_ghost_dev_dirty``), since they are unchanged across + the several axes checked per :meth:`apply_kinetic_bc` call and are + only ever updated in place by :meth:`update_holes`/ + :meth:`update_ghost_particles`. + """ + import cupy as cp + + col_dev = cp.asarray(self.markers[:, axis]) + + if self._holes_ghost_dev_dirty or self._holes_dev is None: + self._holes_dev = cp.asarray(self.holes) + self._ghost_dev = cp.asarray(self.ghost_particles) + self._holes_ghost_dev_dirty = False + holes_dev = self._holes_dev + ghost_dev = self._ghost_dev + + is_r = col_dev > 1.0 + is_l = col_dev < 0.0 + not_hole_or_ghost = ~(holes_dev | ghost_dev) + is_r &= not_hole_or_ghost + is_l &= not_hole_or_ghost + is_out = is_r | is_l + + is_r.get(out=self._is_outside_right) + is_l.get(out=self._is_outside_left) + is_out.get(out=self._is_outside) + + outside_inds = np.nonzero(self._is_outside)[0] + + return outside_inds + @profile def apply_kinetic_bc(self, newton=False): """ @@ -4403,11 +4491,13 @@ def eval_sph( def update_holes(self): """Compute new holes, new number of holes and markers on process""" self._holes[:] = self.markers[:, 0] == -1.0 + self._holes_ghost_dev_dirty = True self.update_valid_mks() def update_ghost_particles(self): """Compute new particles that belong to boundary processes needed for sph evaluation""" self._ghost_particles[:] = self.markers[:, -1] == -2.0 + self._holes_ghost_dev_dirty = True self.update_valid_mks() ### MPI comm for domain decomposition ### diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index e80b8e8be..5345f9113 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -2,6 +2,7 @@ import logging +import cunumpy import numpy as np from cunumpy import PyccelKernel from feectools.ddm.mpi import mpi as MPI @@ -162,6 +163,26 @@ def __init__( else: self._box_comm = False + @staticmethod + def _reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim): + """Device version of the per-step marker buffer bookkeeping at the top + of :meth:`__call__` (save initial phase-space coordinates, zero the + boundary-shift columns, zero the residual/scratch columns). + + ``markers`` is a pinned-memory-backed host array (see + :func:`struphy.pic.base._pinned_zeros`), so a full round trip through + the device is fast (~5 ms for the 137 MiB ``PressureLessSPH`` marker + array) and cheaper than the equivalent strided in-place NumPy writes + (~35 ms), which touch three disjoint, non-contiguous column ranges. + """ + import cupy as cp + + dev = cp.asarray(markers) + dev[:, init_slice] = dev[:, : 3 + vdim] + dev[:, shift_slice] = 0.0 + dev[:, residual_idx:-2] = 0.0 + dev.get(out=markers) + @profile def __call__(self, dt: float): """ @@ -184,14 +205,17 @@ def __call__(self, dt: float): init_slice = slice(first_pusher_idx, first_shift_idx) shift_slice = slice(first_shift_idx, residual_idx) - # save initial phase space coordinates - markers[:, init_slice] = markers[:, : 3 + vdim] + if cunumpy.cupy_backend: + self._reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim) + else: + # save initial phase space coordinates + markers[:, init_slice] = markers[:, : 3 + vdim] - # set boundary shifts to zero - markers[:, shift_slice] = 0.0 + # set boundary shifts to zero + markers[:, shift_slice] = 0.0 - # clear buffer columns starting from residual index, dont clear ID (last column) and loc_box - markers[:, residual_idx:-2] = 0.0 + # clear buffer columns starting from residual index, dont clear ID (last column) and loc_box + markers[:, residual_idx:-2] = 0.0 rank = self.particles.mpi_rank logger.debug(f"rank {rank}: starting {self.kernel} ...") From de2fc6a82cdd5363ba13ea11cbe29696b1c5b2f8 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Fri, 14 Aug 2026 11:57:28 +0200 Subject: [PATCH 020/156] np -> xp --- src/struphy/pic/base.py | 4251 +++++++++++----------------------- src/struphy/pic/particles.py | 23 +- src/struphy/pic/sorting.py | 17 +- 3 files changed, 1354 insertions(+), 2937 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index f90696136..4079f5ba3 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -5,6 +5,7 @@ from abc import ABCMeta, abstractmethod import h5py +import numpy as np import scipy.special as sp try: @@ -15,10 +16,8 @@ class Intracomm: x = None -import cunumpy -import numpy as np +import cunumpy as xp from cunumpy import PyccelKernel -from cunumpy import to_cunumpy from feectools.ddm.mpi import MockComm from feectools.ddm.mpi import mpi as MPI from line_profiler import profile @@ -82,7 +81,7 @@ def _dev(*arrays): """Convert host (marker) coordinate arrays to the active array backend, for feeding into equilibrium/domain/perturbation functions that follow the global backend rather than the (always host-resident) markers.""" - out = tuple(to_cunumpy(a) for a in arrays) + out = tuple(xp.to_cunumpy(a) for a in arrays) return out[0] if len(out) == 1 else out @@ -105,7 +104,7 @@ def _pinned_zeros(shape, dtype=float): only tie up a scarcer resource for no benefit; a plain allocation is used instead. """ - if not cunumpy.cupy_backend: + if not xp.cupy_backend: return np.zeros(shape, dtype=dtype) import cupy as cp @@ -318,15 +317,11 @@ def __init__( if domain_decomp is None: self._domain_array, self._nprocs = self._get_domain_decomp(self.sorting_params.dims_mask) else: - # domain_decomp[0] may come from a Derham grid living on the device - # (ARRAY_BACKEND=cupy); everything below operates on markers, which - # are always host arrays (there is no device particle kernel), so - # domain_array is brought to the host once here. - self._domain_array = _to_numpy_for_kernel(domain_decomp[0]) + self._domain_array = domain_decomp[0] self._nprocs = domain_decomp[1] # total number of cells (equal to mpi_size if no grid) - n_cells = np.sum(np.prod(self.domain_array[:, 2::3], axis=1, dtype=int)) * self.num_clones + n_cells = xp.sum(xp.prod(self.domain_array[:, 2::3], axis=1, dtype=int)) * self.num_clones # total number of boxes if self.boxes_per_dim is None: @@ -338,7 +333,7 @@ def __init__( assert all([nboxes % nproc == 0 for nboxes, nproc in zip(self.boxes_per_dim, self.nprocs)]), ( f"Number of boxes {self.boxes_per_dim =} must be divisible by number of processes {self.nprocs =} in each direction." ) - n_boxes = np.prod(np.array(self.boxes_per_dim), dtype=int) * self.num_clones + n_boxes = xp.prod(xp.array(self.boxes_per_dim), dtype=int) * self.num_clones # total number of markers (Np) and particles per cell (ppc) Np = self.loading_params.Np @@ -439,9 +434,9 @@ def __init__( self._generate_sampling_moments() # create buffers for mpi_sort_markers - self._sorting_etas = np.zeros((self.markers.shape[0], 3), dtype=float) - self._is_on_proc_domain = np.zeros((self.markers.shape[0], 3), dtype=bool) - self._can_stay = np.zeros(self.markers.shape[0], dtype=bool) + self._sorting_etas = xp.zeros((self.markers.shape[0], 3), dtype=float) + self._is_on_proc_domain = xp.zeros((self.markers.shape[0], 3), dtype=bool) + self._can_stay = xp.zeros(self.markers.shape[0], dtype=bool) self._reqs = [None] * self.mpi_size self._recvbufs = [None] * self.mpi_size self._send_to_i = [None] * self.mpi_size @@ -821,21 +816,10 @@ def valid_mks(self): self._valid_mks = ~np.logical_or(self.holes, self.ghost_particles) return self._valid_mks -<<<<<<< HEAD - def update_valid_mks(self): - self._valid_mks[:] = ~np.logical_or(self.holes, self.ghost_particles) - - @property - def n_mks_loc(self): - """Number of valid markers on process (without holes and ghosts).""" - # print(f"{self.kinds} on clone {self.clone_id}: counting valid markers: {np.count_nonzero(self.valid_mks)} valid markers on process {self.mpi_rank} found.") - return np.count_nonzero(self.valid_mks) -======= @property def n_mks_loc(self): """Number of valid markers on process (without holes and ghosts).""" return xp.count_nonzero(self.valid_mks) ->>>>>>> devel @property def n_mks_on_each_proc(self): @@ -845,7 +829,7 @@ def n_mks_on_each_proc(self): @property def n_mks_on_clone(self): """Number of valid markers on current clone (without holes and ghosts).""" - return np.sum(self.n_mks_on_each_proc) + return xp.sum(self.n_mks_on_each_proc) @property def n_mks_on_each_clone(self): @@ -855,7 +839,7 @@ def n_mks_on_each_clone(self): @property def n_mks_global(self): """Number of valid markers on current clone (without holes and ghosts).""" - return np.sum(self.n_mks_on_each_clone) + return xp.sum(self.n_mks_on_each_clone) @property def positions(self): @@ -864,7 +848,7 @@ def positions(self): @positions.setter def positions(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, xp.ndarray) assert new.shape == (self.n_mks_loc, 3) self._markers[self.valid_mks, self.index["pos"]] = new @@ -875,19 +859,10 @@ def velocities(self): @velocities.setter def velocities(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, xp.ndarray) assert new.shape == (self.n_mks_loc, self.vdim), f"{self.n_mks_loc =} and {self.vdim =} but {new.shape =}" self._markers[self.valid_mks, self.index["vel"]] = new -<<<<<<< HEAD - def set_velocities_comp(self, velocity, comp): - new = np.ones(shape=(self.velocities.shape[0], 1)) * velocity - - for c in comp: - self._markers[self.valid_mks, slice(3 + c, 3 + c + 1)] = new - -======= ->>>>>>> devel @property def phasespace_coords(self): """Array holding the marker positions and velocities in logical space. The i-th row holds the i-th marker info.""" @@ -895,7 +870,7 @@ def phasespace_coords(self): @phasespace_coords.setter def phasespace_coords(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, xp.ndarray) assert new.shape == (self.n_mks_loc, 3 + self.vdim) self._markers[self.valid_mks, self.index["coords"]] = new @@ -906,7 +881,7 @@ def weights(self): @weights.setter def weights(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, xp.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["weights"]] = new @@ -915,15 +890,9 @@ def sampling_density_values(self): """Array holding the current marker 0form sampling density s0. The i-th row holds the i-th marker info.""" return self.markers[self.valid_mks, self.index["s0"]] -<<<<<<< HEAD - @sampling_density.setter - def sampling_density(self, new): - assert isinstance(new, np.ndarray) -======= @sampling_density_values.setter def sampling_density_values(self, new): assert isinstance(new, xp.ndarray) ->>>>>>> devel assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["s0"]] = new @@ -934,7 +903,7 @@ def weights0(self): @weights0.setter def weights0(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, xp.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["w0"]] = new @@ -945,7 +914,7 @@ def marker_ids(self): @marker_ids.setter def marker_ids(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, xp.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["ids"]] = new @@ -956,23 +925,23 @@ def f_coords(self): @f_coords.setter def f_coords(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, xp.ndarray) self.markers[self.valid_mks, self.f_coords_index] = new @property def f_jacobian_coords(self): """Coordinates of the velocity jacobian determinant of the distribution fuction.""" if isinstance(self.f_jacobian_coords_index, list): - return self.markers[np.ix_(~self.holes, self.f_jacobian_coords_index)] + return self.markers[xp.ix_(~self.holes, self.f_jacobian_coords_index)] else: return self.markers[~self.holes, self.f_jacobian_coords_index] @f_jacobian_coords.setter def f_jacobian_coords(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, xp.ndarray) if isinstance(self.f_jacobian_coords_index, list): self.markers[ - np.ix_( + xp.ix_( ~self.holes, self.f_jacobian_coords_index, ) @@ -1229,7 +1198,7 @@ def draw_markers( self._load_tesselation() if isinstance(self, ParticlesSPH): self._set_initial_condition() - self.velocities = xp.array(self.u_init(self.positions)).T + self.velocities = _to_numpy_for_kernel(self.u_init(_dev(self.positions))).T # set markers ID in last column self.marker_ids = _first_marker_id + xp.arange(n_mks_load_loc, dtype=float) else: @@ -1242,7 +1211,7 @@ def draw_markers( # set seed _seed = self.loading_params.seed if _seed is not None: - xp.random.seed(_seed) + np.random.seed(_seed) # counting integers num_loaded_particles_loc = 0 # number of particles alreday loaded (local) @@ -1253,15 +1222,15 @@ def draw_markers( while num_loaded_particles_glob < int(self.Np): # Generate a chunk of random particles num_to_add_glob = min(chunk_size, int(self.Np) - num_loaded_particles_glob) - temp = xp.random.rand(num_to_add_glob, 3 + self.vdim) + temp = np.random.rand(num_to_add_glob, 3 + self.vdim) # check which particles are on the current process domain - is_on_proc_domain = xp.logical_and( + is_on_proc_domain = np.logical_and( temp[:, :3] > self.domain_array[self.mpi_rank, 0::3], temp[:, :3] < self.domain_array[self.mpi_rank, 1::3], ) - valid_idx = xp.nonzero(xp.all(is_on_proc_domain, axis=1))[0] + valid_idx = np.nonzero(np.all(is_on_proc_domain, axis=1))[0] valid_particles = temp[valid_idx] - valid_particles = xp.array_split(valid_particles, self.num_clones)[self.clone_id] + valid_particles = np.array_split(valid_particles, self.num_clones)[self.clone_id] num_valid = valid_particles.shape[0] # Add the valid particles to the phasespace_coords array @@ -1318,7 +1287,7 @@ def draw_markers( # initial velocities - SPH case: v(0) = u(x(0)) for given velocity u(x) if isinstance(self, ParticlesSPH): self._set_initial_condition() - self.velocities = xp.array(self.u_init(self.positions)).T + self.velocities = _to_numpy_for_kernel(self.u_init(_dev(self.positions))).T else: # inverse transform sampling in velocity space # Avoid exact 0 or 1 from low-discrepancy sequences: erfinv(±1) @@ -1426,8 +1395,8 @@ def draw_markers( # check if all particle positions are inside the unit cube [0, 1]^3 n_mks_load_loc = self.n_mks_load[self._mpi_rank] - assert xp.all(~self.holes[:n_mks_load_loc]) - assert xp.all(self.holes[n_mks_load_loc:]) + assert np.all(~self.holes[:n_mks_load_loc]) + assert np.all(self.holes[n_mks_load_loc:]) if self._initialized_sorting and sort: logger.info("\nSorting the markers after initial draw") @@ -1476,7 +1445,7 @@ def initialize_weights( else: assert self.domain is not None, "A domain is needed to initialize weights." - if xp.size(self.markers_wo_holes_and_ghost) == 0: + if np.size(self.markers_wo_holes_and_ghost) == 0: return # set initial condition @@ -1494,21 +1463,23 @@ def initialize_weights( # evaluate initial distribution function if isinstance(self, ParticlesSPH): - f_init = self.f_init(self.positions) + f_init = _to_numpy_for_kernel(self.f_init(_dev(self.positions))) else: - f_init = self.f_init(*self.f_coords.T) + f_init = _to_numpy_for_kernel(self.f_init(*_dev(*self.f_coords.T))) # if f_init is vol-form, transform to 0-form if self.is_volume_form[0]: - f_init /= self.domain.jacobian_det(self.positions) + f_init /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions))) if self.is_volume_form[1]: - f_init /= self.f_init.velocity_jacobian_det( - *self.f_jacobian_coords.T, + f_init /= _to_numpy_for_kernel( + self.f_init.velocity_jacobian_det(*_dev(*self.f_jacobian_coords.T)), ) # compute s0 and save at vdim + 4 - self.sampling_density_values = self.s0(*self.phasespace_coords.T, flat_eval=True) + self.sampling_density_values = _to_numpy_for_kernel( + self.s0(*_dev(*self.phasespace_coords.T), flat_eval=True), + ) # compute w0 and save at vdim + 5 self.weights0 = f_init / self.sampling_density_values / self.Np @@ -1537,23 +1508,23 @@ def update_weights(self): """ from struphy.pic.particles import ParticlesSPH - if xp.size(self.markers_wo_holes_and_ghost) == 0: + if np.size(self.markers_wo_holes_and_ghost) == 0: return if isinstance(self, ParticlesSPH): - f0 = self.f0.n0(self.positions) + f0 = _to_numpy_for_kernel(self.f0.n0(_dev(self.positions))) else: # in case of CanonicalMaxwellian, evaluate constants_of_motion # if isinstance(self.f0, CanonicalMaxwellian): # self.save_constants_of_motion() - f0 = self.f0(*self.f_coords.T) + f0 = _to_numpy_for_kernel(self.f0(*_dev(*self.f_coords.T))) # if f_init is vol-form, transform to 0-form if self.is_volume_form[0]: - f0 /= self.domain.jacobian_det(self.positions) + f0 /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions))) if self.is_volume_form[1]: - f0 /= self.f0.velocity_jacobian_det(*self.f_jacobian_coords.T) + f0 /= _to_numpy_for_kernel(self.f0.velocity_jacobian_det(*_dev(*self.f_jacobian_coords.T))) self.weights = self.weights0 - f0 / self.sampling_density_values / self.Np @@ -1591,7 +1562,7 @@ def binning( The reconstructed delta-f distribution function. """ - assert xp.count_nonzero(components) == len(bin_edges) + assert np.count_nonzero(components) == len(bin_edges) # volume of a bin bin_vol = 1.0 @@ -1615,7 +1586,7 @@ def binning( elif quantity == "energy_tensor": multiplier = self.velocities[:, v_axis[0]] * self.velocities[:, v_axis[1]] elif quantity == "heat_flux": - velocity_norm2 = xp.linalg.norm(self.velocities, axis=1) ** 2 + velocity_norm2 = np.linalg.norm(self.velocities, axis=1) ** 2 multiplier = velocity_norm2 * self.velocities[:, v_axis[0]] # compute weights of histogram: @@ -1623,19 +1594,19 @@ def binning( _weights = self.weights * self.Np * multiplier if divide_by_jac: - _weights /= self.domain.jacobian_det(self.positions, remove_outside=False) + _weights /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions), remove_outside=False)) # _weights /= self.velocity_jacobian_det(*self.phasespace_coords.T) - _weights0 /= self.domain.jacobian_det(self.positions, remove_outside=False) + _weights0 /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions), remove_outside=False)) # _weights0 /= self.velocity_jacobian_det(*self.phasespace_coords.T) - f_slice = xp.histogramdd( + f_slice = np.histogramdd( self.markers_wo_holes_and_ghost[:, slicing], bins=bin_edges, weights=_weights0, )[0] - df_slice = xp.histogramdd( + df_slice = np.histogramdd( self.markers_wo_holes_and_ghost[:, slicing], bins=bin_edges, weights=_weights, @@ -1662,7 +1633,7 @@ def show_distribution_function(self, components, bin_edges): import matplotlib.pyplot as plt - n_dim = xp.count_nonzero(components) + n_dim = np.count_nonzero(components) assert n_dim == 1 or n_dim == 2, f"Distribution function can only be shown in 1D or 2D slices, not {n_dim}." @@ -1796,7 +1767,7 @@ def apply_kinetic_bc(self, newton=False): self._particle_refilling() self._markers[self._is_outside, :-1] = -1.0 - self._n_lost_markers += len(xp.nonzero(self._is_outside)[0]) + self._n_lost_markers += len(np.nonzero(self._is_outside)[0]) for axis in self._periodic_axes: outside_inds = self._find_outside_particles(axis) @@ -1807,8 +1778,8 @@ def apply_kinetic_bc(self, newton=False): self.markers[outside_inds, axis] = self.markers[outside_inds, axis] % 1.0 # set shift for alpha-weighted mid-point computation - outside_right_inds = xp.nonzero(self._is_outside_right)[0] - outside_left_inds = xp.nonzero(self._is_outside_left)[0] + outside_right_inds = np.nonzero(self._is_outside_right)[0] + outside_left_inds = np.nonzero(self._is_outside_left)[0] if newton: self.markers[ outside_right_inds, @@ -1862,6 +1833,7 @@ def update_holes(self): Must be called after any operation that creates, removes or moves markers (e.g. sorting, boundary conditions, refilling), since holes are tracked per row index.""" self._holes[:] = self.markers[:, 0] == -1.0 + self._holes_ghost_dev_dirty = True self._update_valid_mks() def set_velocities_comp(self, velocity, comp): @@ -2323,7 +2295,7 @@ def gather_scalar_in_intercomm_array(self, scalar: int, out: xp.ndarray = None): def _update_valid_mks(self): """Refresh :attr:`~struphy.pic.base.Particles.valid_mks`: a row is a valid marker if and only if it is neither a hole nor a ghost particle.""" - self._valid_mks[:] = ~xp.logical_or(self.holes, self.ghost_particles) + self._valid_mks[:] = ~np.logical_or(self.holes, self.ghost_particles) def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): """ @@ -2337,7 +2309,7 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): Returns ------- - dom_arr : np.ndarray + dom_arr : xp.ndarray A 2d array of shape (#MPI processes, 9). The row index denotes the process rank. The columns are for n=0,1,2: - arr[i, 3*n + 0] holds the LEFT domain boundary of process i in direction eta_(n+1). - arr[i, 3*n + 1] holds the RIGHT domain boundary of process i in direction eta_(n+1). @@ -2349,7 +2321,7 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): if mpi_dims_mask is None: mpi_dims_mask = [True, True, True] - dom_arr = np.zeros((self.mpi_size, 9), dtype=float) + dom_arr = xp.zeros((self.mpi_size, 9), dtype=float) # factorize mpi size factors = factorint(self.mpi_size) @@ -2373,10 +2345,10 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): mm = (mm + 1) % 3 nprocs[mm] *= fac - assert np.prod(nprocs) == self.mpi_size + assert xp.prod(nprocs) == self.mpi_size # domain decomposition - breaks = [np.linspace(0.0, 1.0, nproc + 1) for nproc in nprocs] + breaks = [xp.linspace(0.0, 1.0, nproc + 1) for nproc in nprocs] # fill domain array for n in range(self.mpi_size): @@ -2413,14 +2385,14 @@ def _n_mks_load_and_Np_per_clone(self): """Return two arrays: 1) an array of sub_comm.size where the i-th entry corresponds to the number of markers drawn on process i, and 2) an array of size num_clones where the i-th entry corresponds to the number of markers on clone i.""" # number of cells on current process - n_cells_loc = np.prod( + n_cells_loc = xp.prod( self.domain_array[self.mpi_rank, 2::3], dtype=int, ) # array of number of markers on each process at loading stage if self.clone_config is not None: - _n_cells_clone = np.sum(np.prod(self.domain_array[:, 2::3], axis=1, dtype=int)) + _n_cells_clone = xp.sum(xp.prod(self.domain_array[:, 2::3], axis=1, dtype=int)) _n_mks_load_tot = self.clone_config.get_Np_clone(self.Np) _ppc = _n_mks_load_tot / _n_cells_clone else: @@ -2430,19 +2402,14 @@ def _n_mks_load_and_Np_per_clone(self): n_mks_load = self.gather_scalar_in_subcomm_array(int(_ppc * n_cells_loc)) # add deviation from Np to rank 0 - n_mks_load[0] += _n_mks_load_tot - np.sum(n_mks_load) + n_mks_load[0] += _n_mks_load_tot - xp.sum(n_mks_load) # check if all markers are there - assert np.sum(n_mks_load) == _n_mks_load_tot + assert xp.sum(n_mks_load) == _n_mks_load_tot # Np on each clone -<<<<<<< HEAD - Np_per_clone = self._gather_scalar_in_intercomm_array(_n_mks_load_tot) - assert np.sum(Np_per_clone) == self.Np -======= Np_per_clone = self.gather_scalar_in_intercomm_array(_n_mks_load_tot) assert xp.sum(Np_per_clone) == self.Np ->>>>>>> devel return n_mks_load, Np_per_clone @@ -2456,7 +2423,7 @@ def _allocate_marker_array(self, dry_run: bool = False): # number of markers on the local process at loading stage n_mks_load_loc = self.n_mks_load[self._mpi_rank] - bufsize = self.bufsize + 1.0 / np.sqrt(n_mks_load_loc) + bufsize = self.bufsize + 1.0 / xp.sqrt(n_mks_load_loc) # allocate markers array (3 x positions, vdim x velocities, weight, s0, w0, ..., ID) with buffer self._n_rows = round(float(n_mks_load_loc * (1 + bufsize))) @@ -2471,22 +2438,22 @@ def _allocate_marker_array(self, dry_run: bool = False): self._markers = _pinned_zeros((self.n_rows, self.n_cols), dtype=float) - # allocate auxiliary arrays + # allocate auxiliary arrays (host-resident: read/written by compiled, + # host-only Pyccel kernels via args_markers, see _to_numpy_for_kernel) self._holes = np.zeros(self.n_rows, dtype=bool) self._ghost_particles = np.zeros(self.n_rows, dtype=bool) self._valid_mks = np.zeros(self.n_rows, dtype=bool) + self._is_outside_right = np.zeros(self.n_rows, dtype=bool) + self._is_outside_left = np.zeros(self.n_rows, dtype=bool) + self._is_outside = np.zeros(self.n_rows, dtype=bool) # device-resident copies of _holes/_ghost_particles used by # _find_outside_particles_gpu; re-synced lazily, only when stale - # (see _holes_ghost_dev and the dirty flag set in update_holes()/ - # update_ghost_particles(), the only two places that mutate the - # host arrays in place). + # (see the dirty flag set in update_holes()/_update_ghost_particles(), + # the only two places that mutate the host arrays in place). self._holes_dev = None self._ghost_dev = None self._holes_ghost_dev_dirty = True - self._is_outside_right = np.zeros(self.n_rows, dtype=bool) - self._is_outside_left = np.zeros(self.n_rows, dtype=bool) - self._is_outside = np.zeros(self.n_rows, dtype=bool) # create array container (3 x positions, vdim x velocities, weight, s0, w0, ID) for removed markers self._n_lost_markers = 0 @@ -2602,16 +2569,16 @@ def _generate_sampling_moments(self): # assert len(ns) == len(us) == len(vths) - # ns = np.array(ns) - # us = np.array(us) - # vths = np.array(vths) + # ns = xp.array(ns) + # us = xp.array(us) + # vths = xp.array(vths) # Use the mean of shifts and thermal velocity such that outermost shift+thermal is # new shift + new thermal - # mean_us = np.mean(us, axis=0) - # us_ext = us + vths * np.where(us >= 0, 1, -1) + # mean_us = xp.mean(us, axis=0) + # us_ext = us + vths * xp.where(us >= 0, 1, -1) # us_ext_dist = us_ext - mean_us[None, :] - # new_vths = np.max(np.abs(us_ext_dist), axis=0) + # new_vths = xp.max(xp.abs(us_ext_dist), axis=0) # new_moments = [] @@ -2678,11 +2645,6 @@ def _set_initial_condition(self): # TODO: add other velocity components def _f_init(*etas, flat_eval=False): - # self.f0/_density evaluate on the device (equilibrium and - # perturbation functions follow the global array backend), - # while markers are always host-resident; convert at this - # marker/field evaluation boundary and convert the result back. - etas = tuple(to_cunumpy(eta) for eta in etas) if len(etas) == 1: if _density is None: out = self.f0.n0(etas[0]) @@ -2707,16 +2669,10 @@ def _f_init(*etas, flat_eval=False): out = out0 + out1 if flat_eval: - out = np.squeeze(out) - # Returned in the same (active) backend as the converted - # `etas` above -- callers that need markers/host data convert - # explicitly (see _to_numpy_for_kernel at call sites), since - # this closure is also reused as a field function fed back - # into domain/grid machinery that expects the active backend. + out = xp.squeeze(out) return out def _u_init(*etas, flat_eval=False): - etas = tuple(to_cunumpy(eta) for eta in etas) if len(etas) == 1: if _u1 is None: out = self.f0.uv(etas[0]) @@ -2741,12 +2697,7 @@ def _u_init(*etas, flat_eval=False): out = out0 + out1 if flat_eval: - out = np.squeeze(out) - # Returned in the same (active) backend as the converted - # `etas` above -- callers that need markers/host data convert - # explicitly (see _to_numpy_for_kernel at call sites), since - # this closure is also reused as a field function fed back - # into domain/grid machinery that expects the active backend. + out = xp.squeeze(out) return out self._f_init = _f_init @@ -2755,7 +2706,7 @@ def _u_init(*etas, flat_eval=False): def _load_external( self, n_mks_load_loc: int, - n_mks_load_cum_sum: np.ndarray, + n_mks_load_cum_sum: xp.ndarray, ): """Load markers from external .hdf5 file. @@ -2764,7 +2715,7 @@ def _load_external( n_mks_load_loc: int Number of markers on the local process at loading stage. - n_mks_load_cum_sum: np.ndarray + n_mks_load_cum_sum: xp.ndarray Cumulative sum of number of markers on each process at loading stage. """ if self.mpi_rank == 0: @@ -2783,7 +2734,7 @@ def _load_external( tag=123, ) else: - recvbuf = np.zeros( + recvbuf = xp.zeros( (n_mks_load_loc, self.markers.shape[1]), dtype=float, ) @@ -2830,2878 +2781,1443 @@ def _load_tesselation(self, n_quad: int = 1): self._markers[: eta3.size, 2] = eta3 self._update_valid_mks() -<<<<<<< HEAD - def draw_markers( - self, - sort: bool = True, - ): - r""" - Drawing markers - - * for PIC: according to the volume density :math:`s^\textrm{vol}_{\textnormal{in}}` - * for SPH: from unity/disc in space and according to the vector-field representation of the fluid velocity - - In Struphy, the initial marker distribution :math:`s^\textrm{vol}_{\textnormal{in}}` is always of the form + def _reset_marker_ids(self): + """Reset the marker ids (last column in marker array) according to the current distribution of particles. + The first marker on rank 0 gets the id '0', the last marker on the last rank gets the id 'n_mks_global - 1'.""" + n_mks_proc_cumsum = xp.cumsum(self.n_mks_on_each_proc) + n_mks_clone_cumsum = xp.cumsum(self.n_mks_on_each_clone) + first_marker_id = (n_mks_clone_cumsum - self.n_mks_on_each_clone)[self.clone_id] + ( + n_mks_proc_cumsum - self.n_mks_on_each_proc + )[self.mpi_rank] + self.marker_ids = first_marker_id + xp.arange(self.n_mks_loc, dtype=int) - .. math:: + def _find_outside_particles(self, axis): + """Find markers whose ``axis``-th logical coordinate lies outside ``[0, 1]`` + (holes and ghost particles are excluded), updating + :attr:`_is_outside_left`/:attr:`_is_outside_right`/:attr:`_is_outside` accordingly. - s^\textrm{vol}_{\textnormal{in}}(\eta,v) = n^3(\eta)\, \mathcal M(v)\,, + Parameters + ---------- + axis : int + Column of the markers array (0, 1 or 2) holding the logical coordinate to check. - with :math:`\mathcal M(v)` a multi-variate Gaussian: + Returns + ------- + outside_inds : numpy.ndarray[int] + Row indices of the markers that are outside the logical unit cube. + """ + if xp.cupy_backend: + return self._find_outside_particles_gpu(axis) - .. math:: + # determine particles outside of the logical unit cube + self._is_outside_right[:] = self.markers[:, axis] > 1.0 + self._is_outside_left[:] = self.markers[:, axis] < 0.0 - \mathcal M(v) = \prod_{i=1}^{d_v} \frac{1}{\sqrt{2\pi}\,v_{\mathrm{th},i}} - \exp\left[-\frac{(v_i-u_i)^2}{2 v_{\mathrm{th},i}^2}\right]\,, + self._is_outside_right[self.holes] = False + self._is_outside_right[self.ghost_particles] = False + self._is_outside_left[self.holes] = False + self._is_outside_left[self.ghost_particles] = False - where :math:`d_v` stands for the dimension in velocity space, :math:`u_i` are velocity constant shifts - and :math:`v_{\mathrm{th},i}` are constant thermal velocities (standard deviations). - The function :math:`n^3:(0,1)^3 \to \mathbb R^+` is a normalized 3-form on the unit cube, + self._is_outside[:] = np.logical_or( + self._is_outside_right, + self._is_outside_left, + ) - .. math:: + # indices or particles that are outside of the logical unit cube + outside_inds = np.nonzero(self._is_outside)[0] - \int_{(0,1)^3} n^3(\eta)\,\textnormal d \eta = 1\,. + return outside_inds - The following choices are available in Struphy: + def _find_outside_particles_gpu(self, axis): + """Device version of :meth:`_find_outside_particles`. - 1. Uniform distribution on the unit cube: :math:`n^3(\eta) = 1` + ``self.markers[:, axis]`` is a single column out of ``n_cols``, so + reading it is a heavily strided gather; the reference (CPU) version + pays that cost twice (once each for the ``>`` and ``<`` comparison). + Reading it once into a device array and doing both comparisons plus + the hole/ghost masking there is measurably faster end-to-end even + after paying for the host<->device copies, because ``self._markers`` + is pinned memory (see :func:`_pinned_zeros`) — the transfers alone + run at a few hundred MiB, not tens of ms. - 2. Uniform distribution on the disc: :math:`n^3(\eta) = 2\eta_1` (radial coordinate = volume element of square-to-disc mapping) + ``holes``/``ghost_particles`` are re-transferred only when stale + (see ``_holes_ghost_dev_dirty``), since they are unchanged across + the several axes checked per :meth:`apply_kinetic_bc` call and are + only ever updated in place by :meth:`update_holes`/ + :meth:`_update_ghost_particles`. + """ + import cupy as cp - Velocities are sampled via inverse transform sampling. - In case of Particles6D, velocities are sampled as a Maxwellian in each 3 directions, + col_dev = cp.asarray(self.markers[:, axis]) - .. math:: + if self._holes_ghost_dev_dirty or self._holes_dev is None: + self._holes_dev = cp.asarray(self.holes) + self._ghost_dev = cp.asarray(self.ghost_particles) + self._holes_ghost_dev_dirty = False + holes_dev = self._holes_dev + ghost_dev = self._ghost_dev - r_i = \int^{v_i}_{-\infty} \mathcal M(v^\prime_i) \textnormal{d} v^\prime_i = \frac{1}{2}\left[ 1 + \text{erf}\left(\frac{v_i - u_i}{\sqrt{2}v_{\mathrm{th},i}}\right)\right] \,, + is_r = col_dev > 1.0 + is_l = col_dev < 0.0 + not_hole_or_ghost = ~(holes_dev | ghost_dev) + is_r &= not_hole_or_ghost + is_l &= not_hole_or_ghost + is_out = is_r | is_l - where :math:`r_i \in \mathcal R(0,1)` is a uniformly drawn random number in the unit interval. So then + is_r.get(out=self._is_outside_right) + is_l.get(out=self._is_outside_left) + is_out.get(out=self._is_outside) - .. math:: + outside_inds = np.nonzero(self._is_outside)[0] - v_i = \text{erfinv}(2r_i - 1)\sqrt{2}v_{\mathrm{th},i} + u_i \,. + return outside_inds - In case of Particles5D, parallel velocity is sampled as a Maxwellian and perpendicular particle speed :math:`v_\perp = \sqrt{v_1^2 + v_2^2}` - is sampled as a 2D Maxwellian in polar coordinates, + def _particle_refilling(self): + r""" + When particles move outside of the domain, refills them. + TODO: Currently only valid for HollowTorus geometry with AdhocTorus equilibrium. + + In case of guiding-center orbit, refills particles at the opposite poloidal angle of the same magnetic flux surface. .. math:: - \mathcal{M}(v_1, v_2) \, \textnormal{d} v_1 \textnormal{d} v_2 &= \prod_{i=1}^{2} \frac{1}{\sqrt{2\pi}}\frac{1}{v_{\mathrm{th},i}} - \exp\left[-\frac{(v_i-u_i)^2}{2 v_{\mathrm{th},i}^2}\right] \textnormal{d} v_i\,, - \\ - &= \frac{1}{v_\mathrm{th}^2}v_\perp \exp\left[-\frac{(v_\perp-u)^2}{2 v_\mathrm{th}^2}\right] \textnormal{d} v_\perp\,, + \theta_\text{refill} &= - \theta_\text{loss} \\ - &= \mathcal{M}^{\textnormal{pol}}(v_\perp) \, \textnormal{d} v_\perp \,. + \phi_\text{refill} &= -2 q(r_\text{loss}) \theta_\text{loss} - Then, + In case of full orbit, refills particles at the same gyro orbit until their guiding-centers are also outside of the domain. + When their guiding-centers also reach at the boundary, refills them as we did with guiding-center orbit. + """ - .. math:: + for kind in self.bc_refill: + # sorting out particles which are out of the domain + if kind == "inner": + outside_inds = np.nonzero(self._is_outside_left)[0] + self.markers[outside_inds, 0] = 1e-4 + r_loss = self.domain.params["a1"] - r = \int^{v_\perp}_0 \mathcal{M}^{\textnormal{pol}} \textnormal{d} v_\perp = 1 - \exp\left[-\frac{(v_\perp-u)^2}{2 v_\mathrm{th}^2}\right] \,. + else: + outside_inds = np.nonzero(self._is_outside_right)[0] + self.markers[outside_inds, 0] = 1 - 1e-4 + r_loss = 1.0 - So then, + if len(outside_inds) == 0: + continue + + # in case of Particles6D, do gyro boundary transfer + if self.vdim == 3: + gyro_inside_inds = self._gyro_transfer(outside_inds) + + # mark the particle as done for multiple step pushers + self.markers[outside_inds[gyro_inside_inds], self.first_pusher_idx] = -1.0 + self._is_outside[outside_inds[gyro_inside_inds]] = False + + # exclude particles whose guiding center positions are still inside. + if len(gyro_inside_inds) > 0: + outside_inds = outside_inds[~gyro_inside_inds] + + # do phi boundary transfer = phi_loss - 2*q(r_loss)*theta_loss + self.markers[outside_inds, 2] -= 2 * self.equil.q_r(r_loss) * self.markers[outside_inds, 1] + + # theta_boudary_transfer = - theta_loss + self.markers[outside_inds, 1] = 1.0 - self.markers[outside_inds, 1] + + # mark the particle as done for multiple step pushers + self.markers[outside_inds, self.first_pusher_idx] = -1.0 + self._is_outside[outside_inds] = False + + def _gyro_transfer(self, outside_inds): + r"""Refills particles at the same gyro orbit. + Their perpendicular velocity directions are also changed accordingly: + + First, refills the particles at the other side of the cross point (between gyro circle and domain boundary), .. math:: - v_\perp = \sqrt{- \ln(1-r)}\sqrt{2}v_\mathrm{th} + u \,. + \theta_\text{refill} = \theta_\text{gc} - \left(\theta_\text{loss} - \theta_\text{gc} \right) \,. - All needed parameters can be set in the parameter file, see :ref:`params_yml`. + Then changes the direction of the perpendicular velocity, - An initial sorting will be performed if sort is given as True (default) and sorting_params were given to the init. + .. math:: + + \vec{v}_{\perp, \text{refill}} = \frac{\vec{\rho}_g}{|\vec{\rho}_g|} \times \vec{b}_0 |\vec{v}_{\perp, \text{loss}}| \,, + + where :math:`\vec{\rho}_g = \vec{x}_\text{refill} - \vec{X}_\text{gc}` is the cartesian radial vector. Parameters ---------- - sort : Bool - Wether to sort the particules in boxes after initial drawing (only if sorting params were passed) + outside_inds : xp.array (int) + An array of indices of particles which are outside of the domain. + + Returns + ------- + out : xp.array (bool) + An array of indices of particles where its guiding centers are outside of the domain. """ - # number of markers on the local process at loading stage - n_mks_load_loc = self.n_mks_load[self.mpi_rank] - # Np_per_clone_loc = self.Np_per_clone[self.clone_id] + # incoming markers must be "Particles6D". + assert self.vdim == 3 - # fill holes in markers array with -1 (all holes are at end of array at loading stage) - self._markers[n_mks_load_loc:] = -1.0 + # TODO: currently assumes periodic boundary condition along poloidal and toroidal angle + self.markers[outside_inds, 1:3] = self.markers[outside_inds, 1:3] % 1 - # number of holes and markers on process - self.update_holes() - self.update_ghost_particles() + v = self.markers[outside_inds, 3:6].T - # cumulative sum of number of markers on each process at loading stage. - n_mks_load_cum_sum = np.cumsum(self.n_mks_load) - Np_per_clone_cum_sum = np.cumsum(self.Np_per_clone) - _first_marker_id = (Np_per_clone_cum_sum - self.Np_per_clone)[self.clone_id] + ( - n_mks_load_cum_sum - self.n_mks_load - )[self._mpi_rank] + # eval cartesian equilibrium magnetic field at the marker positions + assert isinstance(self.equil, FluidEquilibriumWithB), "Gyro transfer function needs a magnetic background." + b_cart, xyz = self.equil.b_cart(self.markers[outside_inds, :]) - logger.debug("\nMARKERS:") - logger.debug(f"{'name:':<25}{self.name}") - logger.debug(f"{'Np:':<25}{self.Np}") - logger.debug(f"{'ppc:':<25}{self.ppc}") - logger.debug(f"{'ppb:':<25}{self.ppb}") - logger.debug(f"{'bc:':<25}{self.bc}") - logger.debug(f"{'bc_refill:':<25}{self.bc_refill}") - logger.debug(f"{'loading:':<25}{self.loading}") - logger.debug(f"{'type:':<25}{self.type}") - logger.debug(f"{'control_variate:':<25}{self.control_variate}") - logger.debug(f"{'domain_array[0]:':<25}{self.domain_array[0]}") - logger.debug(f"{'boxes_per_dim:':<25}{self.boxes_per_dim}") - logger.debug(f"{'mpi_dims_mask:':<25}{self.mpi_dims_mask}") + # calculate magnetic field amplitude and normalized magnetic field + absB0 = xp.sqrt(b_cart[0] ** 2 + b_cart[1] ** 2 + b_cart[2] ** 2) + norm_b_cart = b_cart / absB0 - if self.loading == "external": - self._load_external() - elif self.loading == "restart": - self._load_restart() - elif self.loading == "tesselation": - self._load_tesselation() - if self.type == "sph": - self._set_initial_condition() - self.velocities = _to_numpy_for_kernel(self.u_init(self.positions)).T - # set markers ID in last column - self.marker_ids = _first_marker_id + np.arange(n_mks_load_loc, dtype=float) - else: - logger.debug("\nLoading fresh markers:") - for key, val in self.loading_params.__dict__.items(): - logger.debug(f"{key + ' :':<25}{val}") + # calculate parallel and perpendicular velocities + v_parallel = xp.einsum("ij,ij->j", v, norm_b_cart) + v_perp = xp.cross(norm_b_cart, xp.cross(v, norm_b_cart, axis=0), axis=0) + v_perp_square = xp.sqrt(v_perp[0] ** 2 + v_perp[1] ** 2 + v_perp[2] ** 2) - # 1. standard random number generator (pseudo-random) - if self.loading == "pseudo_random": - # set seed - _seed = self.loading_params.seed - if _seed is not None: - np.random.seed(_seed) + assert xp.all(xp.isclose(v_perp, v - norm_b_cart * v_parallel)) - # counting integers - num_loaded_particles_loc = 0 # number of particles alreday loaded (local) - num_loaded_particles_glob = 0 # number of particles already loaded (each clone) - chunk_size = 10000 # TODO: number of particle chunk + # calculate Larmor radius + Larmor_r = xp.cross(norm_b_cart, v_perp, axis=0) / absB0 * self.equation_params.epsilon - # Total number of markers to draw (sum over all clones) - while num_loaded_particles_glob < int(self.Np): - # Generate a chunk of random particles - num_to_add_glob = min(chunk_size, int(self.Np) - num_loaded_particles_glob) - temp = np.random.rand(num_to_add_glob, 3 + self.vdim) - # check which particles are on the current process domain - is_on_proc_domain = np.logical_and( - temp[:, :3] > self.domain_array[self.mpi_rank, 0::3], - temp[:, :3] < self.domain_array[self.mpi_rank, 1::3], - ) - valid_idx = np.nonzero(np.all(is_on_proc_domain, axis=1))[0] - valid_particles = temp[valid_idx] - valid_particles = np.array_split(valid_particles, self.num_clones)[self.clone_id] - num_valid = valid_particles.shape[0] + # transform cartesian coordinates to logical coordinates + # TODO: currently only possible with the geomoetry where its inverse map is defined. + assert hasattr(self.domain, "inverse_map") - # Add the valid particles to the phasespace_coords array - self._markers[ - num_loaded_particles_loc : num_loaded_particles_loc + num_valid, - : 3 + self.vdim, - ] = valid_particles - num_loaded_particles_glob += num_to_add_glob - num_loaded_particles_loc += num_valid + xyz -= Larmor_r - # make sure all particles are loaded - assert self.Np == int(num_loaded_particles_glob), f"{self.Np =}, {int(num_loaded_particles_glob) =}" + gc_etas = self.domain.inverse_map(*xyz, bounded=False) - # set new n_mks_load - self._gather_scalar_in_subcomm_array(num_loaded_particles_loc, out=self.n_mks_load) - n_mks_load_loc = self.n_mks_load[self.mpi_rank] - n_mks_load_cum_sum = np.cumsum(self.n_mks_load) + # gyro transfer + self.markers[outside_inds, 1] = (gc_etas[1] - (self.markers[outside_inds, 1] - gc_etas[1]) % 1) % 1 - # set new holes in markers array to -1 - self._markers[num_loaded_particles_loc:] = -1.0 - self.update_holes() + new_xyz = self.domain(self.markers[outside_inds, :]) - # 2. plain sobol numbers with skip of first 1000 numbers - elif self.loading == "sobol_standard": - self.phasespace_coords = sobol_seq.i4_sobol_generate( - 3 + self.vdim, - n_mks_load_loc, - 1000 + (n_mks_load_cum_sum - self.n_mks_load)[self._mpi_rank], - ) + # eval cartesian equilibrium magnetic field at the marker positions + b_cart = self.equil.b_cart(self.markers[outside_inds, :])[0] - # 3. symmetric sobol numbers in all 6 dimensions with skip of first 1000 numbers - elif self.loading == "sobol_antithetic": - assert self.vdim == 3, NotImplementedError( - '"sobol_antithetic" requires vdim=3 at the moment.', - ) + # calculate magnetic field amplitude and normalized magnetic field + absB0 = xp.sqrt(b_cart[0] ** 2 + b_cart[1] ** 2 + b_cart[2] ** 2) + norm_b_cart = b_cart / absB0 - temp_markers = sobol_seq.i4_sobol_generate( - 3 + self.vdim, - n_mks_load_loc // 64, - 1000 + (n_mks_load_cum_sum - self.n_mks_load)[self._mpi_rank] // 64, - ) + Larmor_r = new_xyz - xyz + Larmor_r /= xp.sqrt(Larmor_r[0] ** 2 + Larmor_r[1] ** 2 + Larmor_r[2] ** 2) - sampling_kernels.set_particles_symmetric_3d_3v( - temp_markers, - self.markers, - ) + new_v_perp = xp.cross(Larmor_r, norm_b_cart, axis=0) * v_perp_square - # 4. Wrong specification - else: - raise ValueError( - "Specified particle loading method does not exist!", - ) + self.markers[outside_inds, 3:6] = (norm_b_cart * v_parallel).T + new_v_perp.T - # initial velocities - SPH case: v(0) = u(x(0)) for given velocity u(x) - if self.type == "sph": - self._set_initial_condition() - self.velocities = _to_numpy_for_kernel(self.u_init(self.positions)).T - else: - # inverse transform sampling in velocity space - # Avoid exact 0 or 1 from low-discrepancy sequences: erfinv(±1) - # and log(0) produce infinities or invalid polar velocities. - eps = np.finfo(float).eps - self._markers[:n_mks_load_loc, 3 : 3 + self.vdim] = np.clip( - self._markers[:n_mks_load_loc, 3 : 3 + self.vdim], - eps, - 1.0 - eps, - ) + return xp.logical_and(1.0 > gc_etas[0], gc_etas[0] > 0.0) - u_mean = np.array(self.loading_params.moments[: self.vdim]) - v_th = np.array(self.loading_params.moments[self.vdim :]) + def _sort_boxed_particles_numpy(self): + """Sort the particles by box using numpy.argsort.""" + sorting_axis = self._sorting_boxes.box_index - # Particles6D: (1d Maxwellian, 1d Maxwellian, 1d Maxwellian) - if self.vdim == 3: - self.velocities = ( - sp.erfinv( - 2 * self.velocities - 1, - ) - * np.sqrt(2) - * v_th - + u_mean - ) - # Particles5D: (1d Maxwellian, polar Maxwellian as volume-form) - elif self.vdim == 2: - self._markers[:n_mks_load_loc, 3] = ( - sp.erfinv( - 2 * self.velocities[:, 0] - 1, - ) - * np.sqrt(2) - * v_th[0] - + u_mean[0] - ) + if not hasattr(self, "_argsort_array"): + self._argsort_array = xp.zeros(self.markers.shape[0], dtype=int) + self._argsort_array[:] = self._markers[:, sorting_axis].argsort() - self._markers[:n_mks_load_loc, 4] = ( - np.sqrt( - -np.log(1.0 - self.velocities[:, 1]), - ) - * np.sqrt(2) - * v_th[1] - ) + self._markers[:, :] = self._markers[self._argsort_array] - # v_perp is a polar velocity coordinate and must be >= 0. - # A mean shift in this coordinate is not physically consistent - # with the polar Maxwellian used later in gaussian(..., polar=True). - if abs(float(u_mean[1])) > 0.0: - raise ValueError( - "For Particles5D, the second velocity coordinate is polar " - "(v_perp), so loading_params.moments[1] must be 0.0." - ) - elif self.vdim == 0: - pass - else: - raise NotImplementedError( - "Inverse transform sampling of given vdim is not implemented!", - ) + def _check_and_assign_particles_to_boxes(self): + """Check whether the box array has enough columns (detect load imbalance wrt to sorting boxes), + and then assign the particles to boxes.""" - # inversion method for drawing uniformly on the disc - if self.spatial == "disc": - self._markers[:n_mks_load_loc, 0] = np.sqrt( - self._markers[:n_mks_load_loc, 0], - ) - else: - assert self.spatial == "uniform", f'Spatial drawing must be "uniform" or "disc", is {self.spatial}.' + from cunumpy.xp import array_backend - self.marker_ids = _first_marker_id + np.arange(n_mks_load_loc, dtype=float) + if array_backend.backend == "numpy": + bcount = xp.bincount(xp.int64(self.markers_wo_holes[:, -2])) + else: + import cupy as cp - # set specific initial condition for some particles - if self.loading_params.specific_markers is not None: - specific_markers = self.loading_params.specific_markers + indices = self.markers_wo_holes[:, -2] + indices = indices.astype(cp.int64) + bcount = cp.bincount(indices) - counter = 0 - for i in range(len(specific_markers)): - if i == int(self.markers[counter, -1]): - for j in range(3 + self.vdim): - if specific_markers[i][j] is not None: - self._markers[ - counter, - j, - ] = specific_markers[i][j] + max_in_box = xp.max(bcount) + if max_in_box > self._sorting_boxes.boxes.shape[1]: + warnings.warn( + f'Strong load imbalance detected in sorting boxes: \ +max number of markers in a box ({max_in_box}) on rank {self.mpi_rank} \ +exceeds the column-size of the box array ({self._sorting_boxes.boxes.shape[1]}). \ +Increasing the value of "box_bufsize" in the markers parameters for the next run.', + ) + self.mpi_comm.Abort() - counter += 1 + assign_particles_to_boxes( + self.markers, + self.holes, + self._sorting_boxes._boxes, + self._sorting_boxes._next_index, + ) - # check if all particle positions are inside the unit cube [0, 1]^3 - n_mks_load_loc = self.n_mks_load[self._mpi_rank] + def _update_ghost_particles(self): + """Refresh :attr:`~struphy.pic.base.Particles.ghost_particles`: a marker is flagged + as a ghost particle when its ID column (last column) equals -2, the marker set by + :meth:`_prepare_ghost_particles`/:meth:`_sendrecv_markers_boxes` for SPH ghost-box + particles received from a neighbouring process.""" + self._ghost_particles[:] = self.markers[:, -1] == -2.0 + self._holes_ghost_dev_dirty = True + self._update_valid_mks() - assert np.all(~self.holes[:n_mks_load_loc]) - assert np.all(self.holes[n_mks_load_loc:]) + def _remove_ghost_particles(self): + """Discard all current ghost particles: turn their marker-array rows into new + holes (so the space can be reused before the next SPH ghost-box update).""" + self._update_ghost_particles() + new_holes = np.nonzero(self.ghost_particles) + self._markers[new_holes] = -1.0 + self.update_holes() - if self._initialized_sorting and sort: - logger.info("\nSorting the markers after initial draw") - if self.mpi_comm is not None: - self.mpi_sort_markers() - self.do_sort() - logger.info("Done.") + def _prepare_ghost_particles(self): + """Markers for boundary conditions and MPI communication. - @profile - def mpi_sort_markers( - self, - apply_bc: bool = True, - alpha: tuple | list | int | float = 1.0, - do_test: bool = False, - remove_ghost: bool = True, - ): + Does the following: + 1. determine which markers belong to boxes that are at the boundary and put these markers in a new array (e.g. markers_x_m) + 2. set their last index to -2 to indicate that they will be "ghost particles" after sending + 3. set their new box number (boundary conditions enter here) + 4. optional: mirror position for boundary conditions """ - Sorts markers according to MPI domain decomposition. - - Markers are sent to the process corresponding to the alpha-weighted position - alpha*markers[:, 0:3] + (1 - alpha)*markers[:, first_pusher_idx:first_pusher_idx + 3]. - - Periodic boundary conditions are taken into account - when computing the alpha-weighted position. + shifts = self.sorting_boxes.bc_sph_index_shifts - Parameters - ---------- - appl_bc : bool - Whether to apply kinetic boundary conditions before sorting. + ## Faces - alpha : tuple | list | int | float - For i=1,2,3 the sorting is according to alpha[i]*markers[:, i] + (1 - alpha[i])*markers[:, first_pusher_idx + i]. - If int or float then alpha = (alpha, alpha, alpha). alpha must be between 0 and 1. + # ghost marker arrays + self._markers_x_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m) + self._markers_x_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p) + self._markers_y_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_m) + self._markers_y_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_p) + self._markers_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_z_m) + self._markers_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_z_p) - do_test : bool - Check if all markers are on the right process after sorting. + # Put last index to -2 to indicate that they are ghosts on the new process + self._markers_x_m[:, -1] = -2.0 + self._markers_x_p[:, -1] = -2.0 + self._markers_y_m[:, -1] = -2.0 + self._markers_y_p[:, -1] = -2.0 + self._markers_z_m[:, -1] = -2.0 + self._markers_z_p[:, -1] = -2.0 - remove_ghost : bool - Remove ghost particles before send. - """ - if remove_ghost: - self.remove_ghost_particles() + # Adjust box number + self._markers_x_m[:, self._sorting_boxes.box_index] += shifts["x_m"] + self._markers_x_p[:, self._sorting_boxes.box_index] -= shifts["x_p"] + self._markers_y_m[:, self._sorting_boxes.box_index] += shifts["y_m"] + self._markers_y_p[:, self._sorting_boxes.box_index] -= shifts["y_p"] + self._markers_z_m[:, self._sorting_boxes.box_index] += shifts["z_m"] + self._markers_z_p[:, self._sorting_boxes.box_index] -= shifts["z_p"] - self._Barrier() + # Mirror position for boundary condition + if self.bc_sph[0] in ("mirror", "fixed", "noslip"): + self._mirror_particles( + "_markers_x_m", + "_markers_x_p", + is_domain_boundary=self.sorting_boxes.is_domain_boundary, + mean_velocity_index=self.mean_velocity_index, + ) - # before sorting, apply kinetic bc - if apply_bc: - self.apply_kinetic_bc() + if self.bc_sph[1] in ("mirror", "fixed", "noslip"): + self._mirror_particles( + "_markers_y_m", + "_markers_y_p", + is_domain_boundary=self.sorting_boxes.is_domain_boundary, + mean_velocity_index=self.mean_velocity_index, + ) - if isinstance(alpha, int) or isinstance(alpha, float): - alpha = (alpha, alpha, alpha) + if self.bc_sph[2] in ("mirror", "fixed", "noslip"): + self._mirror_particles( + "_markers_z_m", + "_markers_z_p", + is_domain_boundary=self.sorting_boxes.is_domain_boundary, + mean_velocity_index=self.mean_velocity_index, + ) - # create new markers_to_be_sent array and make corresponding holes in markers array - hole_inds_after_send, send_inds = self.sendrecv_determine_mtbs(alpha=alpha) + ## Edges x-y - # determine where to send markers_to_be_sent - send_info = self.sendrecv_get_destinations(send_inds) + # ghost marker arrays + self._markers_x_m_y_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_m) + self._markers_x_m_y_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_p) + self._markers_x_p_y_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_m) + self._markers_x_p_y_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_p) - # set new holes in markers array to -1 - self._markers[send_inds] = -1.0 + # Put last index to -2 to indicate that they are ghosts on the new process + self._markers_x_m_y_m[:, -1] = -2.0 + self._markers_x_m_y_p[:, -1] = -2.0 + self._markers_x_p_y_m[:, -1] = -2.0 + self._markers_x_p_y_p[:, -1] = -2.0 - # transpose send_info - recv_info = self.sendrecv_all_to_all(send_info) + # Adjust box number + self._markers_x_m_y_m[:, self._sorting_boxes.box_index] += shifts["x_m"] + shifts["y_m"] + self._markers_x_m_y_p[:, self._sorting_boxes.box_index] += shifts["x_m"] - shifts["y_p"] + self._markers_x_p_y_m[:, self._sorting_boxes.box_index] += -shifts["x_p"] + shifts["y_m"] + self._markers_x_p_y_p[:, self._sorting_boxes.box_index] += -shifts["x_p"] - shifts["y_p"] - # send and receive markers - self.sendrecv_markers(recv_info, hole_inds_after_send) + # Mirror position for boundary condition + if self.bc_sph[0] in ("mirror", "fixed", "noslip") or self.bc_sph[1] in ("mirror", "fixed", "noslip"): + self._mirror_particles( + "_markers_x_m_y_m", + "_markers_x_m_y_p", + "_markers_x_p_y_m", + "_markers_x_p_y_p", + is_domain_boundary=self.sorting_boxes.is_domain_boundary, + mean_velocity_index=self.mean_velocity_index, + ) - # new holes and new number of holes and markers on process - self.update_holes() + ## Edges x-z - # refresh ghost mask: received markers may land in rows that previously held - # ghost particles. update_holes alone recomputes valid_mks from a stale - # _ghost_particles mask, which would wrongly exclude these incoming real markers. - self.update_ghost_particles() + # ghost marker arrays + self._markers_x_m_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_z_m) + self._markers_x_m_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_z_p) + self._markers_x_p_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_z_m) + self._markers_x_p_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_z_p) - # check if all markers are on the right process after sorting - if do_test: - all_on_right_proc = np.all( - np.logical_and( - self.positions > self.domain_array[self.mpi_rank, 0::3], - self.positions < self.domain_array[self.mpi_rank, 1::3], - ), - ) + # Put last index to -2 to indicate that they are ghosts on the new process + self._markers_x_m_z_m[:, -1] = -2.0 + self._markers_x_m_z_p[:, -1] = -2.0 + self._markers_x_p_z_m[:, -1] = -2.0 + self._markers_x_p_z_p[:, -1] = -2.0 - assert all_on_right_proc - # assert self.phasespace_coords.size > 0, f'No particles on process {self.mpi_rank}, please rebalance, aborting ...' + # Adjust box number + self._markers_x_m_z_m[:, self._sorting_boxes.box_index] += shifts["x_m"] + shifts["z_m"] + self._markers_x_m_z_p[:, self._sorting_boxes.box_index] += shifts["x_m"] - shifts["z_p"] + self._markers_x_p_z_m[:, self._sorting_boxes.box_index] += -shifts["x_p"] + shifts["z_m"] + self._markers_x_p_z_p[:, self._sorting_boxes.box_index] += -shifts["x_p"] - shifts["z_p"] - self._Barrier() + # Mirror position for boundary condition + if self.bc_sph[0] in ("mirror", "fixed", "noslip") or self.bc_sph[2] in ("mirror", "fixed", "noslip"): + self._mirror_particles( + "_markers_x_m_z_m", + "_markers_x_m_z_p", + "_markers_x_p_z_m", + "_markers_x_p_z_p", + is_domain_boundary=self.sorting_boxes.is_domain_boundary, + mean_velocity_index=self.mean_velocity_index, + ) - def initialize_weights( - self, - *, - bckgr_params: dict = None, - pert_params: dict = None, - # reject_weights: bool = False, - # threshold: float = 1e-8, - ): - r""" - Computes the initial weights + ## Edges y-z - .. math:: + # ghost marker arrays + self._markers_y_m_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_m_z_m) + self._markers_y_m_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_m_z_p) + self._markers_y_p_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_p_z_m) + self._markers_y_p_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_p_z_p) - w_{k0} := \frac{f^0(t, q_k(t)) }{s^0(t, q_k(t)) } = \frac{f^0(0, q_k(0)) }{s^0(0, q_k(0)) } = \frac{f^0_{\textnormal{in}}(q_{k0}) }{s^0_{\textnormal{in}}(q_{k0}) } + # Put last index to -2 to indicate that they are ghosts on the new process + self._markers_y_m_z_m[:, -1] = -2.0 + self._markers_y_m_z_p[:, -1] = -2.0 + self._markers_y_p_z_m[:, -1] = -2.0 + self._markers_y_p_z_p[:, -1] = -2.0 - from the initial distribution function :math:`f^0_{\textnormal{in}}` specified in the parmeter file - and from the initial volume density :math:`s^n_{\textnormal{vol}}` specified in :meth:`~struphy.pic.base.Particles.draw_markers`. - Moreover, it sets the corresponding columns for "w0", "s0" and "weights" in the markers array. - If :attr:`~struphy.pic.base.Particles.control_variate` is True, the background :attr:`~struphy.pic.base.Particles.f0` is subtracted. + # Adjust box number + self._markers_y_m_z_m[:, self._sorting_boxes.box_index] += shifts["y_m"] + shifts["z_m"] + self._markers_y_m_z_p[:, self._sorting_boxes.box_index] += shifts["y_m"] - shifts["z_p"] + self._markers_y_p_z_m[:, self._sorting_boxes.box_index] += -shifts["y_p"] + shifts["z_m"] + self._markers_y_p_z_p[:, self._sorting_boxes.box_index] += -shifts["y_p"] - shifts["z_p"] - Parameters - ---------- - bckgr_params : dict - Kinetic background parameters. + # Mirror position for boundary condition + if self.bc_sph[1] in ("mirror", "fixed", "noslip") or self.bc_sph[2] in ("mirror", "fixed", "noslip"): + self._mirror_particles( + "_markers_y_m_z_m", + "_markers_y_m_z_p", + "_markers_y_p_z_m", + "_markers_y_p_z_p", + is_domain_boundary=self.sorting_boxes.is_domain_boundary, + mean_velocity_index=self.mean_velocity_index, + ) - pert_params : dict - Kinetic perturbation parameters for initial condition. - """ + ## Corners - if self.loading == "tesselation": - if not self.is_volume_form[0]: - fvol = TransformedPformComponent([self.f_init], "0", "3", domain=self.domain) - else: - fvol = self.f_init - cell_avg = self.tesselation.cell_averages(fvol, n_quad=self.loading_params.n_quad) - self.weights0 = cell_avg.flatten() - else: - assert self.domain is not None, "A domain is needed to initialize weights." + # ghost marker arrays + self._markers_x_m_y_m_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_m_z_m) + self._markers_x_m_y_m_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_m_z_p) + self._markers_x_m_y_p_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_p_z_m) + self._markers_x_m_y_p_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_p_z_p) + self._markers_x_p_y_m_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_m_z_m) + self._markers_x_p_y_m_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_m_z_p) + self._markers_x_p_y_p_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_p_z_m) + self._markers_x_p_y_p_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_p_z_p) - # set initial condition - if bckgr_params is not None: - self._bckgr_params = bckgr_params + # Put last index to -2 to indicate that they are ghosts on the new process + self._markers_x_m_y_m_z_m[:, -1] = -2.0 + self._markers_x_m_y_m_z_p[:, -1] = -2.0 + self._markers_x_m_y_p_z_m[:, -1] = -2.0 + self._markers_x_m_y_p_z_p[:, -1] = -2.0 + self._markers_x_p_y_m_z_m[:, -1] = -2.0 + self._markers_x_p_y_m_z_p[:, -1] = -2.0 + self._markers_x_p_y_p_z_m[:, -1] = -2.0 + self._markers_x_p_y_p_z_p[:, -1] = -2.0 - if pert_params is not None: - self._pert_params = pert_params + # Adjust box number + self._markers_x_m_y_m_z_m[:, self._sorting_boxes.box_index] += shifts["x_m"] + shifts["y_m"] + shifts["z_m"] + self._markers_x_m_y_m_z_p[:, self._sorting_boxes.box_index] += shifts["x_m"] + shifts["y_m"] - shifts["z_p"] + self._markers_x_m_y_p_z_m[:, self._sorting_boxes.box_index] += shifts["x_m"] - shifts["y_p"] + shifts["z_m"] + self._markers_x_m_y_p_z_p[:, self._sorting_boxes.box_index] += shifts["x_m"] - shifts["y_p"] - shifts["z_p"] + self._markers_x_p_y_m_z_m[:, self._sorting_boxes.box_index] += -shifts["x_p"] + shifts["y_m"] + shifts["z_m"] + self._markers_x_p_y_m_z_p[:, self._sorting_boxes.box_index] += -shifts["x_p"] + shifts["y_m"] - shifts["z_p"] + self._markers_x_p_y_p_z_m[:, self._sorting_boxes.box_index] += -shifts["x_p"] - shifts["y_p"] + shifts["z_m"] + self._markers_x_p_y_p_z_p[:, self._sorting_boxes.box_index] += -shifts["x_p"] - shifts["y_p"] - shifts["z_p"] - if self.type != "sph": - self._set_initial_condition() + # Mirror position for boundary condition + if any([bci in ("mirror", "fixed", "noslip") for bci in self.bc_sph]): + self._mirror_particles( + "_markers_x_m_y_m_z_m", + "_markers_x_m_y_m_z_p", + "_markers_x_m_y_p_z_m", + "_markers_x_m_y_p_z_p", + "_markers_x_p_y_m_z_m", + "_markers_x_p_y_m_z_p", + "_markers_x_p_y_p_z_m", + "_markers_x_p_y_p_z_p", + is_domain_boundary=self.sorting_boxes.is_domain_boundary, + mean_velocity_index=self.mean_velocity_index, + ) - # evaluate initial distribution function - # NOTE: self.domain/self.f0/self.s0 evaluate on the device (they - # follow the global array backend), while markers are always - # host-resident, so results are converted back to NumPy at this - # marker/field evaluation boundary. - if self.type == "sph": - f_init = _to_numpy_for_kernel(self.f_init(_dev(self.positions))) - else: - f_init = _to_numpy_for_kernel(self.f_init(*_dev(*self.f_coords.T))) + def _mirror_particles( + self, *marker_array_names, is_domain_boundary: dict | None = None, mean_velocity_index: int | None = None + ): + """ + Mirror the positions and velocities of the particles in the ghost marker arrays for the boundary conditions. + For "mirror" boundary condition, the positions are mirrored and the velocities are unchanged. + For "fixed" boundary condition, the positions are mirrored and the velocities are set to zero (or to the value of f_init if provided). + For "noslip" boundary condition, the positions are mirrored and the velocities are inverted to have zero velocity at the boundary. - # if f_init is vol-form, transform to 0-form - if self.is_volume_form[0]: - f_init /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions))) + Parameters + ---------- + marker_array_names : str + The names of the marker arrays to be mirrored (e.g. "_markers_x_m", "_markers_x_p", etc.). - if self.is_volume_form[1]: - f_init /= _to_numpy_for_kernel( - self.f_init.velocity_jacobian_det( - *_dev(*self.f_jacobian_coords.T), - ) - ) + is_domain_boundary : dict + A dictionary indicating whether the boundary condition is applied at the domain boundary (e.g. {"x_m": True, "x_p": True, "y_m": True, "y_p": True, "z_m": True, "z_p": True}). - # compute s0 and save at vdim + 4 - self.sampling_density = _to_numpy_for_kernel(self.s0(*_dev(*self.phasespace_coords.T), flat_eval=True)) + mean_velocity_index : int, optional + The index of the mean velocity in the marker array (if applicable), by default None. + """ + self._fixed_markers_set = {} - # compute w0 and save at vdim + 5 - self.weights0 = f_init / self.sampling_density / self.Np + for arr_name in marker_array_names: + assert isinstance(arr_name, str) + arr = getattr(self, arr_name) - if self.reject_weights: - reject = self.markers[:, self.index["w0"]] < self.threshold - self._markers[reject] = -1.0 - self.update_holes() - self.reset_marker_ids() - logger.info( - f"\nWeights < {self.threshold} have been rejected, number of valid markers on process {self.mpi_rank} is {self.n_mks_loc}.", - ) - - # compute (time-dependent) weights at vdim + 3 - if self.control_variate: - self.update_weights() - else: - self.weights = self.weights0 + if arr.size == 0: + continue - @profile - def update_weights(self): - """ - Applies the control variate method, i.e. updates the time-dependent marker weights - according to the algorithm in :ref:`control_var`. - The background :attr:`~struphy.pic.base.Particles.f0` is used for this. - """ + # x-direction + if self.bc_sph[0] in ("mirror", "fixed", "noslip"): + if "x_m" in arr_name and is_domain_boundary["x_m"]: + arr[:, 0] *= -1.0 + if self.bc_sph[0] == "fixed" and arr_name not in self._fixed_markers_set: + boundary_values = _to_numpy_for_kernel( + self.f_init( + *_dev(*arr[:, :3].T), + flat_eval=True, + ), + ) # evaluation outside of the unit cube - maybe not working for all f_init! + arr[:, self.index["weights"]] = ( + -boundary_values + / _to_numpy_for_kernel( + self.s0( + *_dev(*arr[:, :3].T), + flat_eval=True, + remove_holes=False, + ), + ) + / self.Np + ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right + self._fixed_markers_set[arr_name] = True + elif self.bc_sph[0] == "noslip": + # invert the velocities to have zero velocity at the boundary + arr[:, 3] *= -1.0 + arr[:, 4] *= -1.0 + arr[:, 5] *= -1.0 + if mean_velocity_index is not None: + arr[:, mean_velocity_index] *= -1.0 + arr[:, mean_velocity_index + 1] *= -1.0 + arr[:, mean_velocity_index + 2] *= -1.0 - if self.type == "sph": - f0 = _to_numpy_for_kernel(self.f0.n0(_dev(self.positions))) - else: - # in case of CanonicalMaxwellian, evaluate constants_of_motion - if self.f0.coords == "constants_of_motion": - self.save_constants_of_motion() - f0 = _to_numpy_for_kernel(self.f0(*_dev(*self.f_coords.T))) + elif "x_p" in arr_name and is_domain_boundary["x_p"]: + arr[:, 0] = 2.0 - arr[:, 0] + if self.bc_sph[0] == "fixed" and arr_name not in self._fixed_markers_set: + boundary_values = _to_numpy_for_kernel( + self.f_init( + *_dev(*arr[:, :3].T), + flat_eval=True, + ), + ) # evaluation outside of the unit cube - maybe not working for all f_init! + arr[:, self.index["weights"]] = ( + -boundary_values + / _to_numpy_for_kernel( + self.s0( + *_dev(*arr[:, :3].T), + flat_eval=True, + remove_holes=False, + ), + ) + / self.Np + ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right + self._fixed_markers_set[arr_name] = True + elif self.bc_sph[0] == "noslip": + # invert the velocities to have zero velocity at the boundary + arr[:, 3] *= -1.0 + arr[:, 4] *= -1.0 + arr[:, 5] *= -1.0 + if mean_velocity_index is not None: + arr[:, mean_velocity_index] *= -1.0 + arr[:, mean_velocity_index + 1] *= -1.0 + arr[:, mean_velocity_index + 2] *= -1.0 - # if f_init is vol-form, transform to 0-form - if self.is_volume_form[0]: - f0 /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions))) + # y-direction + if self.bc_sph[1] in ("mirror", "fixed", "noslip"): + if "y_m" in arr_name and is_domain_boundary["y_m"]: + arr[:, 1] *= -1.0 + if self.bc_sph[1] == "fixed" and arr_name not in self._fixed_markers_set: + boundary_values = _to_numpy_for_kernel( + self.f_init( + *_dev(*arr[:, :3].T), + flat_eval=True, + ), + ) # evaluation outside of the unit cube - maybe not working for all f_init! + arr[:, self.index["weights"]] = ( + -boundary_values + / _to_numpy_for_kernel( + self.s0( + *_dev(*arr[:, :3].T), + flat_eval=True, + remove_holes=False, + ), + ) + / self.Np + ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right + self._fixed_markers_set[arr_name] = True + elif self.bc_sph[1] == "noslip": + # invert the velocities to have zero velocity at the boundary + arr[:, 3] *= -1.0 + arr[:, 4] *= -1.0 + arr[:, 5] *= -1.0 + if mean_velocity_index is not None: + arr[:, mean_velocity_index] *= -1.0 + arr[:, mean_velocity_index + 1] *= -1.0 + arr[:, mean_velocity_index + 2] *= -1.0 - if self.is_volume_form[1]: - f0 /= _to_numpy_for_kernel(self.f0.velocity_jacobian_det(*_dev(*self.f_jacobian_coords.T))) + elif "y_p" in arr_name and is_domain_boundary["y_p"]: + arr[:, 1] = 2.0 - arr[:, 1] + if self.bc_sph[1] == "fixed" and arr_name not in self._fixed_markers_set: + boundary_values = _to_numpy_for_kernel( + self.f_init( + *_dev(*arr[:, :3].T), + flat_eval=True, + ), + ) # evaluation outside of the unit cube - maybe not working for all f_init! + arr[:, self.index["weights"]] = ( + -boundary_values + / _to_numpy_for_kernel( + self.s0( + *_dev(*arr[:, :3].T), + flat_eval=True, + remove_holes=False, + ), + ) + / self.Np + ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right + self._fixed_markers_set[arr_name] = True + elif self.bc_sph[1] == "noslip": + # invert the velocities to have zero velocity at the boundary + arr[:, 3] *= -1.0 + arr[:, 4] *= -1.0 + arr[:, 5] *= -1.0 + if mean_velocity_index is not None: + arr[:, mean_velocity_index] *= -1.0 + arr[:, mean_velocity_index + 1] *= -1.0 + arr[:, mean_velocity_index + 2] *= -1.0 - self.weights = self.weights0 - f0 / self.sampling_density / self.Np + # z-direction + if self.bc_sph[2] in ("mirror", "fixed", "noslip"): + if "z_m" in arr_name and is_domain_boundary["z_m"]: + arr[:, 2] *= -1.0 + if self.bc_sph[2] == "fixed" and arr_name not in self._fixed_markers_set: + boundary_values = _to_numpy_for_kernel( + self.f_init( + *_dev(*arr[:, :3].T), + flat_eval=True, + ), + ) # evaluation outside of the unit cube - maybe not working for all f_init! + arr[:, self.index["weights"]] = ( + -boundary_values + / _to_numpy_for_kernel( + self.s0( + *_dev(*arr[:, :3].T), + flat_eval=True, + remove_holes=False, + ), + ) + / self.Np + ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right + self._fixed_markers_set[arr_name] = True + elif self.bc_sph[2] == "noslip": + # invert the velocities to have zero velocity at the boundary + arr[:, 3] *= -1.0 + arr[:, 4] *= -1.0 + arr[:, 5] *= -1.0 + if mean_velocity_index is not None: + arr[:, mean_velocity_index] *= -1.0 + arr[:, mean_velocity_index + 1] *= -1.0 + arr[:, mean_velocity_index + 2] *= -1.0 - def reset_marker_ids(self): -======= - def _reset_marker_ids(self): ->>>>>>> devel - """Reset the marker ids (last column in marker array) according to the current distribution of particles. - The first marker on rank 0 gets the id '0', the last marker on the last rank gets the id 'n_mks_global - 1'.""" - n_mks_proc_cumsum = np.cumsum(self.n_mks_on_each_proc) - n_mks_clone_cumsum = np.cumsum(self.n_mks_on_each_clone) - first_marker_id = (n_mks_clone_cumsum - self.n_mks_on_each_clone)[self.clone_id] + ( - n_mks_proc_cumsum - self.n_mks_on_each_proc - )[self.mpi_rank] - self.marker_ids = first_marker_id + np.arange(self.n_mks_loc, dtype=int) + elif "z_p" in arr_name and is_domain_boundary["z_p"]: + arr[:, 2] = 2.0 - arr[:, 2] + if self.bc_sph[2] == "fixed" and arr_name not in self._fixed_markers_set: + boundary_values = _to_numpy_for_kernel( + self.f_init( + *_dev(*arr[:, :3].T), + flat_eval=True, + ), + ) # evaluation outside of the unit cube - maybe not working for all f_init! + arr[:, self.index["weights"]] = ( + -boundary_values + / _to_numpy_for_kernel( + self.s0( + *_dev(*arr[:, :3].T), + flat_eval=True, + remove_holes=False, + ), + ) + / self.Np + ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right + self._fixed_markers_set[arr_name] = True + elif self.bc_sph[2] == "noslip": + # invert the velocities to have zero velocity at the boundary + arr[:, 3] *= -1.0 + arr[:, 4] *= -1.0 + arr[:, 5] *= -1.0 + if mean_velocity_index is not None: + arr[:, mean_velocity_index] *= -1.0 + arr[:, mean_velocity_index + 1] *= -1.0 + arr[:, mean_velocity_index + 2] *= -1.0 -<<<<<<< HEAD - @profile - def binning( - self, - components: tuple[bool], - bin_edges: tuple[np.ndarray], - output_quantity: LiteralOptions.BinningQuantity = "density", - divide_by_jac: bool = True, - ): - r"""Computes full-f and delta-f distribution functions via marker binning in logical space. - Numpy's histogramdd is used, following the algorithm outlined in :ref:`binning`. -======= - def _find_outside_particles(self, axis): - """Find markers whose ``axis``-th logical coordinate lies outside ``[0, 1]`` - (holes and ghost particles are excluded), updating - :attr:`_is_outside_left`/:attr:`_is_outside_right`/:attr:`_is_outside` accordingly. ->>>>>>> devel + def _determine_markers_in_box(self, list_boxes): + """Gather the markers currently sorted into any of the given boxes into a new array + (used to collect the particles on a domain/process boundary before turning them + into ghost particles). Parameters ---------- - axis : int - Column of the markers array (0, 1 or 2) holding the logical coordinate to check. + list_boxes : list[int] + Flat box indices (as computed by + :func:`~struphy.pic.sorting_kernels.flatten_index`) whose particles are collected. Returns ------- - outside_inds : xp.ndarray[int] - Row indices of the markers that are outside the logical unit cube. + markers_in_box : xp.ndarray + Copy of the marker-array rows belonging to any of ``list_boxes``. """ -<<<<<<< HEAD + indices = [] + for i in list_boxes: + indices += list(self._sorting_boxes._boxes[i][self._sorting_boxes._boxes[i] != -1]) - assert np.count_nonzero(np.array(components)) == len(bin_edges) + indices = xp.array(indices, dtype=int) + markers_in_box = self.markers[indices] + return markers_in_box - # bin_edges is caller-supplied and may follow the active array - # backend (e.g. built with xp.linspace under CuPy); markers and the - # rest of this method are always host-resident, so bring it to NumPy - # here, at the marker/caller-data boundary. - bin_edges = tuple(_to_numpy_for_kernel(be) for be in bin_edges) + def _get_destinations_box(self): + """Route the ghost markers prepared by :meth:`_prepare_ghost_particles` (one array + per face/edge/corner) to the neighbouring process on that side (found earlier by + :meth:`_get_neighbouring_proc`), accumulating, per destination rank, the number of + markers to send (:attr:`_send_info_box`) and the markers themselves + (:attr:`_send_list_box`, used by :meth:`_self_communication_boxes` and + :meth:`_sendrecv_markers_boxes`).""" + self._send_info_box = xp.zeros(self.mpi_size, dtype=int) + self._send_list_box = [xp.zeros((0, self.n_cols))] * self.mpi_size - # volume of a bin - bin_vol = 1.0 - for be in bin_edges: - bin_vol *= be[1] - be[0] + # Faces + # if self._x_m_proc is not None: + self._send_info_box[self._x_m_proc] += len(self._markers_x_m) + self._send_list_box[self._x_m_proc] = xp.concatenate((self._send_list_box[self._x_m_proc], self._markers_x_m)) - # extend components list to number of columns of markers array - _n = len(components) - slicing = components + [False] * (self.markers.shape[1] - _n) + # if self._x_p_proc is not None: + self._send_info_box[self._x_p_proc] += len(self._markers_x_p) + self._send_list_box[self._x_p_proc] = xp.concatenate((self._send_list_box[self._x_p_proc], self._markers_x_p)) - # determine type of output quantity - # Note: "density" Literal does not have "_" - quantity, *v_axis = output_quantity.rsplit(sep="_", maxsplit=1) - v_axis = [int(char) - 1 for char in "".join(v_axis)] # convert dimension axis to index + # if self._y_m_proc is not None: + self._send_info_box[self._y_m_proc] += len(self._markers_y_m) + self._send_list_box[self._y_m_proc] = xp.concatenate((self._send_list_box[self._y_m_proc], self._markers_y_m)) - # determine histogram weights multiplier - if quantity == "density": - multiplier = 1 - elif quantity == "current": - multiplier = self.velocities[:, v_axis[0]] - elif quantity == "energy_tensor": - multiplier = self.velocities[:, v_axis[0]] * self.velocities[:, v_axis[1]] - elif quantity == "heat_flux": - velocity_norm2 = np.linalg.norm(self.velocities, axis=1) ** 2 - multiplier = velocity_norm2 * self.velocities[:, v_axis[0]] + # if self._y_p_proc is not None: + self._send_info_box[self._y_p_proc] += len(self._markers_y_p) + self._send_list_box[self._y_p_proc] = xp.concatenate((self._send_list_box[self._y_p_proc], self._markers_y_p)) - # compute weights of histogram: - _weights0 = self.weights0 * self.Np * multiplier - _weights = self.weights * self.Np * multiplier + # if self._z_m_proc is not None: + self._send_info_box[self._z_m_proc] += len(self._markers_z_m) + self._send_list_box[self._z_m_proc] = xp.concatenate((self._send_list_box[self._z_m_proc], self._markers_z_m)) - if divide_by_jac: - _weights /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions), remove_outside=False)) - # _weights /= self.velocity_jacobian_det(*self.phasespace_coords.T) + # if self._z_p_proc is not None: + self._send_info_box[self._z_p_proc] += len(self._markers_z_p) + self._send_list_box[self._z_p_proc] = xp.concatenate((self._send_list_box[self._z_p_proc], self._markers_z_p)) - _weights0 /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions), remove_outside=False)) - # _weights0 /= self.velocity_jacobian_det(*self.phasespace_coords.T) + # x-y edges + # if self._x_m_y_m_proc is not None: + self._send_info_box[self._x_m_y_m_proc] += len(self._markers_x_m_y_m) + self._send_list_box[self._x_m_y_m_proc] = xp.concatenate( + (self._send_list_box[self._x_m_y_m_proc], self._markers_x_m_y_m), + ) - f_slice = np.histogramdd( - self.markers_wo_holes_and_ghost[:, slicing], - bins=bin_edges, - weights=_weights0, - )[0] + # if self._x_m_y_p_proc is not None: + self._send_info_box[self._x_m_y_p_proc] += len(self._markers_x_m_y_p) + self._send_list_box[self._x_m_y_p_proc] = xp.concatenate( + (self._send_list_box[self._x_m_y_p_proc], self._markers_x_m_y_p), + ) - df_slice = np.histogramdd( - self.markers_wo_holes_and_ghost[:, slicing], - bins=bin_edges, - weights=_weights, - )[0] - - f_slice /= self.Np * bin_vol - df_slice /= self.Np * bin_vol - - return f_slice, df_slice - - def show_distribution_function(self, components, bin_edges): - """ - 1D and 2D plots of slices of the distribution function via marker binning. - This routine is mainly for de-bugging. + # if self._x_p_y_m_proc is not None: + self._send_info_box[self._x_p_y_m_proc] += len(self._markers_x_p_y_m) + self._send_list_box[self._x_p_y_m_proc] = xp.concatenate( + (self._send_list_box[self._x_p_y_m_proc], self._markers_x_p_y_m), + ) - Parameters - ---------- - components : list[bool] - List of length 6 giving the directions in phase space in which to bin. + # if self._x_p_y_p_proc is not None: + self._send_info_box[self._x_p_y_p_proc] += len(self._markers_x_p_y_p) + self._send_list_box[self._x_p_y_p_proc] = xp.concatenate( + (self._send_list_box[self._x_p_y_p_proc], self._markers_x_p_y_p), + ) - bin_edges : list[array] - List of bin edges (resolution) having the length of True entries in components. - """ + # x-z edges + # if self._x_m_z_m_proc is not None: + self._send_info_box[self._x_m_z_m_proc] += len(self._markers_x_m_z_m) + self._send_list_box[self._x_m_z_m_proc] = xp.concatenate( + (self._send_list_box[self._x_m_z_m_proc], self._markers_x_m_z_m), + ) - import matplotlib.pyplot as plt + # if self._x_m_z_p_proc is not None: + self._send_info_box[self._x_m_z_p_proc] += len(self._markers_x_m_z_p) + self._send_list_box[self._x_m_z_p_proc] = xp.concatenate( + (self._send_list_box[self._x_m_z_p_proc], self._markers_x_m_z_p), + ) - n_dim = np.count_nonzero(components) + # if self._x_p_z_m_proc is not None: + self._send_info_box[self._x_p_z_m_proc] += len(self._markers_x_p_z_m) + self._send_list_box[self._x_p_z_m_proc] = xp.concatenate( + (self._send_list_box[self._x_p_z_m_proc], self._markers_x_p_z_m), + ) - assert n_dim == 1 or n_dim == 2, f"Distribution function can only be shown in 1D or 2D slices, not {n_dim}." + # if self._x_p_z_p_proc is not None: + self._send_info_box[self._x_p_z_p_proc] += len(self._markers_x_p_z_p) + self._send_list_box[self._x_p_z_p_proc] = xp.concatenate( + (self._send_list_box[self._x_p_z_p_proc], self._markers_x_p_z_p), + ) - f_slice, df_slice = self.binning(components, bin_edges) + # y-z edges + # if self._y_m_z_m_proc is not None: + self._send_info_box[self._y_m_z_m_proc] += len(self._markers_y_m_z_m) + self._send_list_box[self._y_m_z_m_proc] = xp.concatenate( + (self._send_list_box[self._y_m_z_m_proc], self._markers_y_m_z_m), + ) - bin_centers = [bi[:-1] + (bi[1] - bi[0]) / 2 for bi in bin_edges] + # if self._y_m_z_p_proc is not None: + self._send_info_box[self._y_m_z_p_proc] += len(self._markers_y_m_z_p) + self._send_list_box[self._y_m_z_p_proc] = xp.concatenate( + (self._send_list_box[self._y_m_z_p_proc], self._markers_y_m_z_p), + ) - labels = { - 0: r"$\eta_1$", - 1: r"$\eta_2$", - 2: r"$\eta_3$", - 3: "$v_1$", - 4: "$v_2$", - 5: "$v_3$", - } - indices = np.nonzero(components)[0] + # if self._y_p_z_m_proc is not None: + self._send_info_box[self._y_p_z_m_proc] += len(self._markers_y_p_z_m) + self._send_list_box[self._y_p_z_m_proc] = xp.concatenate( + (self._send_list_box[self._y_p_z_m_proc], self._markers_y_p_z_m), + ) - if n_dim == 1: - plt.plot(bin_centers[0], f_slice) - plt.xlabel(labels[indices[0]]) - else: - plt.contourf(bin_centers[0], bin_centers[1], df_slice.T, levels=20) - plt.colorbar() - # plt.axis('square') - plt.xlabel(labels[indices[0]]) - plt.ylabel(labels[indices[1]]) + # if self._y_p_z_p_proc is not None: + self._send_info_box[self._y_p_z_p_proc] += len(self._markers_y_p_z_p) + self._send_list_box[self._y_p_z_p_proc] = xp.concatenate( + (self._send_list_box[self._y_p_z_p_proc], self._markers_y_p_z_p), + ) - plt.show() + # corners + # if self._x_m_y_m_z_m_proc is not None: + self._send_info_box[self._x_m_y_m_z_m_proc] += len(self._markers_x_m_y_m_z_m) + self._send_list_box[self._x_m_y_m_z_m_proc] = xp.concatenate( + (self._send_list_box[self._x_m_y_m_z_m_proc], self._markers_x_m_y_m_z_m), + ) - def _find_outside_particles(self, axis): - if cunumpy.cupy_backend: - return self._find_outside_particles_gpu(axis) + # if self._x_m_y_m_z_p_proc is not None: + self._send_info_box[self._x_m_y_m_z_p_proc] += len(self._markers_x_m_y_m_z_p) + self._send_list_box[self._x_m_y_m_z_p_proc] = xp.concatenate( + (self._send_list_box[self._x_m_y_m_z_p_proc], self._markers_x_m_y_m_z_p), + ) -======= ->>>>>>> devel - # determine particles outside of the logical unit cube - self._is_outside_right[:] = self.markers[:, axis] > 1.0 - self._is_outside_left[:] = self.markers[:, axis] < 0.0 + # if self._x_m_y_p_z_m_proc is not None: + self._send_info_box[self._x_m_y_p_z_m_proc] += len(self._markers_x_m_y_p_z_m) + self._send_list_box[self._x_m_y_p_z_m_proc] = xp.concatenate( + (self._send_list_box[self._x_m_y_p_z_m_proc], self._markers_x_m_y_p_z_m), + ) - self._is_outside_right[self.holes] = False - self._is_outside_right[self.ghost_particles] = False - self._is_outside_left[self.holes] = False - self._is_outside_left[self.ghost_particles] = False + # if self._x_m_y_p_z_p_proc is not None: + self._send_info_box[self._x_m_y_p_z_p_proc] += len(self._markers_x_m_y_p_z_p) + self._send_list_box[self._x_m_y_p_z_p_proc] = xp.concatenate( + (self._send_list_box[self._x_m_y_p_z_p_proc], self._markers_x_m_y_p_z_p), + ) - self._is_outside[:] = np.logical_or( - self._is_outside_right, - self._is_outside_left, + # if self._x_p_y_m_z_m_proc is not None: + self._send_info_box[self._x_p_y_m_z_m_proc] += len(self._markers_x_p_y_m_z_m) + self._send_list_box[self._x_p_y_m_z_m_proc] = xp.concatenate( + (self._send_list_box[self._x_p_y_m_z_m_proc], self._markers_x_p_y_m_z_m), ) - # indices or particles that are outside of the logical unit cube - outside_inds = np.nonzero(self._is_outside)[0] + # if self._x_p_y_m_z_p_proc is not None: + self._send_info_box[self._x_p_y_m_z_p_proc] += len(self._markers_x_p_y_m_z_p) + self._send_list_box[self._x_p_y_m_z_p_proc] = xp.concatenate( + (self._send_list_box[self._x_p_y_m_z_p_proc], self._markers_x_p_y_m_z_p), + ) - return outside_inds + # if self._x_p_y_p_z_m_proc is not None: + self._send_info_box[self._x_p_y_p_z_m_proc] += len(self._markers_x_p_y_p_z_m) + self._send_list_box[self._x_p_y_p_z_m_proc] = xp.concatenate( + (self._send_list_box[self._x_p_y_p_z_m_proc], self._markers_x_p_y_p_z_m), + ) - def _find_outside_particles_gpu(self, axis): - """Device version of :meth:`_find_outside_particles`. + # if self._x_p_y_p_z_p_proc is not None: + self._send_info_box[self._x_p_y_p_z_p_proc] += len(self._markers_x_p_y_p_z_p) + self._send_list_box[self._x_p_y_p_z_p_proc] = xp.concatenate( + (self._send_list_box[self._x_p_y_p_z_p_proc], self._markers_x_p_y_p_z_p), + ) - ``self.markers[:, axis]`` is a single column out of ``n_cols``, so - reading it is a heavily strided gather; the reference (CPU) version - pays that cost twice (once each for the ``>`` and ``<`` comparison). - Reading it once into a device array and doing both comparisons plus - the hole/ghost masking there is measurably faster end-to-end even - after paying for the host<->device copies, because ``self._markers`` - is pinned memory (see :func:`_pinned_zeros`) — the transfers alone - run at a few hundred MiB, not tens of ms. + def _self_communication_boxes(self): + """Communicate the particles in case a process is it's own neighbour + (in case of periodicity with low number of procs/boxes)""" - ``holes``/``ghost_particles`` are re-transferred only when stale - (see ``_holes_ghost_dev_dirty``), since they are unchanged across - the several axes checked per :meth:`apply_kinetic_bc` call and are - only ever updated in place by :meth:`update_holes`/ - :meth:`update_ghost_particles`. - """ - import cupy as cp + if self._send_info_box[self.mpi_rank] > 0: + self.update_holes() + holes_inds = xp.nonzero(self.holes)[0] - col_dev = cp.asarray(self.markers[:, axis]) + if holes_inds.size < self._send_info_box[self.mpi_rank]: + warnings.warn( + f'Strong load imbalance detected: \ +number of holes ({holes_inds.size}) on rank {self.mpi_rank} \ +is smaller than number of incoming particles ({self._send_info_box[self.mpi_rank]}). \ +Increasing the value of "bufsize" in the markers parameters for the next run.', + ) + self.mpi_comm.Abort() - if self._holes_ghost_dev_dirty or self._holes_dev is None: - self._holes_dev = cp.asarray(self.holes) - self._ghost_dev = cp.asarray(self.ghost_particles) - self._holes_ghost_dev_dirty = False - holes_dev = self._holes_dev - ghost_dev = self._ghost_dev + # _tmp = self.markers.copy() + # _n_rows_old = _tmp.shape[0] + # logger.info(f"old: {self.markers.shape = }") + # self._bufsize *= 2.0 + # self._allocate_marker_array() + # logger.info(f"new: {self.markers.shape = }\n") + # self.markers[:] = -1.0 + # self.markers[:_n_rows_old] = _tmp + # self.update_holes() + # self._update_ghost_particles() + # self._update_valid_mks() + # holes_inds = xp.nonzero(self.holes)[0] - is_r = col_dev > 1.0 - is_l = col_dev < 0.0 - not_hole_or_ghost = ~(holes_dev | ghost_dev) - is_r &= not_hole_or_ghost - is_l &= not_hole_or_ghost - is_out = is_r | is_l + self.markers[holes_inds[xp.arange(self._send_info_box[self.mpi_rank])]] = self._send_list_box[self.mpi_rank] - is_r.get(out=self._is_outside_right) - is_l.get(out=self._is_outside_left) - is_out.get(out=self._is_outside) + def _sendrecv_all_to_all_boxes(self): + """ + Distribute info on how many markers will be sent/received to/from each process via all-to-all + for the communication of particles in boundary boxes. + """ - outside_inds = np.nonzero(self._is_outside)[0] + self._recv_info_box = xp.zeros(self.mpi_comm.Get_size(), dtype=int) - return outside_inds + self.mpi_comm.Alltoall(self._send_info_box, self._recv_info_box) -<<<<<<< HEAD - @profile - def apply_kinetic_bc(self, newton=False): + def _sendrecv_markers_boxes(self): """ - Apply boundary conditions to markers that are outside of the logical unit cube. - - Parameters - ---------- - newton : bool - Whether the shift due to boundary conditions should be computed - for a Newton step or for a strandard (explicit or Picard) step. + Use non-blocking communication. In-place modification of markers + for the communication of particles in boundary boxes. """ - # apply boundary conditions - for axis in self._remove_axes: - outside_inds = self._find_outside_particles(axis) + # i-th entry holds the number (not the index) of the first hole to be filled by data from process i + first_hole = xp.cumsum(self._recv_info_box) - self._recv_info_box + hole_inds = xp.nonzero(self._holes)[0] + # Initialize send and receive commands + reqs = [] + recvbufs = [] + for i, (data, N_recv) in enumerate(zip(self._send_list_box, list(self._recv_info_box))): + if i == self.mpi_comm.Get_rank(): + reqs += [None] + recvbufs += [None] + else: + self.mpi_comm.Isend(data, dest=i, tag=self.mpi_comm.Get_rank()) - if len(outside_inds) == 0: - continue + recvbufs += [xp.zeros((N_recv, self._markers.shape[1]), dtype=float)] + reqs += [self.mpi_comm.Irecv(recvbufs[-1], source=i, tag=i)] - if self.bc_refill is not None: - self.particle_refilling() + # Wait for buffer, then put markers into holes + test_reqs = [False] * (self._recv_info_box.size - 1) + while len(test_reqs) > 0: + # loop over all receive requests + for i, req in enumerate(reqs): + if req is None: + continue + else: + # check if data has been received + if req.Test(): + if hole_inds.size < first_hole[i] + self._recv_info_box[i]: + warnings.warn( + f'Strong load imbalance detected: \ +number of holes ({hole_inds.size}) on rank {self.mpi_rank} \ +is smaller than number of incoming particles ({first_hole[i] + self._recv_info_box[i]}). \ +Increasing the value of "bufsize" in the markers parameters for the next run.', + ) + self.mpi_comm.Abort() + # exit() - self._markers[self._is_outside, :-1] = -1.0 - self._n_lost_markers += len(np.nonzero(self._is_outside)[0]) + self._markers[hole_inds[first_hole[i] + xp.arange(self._recv_info_box[i])]] = recvbufs[i] - for axis in self._periodic_axes: - outside_inds = self._find_outside_particles(axis) + test_reqs.pop() + reqs[i] = None - if len(outside_inds) == 0: - continue + self._Barrier() - self.markers[outside_inds, axis] = self.markers[outside_inds, axis] % 1.0 + def _get_neighbouring_proc(self): + """Find the neighbouring processes for the sending of boxes. - # set shift for alpha-weighted mid-point computation - outside_right_inds = np.nonzero(self._is_outside_right)[0] - outside_left_inds = np.nonzero(self._is_outside_left)[0] - if newton: - self.markers[ - outside_right_inds, - self.first_pusher_idx + 3 + self.vdim + axis, - ] += 1.0 - self.markers[ - outside_left_inds, - self.first_pusher_idx + 3 + self.vdim + axis, - ] += -1.0 - else: - self.markers[ - :, - self.first_pusher_idx + 3 + self.vdim + axis, - ] = 0.0 - self.markers[ - outside_right_inds, - self.first_pusher_idx + 3 + self.vdim + axis, - ] = 1.0 - self.markers[ - outside_left_inds, - self.first_pusher_idx + 3 + self.vdim + axis, - ] = -1.0 - - # put all coordinate inside the unit cube (avoid wrong Jacobian evaluations) - outside_inds_per_axis = {} - for axis in self._reflect_axes: - outside_inds = self._find_outside_particles(axis) + The left (right) neighbour in direction 1 is called x_m_proc (x_p_proc), etc. + By default every process is its own neighbour. + """ + # Faces + self._x_m_proc = None + self._x_p_proc = None + self._y_m_proc = None + self._y_p_proc = None + self._z_m_proc = None + self._z_p_proc = None + # Edges + self._x_m_y_m_proc = None + self._x_m_y_p_proc = None + self._x_p_y_m_proc = None + self._x_p_y_p_proc = None + self._x_m_z_m_proc = None + self._x_m_z_p_proc = None + self._x_p_z_m_proc = None + self._x_p_z_p_proc = None + self._y_m_z_m_proc = None + self._y_m_z_p_proc = None + self._y_p_z_m_proc = None + self._y_p_z_p_proc = None + # Corners + self._x_m_y_m_z_m_proc = None + self._x_m_y_m_z_p_proc = None + self._x_m_y_p_z_m_proc = None + self._x_p_y_m_z_m_proc = None + self._x_m_y_p_z_p_proc = None + self._x_p_y_m_z_p_proc = None + self._x_p_y_p_z_m_proc = None + self._x_p_y_p_z_p_proc = None - self.markers[self._is_outside_left, axis] *= -1.0 - self.markers[self._is_outside_right, axis] *= -1.0 - self.markers[self._is_outside_right, axis] += 2.0 + # periodicitiy for distance computation + periodic1 = self.bc_sph[0] == "periodic" + periodic2 = self.bc_sph[1] == "periodic" + periodic3 = self.bc_sph[2] == "periodic" - self.markers[self._is_outside, self.first_pusher_idx] = -1.0 + # Determine which proc are on which side + dd = self.domain_array + rank = self.mpi_rank - outside_inds_per_axis[axis] = outside_inds + x_l = dd[rank][0] + x_r = dd[rank][1] + y_l = dd[rank][3] + y_r = dd[rank][4] + z_l = dd[rank][6] + z_r = dd[rank][7] + for i in range(self.mpi_size): + xl_i = dd[i][0] + xr_i = dd[i][1] + yl_i = dd[i][3] + yr_i = dd[i][4] + zl_i = dd[i][6] + zr_i = dd[i][7] - for axis in self._reflect_axes: - if len(outside_inds_per_axis[axis]) == 0: - continue - # flip velocity - reflect( - self.markers, - self.domain.args_domain, - outside_inds_per_axis[axis], - axis, - ) + is_same_x_l = abs(distance(xl_i, x_l, periodic1)) < 1e-5 + is_same_x_r = abs(distance(xr_i, x_r, periodic1)) < 1e-5 + is_same_y_l = abs(distance(yl_i, y_l, periodic2)) < 1e-5 + is_same_y_r = abs(distance(yr_i, y_r, periodic2)) < 1e-5 + is_same_z_l = abs(distance(zl_i, z_l, periodic3)) < 1e-5 + is_same_z_r = abs(distance(zr_i, z_r, periodic3)) < 1e-5 - def particle_refilling(self): -======= - def _particle_refilling(self): ->>>>>>> devel - r""" - When particles move outside of the domain, refills them. - TODO: Currently only valid for HollowTorus geometry with AdhocTorus equilibrium. + is_neigh_x_l = abs(distance(xr_i, x_l, periodic1)) < 1e-5 + is_neigh_x_r = abs(distance(xl_i, x_r, periodic1)) < 1e-5 + is_neigh_y_l = abs(distance(yr_i, y_l, periodic2)) < 1e-5 + is_neigh_y_r = abs(distance(yl_i, y_r, periodic2)) < 1e-5 + is_neigh_z_l = abs(distance(zr_i, z_l, periodic3)) < 1e-5 + is_neigh_z_r = abs(distance(zl_i, z_r, periodic3)) < 1e-5 - In case of guiding-center orbit, refills particles at the opposite poloidal angle of the same magnetic flux surface. + # Faces - .. math:: + # Process on the left (minus axis) in the x direction + if is_same_y_l and is_same_y_r and is_same_z_l and is_same_z_r and is_neigh_x_l: + self._x_m_proc = i - \theta_\text{refill} &= - \theta_\text{loss} - \\ - \phi_\text{refill} &= -2 q(r_\text{loss}) \theta_\text{loss} + # Process on the right (plus axis) in the x direction + if is_same_y_l and is_same_y_r and is_same_z_l and is_same_z_r and is_neigh_x_r: + self._x_p_proc = i - In case of full orbit, refills particles at the same gyro orbit until their guiding-centers are also outside of the domain. - When their guiding-centers also reach at the boundary, refills them as we did with guiding-center orbit. - """ + # Process on the left (minus axis) in the y direction + if is_same_x_l and is_same_x_r and is_same_z_l and is_same_z_r and is_neigh_y_l: + self._y_m_proc = i - for kind in self.bc_refill: - # sorting out particles which are out of the domain - if kind == "inner": - outside_inds = np.nonzero(self._is_outside_left)[0] - self.markers[outside_inds, 0] = 1e-4 - r_loss = self.domain.params["a1"] + # Process on the right (plus axis) in the y direction + if is_same_x_l and is_same_x_r and is_same_z_l and is_same_z_r and is_neigh_y_r: + self._y_p_proc = i - else: - outside_inds = np.nonzero(self._is_outside_right)[0] - self.markers[outside_inds, 0] = 1 - 1e-4 - r_loss = 1.0 + # Process on the left (minus axis) in the z direction + if is_same_x_l and is_same_x_r and is_same_y_l and is_same_y_r and is_neigh_z_l: + self._z_m_proc = i - if len(outside_inds) == 0: - continue + # Process on the right (plus axis) in the z direction + if is_same_x_l and is_same_x_r and is_same_y_l and is_same_y_r and is_neigh_z_r: + self._z_p_proc = i - # in case of Particles6D, do gyro boundary transfer - if self.vdim == 3: - gyro_inside_inds = self._gyro_transfer(outside_inds) + # Edges - # mark the particle as done for multiple step pushers - self.markers[outside_inds[gyro_inside_inds], self.first_pusher_idx] = -1.0 - self._is_outside[outside_inds[gyro_inside_inds]] = False + # Process on the left in x and left in y axis + if is_same_z_l and is_same_z_r and is_neigh_x_l and is_neigh_y_l: + self._x_m_y_m_proc = i - # exclude particles whose guiding center positions are still inside. - if len(gyro_inside_inds) > 0: - outside_inds = outside_inds[~gyro_inside_inds] + # Process on the left in x and right in y axis + if is_same_z_l and is_same_z_r and is_neigh_x_l and is_neigh_y_r: + self._x_m_y_p_proc = i - # do phi boundary transfer = phi_loss - 2*q(r_loss)*theta_loss - self.markers[outside_inds, 2] -= 2 * self.equil.q_r(r_loss) * self.markers[outside_inds, 1] + # Process on the right in x and left in y axis + if is_same_z_l and is_same_z_r and is_neigh_x_r and is_neigh_y_l: + self._x_p_y_m_proc = i - # theta_boudary_transfer = - theta_loss - self.markers[outside_inds, 1] = 1.0 - self.markers[outside_inds, 1] + # Process on the right in x and right in y axis + if is_same_z_l and is_same_z_r and is_neigh_x_r and is_neigh_y_r: + self._x_p_y_p_proc = i - # mark the particle as done for multiple step pushers - self.markers[outside_inds, self.first_pusher_idx] = -1.0 - self._is_outside[outside_inds] = False + # Process on the left in x and left in z axis + if is_same_y_l and is_same_y_r and is_neigh_x_l and is_neigh_z_l: + self._x_m_z_m_proc = i - def _gyro_transfer(self, outside_inds): - r"""Refills particles at the same gyro orbit. - Their perpendicular velocity directions are also changed accordingly: + # Process on the left in x and right in z axis + if is_same_y_l and is_same_y_r and is_neigh_x_l and is_neigh_z_r: + self._x_m_z_p_proc = i - First, refills the particles at the other side of the cross point (between gyro circle and domain boundary), + # Process on the right in x and left in z axis + if is_same_y_l and is_same_y_r and is_neigh_x_r and is_neigh_z_l: + self._x_p_z_m_proc = i - .. math:: + # Process on the right in x and right in z axis + if is_same_y_l and is_same_y_r and is_neigh_x_r and is_neigh_z_r: + self._x_p_z_p_proc = i - \theta_\text{refill} = \theta_\text{gc} - \left(\theta_\text{loss} - \theta_\text{gc} \right) \,. + # Process on the left in y and left in z axis + if is_same_x_l and is_same_x_r and is_neigh_y_l and is_neigh_z_l: + self._y_m_z_m_proc = i - Then changes the direction of the perpendicular velocity, + # Process on the left in y and right in z axis + if is_same_x_l and is_same_x_r and is_neigh_y_l and is_neigh_z_r: + self._y_m_z_p_proc = i - .. math:: + # Process on the right in y and left in z axis + if is_same_x_l and is_same_x_r and is_neigh_y_r and is_neigh_z_l: + self._y_p_z_m_proc = i - \vec{v}_{\perp, \text{refill}} = \frac{\vec{\rho}_g}{|\vec{\rho}_g|} \times \vec{b}_0 |\vec{v}_{\perp, \text{loss}}| \,, + # Process on the right in y and right in z axis + if is_same_x_l and is_same_x_r and is_neigh_y_r and is_neigh_z_r: + self._y_p_z_p_proc = i - where :math:`\vec{\rho}_g = \vec{x}_\text{refill} - \vec{X}_\text{gc}` is the cartesian radial vector. + # Corners - Parameters - ---------- - outside_inds : np.array (int) - An array of indices of particles which are outside of the domain. + # Process on the left in x, left in y and left in z axis + if is_neigh_x_l and is_neigh_y_l and is_neigh_z_l: + self._x_m_y_m_z_m_proc = i - Returns - ------- - out : np.array (bool) - An array of indices of particles where its guiding centers are outside of the domain. - """ + # Process on the left in x, left in y and right in z axis + if is_neigh_x_l and is_neigh_y_l and is_neigh_z_r: + self._x_m_y_m_z_p_proc = i - # incoming markers must be "Particles6D". - assert self.vdim == 3 + # Process on the left in x, right in y and left in z axis + if is_neigh_x_l and is_neigh_y_r and is_neigh_z_l: + self._x_m_y_p_z_m_proc = i - # TODO: currently assumes periodic boundary condition along poloidal and toroidal angle - self.markers[outside_inds, 1:3] = self.markers[outside_inds, 1:3] % 1 + # Process on the left in x, right in y and right in z axis + if is_neigh_x_l and is_neigh_y_r and is_neigh_z_r: + self._x_m_y_p_z_p_proc = i - v = self.markers[outside_inds, 3:6].T + # Process on the right in x, left in y and left in z axis + if is_neigh_x_r and is_neigh_y_l and is_neigh_z_l: + self._x_p_y_m_z_m_proc = i - # eval cartesian equilibrium magnetic field at the marker positions - assert isinstance(self.equil, FluidEquilibriumWithB), "Gyro transfer function needs a magnetic background." - b_cart, xyz = self.equil.b_cart(self.markers[outside_inds, :]) + # Process on the right in x, left in y and right in z axis + if is_neigh_x_r and is_neigh_y_l and is_neigh_z_r: + self._x_p_y_m_z_p_proc = i - # calculate magnetic field amplitude and normalized magnetic field - absB0 = np.sqrt(b_cart[0] ** 2 + b_cart[1] ** 2 + b_cart[2] ** 2) - norm_b_cart = b_cart / absB0 + # Process on the right in x, right in y and left in z axis + if is_neigh_x_r and is_neigh_y_r and is_neigh_z_l: + self._x_p_y_p_z_m_proc = i - # calculate parallel and perpendicular velocities - v_parallel = np.einsum("ij,ij->j", v, norm_b_cart) - v_perp = np.cross(norm_b_cart, np.cross(v, norm_b_cart, axis=0), axis=0) - v_perp_square = np.sqrt(v_perp[0] ** 2 + v_perp[1] ** 2 + v_perp[2] ** 2) + # Process on the right in x, right in y and right in z axis + if is_neigh_x_r and is_neigh_y_r and is_neigh_z_r: + self._x_p_y_p_z_p_proc = i - assert np.all(np.isclose(v_perp, v - norm_b_cart * v_parallel)) - - # calculate Larmor radius -<<<<<<< HEAD - Larmor_r = np.cross(norm_b_cart, v_perp, axis=0) / absB0 * self._epsilon -======= - Larmor_r = xp.cross(norm_b_cart, v_perp, axis=0) / absB0 * self.equation_params.epsilon ->>>>>>> devel - - # transform cartesian coordinates to logical coordinates - # TODO: currently only possible with the geomoetry where its inverse map is defined. - assert hasattr(self.domain, "inverse_map") - - xyz -= Larmor_r - - gc_etas = self.domain.inverse_map(*xyz, bounded=False) - - # gyro transfer - self.markers[outside_inds, 1] = (gc_etas[1] - (self.markers[outside_inds, 1] - gc_etas[1]) % 1) % 1 - - new_xyz = self.domain(self.markers[outside_inds, :]) - - # eval cartesian equilibrium magnetic field at the marker positions - b_cart = self.equil.b_cart(self.markers[outside_inds, :])[0] - - # calculate magnetic field amplitude and normalized magnetic field - absB0 = np.sqrt(b_cart[0] ** 2 + b_cart[1] ** 2 + b_cart[2] ** 2) - norm_b_cart = b_cart / absB0 - - Larmor_r = new_xyz - xyz - Larmor_r /= np.sqrt(Larmor_r[0] ** 2 + Larmor_r[1] ** 2 + Larmor_r[2] ** 2) - - new_v_perp = np.cross(Larmor_r, norm_b_cart, axis=0) * v_perp_square - - self.markers[outside_inds, 3:6] = (norm_b_cart * v_parallel).T + new_v_perp.T - - return np.logical_and(1.0 > gc_etas[0], gc_etas[0] > 0.0) - -<<<<<<< HEAD - class SortingBoxes: - """Boxes used for the sorting of the particles. - - Boxes are represented as a 2D array of integers, where - each line coresponds to one box, and all entries of line i that are not -1 - correspond to a particles in the i-th box. - - Parameters - ---------- - markers_shape : tuple - shape of 2D marker array. - - is_sph : bool - True if particle type is "sph". - - nx : int - number of boxes in the x direction. - - ny : int - number of boxes in the y direction. - - nz : int - number of boxes in the z direction. - - bc_sph : list - Boundary condition for sph density evaluation. - Either 'periodic', 'mirror', 'fixed' or 'noslip' in each direction. - - is_domain_boundary: dict - Has two booleans for each direction; True when the boundary of the MPI process is a domain boundary. - - comm : Intracomm - MPI communicator or None. - - box_index : int - Column index of the particles array to store the box number, counted from - the end (e.g. -2 for the second-to-last). - - box_bufsize : float - additional buffer space in the size of the boxes""" - - def __init__( - self, - markers_shape: tuple, - is_sph: bool, - *, - nx: int = 1, - ny: int = 1, - nz: int = 1, - bc_sph: list = None, - is_domain_boundary: dict = None, - comm: Intracomm = None, - box_index: "int" = -2, - box_bufsize: "float" = 2.0, - ): - self._markers_shape = markers_shape - self._nx = nx - self._ny = ny - self._nz = nz - self._comm = comm - self._box_index = box_index - self._box_bufsize = box_bufsize - - if bc_sph is None: - bc_sph = ["periodic"] * 3 - self._bc_sph = bc_sph - - if is_domain_boundary is None: - is_domain_boundary = {} - is_domain_boundary["x_m"] = True - is_domain_boundary["x_p"] = True - is_domain_boundary["y_m"] = True - is_domain_boundary["y_p"] = True - is_domain_boundary["z_m"] = True - is_domain_boundary["z_p"] = True - - self._is_domain_boundary = is_domain_boundary - - if comm is None: - self._rank = 0 - else: - self._rank = comm.Get_rank() - - self._set_boxes() - - self._communicate = is_sph - - if self.communicate: - self._set_boundary_boxes() - - @property - def nx(self): - return self._nx - - @property - def ny(self): - return self._ny - - @property - def nz(self): - return self._nz - - @property - def comm(self): - return self._comm - - @property - def box_index(self): - return self._box_index - - @property - def boxes(self): - if not hasattr(self, "_boxes"): - self._set_boxes() - return self._boxes - - @property - def neighbours(self): - if not hasattr(self, "_neighbours"): - self._set_boxes() - return self._neighbours - - @property - def communicate(self): - return self._communicate - - @property - def is_domain_boundary(self): - """Dict with two booleans for each direction (e.g. 'x_m' and 'x_p'); True when the boundary of the MPI process is a domain boundary (0.0 or 1.0).""" - return self._is_domain_boundary - - @property - def bc_sph(self): - """List of boundary conditions for sph evaluation in each direction.""" - return self._bc_sph - - @property - def bc_sph_index_shifts(self): - """Dictionary holding the index shifts of box number for ghost particles in each direction.""" - if not hasattr(self, "_bc_sph_index_shifts"): - self._compute_sph_index_shifts() - return self._bc_sph_index_shifts - - def _compute_sph_index_shifts(self): - """The index shifts are applied to ghost particles to indicate their new box after sending.""" - self._bc_sph_index_shifts = {} - self._bc_sph_index_shifts["x_m"] = flatten_index(self.nx, 0, 0, self.nx, self.ny, self.nz) - self._bc_sph_index_shifts["x_p"] = flatten_index(self.nx, 0, 0, self.nx, self.ny, self.nz) - self._bc_sph_index_shifts["y_m"] = flatten_index(0, self.ny, 0, self.nx, self.ny, self.nz) - self._bc_sph_index_shifts["y_p"] = flatten_index(0, self.ny, 0, self.nx, self.ny, self.nz) - self._bc_sph_index_shifts["z_m"] = flatten_index(0, 0, self.nz, self.nx, self.ny, self.nz) - self._bc_sph_index_shifts["z_p"] = flatten_index(0, 0, self.nz, self.nx, self.ny, self.nz) - - if self.bc_sph[0] in ("mirror", "fixed", "noslip"): - if self.is_domain_boundary["x_m"]: - self._bc_sph_index_shifts["x_m"] = flatten_index(-1, 0, 0, self.nx, self.ny, self.nz) - if self.is_domain_boundary["x_p"]: - self._bc_sph_index_shifts["x_p"] = flatten_index(-1, 0, 0, self.nx, self.ny, self.nz) - - if self.bc_sph[1] in ("mirror", "fixed", "noslip"): - if self.is_domain_boundary["y_m"]: - self._bc_sph_index_shifts["y_m"] = flatten_index(0, -1, 0, self.nx, self.ny, self.nz) - if self.is_domain_boundary["y_p"]: - self._bc_sph_index_shifts["y_p"] = flatten_index(0, -1, 0, self.nx, self.ny, self.nz) - - if self.bc_sph[2] in ("mirror", "fixed", "noslip"): - if self.is_domain_boundary["z_m"]: - self._bc_sph_index_shifts["z_m"] = flatten_index(0, 0, -1, self.nx, self.ny, self.nz) - if self.is_domain_boundary["z_p"]: - self._bc_sph_index_shifts["z_p"] = flatten_index(0, 0, -1, self.nx, self.ny, self.nz) - - def _set_boxes(self): - """ "(Re)set the box structure.""" - self._n_boxes = (self._nx + 2) * (self._ny + 2) * (self._nz + 2) - n_box_in = self._nx * self._ny * self._nz - - n_particles = self._markers_shape[0] - n_mkr = int(n_particles / n_box_in) + 1 - n_cols = round( - float(n_mkr) * (1 + 1 / float(np.sqrt(n_mkr)) + self._box_bufsize), - ) - - # cartesian boxes - self._boxes = np.zeros((self._n_boxes + 1, n_cols), dtype=int) - - # TODO: there is still a bug here - # the row number in self._boxes should not be n_boxes + 1; this is just a temporary fix to avoid an error that I dont understand. - # Must be fixed soon! - - self._next_index = np.zeros((self._n_boxes + 1), dtype=int) - self._cumul_next_index = np.zeros((self._n_boxes + 2), dtype=int) - self._neighbours = np.zeros((self._n_boxes, 27), dtype=int) - - # A particle on box i only sees particles in boxes that belong to neighbours[i] - initialize_neighbours(self._neighbours, self.nx, self.ny, self.nz) - # logger.info(f"{self._rank = }\n{self._neighbours = }") - - self._swap_line_1 = np.zeros(self._markers_shape[1]) - self._swap_line_2 = np.zeros(self._markers_shape[1]) - - def _set_boundary_boxes(self): - """Gather all the boxes that are part of a boundary""" - gather_x_boxes = self.nx > 1 - gather_y_boxes = self.ny > 1 - gather_z_boxes = self.nz > 1 - - # x boundary - # negative direction - self._bnd_boxes_x_m = [] - # positive direction - self._bnd_boxes_x_p = [] - - if gather_x_boxes: - for j in range(1, self.ny + 1): - for k in range(1, self.nz + 1): - self._bnd_boxes_x_m.append(flatten_index(1, j, k, self.nx, self.ny, self.nz)) - self._bnd_boxes_x_p.append(flatten_index(self.nx, j, k, self.nx, self.ny, self.nz)) - - logger.debug(f"eta1 boundary on {self._rank =}:\n{self._bnd_boxes_x_m =}\n{self._bnd_boxes_x_p =}") - - # y boundary - # negative direction - self._bnd_boxes_y_m = [] - # positive direction - self._bnd_boxes_y_p = [] - - if gather_y_boxes: - for i in range(1, self.nx + 1): - for k in range(1, self.nz + 1): - self._bnd_boxes_y_m.append(flatten_index(i, 1, k, self.nx, self.ny, self.nz)) - self._bnd_boxes_y_p.append(flatten_index(i, self.ny, k, self.nx, self.ny, self.nz)) - - logger.debug(f"eta2 boundary on {self._rank =}:\n{self._bnd_boxes_y_m =}\n{self._bnd_boxes_y_p =}") - - # z boundary - # negative direction - self._bnd_boxes_z_m = [] - # positive direction - self._bnd_boxes_z_p = [] - - if gather_z_boxes: - for i in range(1, self.nx + 1): - for j in range(1, self.ny + 1): - self._bnd_boxes_z_m.append(flatten_index(i, j, 1, self.nx, self.ny, self.nz)) - self._bnd_boxes_z_p.append(flatten_index(i, j, self.nz, self.nx, self.ny, self.nz)) - - logger.debug(f"eta3 boundary on {self._rank =}:\n{self._bnd_boxes_z_m =}\n{self._bnd_boxes_z_p =}") - - # x-y edges - self._bnd_boxes_x_m_y_m = [] - self._bnd_boxes_x_m_y_p = [] - self._bnd_boxes_x_p_y_m = [] - self._bnd_boxes_x_p_y_p = [] - - if gather_x_boxes and gather_y_boxes: - for k in range(1, self.nz + 1): - self._bnd_boxes_x_m_y_m.append(flatten_index(1, 1, k, self.nx, self.ny, self.nz)) - self._bnd_boxes_x_m_y_p.append(flatten_index(1, self.ny, k, self.nx, self.ny, self.nz)) - self._bnd_boxes_x_p_y_m.append(flatten_index(self.nx, 1, k, self.nx, self.ny, self.nz)) - self._bnd_boxes_x_p_y_p.append(flatten_index(self.nx, self.ny, k, self.nx, self.ny, self.nz)) - - logger.debug( - ( - f"eta1-eta2 edge on {self._rank =}:\n{self._bnd_boxes_x_m_y_m =}" - f"\n{self._bnd_boxes_x_m_y_p =}" - f"\n{self._bnd_boxes_x_p_y_m =}" - f"\n{self._bnd_boxes_x_p_y_p =}" - ), - ) - - # x-z edges - self._bnd_boxes_x_m_z_m = [] - self._bnd_boxes_x_m_z_p = [] - self._bnd_boxes_x_p_z_m = [] - self._bnd_boxes_x_p_z_p = [] - - if gather_x_boxes and gather_z_boxes: - for j in range(1, self.ny + 1): - self._bnd_boxes_x_m_z_m.append(flatten_index(1, j, 1, self.nx, self.ny, self.nz)) - self._bnd_boxes_x_m_z_p.append(flatten_index(1, j, self.nz, self.nx, self.ny, self.nz)) - self._bnd_boxes_x_p_z_m.append(flatten_index(self.nx, j, 1, self.nx, self.ny, self.nz)) - self._bnd_boxes_x_p_z_p.append(flatten_index(self.nx, j, self.nz, self.nx, self.ny, self.nz)) - - logger.debug( - ( - f"eta1-eta3 edge on {self._rank =}:\n{self._bnd_boxes_x_m_z_m =}" - f"\n{self._bnd_boxes_x_m_z_p =}" - f"\n{self._bnd_boxes_x_p_z_m =}" - f"\n{self._bnd_boxes_x_p_z_p =}" - ), - ) - - # y-z edges - self._bnd_boxes_y_m_z_m = [] - self._bnd_boxes_y_m_z_p = [] - self._bnd_boxes_y_p_z_m = [] - self._bnd_boxes_y_p_z_p = [] - - if gather_y_boxes and gather_z_boxes: - for i in range(1, self.nx + 1): - self._bnd_boxes_y_m_z_m.append(flatten_index(i, 1, 1, self.nx, self.ny, self.nz)) - self._bnd_boxes_y_m_z_p.append(flatten_index(i, 1, self.nz, self.nx, self.ny, self.nz)) - self._bnd_boxes_y_p_z_m.append(flatten_index(i, self.ny, 1, self.nx, self.ny, self.nz)) - self._bnd_boxes_y_p_z_p.append(flatten_index(i, self.ny, self.nz, self.nx, self.ny, self.nz)) - - logger.debug( - ( - f"eta2-eta3 edge on {self._rank =}:\n{self._bnd_boxes_y_m_z_m =}" - f"\n{self._bnd_boxes_y_m_z_p =}" - f"\n{self._bnd_boxes_y_p_z_m =}" - f"\n{self._bnd_boxes_y_p_z_p =}" - ), - ) - - # corners - self._bnd_boxes_x_m_y_m_z_m = [] - self._bnd_boxes_x_m_y_m_z_p = [] - self._bnd_boxes_x_m_y_p_z_m = [] - self._bnd_boxes_x_p_y_m_z_m = [] - self._bnd_boxes_x_m_y_p_z_p = [] - self._bnd_boxes_x_p_y_m_z_p = [] - self._bnd_boxes_x_p_y_p_z_m = [] - self._bnd_boxes_x_p_y_p_z_p = [] - - if gather_x_boxes and gather_y_boxes and gather_z_boxes: - self._bnd_boxes_x_m_y_m_z_m = [flatten_index(1, 1, 1, self.nx, self.ny, self.nz)] - self._bnd_boxes_x_m_y_m_z_p = [flatten_index(1, 1, self.nz, self.nx, self.ny, self.nz)] - self._bnd_boxes_x_m_y_p_z_m = [flatten_index(1, self.ny, 1, self.nx, self.ny, self.nz)] - self._bnd_boxes_x_p_y_m_z_m = [flatten_index(self.nx, 1, 1, self.nx, self.ny, self.nz)] - self._bnd_boxes_x_m_y_p_z_p = [flatten_index(1, self.ny, self.nz, self.nx, self.ny, self.nz)] - self._bnd_boxes_x_p_y_m_z_p = [flatten_index(self.nx, 1, self.nz, self.nx, self.ny, self.nz)] - self._bnd_boxes_x_p_y_p_z_m = [flatten_index(self.nx, self.ny, 1, self.nx, self.ny, self.nz)] - self._bnd_boxes_x_p_y_p_z_p = [flatten_index(self.nx, self.ny, self.nz, self.nx, self.ny, self.nz)] - - logger.debug( - ( - f"corners on {self._rank =}:\n{self._bnd_boxes_x_m_y_m_z_m =}" - f"\n{self._bnd_boxes_x_m_y_m_z_p =}" - f"\n{self._bnd_boxes_x_m_y_p_z_m =}" - f"\n{self._bnd_boxes_x_p_y_m_z_m =}" - f"\n{self._bnd_boxes_x_m_y_p_z_p =}" - f"\n{self._bnd_boxes_x_p_y_m_z_p =}" - f"\n{self._bnd_boxes_x_p_y_p_z_m =}" - f"\n{self._bnd_boxes_x_p_y_p_z_p =}" - ), - ) - -======= ->>>>>>> devel - def _sort_boxed_particles_numpy(self): - """Sort the particles by box using numpy.argsort.""" - sorting_axis = self._sorting_boxes.box_index - - if not hasattr(self, "_argsort_array"): - self._argsort_array = np.zeros(self.markers.shape[0], dtype=int) - self._argsort_array[:] = self._markers[:, sorting_axis].argsort() - - self._markers[:, :] = self._markers[self._argsort_array] - -<<<<<<< HEAD - @profile - def put_particles_in_boxes(self): - """Assign the right box to the particles and the list of the particles to each box. - If sorting_boxes was instantiated with an MPI comm, then the particles in the - neighbouring boxes of neighbours processors or also communicated""" - self.remove_ghost_particles() - - assign_box_to_each_particle( - self.markers, - self.holes, - self._sorting_boxes.nx, - self._sorting_boxes.ny, - self._sorting_boxes.nz, - self.domain_array[self.mpi_rank], - ) - - self.check_and_assign_particles_to_boxes() - - if self.sorting_boxes.communicate: - self.communicate_boxes() - self.check_and_assign_particles_to_boxes() - self.update_ghost_particles() - - # if self.verbose: - # valid_box_ids = np.nonzero(self._sorting_boxes._boxes[:, 0] != -1)[0] - # logger.info(f"Boxes holding at least one particle: {valid_box_ids}") - # for i in valid_box_ids: - # n_mks_box = np.count_nonzero(self._sorting_boxes._boxes[i] != -1) - # logger.info(f"Number of markers in box {i} is {n_mks_box}") - - def check_and_assign_particles_to_boxes(self): -======= - def _check_and_assign_particles_to_boxes(self): ->>>>>>> devel - """Check whether the box array has enough columns (detect load imbalance wrt to sorting boxes), - and then assign the particles to boxes.""" - - bcount = np.bincount(np.int64(self.markers_wo_holes[:, -2])) - - max_in_box = np.max(bcount) - if max_in_box > self._sorting_boxes.boxes.shape[1]: - warnings.warn( - f'Strong load imbalance detected in sorting boxes: \ -max number of markers in a box ({max_in_box}) on rank {self.mpi_rank} \ -exceeds the column-size of the box array ({self._sorting_boxes.boxes.shape[1]}). \ -Increasing the value of "box_bufsize" in the markers parameters for the next run.', - ) - self.mpi_comm.Abort() - - assign_particles_to_boxes( - self.markers, - self.holes, - self._sorting_boxes._boxes, - self._sorting_boxes._next_index, - ) - - def _update_ghost_particles(self): - """Refresh :attr:`~struphy.pic.base.Particles.ghost_particles`: a marker is flagged - as a ghost particle when its ID column (last column) equals -2, the marker set by - :meth:`_prepare_ghost_particles`/:meth:`_sendrecv_markers_boxes` for SPH ghost-box - particles received from a neighbouring process.""" - self._ghost_particles[:] = self.markers[:, -1] == -2.0 - self._update_valid_mks() - -<<<<<<< HEAD - self.put_particles_in_boxes() - - if use_numpy_argsort: - self._sort_boxed_particles_numpy() - else: - sort_boxed_particles( - self._markers, - self._sorting_boxes._swap_line_1, - self._sorting_boxes._swap_line_2, - nboxes + 1, - self._sorting_boxes._next_index, - self._sorting_boxes._cumul_next_index, - ) - - # The marker rows have just been reordered. The masks are row-based, - # so they must be rebuilt before any later use of valid_mks/f_coords. - self.update_holes() - self.update_ghost_particles() - self.update_valid_mks() - - def remove_ghost_particles(self): - self.update_ghost_particles() - new_holes = np.nonzero(self.ghost_particles) -======= - def _remove_ghost_particles(self): - """Discard all current ghost particles: turn their marker-array rows into new - holes (so the space can be reused before the next SPH ghost-box update).""" - self._update_ghost_particles() - new_holes = xp.nonzero(self.ghost_particles) ->>>>>>> devel - self._markers[new_holes] = -1.0 - self.update_holes() - - def _prepare_ghost_particles(self): - """Markers for boundary conditions and MPI communication. - - Does the following: - 1. determine which markers belong to boxes that are at the boundary and put these markers in a new array (e.g. markers_x_m) - 2. set their last index to -2 to indicate that they will be "ghost particles" after sending - 3. set their new box number (boundary conditions enter here) - 4. optional: mirror position for boundary conditions - """ - shifts = self.sorting_boxes.bc_sph_index_shifts - - ## Faces - - # ghost marker arrays - self._markers_x_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m) - self._markers_x_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p) - self._markers_y_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_m) - self._markers_y_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_p) - self._markers_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_z_m) - self._markers_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_z_p) - - # Put last index to -2 to indicate that they are ghosts on the new process - self._markers_x_m[:, -1] = -2.0 - self._markers_x_p[:, -1] = -2.0 - self._markers_y_m[:, -1] = -2.0 - self._markers_y_p[:, -1] = -2.0 - self._markers_z_m[:, -1] = -2.0 - self._markers_z_p[:, -1] = -2.0 - - # Adjust box number - self._markers_x_m[:, self._sorting_boxes.box_index] += shifts["x_m"] - self._markers_x_p[:, self._sorting_boxes.box_index] -= shifts["x_p"] - self._markers_y_m[:, self._sorting_boxes.box_index] += shifts["y_m"] - self._markers_y_p[:, self._sorting_boxes.box_index] -= shifts["y_p"] - self._markers_z_m[:, self._sorting_boxes.box_index] += shifts["z_m"] - self._markers_z_p[:, self._sorting_boxes.box_index] -= shifts["z_p"] - - # Mirror position for boundary condition - if self.bc_sph[0] in ("mirror", "fixed", "noslip"): - self._mirror_particles( - "_markers_x_m", - "_markers_x_p", - is_domain_boundary=self.sorting_boxes.is_domain_boundary, - mean_velocity_index=self.mean_velocity_index, - ) - - if self.bc_sph[1] in ("mirror", "fixed", "noslip"): - self._mirror_particles( - "_markers_y_m", - "_markers_y_p", - is_domain_boundary=self.sorting_boxes.is_domain_boundary, - mean_velocity_index=self.mean_velocity_index, - ) - - if self.bc_sph[2] in ("mirror", "fixed", "noslip"): - self._mirror_particles( - "_markers_z_m", - "_markers_z_p", - is_domain_boundary=self.sorting_boxes.is_domain_boundary, - mean_velocity_index=self.mean_velocity_index, - ) - - ## Edges x-y - - # ghost marker arrays - self._markers_x_m_y_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_m) - self._markers_x_m_y_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_p) - self._markers_x_p_y_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_m) - self._markers_x_p_y_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_p) - - # Put last index to -2 to indicate that they are ghosts on the new process - self._markers_x_m_y_m[:, -1] = -2.0 - self._markers_x_m_y_p[:, -1] = -2.0 - self._markers_x_p_y_m[:, -1] = -2.0 - self._markers_x_p_y_p[:, -1] = -2.0 - - # Adjust box number - self._markers_x_m_y_m[:, self._sorting_boxes.box_index] += shifts["x_m"] + shifts["y_m"] - self._markers_x_m_y_p[:, self._sorting_boxes.box_index] += shifts["x_m"] - shifts["y_p"] - self._markers_x_p_y_m[:, self._sorting_boxes.box_index] += -shifts["x_p"] + shifts["y_m"] - self._markers_x_p_y_p[:, self._sorting_boxes.box_index] += -shifts["x_p"] - shifts["y_p"] - - # Mirror position for boundary condition - if self.bc_sph[0] in ("mirror", "fixed", "noslip") or self.bc_sph[1] in ("mirror", "fixed", "noslip"): - self._mirror_particles( - "_markers_x_m_y_m", - "_markers_x_m_y_p", - "_markers_x_p_y_m", - "_markers_x_p_y_p", - is_domain_boundary=self.sorting_boxes.is_domain_boundary, - mean_velocity_index=self.mean_velocity_index, - ) - - ## Edges x-z - - # ghost marker arrays - self._markers_x_m_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_z_m) - self._markers_x_m_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_z_p) - self._markers_x_p_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_z_m) - self._markers_x_p_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_z_p) - - # Put last index to -2 to indicate that they are ghosts on the new process - self._markers_x_m_z_m[:, -1] = -2.0 - self._markers_x_m_z_p[:, -1] = -2.0 - self._markers_x_p_z_m[:, -1] = -2.0 - self._markers_x_p_z_p[:, -1] = -2.0 - - # Adjust box number - self._markers_x_m_z_m[:, self._sorting_boxes.box_index] += shifts["x_m"] + shifts["z_m"] - self._markers_x_m_z_p[:, self._sorting_boxes.box_index] += shifts["x_m"] - shifts["z_p"] - self._markers_x_p_z_m[:, self._sorting_boxes.box_index] += -shifts["x_p"] + shifts["z_m"] - self._markers_x_p_z_p[:, self._sorting_boxes.box_index] += -shifts["x_p"] - shifts["z_p"] - - # Mirror position for boundary condition - if self.bc_sph[0] in ("mirror", "fixed", "noslip") or self.bc_sph[2] in ("mirror", "fixed", "noslip"): - self._mirror_particles( - "_markers_x_m_z_m", - "_markers_x_m_z_p", - "_markers_x_p_z_m", - "_markers_x_p_z_p", - is_domain_boundary=self.sorting_boxes.is_domain_boundary, - mean_velocity_index=self.mean_velocity_index, - ) - - ## Edges y-z - - # ghost marker arrays - self._markers_y_m_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_m_z_m) - self._markers_y_m_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_m_z_p) - self._markers_y_p_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_p_z_m) - self._markers_y_p_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_y_p_z_p) - - # Put last index to -2 to indicate that they are ghosts on the new process - self._markers_y_m_z_m[:, -1] = -2.0 - self._markers_y_m_z_p[:, -1] = -2.0 - self._markers_y_p_z_m[:, -1] = -2.0 - self._markers_y_p_z_p[:, -1] = -2.0 - - # Adjust box number - self._markers_y_m_z_m[:, self._sorting_boxes.box_index] += shifts["y_m"] + shifts["z_m"] - self._markers_y_m_z_p[:, self._sorting_boxes.box_index] += shifts["y_m"] - shifts["z_p"] - self._markers_y_p_z_m[:, self._sorting_boxes.box_index] += -shifts["y_p"] + shifts["z_m"] - self._markers_y_p_z_p[:, self._sorting_boxes.box_index] += -shifts["y_p"] - shifts["z_p"] - - # Mirror position for boundary condition - if self.bc_sph[1] in ("mirror", "fixed", "noslip") or self.bc_sph[2] in ("mirror", "fixed", "noslip"): - self._mirror_particles( - "_markers_y_m_z_m", - "_markers_y_m_z_p", - "_markers_y_p_z_m", - "_markers_y_p_z_p", - is_domain_boundary=self.sorting_boxes.is_domain_boundary, - mean_velocity_index=self.mean_velocity_index, - ) - - ## Corners - - # ghost marker arrays - self._markers_x_m_y_m_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_m_z_m) - self._markers_x_m_y_m_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_m_z_p) - self._markers_x_m_y_p_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_p_z_m) - self._markers_x_m_y_p_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_m_y_p_z_p) - self._markers_x_p_y_m_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_m_z_m) - self._markers_x_p_y_m_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_m_z_p) - self._markers_x_p_y_p_z_m = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_p_z_m) - self._markers_x_p_y_p_z_p = self._determine_markers_in_box(self._sorting_boxes._bnd_boxes_x_p_y_p_z_p) - - # Put last index to -2 to indicate that they are ghosts on the new process - self._markers_x_m_y_m_z_m[:, -1] = -2.0 - self._markers_x_m_y_m_z_p[:, -1] = -2.0 - self._markers_x_m_y_p_z_m[:, -1] = -2.0 - self._markers_x_m_y_p_z_p[:, -1] = -2.0 - self._markers_x_p_y_m_z_m[:, -1] = -2.0 - self._markers_x_p_y_m_z_p[:, -1] = -2.0 - self._markers_x_p_y_p_z_m[:, -1] = -2.0 - self._markers_x_p_y_p_z_p[:, -1] = -2.0 - - # Adjust box number - self._markers_x_m_y_m_z_m[:, self._sorting_boxes.box_index] += shifts["x_m"] + shifts["y_m"] + shifts["z_m"] - self._markers_x_m_y_m_z_p[:, self._sorting_boxes.box_index] += shifts["x_m"] + shifts["y_m"] - shifts["z_p"] - self._markers_x_m_y_p_z_m[:, self._sorting_boxes.box_index] += shifts["x_m"] - shifts["y_p"] + shifts["z_m"] - self._markers_x_m_y_p_z_p[:, self._sorting_boxes.box_index] += shifts["x_m"] - shifts["y_p"] - shifts["z_p"] - self._markers_x_p_y_m_z_m[:, self._sorting_boxes.box_index] += -shifts["x_p"] + shifts["y_m"] + shifts["z_m"] - self._markers_x_p_y_m_z_p[:, self._sorting_boxes.box_index] += -shifts["x_p"] + shifts["y_m"] - shifts["z_p"] - self._markers_x_p_y_p_z_m[:, self._sorting_boxes.box_index] += -shifts["x_p"] - shifts["y_p"] + shifts["z_m"] - self._markers_x_p_y_p_z_p[:, self._sorting_boxes.box_index] += -shifts["x_p"] - shifts["y_p"] - shifts["z_p"] - - # Mirror position for boundary condition - if any([bci in ("mirror", "fixed", "noslip") for bci in self.bc_sph]): - self._mirror_particles( - "_markers_x_m_y_m_z_m", - "_markers_x_m_y_m_z_p", - "_markers_x_m_y_p_z_m", - "_markers_x_m_y_p_z_p", - "_markers_x_p_y_m_z_m", - "_markers_x_p_y_m_z_p", - "_markers_x_p_y_p_z_m", - "_markers_x_p_y_p_z_p", - is_domain_boundary=self.sorting_boxes.is_domain_boundary, - mean_velocity_index=self.mean_velocity_index, - ) - - def _mirror_particles( - self, *marker_array_names, is_domain_boundary: dict | None = None, mean_velocity_index: int | None = None - ): - """ - Mirror the positions and velocities of the particles in the ghost marker arrays for the boundary conditions. - For "mirror" boundary condition, the positions are mirrored and the velocities are unchanged. - For "fixed" boundary condition, the positions are mirrored and the velocities are set to zero (or to the value of f_init if provided). - For "noslip" boundary condition, the positions are mirrored and the velocities are inverted to have zero velocity at the boundary. - - Parameters - ---------- - marker_array_names : str - The names of the marker arrays to be mirrored (e.g. "_markers_x_m", "_markers_x_p", etc.). - - is_domain_boundary : dict - A dictionary indicating whether the boundary condition is applied at the domain boundary (e.g. {"x_m": True, "x_p": True, "y_m": True, "y_p": True, "z_m": True, "z_p": True}). - - mean_velocity_index : int, optional - The index of the mean velocity in the marker array (if applicable), by default None. - """ - self._fixed_markers_set = {} - - for arr_name in marker_array_names: - assert isinstance(arr_name, str) - arr = getattr(self, arr_name) - - if arr.size == 0: - continue - - # x-direction - if self.bc_sph[0] in ("mirror", "fixed", "noslip"): - if "x_m" in arr_name and is_domain_boundary["x_m"]: - arr[:, 0] *= -1.0 - if self.bc_sph[0] == "fixed" and arr_name not in self._fixed_markers_set: - # f_init/s0 evaluate on the active array backend; arr is - # always host-resident, so convert at this boundary. - boundary_values = _to_numpy_for_kernel(self.f_init( - *_dev(*arr[:, :3].T), - flat_eval=True, - )) # evaluation outside of the unit cube - maybe not working for all f_init! - arr[:, self.index["weights"]] = ( - -boundary_values - / _to_numpy_for_kernel(self.s0( - *_dev(*arr[:, :3].T), - flat_eval=True, - remove_holes=False, - )) - / self.Np - ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right - self._fixed_markers_set[arr_name] = True - elif self.bc_sph[0] == "noslip": - # invert the velocities to have zero velocity at the boundary - arr[:, 3] *= -1.0 - arr[:, 4] *= -1.0 - arr[:, 5] *= -1.0 - if mean_velocity_index is not None: - arr[:, mean_velocity_index] *= -1.0 - arr[:, mean_velocity_index + 1] *= -1.0 - arr[:, mean_velocity_index + 2] *= -1.0 - - elif "x_p" in arr_name and is_domain_boundary["x_p"]: - arr[:, 0] = 2.0 - arr[:, 0] - if self.bc_sph[0] == "fixed" and arr_name not in self._fixed_markers_set: - # f_init/s0 evaluate on the active array backend; arr is - # always host-resident, so convert at this boundary. - boundary_values = _to_numpy_for_kernel(self.f_init( - *_dev(*arr[:, :3].T), - flat_eval=True, - )) # evaluation outside of the unit cube - maybe not working for all f_init! - arr[:, self.index["weights"]] = ( - -boundary_values - / _to_numpy_for_kernel(self.s0( - *_dev(*arr[:, :3].T), - flat_eval=True, - remove_holes=False, - )) - / self.Np - ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right - self._fixed_markers_set[arr_name] = True - elif self.bc_sph[0] == "noslip": - # invert the velocities to have zero velocity at the boundary - arr[:, 3] *= -1.0 - arr[:, 4] *= -1.0 - arr[:, 5] *= -1.0 - if mean_velocity_index is not None: - arr[:, mean_velocity_index] *= -1.0 - arr[:, mean_velocity_index + 1] *= -1.0 - arr[:, mean_velocity_index + 2] *= -1.0 - - # y-direction - if self.bc_sph[1] in ("mirror", "fixed", "noslip"): - if "y_m" in arr_name and is_domain_boundary["y_m"]: - arr[:, 1] *= -1.0 - if self.bc_sph[1] == "fixed" and arr_name not in self._fixed_markers_set: - # f_init/s0 evaluate on the active array backend; arr is - # always host-resident, so convert at this boundary. - boundary_values = _to_numpy_for_kernel(self.f_init( - *_dev(*arr[:, :3].T), - flat_eval=True, - )) # evaluation outside of the unit cube - maybe not working for all f_init! - arr[:, self.index["weights"]] = ( - -boundary_values - / _to_numpy_for_kernel(self.s0( - *_dev(*arr[:, :3].T), - flat_eval=True, - remove_holes=False, - )) - / self.Np - ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right - self._fixed_markers_set[arr_name] = True - elif self.bc_sph[1] == "noslip": - # invert the velocities to have zero velocity at the boundary - arr[:, 3] *= -1.0 - arr[:, 4] *= -1.0 - arr[:, 5] *= -1.0 - if mean_velocity_index is not None: - arr[:, mean_velocity_index] *= -1.0 - arr[:, mean_velocity_index + 1] *= -1.0 - arr[:, mean_velocity_index + 2] *= -1.0 - - elif "y_p" in arr_name and is_domain_boundary["y_p"]: - arr[:, 1] = 2.0 - arr[:, 1] - if self.bc_sph[1] == "fixed" and arr_name not in self._fixed_markers_set: - # f_init/s0 evaluate on the active array backend; arr is - # always host-resident, so convert at this boundary. - boundary_values = _to_numpy_for_kernel(self.f_init( - *_dev(*arr[:, :3].T), - flat_eval=True, - )) # evaluation outside of the unit cube - maybe not working for all f_init! - arr[:, self.index["weights"]] = ( - -boundary_values - / _to_numpy_for_kernel(self.s0( - *_dev(*arr[:, :3].T), - flat_eval=True, - remove_holes=False, - )) - / self.Np - ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right - self._fixed_markers_set[arr_name] = True - elif self.bc_sph[1] == "noslip": - # invert the velocities to have zero velocity at the boundary - arr[:, 3] *= -1.0 - arr[:, 4] *= -1.0 - arr[:, 5] *= -1.0 - if mean_velocity_index is not None: - arr[:, mean_velocity_index] *= -1.0 - arr[:, mean_velocity_index + 1] *= -1.0 - arr[:, mean_velocity_index + 2] *= -1.0 - - # z-direction - if self.bc_sph[2] in ("mirror", "fixed", "noslip"): - if "z_m" in arr_name and is_domain_boundary["z_m"]: - arr[:, 2] *= -1.0 - if self.bc_sph[2] == "fixed" and arr_name not in self._fixed_markers_set: - # f_init/s0 evaluate on the active array backend; arr is - # always host-resident, so convert at this boundary. - boundary_values = _to_numpy_for_kernel(self.f_init( - *_dev(*arr[:, :3].T), - flat_eval=True, - )) # evaluation outside of the unit cube - maybe not working for all f_init! - arr[:, self.index["weights"]] = ( - -boundary_values - / _to_numpy_for_kernel(self.s0( - *_dev(*arr[:, :3].T), - flat_eval=True, - remove_holes=False, - )) - / self.Np - ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right - self._fixed_markers_set[arr_name] = True - elif self.bc_sph[2] == "noslip": - # invert the velocities to have zero velocity at the boundary - arr[:, 3] *= -1.0 - arr[:, 4] *= -1.0 - arr[:, 5] *= -1.0 - if mean_velocity_index is not None: - arr[:, mean_velocity_index] *= -1.0 - arr[:, mean_velocity_index + 1] *= -1.0 - arr[:, mean_velocity_index + 2] *= -1.0 - - elif "z_p" in arr_name and is_domain_boundary["z_p"]: - arr[:, 2] = 2.0 - arr[:, 2] - if self.bc_sph[2] == "fixed" and arr_name not in self._fixed_markers_set: - # f_init/s0 evaluate on the active array backend; arr is - # always host-resident, so convert at this boundary. - boundary_values = _to_numpy_for_kernel(self.f_init( - *_dev(*arr[:, :3].T), - flat_eval=True, - )) # evaluation outside of the unit cube - maybe not working for all f_init! - arr[:, self.index["weights"]] = ( - -boundary_values - / _to_numpy_for_kernel(self.s0( - *_dev(*arr[:, :3].T), - flat_eval=True, - remove_holes=False, - )) - / self.Np - ) # clarify in case of tesselation: multiple by tile volume (=1/Np) to get the integral value right - self._fixed_markers_set[arr_name] = True - elif self.bc_sph[2] == "noslip": - # invert the velocities to have zero velocity at the boundary - arr[:, 3] *= -1.0 - arr[:, 4] *= -1.0 - arr[:, 5] *= -1.0 - if mean_velocity_index is not None: - arr[:, mean_velocity_index] *= -1.0 - arr[:, mean_velocity_index + 1] *= -1.0 - arr[:, mean_velocity_index + 2] *= -1.0 - - def _determine_markers_in_box(self, list_boxes): - """Gather the markers currently sorted into any of the given boxes into a new array - (used to collect the particles on a domain/process boundary before turning them - into ghost particles). - - Parameters - ---------- - list_boxes : list[int] - Flat box indices (as computed by - :func:`~struphy.pic.sorting_kernels.flatten_index`) whose particles are collected. - - Returns - ------- - markers_in_box : xp.ndarray - Copy of the marker-array rows belonging to any of ``list_boxes``. - """ - indices = [] - for i in list_boxes: - indices += list(self._sorting_boxes._boxes[i][self._sorting_boxes._boxes[i] != -1]) - - indices = np.array(indices, dtype=int) - markers_in_box = self.markers[indices] - return markers_in_box - -<<<<<<< HEAD - def get_destinations_box(self): - """Find the destination proc for the particles to communicate for the box structure.""" - self._send_info_box = np.zeros(self.mpi_size, dtype=int) - self._send_list_box = [np.zeros((0, self.n_cols))] * self.mpi_size -======= - def _get_destinations_box(self): - """Route the ghost markers prepared by :meth:`_prepare_ghost_particles` (one array - per face/edge/corner) to the neighbouring process on that side (found earlier by - :meth:`_get_neighbouring_proc`), accumulating, per destination rank, the number of - markers to send (:attr:`_send_info_box`) and the markers themselves - (:attr:`_send_list_box`, used by :meth:`_self_communication_boxes` and - :meth:`_sendrecv_markers_boxes`).""" - self._send_info_box = xp.zeros(self.mpi_size, dtype=int) - self._send_list_box = [xp.zeros((0, self.n_cols))] * self.mpi_size ->>>>>>> devel - - # Faces - # if self._x_m_proc is not None: - self._send_info_box[self._x_m_proc] += len(self._markers_x_m) - self._send_list_box[self._x_m_proc] = np.concatenate((self._send_list_box[self._x_m_proc], self._markers_x_m)) - - # if self._x_p_proc is not None: - self._send_info_box[self._x_p_proc] += len(self._markers_x_p) - self._send_list_box[self._x_p_proc] = np.concatenate((self._send_list_box[self._x_p_proc], self._markers_x_p)) - - # if self._y_m_proc is not None: - self._send_info_box[self._y_m_proc] += len(self._markers_y_m) - self._send_list_box[self._y_m_proc] = np.concatenate((self._send_list_box[self._y_m_proc], self._markers_y_m)) - - # if self._y_p_proc is not None: - self._send_info_box[self._y_p_proc] += len(self._markers_y_p) - self._send_list_box[self._y_p_proc] = np.concatenate((self._send_list_box[self._y_p_proc], self._markers_y_p)) - - # if self._z_m_proc is not None: - self._send_info_box[self._z_m_proc] += len(self._markers_z_m) - self._send_list_box[self._z_m_proc] = np.concatenate((self._send_list_box[self._z_m_proc], self._markers_z_m)) - - # if self._z_p_proc is not None: - self._send_info_box[self._z_p_proc] += len(self._markers_z_p) - self._send_list_box[self._z_p_proc] = np.concatenate((self._send_list_box[self._z_p_proc], self._markers_z_p)) - - # x-y edges - # if self._x_m_y_m_proc is not None: - self._send_info_box[self._x_m_y_m_proc] += len(self._markers_x_m_y_m) - self._send_list_box[self._x_m_y_m_proc] = np.concatenate( - (self._send_list_box[self._x_m_y_m_proc], self._markers_x_m_y_m), - ) - - # if self._x_m_y_p_proc is not None: - self._send_info_box[self._x_m_y_p_proc] += len(self._markers_x_m_y_p) - self._send_list_box[self._x_m_y_p_proc] = np.concatenate( - (self._send_list_box[self._x_m_y_p_proc], self._markers_x_m_y_p), - ) - - # if self._x_p_y_m_proc is not None: - self._send_info_box[self._x_p_y_m_proc] += len(self._markers_x_p_y_m) - self._send_list_box[self._x_p_y_m_proc] = np.concatenate( - (self._send_list_box[self._x_p_y_m_proc], self._markers_x_p_y_m), - ) - - # if self._x_p_y_p_proc is not None: - self._send_info_box[self._x_p_y_p_proc] += len(self._markers_x_p_y_p) - self._send_list_box[self._x_p_y_p_proc] = np.concatenate( - (self._send_list_box[self._x_p_y_p_proc], self._markers_x_p_y_p), - ) - - # x-z edges - # if self._x_m_z_m_proc is not None: - self._send_info_box[self._x_m_z_m_proc] += len(self._markers_x_m_z_m) - self._send_list_box[self._x_m_z_m_proc] = np.concatenate( - (self._send_list_box[self._x_m_z_m_proc], self._markers_x_m_z_m), - ) - - # if self._x_m_z_p_proc is not None: - self._send_info_box[self._x_m_z_p_proc] += len(self._markers_x_m_z_p) - self._send_list_box[self._x_m_z_p_proc] = np.concatenate( - (self._send_list_box[self._x_m_z_p_proc], self._markers_x_m_z_p), - ) - - # if self._x_p_z_m_proc is not None: - self._send_info_box[self._x_p_z_m_proc] += len(self._markers_x_p_z_m) - self._send_list_box[self._x_p_z_m_proc] = np.concatenate( - (self._send_list_box[self._x_p_z_m_proc], self._markers_x_p_z_m), - ) - - # if self._x_p_z_p_proc is not None: - self._send_info_box[self._x_p_z_p_proc] += len(self._markers_x_p_z_p) - self._send_list_box[self._x_p_z_p_proc] = np.concatenate( - (self._send_list_box[self._x_p_z_p_proc], self._markers_x_p_z_p), - ) - - # y-z edges - # if self._y_m_z_m_proc is not None: - self._send_info_box[self._y_m_z_m_proc] += len(self._markers_y_m_z_m) - self._send_list_box[self._y_m_z_m_proc] = np.concatenate( - (self._send_list_box[self._y_m_z_m_proc], self._markers_y_m_z_m), - ) - - # if self._y_m_z_p_proc is not None: - self._send_info_box[self._y_m_z_p_proc] += len(self._markers_y_m_z_p) - self._send_list_box[self._y_m_z_p_proc] = np.concatenate( - (self._send_list_box[self._y_m_z_p_proc], self._markers_y_m_z_p), - ) - - # if self._y_p_z_m_proc is not None: - self._send_info_box[self._y_p_z_m_proc] += len(self._markers_y_p_z_m) - self._send_list_box[self._y_p_z_m_proc] = np.concatenate( - (self._send_list_box[self._y_p_z_m_proc], self._markers_y_p_z_m), - ) - - # if self._y_p_z_p_proc is not None: - self._send_info_box[self._y_p_z_p_proc] += len(self._markers_y_p_z_p) - self._send_list_box[self._y_p_z_p_proc] = np.concatenate( - (self._send_list_box[self._y_p_z_p_proc], self._markers_y_p_z_p), - ) - - # corners - # if self._x_m_y_m_z_m_proc is not None: - self._send_info_box[self._x_m_y_m_z_m_proc] += len(self._markers_x_m_y_m_z_m) - self._send_list_box[self._x_m_y_m_z_m_proc] = np.concatenate( - (self._send_list_box[self._x_m_y_m_z_m_proc], self._markers_x_m_y_m_z_m), - ) - - # if self._x_m_y_m_z_p_proc is not None: - self._send_info_box[self._x_m_y_m_z_p_proc] += len(self._markers_x_m_y_m_z_p) - self._send_list_box[self._x_m_y_m_z_p_proc] = np.concatenate( - (self._send_list_box[self._x_m_y_m_z_p_proc], self._markers_x_m_y_m_z_p), - ) - - # if self._x_m_y_p_z_m_proc is not None: - self._send_info_box[self._x_m_y_p_z_m_proc] += len(self._markers_x_m_y_p_z_m) - self._send_list_box[self._x_m_y_p_z_m_proc] = np.concatenate( - (self._send_list_box[self._x_m_y_p_z_m_proc], self._markers_x_m_y_p_z_m), - ) - - # if self._x_m_y_p_z_p_proc is not None: - self._send_info_box[self._x_m_y_p_z_p_proc] += len(self._markers_x_m_y_p_z_p) - self._send_list_box[self._x_m_y_p_z_p_proc] = np.concatenate( - (self._send_list_box[self._x_m_y_p_z_p_proc], self._markers_x_m_y_p_z_p), - ) - - # if self._x_p_y_m_z_m_proc is not None: - self._send_info_box[self._x_p_y_m_z_m_proc] += len(self._markers_x_p_y_m_z_m) - self._send_list_box[self._x_p_y_m_z_m_proc] = np.concatenate( - (self._send_list_box[self._x_p_y_m_z_m_proc], self._markers_x_p_y_m_z_m), - ) - - # if self._x_p_y_m_z_p_proc is not None: - self._send_info_box[self._x_p_y_m_z_p_proc] += len(self._markers_x_p_y_m_z_p) - self._send_list_box[self._x_p_y_m_z_p_proc] = np.concatenate( - (self._send_list_box[self._x_p_y_m_z_p_proc], self._markers_x_p_y_m_z_p), - ) - - # if self._x_p_y_p_z_m_proc is not None: - self._send_info_box[self._x_p_y_p_z_m_proc] += len(self._markers_x_p_y_p_z_m) - self._send_list_box[self._x_p_y_p_z_m_proc] = np.concatenate( - (self._send_list_box[self._x_p_y_p_z_m_proc], self._markers_x_p_y_p_z_m), - ) - - # if self._x_p_y_p_z_p_proc is not None: - self._send_info_box[self._x_p_y_p_z_p_proc] += len(self._markers_x_p_y_p_z_p) - self._send_list_box[self._x_p_y_p_z_p_proc] = np.concatenate( - (self._send_list_box[self._x_p_y_p_z_p_proc], self._markers_x_p_y_p_z_p), - ) - - def _self_communication_boxes(self): - """Communicate the particles in case a process is it's own neighbour - (in case of periodicity with low number of procs/boxes)""" - - if self._send_info_box[self.mpi_rank] > 0: - self.update_holes() - holes_inds = np.nonzero(self.holes)[0] - - if holes_inds.size < self._send_info_box[self.mpi_rank]: - warnings.warn( - f'Strong load imbalance detected: \ -number of holes ({holes_inds.size}) on rank {self.mpi_rank} \ -is smaller than number of incoming particles ({self._send_info_box[self.mpi_rank]}). \ -Increasing the value of "bufsize" in the markers parameters for the next run.', - ) - self.mpi_comm.Abort() - - # _tmp = self.markers.copy() - # _n_rows_old = _tmp.shape[0] - # logger.info(f"old: {self.markers.shape = }") - # self._bufsize *= 2.0 - # self._allocate_marker_array() - # logger.info(f"new: {self.markers.shape = }\n") - # self.markers[:] = -1.0 - # self.markers[:_n_rows_old] = _tmp - # self.update_holes() -<<<<<<< HEAD - # self.update_ghost_particles() - # self.update_valid_mks() - # holes_inds = np.nonzero(self.holes)[0] -======= - # self._update_ghost_particles() - # self._update_valid_mks() - # holes_inds = xp.nonzero(self.holes)[0] ->>>>>>> devel - - self.markers[holes_inds[np.arange(self._send_info_box[self.mpi_rank])]] = self._send_list_box[self.mpi_rank] - -<<<<<<< HEAD - @profile - def communicate_boxes(self): - # if verbose: - # n_valid = np.count_nonzero(self.valid_mks) - # n_holes = np.count_nonzero(self.holes) - # n_ghosts = np.count_nonzero(self.ghost_particles) - # logger.info(f"before communicate_boxes: {self.mpi_rank = }, {n_valid = } {n_holes = }, {n_ghosts = }") - - self.prepare_ghost_particles() - self.get_destinations_box() - self.self_communication_boxes() - self.update_holes() - if self.mpi_comm is not None: - self._Barrier() - self.sendrecv_all_to_all_boxes() - self.sendrecv_markers_boxes() - self.update_holes() - self.update_ghost_particles() - - # if verbose: - # n_valid = np.count_nonzero(self.valid_mks) - # n_holes = np.count_nonzero(self.holes) - # n_ghosts = np.count_nonzero(self.ghost_particles) - # logger.info(f"after communicate_boxes: {self.mpi_rank = }, {n_valid = }, {n_holes = }, {n_ghosts = }") - - def sendrecv_all_to_all_boxes(self): -======= - def _sendrecv_all_to_all_boxes(self): ->>>>>>> devel - """ - Distribute info on how many markers will be sent/received to/from each process via all-to-all - for the communication of particles in boundary boxes. - """ - - self._recv_info_box = np.zeros(self.mpi_comm.Get_size(), dtype=int) - - self.mpi_comm.Alltoall(self._send_info_box, self._recv_info_box) - - def _sendrecv_markers_boxes(self): - """ - Use non-blocking communication. In-place modification of markers - for the communication of particles in boundary boxes. - """ - - # i-th entry holds the number (not the index) of the first hole to be filled by data from process i - first_hole = np.cumsum(self._recv_info_box) - self._recv_info_box - hole_inds = np.nonzero(self._holes)[0] - # Initialize send and receive commands - reqs = [] - recvbufs = [] - for i, (data, N_recv) in enumerate(zip(self._send_list_box, list(self._recv_info_box))): - if i == self.mpi_comm.Get_rank(): - reqs += [None] - recvbufs += [None] - else: - self.mpi_comm.Isend(data, dest=i, tag=self.mpi_comm.Get_rank()) - - recvbufs += [np.zeros((N_recv, self._markers.shape[1]), dtype=float)] - reqs += [self.mpi_comm.Irecv(recvbufs[-1], source=i, tag=i)] - - # Wait for buffer, then put markers into holes - test_reqs = [False] * (self._recv_info_box.size - 1) - while len(test_reqs) > 0: - # loop over all receive requests - for i, req in enumerate(reqs): - if req is None: - continue - else: - # check if data has been received - if req.Test(): - if hole_inds.size < first_hole[i] + self._recv_info_box[i]: - warnings.warn( - f'Strong load imbalance detected: \ -number of holes ({hole_inds.size}) on rank {self.mpi_rank} \ -is smaller than number of incoming particles ({first_hole[i] + self._recv_info_box[i]}). \ -Increasing the value of "bufsize" in the markers parameters for the next run.', - ) - self.mpi_comm.Abort() - # exit() - - self._markers[hole_inds[first_hole[i] + np.arange(self._recv_info_box[i])]] = recvbufs[i] - - test_reqs.pop() - reqs[i] = None - - self._Barrier() - - def _get_neighbouring_proc(self): - """Find the neighbouring processes for the sending of boxes. - - The left (right) neighbour in direction 1 is called x_m_proc (x_p_proc), etc. - By default every process is its own neighbour. - """ - # Faces - self._x_m_proc = None - self._x_p_proc = None - self._y_m_proc = None - self._y_p_proc = None - self._z_m_proc = None - self._z_p_proc = None - # Edges - self._x_m_y_m_proc = None - self._x_m_y_p_proc = None - self._x_p_y_m_proc = None - self._x_p_y_p_proc = None - self._x_m_z_m_proc = None - self._x_m_z_p_proc = None - self._x_p_z_m_proc = None - self._x_p_z_p_proc = None - self._y_m_z_m_proc = None - self._y_m_z_p_proc = None - self._y_p_z_m_proc = None - self._y_p_z_p_proc = None - # Corners - self._x_m_y_m_z_m_proc = None - self._x_m_y_m_z_p_proc = None - self._x_m_y_p_z_m_proc = None - self._x_p_y_m_z_m_proc = None - self._x_m_y_p_z_p_proc = None - self._x_p_y_m_z_p_proc = None - self._x_p_y_p_z_m_proc = None - self._x_p_y_p_z_p_proc = None - - # periodicitiy for distance computation - periodic1 = self.bc_sph[0] == "periodic" - periodic2 = self.bc_sph[1] == "periodic" - periodic3 = self.bc_sph[2] == "periodic" - - # Determine which proc are on which side - dd = self.domain_array - rank = self.mpi_rank - - x_l = dd[rank][0] - x_r = dd[rank][1] - y_l = dd[rank][3] - y_r = dd[rank][4] - z_l = dd[rank][6] - z_r = dd[rank][7] - for i in range(self.mpi_size): - xl_i = dd[i][0] - xr_i = dd[i][1] - yl_i = dd[i][3] - yr_i = dd[i][4] - zl_i = dd[i][6] - zr_i = dd[i][7] - - is_same_x_l = abs(distance(xl_i, x_l, periodic1)) < 1e-5 - is_same_x_r = abs(distance(xr_i, x_r, periodic1)) < 1e-5 - is_same_y_l = abs(distance(yl_i, y_l, periodic2)) < 1e-5 - is_same_y_r = abs(distance(yr_i, y_r, periodic2)) < 1e-5 - is_same_z_l = abs(distance(zl_i, z_l, periodic3)) < 1e-5 - is_same_z_r = abs(distance(zr_i, z_r, periodic3)) < 1e-5 - - is_neigh_x_l = abs(distance(xr_i, x_l, periodic1)) < 1e-5 - is_neigh_x_r = abs(distance(xl_i, x_r, periodic1)) < 1e-5 - is_neigh_y_l = abs(distance(yr_i, y_l, periodic2)) < 1e-5 - is_neigh_y_r = abs(distance(yl_i, y_r, periodic2)) < 1e-5 - is_neigh_z_l = abs(distance(zr_i, z_l, periodic3)) < 1e-5 - is_neigh_z_r = abs(distance(zl_i, z_r, periodic3)) < 1e-5 - - # Faces - - # Process on the left (minus axis) in the x direction - if is_same_y_l and is_same_y_r and is_same_z_l and is_same_z_r and is_neigh_x_l: - self._x_m_proc = i - - # Process on the right (plus axis) in the x direction - if is_same_y_l and is_same_y_r and is_same_z_l and is_same_z_r and is_neigh_x_r: - self._x_p_proc = i - - # Process on the left (minus axis) in the y direction - if is_same_x_l and is_same_x_r and is_same_z_l and is_same_z_r and is_neigh_y_l: - self._y_m_proc = i - - # Process on the right (plus axis) in the y direction - if is_same_x_l and is_same_x_r and is_same_z_l and is_same_z_r and is_neigh_y_r: - self._y_p_proc = i - - # Process on the left (minus axis) in the z direction - if is_same_x_l and is_same_x_r and is_same_y_l and is_same_y_r and is_neigh_z_l: - self._z_m_proc = i - - # Process on the right (plus axis) in the z direction - if is_same_x_l and is_same_x_r and is_same_y_l and is_same_y_r and is_neigh_z_r: - self._z_p_proc = i - - # Edges - - # Process on the left in x and left in y axis - if is_same_z_l and is_same_z_r and is_neigh_x_l and is_neigh_y_l: - self._x_m_y_m_proc = i - - # Process on the left in x and right in y axis - if is_same_z_l and is_same_z_r and is_neigh_x_l and is_neigh_y_r: - self._x_m_y_p_proc = i - - # Process on the right in x and left in y axis - if is_same_z_l and is_same_z_r and is_neigh_x_r and is_neigh_y_l: - self._x_p_y_m_proc = i - - # Process on the right in x and right in y axis - if is_same_z_l and is_same_z_r and is_neigh_x_r and is_neigh_y_r: - self._x_p_y_p_proc = i - - # Process on the left in x and left in z axis - if is_same_y_l and is_same_y_r and is_neigh_x_l and is_neigh_z_l: - self._x_m_z_m_proc = i - - # Process on the left in x and right in z axis - if is_same_y_l and is_same_y_r and is_neigh_x_l and is_neigh_z_r: - self._x_m_z_p_proc = i - - # Process on the right in x and left in z axis - if is_same_y_l and is_same_y_r and is_neigh_x_r and is_neigh_z_l: - self._x_p_z_m_proc = i - - # Process on the right in x and right in z axis - if is_same_y_l and is_same_y_r and is_neigh_x_r and is_neigh_z_r: - self._x_p_z_p_proc = i - - # Process on the left in y and left in z axis - if is_same_x_l and is_same_x_r and is_neigh_y_l and is_neigh_z_l: - self._y_m_z_m_proc = i - - # Process on the left in y and right in z axis - if is_same_x_l and is_same_x_r and is_neigh_y_l and is_neigh_z_r: - self._y_m_z_p_proc = i - - # Process on the right in y and left in z axis - if is_same_x_l and is_same_x_r and is_neigh_y_r and is_neigh_z_l: - self._y_p_z_m_proc = i - - # Process on the right in y and right in z axis - if is_same_x_l and is_same_x_r and is_neigh_y_r and is_neigh_z_r: - self._y_p_z_p_proc = i - - # Corners - - # Process on the left in x, left in y and left in z axis - if is_neigh_x_l and is_neigh_y_l and is_neigh_z_l: - self._x_m_y_m_z_m_proc = i - - # Process on the left in x, left in y and right in z axis - if is_neigh_x_l and is_neigh_y_l and is_neigh_z_r: - self._x_m_y_m_z_p_proc = i - - # Process on the left in x, right in y and left in z axis - if is_neigh_x_l and is_neigh_y_r and is_neigh_z_l: - self._x_m_y_p_z_m_proc = i - - # Process on the left in x, right in y and right in z axis - if is_neigh_x_l and is_neigh_y_r and is_neigh_z_r: - self._x_m_y_p_z_p_proc = i - - # Process on the right in x, left in y and left in z axis - if is_neigh_x_r and is_neigh_y_l and is_neigh_z_l: - self._x_p_y_m_z_m_proc = i - - # Process on the right in x, left in y and right in z axis - if is_neigh_x_r and is_neigh_y_l and is_neigh_z_r: - self._x_p_y_m_z_p_proc = i - - # Process on the right in x, right in y and left in z axis - if is_neigh_x_r and is_neigh_y_r and is_neigh_z_l: - self._x_p_y_p_z_m_proc = i - - # Process on the right in x, right in y and right in z axis - if is_neigh_x_r and is_neigh_y_r and is_neigh_z_r: - self._x_p_y_p_z_p_proc = i - - # set empty faces in x - if self._x_m_proc is None: - self._x_m_proc = rank - if self._x_p_proc is None: - self._x_p_proc = rank - - # set empty faces in y - if self._y_m_proc is None: - self._y_m_proc = rank - if self._y_p_proc is None: - self._y_p_proc = rank - - # set empty faces in z - if self._z_m_proc is None: - self._z_m_proc = rank - if self._z_p_proc is None: - self._z_p_proc = rank - - # set empty edges in xy - if self._x_m_y_m_proc is None: - if self._x_m_proc == rank: - self._x_m_y_m_proc = self._y_m_proc - elif self._y_m_proc == rank: - self._x_m_y_m_proc = self._x_m_proc - - if self._x_m_y_p_proc is None: - if self._x_m_proc == rank: - self._x_m_y_p_proc = self._y_p_proc - elif self._y_p_proc == rank: - self._x_m_y_p_proc = self._x_m_proc - - if self._x_p_y_m_proc is None: - if self._x_p_proc == rank: - self._x_p_y_m_proc = self._y_m_proc - elif self._y_m_proc == rank: - self._x_p_y_m_proc = self._x_p_proc - - if self._x_p_y_p_proc is None: - if self._x_p_proc == rank: - self._x_p_y_p_proc = self._y_p_proc - elif self._y_p_proc == rank: - self._x_p_y_p_proc = self._x_p_proc - - # set empty edges in xz - if self._x_m_z_m_proc is None: - if self._x_m_proc == rank: - self._x_m_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_m_z_m_proc = self._x_m_proc - - if self._x_m_z_p_proc is None: - if self._x_m_proc == rank: - self._x_m_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_m_z_p_proc = self._x_m_proc - - if self._x_p_z_m_proc is None: - if self._x_p_proc == rank: - self._x_p_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_p_z_m_proc = self._x_p_proc - - if self._x_p_z_p_proc is None: - if self._x_p_proc == rank: - self._x_p_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_p_z_p_proc = self._x_p_proc - - # set empty edges in yz - if self._y_m_z_m_proc is None: - if self._y_m_proc == rank: - self._y_m_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._y_m_z_m_proc = self._y_m_proc - - if self._y_m_z_p_proc is None: - if self._y_m_proc == rank: - self._y_m_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._y_m_z_p_proc = self._y_m_proc - - if self._y_p_z_m_proc is None: - if self._y_p_proc == rank: - self._y_p_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._y_p_z_m_proc = self._y_p_proc - - if self._y_p_z_p_proc is None: - if self._y_p_proc == rank: - self._y_p_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._y_p_z_p_proc = self._y_p_proc - - # set empty corners - if self._x_m_y_m_z_m_proc is None: - if self._x_m_proc == rank: - if self._y_m_proc == rank: - self._x_m_y_m_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_m_y_m_z_m_proc = self._y_m_proc - elif self._y_m_proc == rank: - if self._x_m_proc == rank: - self._x_m_y_m_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_m_y_m_z_m_proc = self._x_m_proc - elif self._z_m_proc == rank: - if self._x_m_proc == rank: - self._x_m_y_m_z_m_proc = self._y_m_proc - elif self._y_m_proc == rank: - self._x_m_y_m_z_m_proc = self._x_m_proc - - if self._x_m_y_m_z_p_proc is None: - if self._x_m_proc == rank: - if self._y_m_proc == rank: - self._x_m_y_m_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_m_y_m_z_p_proc = self._y_m_proc - elif self._y_m_proc == rank: - if self._x_m_proc == rank: - self._x_m_y_m_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_m_y_m_z_p_proc = self._x_m_proc - elif self._z_p_proc == rank: - if self._x_m_proc == rank: - self._x_m_y_m_z_p_proc = self._y_m_proc - elif self._y_m_proc == rank: - self._x_m_y_m_z_p_proc = self._x_m_proc - - if self._x_m_y_p_z_m_proc is None: - if self._x_m_proc == rank: - if self._y_p_proc == rank: - self._x_m_y_p_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_m_y_p_z_m_proc = self._y_p_proc - elif self._y_p_proc == rank: - if self._x_m_proc == rank: - self._x_m_y_p_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_m_y_p_z_m_proc = self._x_m_proc - elif self._z_m_proc == rank: - if self._x_m_proc == rank: - self._x_m_y_p_z_m_proc = self._y_p_proc - elif self._y_p_proc == rank: - self._x_m_y_p_z_m_proc = self._x_m_proc - - if self._x_m_y_p_z_p_proc is None: - if self._x_m_proc == rank: - if self._y_p_proc == rank: - self._x_m_y_p_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_m_y_p_z_p_proc = self._y_p_proc - elif self._y_p_proc == rank: - if self._x_m_proc == rank: - self._x_m_y_p_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_m_y_p_z_p_proc = self._x_m_proc - elif self._z_p_proc == rank: - if self._x_m_proc == rank: - self._x_m_y_p_z_p_proc = self._y_p_proc - elif self._y_p_proc == rank: - self._x_m_y_p_z_p_proc = self._x_m_proc - - if self._x_p_y_m_z_m_proc is None: - if self._x_p_proc == rank: - if self._y_m_proc == rank: - self._x_p_y_m_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_p_y_m_z_m_proc = self._y_m_proc - elif self._y_m_proc == rank: - if self._x_p_proc == rank: - self._x_p_y_m_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_p_y_m_z_m_proc = self._x_p_proc - elif self._z_m_proc == rank: - if self._x_p_proc == rank: - self._x_p_y_m_z_m_proc = self._y_m_proc - elif self._y_m_proc == rank: - self._x_p_y_m_z_m_proc = self._x_p_proc - - if self._x_p_y_m_z_p_proc is None: - if self._x_p_proc == rank: - if self._y_m_proc == rank: - self._x_p_y_m_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_p_y_m_z_p_proc = self._y_m_proc - elif self._y_m_proc == rank: - if self._x_p_proc == rank: - self._x_p_y_m_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_p_y_m_z_p_proc = self._x_p_proc - elif self._z_p_proc == rank: - if self._x_p_proc == rank: - self._x_p_y_m_z_p_proc = self._y_m_proc - elif self._y_m_proc == rank: - self._x_p_y_m_z_p_proc = self._x_p_proc - - if self._x_p_y_p_z_m_proc is None: - if self._x_p_proc == rank: - if self._y_p_proc == rank: - self._x_p_y_p_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_p_y_p_z_m_proc = self._y_p_proc - elif self._y_p_proc == rank: - if self._x_p_proc == rank: - self._x_p_y_p_z_m_proc = self._z_m_proc - elif self._z_m_proc == rank: - self._x_p_y_p_z_m_proc = self._x_p_proc - elif self._z_m_proc == rank: - if self._x_p_proc == rank: - self._x_p_y_p_z_m_proc = self._y_p_proc - elif self._y_p_proc == rank: - self._x_p_y_p_z_m_proc = self._x_p_proc - - if self._x_p_y_p_z_p_proc is None: - if self._x_p_proc == rank: - if self._y_p_proc == rank: - self._x_p_y_p_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_p_y_p_z_p_proc = self._y_p_proc - elif self._y_p_proc == rank: - if self._x_p_proc == rank: - self._x_p_y_p_z_p_proc = self._z_p_proc - elif self._z_p_proc == rank: - self._x_p_y_p_z_p_proc = self._x_p_proc - elif self._z_p_proc == rank: - if self._x_p_proc == rank: - self._x_p_y_p_z_p_proc = self._y_p_proc - elif self._y_p_proc == rank: - self._x_p_y_p_z_p_proc = self._x_p_proc - - @profile - def _communicate_boxes(self): - """Refresh the SPH ghost-box layer: build the outgoing ghost markers - (:meth:`_prepare_ghost_particles`), route them to the neighbouring processes - (:meth:`_get_destinations_box`), deliver the ones staying on this process - (:meth:`_self_communication_boxes`), and, if running under MPI, exchange the rest - with neighbouring processes (:meth:`_sendrecv_all_to_all_boxes`, - :meth:`_sendrecv_markers_boxes`) before marking the received rows as ghost - particles (:meth:`_update_ghost_particles`).""" - # if verbose: - # n_valid = xp.count_nonzero(self.valid_mks) - # n_holes = xp.count_nonzero(self.holes) - # n_ghosts = xp.count_nonzero(self.ghost_particles) - # logger.info(f"before communicate_boxes: {self.mpi_rank = }, {n_valid = } {n_holes = }, {n_ghosts = }") - - self._prepare_ghost_particles() - self._get_destinations_box() - self._self_communication_boxes() - self.update_holes() - if self.mpi_comm is not None: - self._Barrier() - self._sendrecv_all_to_all_boxes() - self._sendrecv_markers_boxes() - self.update_holes() - self._update_ghost_particles() - - # if verbose: - # n_valid = xp.count_nonzero(self.valid_mks) - # n_holes = xp.count_nonzero(self.holes) - # n_ghosts = xp.count_nonzero(self.ghost_particles) - # logger.info(f"after communicate_boxes: {self.mpi_rank = }, {n_valid = }, {n_holes = }, {n_ghosts = }") - -<<<<<<< HEAD - kernel_type : str, optional - Name of the smoothing kernel (must be a key in `self.ker_dct()`). - - derivative : int, optional - Selects whether to evaluate the kernel derivative along a coordinate - direction: 0 (default) returns the scalar density, 1/2/3 returns the - corresponding component of the density gradient with respect to - logical coordinates. - - fast : bool, optional - If True, use the box-based neighbor search (faster for many particles); - if False, use the naive all-pairs evaluation (simpler, slower). - - Returns - ------- - out : np.ndarray - Estimated number density (or requested derivative component) at the - provided evaluation points. The array uses the same shape as `eta1`. - Always a NumPy array: markers are host-resident (there is no device - particle kernel), and `eta1`/`eta2`/`eta3` are converted to NumPy - if they arrive as CuPy arrays. - - Notes - ----- - This method is a thin wrapper around :meth:`eval_sph` and internally - evaluates the column given by `self.index['weights']` (particle weights). - """ - return self.eval_sph( - eta1, - eta2, - eta3, - self.index["weights"], - kernel_type=kernel_type, - derivative=derivative, - h1=h1, - h2=h2, - h3=h3, - fast=fast, - ) - - def eval_velocity( - self, - eta1, - eta2, - eta3, - h1, - h2, - h3, - kernel_type="gaussian_1d", - derivative=0, - fast=True, - ) -> tuple: - """Estimate mean velocity components using SPH smoothing. - - Parameters - ---------- - eta1, eta2, eta3 : array_like - Logical evaluation points. May be 1-D arrays or broadcastable meshgrid - arrays; the returned component arrays match the shape of `eta1`. - - h1, h2, h3 : float - Support radius of the smoothing kernel in each logical dimension. - - kernel_type : str, optional - Name of the smoothing kernel (must be a key in `self.ker_dct()`). - - derivative : int, optional - If 0 (default) evaluate the mean velocity; if 1/2/3 return the - corresponding component of the spatial derivative of the velocity. - - fast : bool, optional - If True use the box-based neighbor search (faster for many particles); - if False use the naive all-pairs evaluation. + # set empty faces in x + if self._x_m_proc is None: + self._x_m_proc = rank + if self._x_p_proc is None: + self._x_p_proc = rank - Returns - ------- - (v1, v2, v3) : tuple of np.ndarray - Three arrays containing the estimated velocity components at the - provided evaluation points. Each array has the same shape as `eta1`. + # set empty faces in y + if self._y_m_proc is None: + self._y_m_proc = rank + if self._y_p_proc is None: + self._y_p_proc = rank - Notes - ----- - This method first computes SPH coefficients by calling - `eval_kernels_sph.sph_mean_velocity_coeffs` (via a Pyccel kernel) to - assemble mean-velocity coefficients into the markers array, then calls - :meth:`eval_sph` for each velocity component. - """ + # set empty faces in z + if self._z_m_proc is None: + self._z_m_proc = rank + if self._z_p_proc is None: + self._z_p_proc = rank - first_free_idx = self.args_markers.first_free_idx - comps = np.array((0, 1, 2)) + # set empty edges in xy + if self._x_m_y_m_proc is None: + if self._x_m_proc == rank: + self._x_m_y_m_proc = self._y_m_proc + elif self._y_m_proc == rank: + self._x_m_y_m_proc = self._x_m_proc - self.put_particles_in_boxes() + if self._x_m_y_p_proc is None: + if self._x_m_proc == rank: + self._x_m_y_p_proc = self._y_p_proc + elif self._y_p_proc == rank: + self._x_m_y_p_proc = self._x_m_proc - func = PyccelKernel(eval_kernels_sph.sph_mean_velocity_coeffs) + if self._x_p_y_m_proc is None: + if self._x_p_proc == rank: + self._x_p_y_m_proc = self._y_m_proc + elif self._y_m_proc == rank: + self._x_p_y_m_proc = self._x_p_proc - func( - alpha=np.array((0.0, 0.0, 0.0)), - column_nr=first_free_idx, - comps=comps, - args_markers=self.args_markers, - args_domain=self.domain.args_domain, - boxes=self.sorting_boxes.boxes, - neighbours=self.sorting_boxes.neighbours, - holes=self.holes, - periodic1=self.boundary_params.bc_sph[0] == "periodic", - periodic2=self.boundary_params.bc_sph[1] == "periodic", - periodic3=self.boundary_params.bc_sph[2] == "periodic", - kernel_type=self.ker_dct()[kernel_type], - h1=h1, - h2=h2, - h3=h3, - ) + if self._x_p_y_p_proc is None: + if self._x_p_proc == rank: + self._x_p_y_p_proc = self._y_p_proc + elif self._y_p_proc == rank: + self._x_p_y_p_proc = self._x_p_proc - v1 = self.eval_sph( - eta1, - eta2, - eta3, - first_free_idx, - kernel_type=kernel_type, - derivative=derivative, - h1=h1, - h2=h2, - h3=h3, - fast=fast, - ) + # set empty edges in xz + if self._x_m_z_m_proc is None: + if self._x_m_proc == rank: + self._x_m_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_m_z_m_proc = self._x_m_proc - v2 = self.eval_sph( - eta1, - eta2, - eta3, - first_free_idx + 1, - kernel_type=kernel_type, - derivative=derivative, - h1=h1, - h2=h2, - h3=h3, - fast=fast, - ) + if self._x_m_z_p_proc is None: + if self._x_m_proc == rank: + self._x_m_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_m_z_p_proc = self._x_m_proc - v3 = self.eval_sph( - eta1, - eta2, - eta3, - first_free_idx + 2, - kernel_type=kernel_type, - derivative=derivative, - h1=h1, - h2=h2, - h3=h3, - fast=fast, - ) + if self._x_p_z_m_proc is None: + if self._x_p_proc == rank: + self._x_p_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_p_z_m_proc = self._x_p_proc - return v1, v2, v3 + if self._x_p_z_p_proc is None: + if self._x_p_proc == rank: + self._x_p_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_p_z_p_proc = self._x_p_proc - def eval_div_viscosity( - self, - eta1, - eta2, - eta3, - h1, - h2, - h3, - kernel_type="gaussian_1d", - mu: float = 1.0, - fast=True, - ) -> tuple: - """Compute divergence of the viscous stress (mu * viscosity tensor). + # set empty edges in yz + if self._y_m_z_m_proc is None: + if self._y_m_proc == rank: + self._y_m_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._y_m_z_m_proc = self._y_m_proc - Parameters - ---------- - eta1, eta2, eta3 : array_like - Logical evaluation points where the divergence is evaluated. + if self._y_m_z_p_proc is None: + if self._y_m_proc == rank: + self._y_m_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._y_m_z_p_proc = self._y_m_proc - h1, h2, h3 : float - Support radius of the smoothing kernel in each logical dimension. + if self._y_p_z_m_proc is None: + if self._y_p_proc == rank: + self._y_p_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._y_p_z_m_proc = self._y_p_proc - kernel_type : str, optional - Name of the smoothing kernel (must be a key in `self.ker_dct()`). + if self._y_p_z_p_proc is None: + if self._y_p_proc == rank: + self._y_p_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._y_p_z_p_proc = self._y_p_proc - mu : float, optional - Dynamic viscosity coefficient used in the viscosity kernel. + # set empty corners + if self._x_m_y_m_z_m_proc is None: + if self._x_m_proc == rank: + if self._y_m_proc == rank: + self._x_m_y_m_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_m_y_m_z_m_proc = self._y_m_proc + elif self._y_m_proc == rank: + if self._x_m_proc == rank: + self._x_m_y_m_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_m_y_m_z_m_proc = self._x_m_proc + elif self._z_m_proc == rank: + if self._x_m_proc == rank: + self._x_m_y_m_z_m_proc = self._y_m_proc + elif self._y_m_proc == rank: + self._x_m_y_m_z_m_proc = self._x_m_proc - fast : bool, optional - If True use the box-based neighbor search; if False use naive - evaluation. + if self._x_m_y_m_z_p_proc is None: + if self._x_m_proc == rank: + if self._y_m_proc == rank: + self._x_m_y_m_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_m_y_m_z_p_proc = self._y_m_proc + elif self._y_m_proc == rank: + if self._x_m_proc == rank: + self._x_m_y_m_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_m_y_m_z_p_proc = self._x_m_proc + elif self._z_p_proc == rank: + if self._x_m_proc == rank: + self._x_m_y_m_z_p_proc = self._y_m_proc + elif self._y_m_proc == rank: + self._x_m_y_m_z_p_proc = self._x_m_proc - Returns - ------- - (gamma_x, gamma_y, gamma_z) : tuple of np.ndarray - Components of the divergence of the viscous stress evaluated at the - provided points. Each array matches the shape of `eta1`. + if self._x_m_y_p_z_m_proc is None: + if self._x_m_proc == rank: + if self._y_p_proc == rank: + self._x_m_y_p_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_m_y_p_z_m_proc = self._y_p_proc + elif self._y_p_proc == rank: + if self._x_m_proc == rank: + self._x_m_y_p_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_m_y_p_z_m_proc = self._x_m_proc + elif self._z_m_proc == rank: + if self._x_m_proc == rank: + self._x_m_y_p_z_m_proc = self._y_p_proc + elif self._y_p_proc == rank: + self._x_m_y_p_z_m_proc = self._x_m_proc - Notes - ----- - The routine populates intermediate marker columns using two Pyccel - kernels: `sph_mean_velocity_coeffs` (mean velocity) and - `sph_viscosity_tensor` (viscosity tensor components). It then evaluates - the necessary derivatives via :meth:`eval_sph` and sums contributions to - produce the three divergence components. - """ + if self._x_m_y_p_z_p_proc is None: + if self._x_m_proc == rank: + if self._y_p_proc == rank: + self._x_m_y_p_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_m_y_p_z_p_proc = self._y_p_proc + elif self._y_p_proc == rank: + if self._x_m_proc == rank: + self._x_m_y_p_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_m_y_p_z_p_proc = self._x_m_proc + elif self._z_p_proc == rank: + if self._x_m_proc == rank: + self._x_m_y_p_z_p_proc = self._y_p_proc + elif self._y_p_proc == rank: + self._x_m_y_p_z_p_proc = self._x_m_proc - first_free_idx = self.args_markers.first_free_idx - self.put_particles_in_boxes() + if self._x_p_y_m_z_m_proc is None: + if self._x_p_proc == rank: + if self._y_m_proc == rank: + self._x_p_y_m_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_p_y_m_z_m_proc = self._y_m_proc + elif self._y_m_proc == rank: + if self._x_p_proc == rank: + self._x_p_y_m_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_p_y_m_z_m_proc = self._x_p_proc + elif self._z_m_proc == rank: + if self._x_p_proc == rank: + self._x_p_y_m_z_m_proc = self._y_m_proc + elif self._y_m_proc == rank: + self._x_p_y_m_z_m_proc = self._x_p_proc - # 1st kernel - func = PyccelKernel(eval_kernels_sph.sph_mean_velocity_coeffs) - comps = np.array((0, 1, 2)) - func( - alpha=np.array((0.0, 0.0, 0.0)), - column_nr=first_free_idx, - comps=comps, - args_markers=self.args_markers, - args_domain=self.domain.args_domain, - boxes=self.sorting_boxes.boxes, - neighbours=self.sorting_boxes.neighbours, - holes=self.holes, - periodic1=self.boundary_params.bc_sph[0] == "periodic", - periodic2=self.boundary_params.bc_sph[1] == "periodic", - periodic3=self.boundary_params.bc_sph[2] == "periodic", - kernel_type=self.ker_dct()[kernel_type], - h1=h1, - h2=h2, - h3=h3, - ) + if self._x_p_y_m_z_p_proc is None: + if self._x_p_proc == rank: + if self._y_m_proc == rank: + self._x_p_y_m_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_p_y_m_z_p_proc = self._y_m_proc + elif self._y_m_proc == rank: + if self._x_p_proc == rank: + self._x_p_y_m_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_p_y_m_z_p_proc = self._x_p_proc + elif self._z_p_proc == rank: + if self._x_p_proc == rank: + self._x_p_y_m_z_p_proc = self._y_m_proc + elif self._y_m_proc == rank: + self._x_p_y_m_z_p_proc = self._x_p_proc - # 2nd kernel - func = PyccelKernel(eval_kernels_sph.sph_viscosity_tensor) - comps = np.arange(9) - func( - alpha=np.array((0.0, 0.0, 0.0)), - column_nr=first_free_idx + 3, - comps=comps, - args_markers=self.args_markers, - args_domain=self.domain.args_domain, - boxes=self.sorting_boxes.boxes, - neighbours=self.sorting_boxes.neighbours, - holes=self.holes, - periodic1=self.boundary_params.bc_sph[0] == "periodic", - periodic2=self.boundary_params.bc_sph[1] == "periodic", - periodic3=self.boundary_params.bc_sph[2] == "periodic", - kernel_type=self.ker_dct()[kernel_type], - h1=h1, - h2=h2, - h3=h3, - mu=mu, - ) + if self._x_p_y_p_z_m_proc is None: + if self._x_p_proc == rank: + if self._y_p_proc == rank: + self._x_p_y_p_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_p_y_p_z_m_proc = self._y_p_proc + elif self._y_p_proc == rank: + if self._x_p_proc == rank: + self._x_p_y_p_z_m_proc = self._z_m_proc + elif self._z_m_proc == rank: + self._x_p_y_p_z_m_proc = self._x_p_proc + elif self._z_m_proc == rank: + if self._x_p_proc == rank: + self._x_p_y_p_z_m_proc = self._y_p_proc + elif self._y_p_proc == rank: + self._x_p_y_p_z_m_proc = self._x_p_proc - # grid evaluation - gamma = [] - for j in range(3): - gamma += [[]] - for k in range(3): - gamma[-1] += [ - self.eval_sph( - eta1, - eta2, - eta3, - first_free_idx + 3 * (j + 1) + k, - kernel_type=kernel_type, - derivative=k + 1, - h1=h1, - h2=h2, - h3=h3, - fast=fast, - ) - ] + if self._x_p_y_p_z_p_proc is None: + if self._x_p_proc == rank: + if self._y_p_proc == rank: + self._x_p_y_p_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_p_y_p_z_p_proc = self._y_p_proc + elif self._y_p_proc == rank: + if self._x_p_proc == rank: + self._x_p_y_p_z_p_proc = self._z_p_proc + elif self._z_p_proc == rank: + self._x_p_y_p_z_p_proc = self._x_p_proc + elif self._z_p_proc == rank: + if self._x_p_proc == rank: + self._x_p_y_p_z_p_proc = self._y_p_proc + elif self._y_p_proc == rank: + self._x_p_y_p_z_p_proc = self._x_p_proc - gamma_x = gamma[0][0] + gamma[0][1] + gamma[0][2] - gamma_y = gamma[1][0] + gamma[1][1] + gamma[1][2] - gamma_z = gamma[2][0] + gamma[2][1] + gamma[2][2] + @profile + def _communicate_boxes(self): + """Refresh the SPH ghost-box layer: build the outgoing ghost markers + (:meth:`_prepare_ghost_particles`), route them to the neighbouring processes + (:meth:`_get_destinations_box`), deliver the ones staying on this process + (:meth:`_self_communication_boxes`), and, if running under MPI, exchange the rest + with neighbouring processes (:meth:`_sendrecv_all_to_all_boxes`, + :meth:`_sendrecv_markers_boxes`) before marking the received rows as ghost + particles (:meth:`_update_ghost_particles`).""" + # if verbose: + # n_valid = xp.count_nonzero(self.valid_mks) + # n_holes = xp.count_nonzero(self.holes) + # n_ghosts = xp.count_nonzero(self.ghost_particles) + # logger.info(f"before communicate_boxes: {self.mpi_rank = }, {n_valid = } {n_holes = }, {n_ghosts = }") - return gamma_x, gamma_y, gamma_z + self._prepare_ghost_particles() + self._get_destinations_box() + self._self_communication_boxes() + self.update_holes() + if self.mpi_comm is not None: + self._Barrier() + self._sendrecv_all_to_all_boxes() + self._sendrecv_markers_boxes() + self.update_holes() + self._update_ghost_particles() + + # if verbose: + # n_valid = xp.count_nonzero(self.valid_mks) + # n_holes = xp.count_nonzero(self.holes) + # n_ghosts = xp.count_nonzero(self.ghost_particles) + # logger.info(f"after communicate_boxes: {self.mpi_rank = }, {n_valid = }, {n_holes = }, {n_ghosts = }") - def eval_sph( -======= def _eval_sph( ->>>>>>> devel self, - eta1: np.ndarray, - eta2: np.ndarray, - eta3: np.ndarray, + eta1: xp.ndarray, + eta2: xp.ndarray, + eta3: xp.ndarray, index: int, - out: np.ndarray = None, + out: xp.ndarray = None, fast: bool = True, kernel_type: str = "gaussian_1d", derivative: int = 0, @@ -5753,18 +4269,12 @@ def _eval_sph( h1, h2, h3 : float Radius of the smoothing kernel in each dimension. """ - # markers are always host-resident (there is no device particle kernel); - # bring evaluation points to the host too so they can be combined with them. - eta1 = _to_numpy_for_kernel(eta1) - eta2 = _to_numpy_for_kernel(eta2) - eta3 = _to_numpy_for_kernel(eta3) - - _shp = np.shape(eta1) - assert _shp == np.shape(eta2) == np.shape(eta3) + _shp = xp.shape(eta1) + assert _shp == xp.shape(eta2) == xp.shape(eta3) if out is not None: - assert _shp == np.shape(out) + assert _shp == xp.shape(out) else: - out = np.zeros_like(eta1) + out = xp.zeros_like(eta1) assert derivative in {0, 1, 2, 3}, f"derivative must be 0, 1, 2 or 3, but is {derivative}." @@ -5833,26 +4343,11 @@ def _eval_sph( ) return out -<<<<<<< HEAD - def update_holes(self): - """Compute new holes, new number of holes and markers on process""" - self._holes[:] = self.markers[:, 0] == -1.0 - self._holes_ghost_dev_dirty = True - self.update_valid_mks() - - def update_ghost_particles(self): - """Compute new particles that belong to boundary processes needed for sph evaluation""" - self._ghost_particles[:] = self.markers[:, -1] == -2.0 - self._holes_ghost_dev_dirty = True - self.update_valid_mks() - -======= ->>>>>>> devel ### MPI comm for domain decomposition ### def _sendrecv_determine_mtbs( self, - alpha: list | tuple | np.ndarray = (1.0, 1.0, 1.0), + alpha: list | tuple | xp.ndarray = (1.0, 1.0, 1.0), ): """ Determine which markers have to be sent from current process and put them in a new array. @@ -5874,12 +4369,12 @@ def _sendrecv_determine_mtbs( Eta-values of shape (n_send, :) according to which the sorting is performed. """ # position that determines the sorting (including periodic shift of boundary conditions) - if not isinstance(alpha, np.ndarray): - alpha = np.array(alpha, dtype=float) + if not isinstance(alpha, xp.ndarray): + alpha = xp.array(alpha, dtype=float) assert alpha.size == 3 - assert np.all(alpha >= 0.0) and np.all(alpha <= 1.0) + assert xp.all(alpha >= 0.0) and xp.all(alpha <= 1.0) bi = self.first_pusher_idx - np.mod( + xp.mod( alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + (1.0 - alpha) * self.markers[:, bi : bi + 3], 1.0, @@ -5887,22 +4382,22 @@ def _sendrecv_determine_mtbs( ) # check which particles are on the current process domain - self._is_on_proc_domain = np.logical_and( + self._is_on_proc_domain = xp.logical_and( self._sorting_etas > self.domain_array[self.mpi_rank, 0::3], self._sorting_etas < self.domain_array[self.mpi_rank, 1::3], ) # to stay on the current process, all three columns must be True - self._can_stay = np.all(self._is_on_proc_domain, axis=1) + self._can_stay = xp.all(self._is_on_proc_domain, axis=1) # holes and ghosts can stay, too self._can_stay[self.holes] = True self._can_stay[self.ghost_particles] = True # True values can stay on the process, False must be sent, already empty rows (-1) cannot be sent - send_inds = np.nonzero(~self._can_stay)[0] + send_inds = xp.nonzero(~self._can_stay)[0] - hole_inds_after_send = np.nonzero(np.logical_or(~self._can_stay, self.holes))[0] + hole_inds_after_send = xp.nonzero(xp.logical_or(~self._can_stay, self.holes))[0] return hole_inds_after_send, send_inds @@ -5921,16 +4416,16 @@ def _sendrecv_get_destinations(self, send_inds): """ # One entry for each process - send_info = np.zeros(self.mpi_size, dtype=int) + send_info = xp.zeros(self.mpi_size, dtype=int) # TODO: do not loop over all processes, start with neighbours and work outwards (using while) for i in range(self.mpi_size): - conds = np.logical_and( + conds = xp.logical_and( self._sorting_etas[send_inds] > self.domain_array[i, 0::3], self._sorting_etas[send_inds] < self.domain_array[i, 1::3], ) - self._send_to_i[i] = np.nonzero(np.all(conds, axis=1))[0] + self._send_to_i[i] = xp.nonzero(xp.all(conds, axis=1))[0] send_info[i] = self._send_to_i[i].size self._send_list[i] = self.markers[send_inds][self._send_to_i[i]] @@ -5952,7 +4447,7 @@ def _sendrecv_all_to_all(self, send_info): Amount of marticles to be received from i-th process. """ - recv_info = np.zeros(self.mpi_size, dtype=int) + recv_info = xp.zeros(self.mpi_size, dtype=int) self.mpi_comm.Alltoall(send_info, recv_info) @@ -5972,7 +4467,7 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): """ # i-th entry holds the number (not the index) of the first hole to be filled by data from process i - first_hole = np.cumsum(recv_info) - recv_info + first_hole = xp.cumsum(recv_info) - recv_info # Initialize send and receive commands for i, (data, N_recv) in enumerate(zip(self._send_list, list(recv_info))): @@ -5982,7 +4477,7 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): else: self.mpi_comm.Isend(data, dest=i, tag=self.mpi_rank) - self._recvbufs[i] = np.zeros((N_recv, self.markers.shape[1]), dtype=float) + self._recvbufs[i] = xp.zeros((N_recv, self.markers.shape[1]), dtype=float) self._reqs[i] = self.mpi_comm.Irecv(self._recvbufs[i], source=i, tag=i) # Wait for buffer, then put markers into holes @@ -6004,71 +4499,11 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): ) self.mpi_comm.Abort() - self.markers[hole_inds_after_send[first_hole[i] + np.arange(recv_info[i])]] = self._recvbufs[i] + self.markers[hole_inds_after_send[first_hole[i] + xp.arange(recv_info[i])]] = self._recvbufs[i] test_reqs.pop() self._reqs[i] = None -<<<<<<< HEAD - def _gather_scalar_in_subcomm_array(self, scalar: int, out: np.ndarray = None): - """Return an array of length sub_comm.size, where the i-th entry corresponds to the value - of the scalar on process i. - - Parameters - ---------- - scalar : int - The scalar value on each process. - - out : np.ndarray - The returned array (optional). - """ - if out is None: - _tmp = np.zeros(self.mpi_size, dtype=int) - else: - assert out.size == self.mpi_size - _tmp = out - - _tmp[self.mpi_rank] = scalar - - if self.mpi_comm is not None: - print(f"{self.mpi_comm = }") - self.mpi_comm.Allgather( - _tmp[self.mpi_rank], - _tmp, - ) - - return _tmp - - def _gather_scalar_in_intercomm_array(self, scalar: int, out: np.ndarray = None): - """Return an array of length inter_comm.size, where the i-th entry corresponds to the value - of the scalar on clone i. - - Parameters - ---------- - scalar : int - The scalar value on each clone. - - out : np.ndarray - The returned array (optional). - """ - if out is None: - _tmp = np.zeros(self.num_clones, dtype=int) - else: - assert out.size == self.num_clones - _tmp = out - - _tmp[self.clone_id] = scalar - - if self.clone_config is not None: - self.clone_config.inter_comm.Allgather( - _tmp[self.clone_id], - _tmp, - ) - - return _tmp - -======= ->>>>>>> devel class Tesselation: """ @@ -6097,7 +4532,7 @@ class Tesselation: comm : Intracomm MPI communicator. - domain_array : np.ndarray + domain_array : xp.ndarray A 2d array[float] of shape (comm.Get_size(), 9) holding info on the domain decomposition. sorting_boxes : SortingBoxes @@ -6109,13 +4544,8 @@ def __init__( tiles_pb: int | float, *, comm: Intracomm = None, -<<<<<<< HEAD - domain_array: np.ndarray = None, - sorting_boxes: Particles.SortingBoxes = None, -======= domain_array: xp.ndarray = None, sorting_boxes: SortingBoxes = None, ->>>>>>> devel ): if isinstance(tiles_pb, int): self._tiles_pb = tiles_pb @@ -6132,8 +4562,8 @@ def __init__( assert domain_array is not None if domain_array is None: - self._starts = np.zeros(3) - self._ends = np.ones(3) + self._starts = xp.zeros(3) + self._ends = xp.ones(3) else: self._starts = domain_array[self.rank, 0::3] self._ends = domain_array[self.rank, 1::3] @@ -6156,9 +4586,9 @@ def __init__( if n_boxes == 1: self._dims_mask = [True] * 3 else: - self._dims_mask = np.array(self.boxes_per_dim) > 1 + self._dims_mask = xp.array(self.boxes_per_dim) > 1 - min_tiles = 2 ** np.count_nonzero(self.dims_mask) + min_tiles = 2 ** xp.count_nonzero(self.dims_mask) assert self.tiles_pb >= min_tiles, ( f"At least {min_tiles} tiles per sorting box is enforced, but you have {self.tiles_pb}!" ) @@ -6187,19 +4617,19 @@ def get_tiles(self): # logger.info(f'{self.dims_mask = }') # tiles in one sorting box - self._nt_per_dim = np.array([1, 1, 1]) - _ids = np.nonzero(self._dims_mask)[0] + self._nt_per_dim = xp.array([1, 1, 1]) + _ids = xp.nonzero(self._dims_mask)[0] for fac in factors_vec: _nt = self.nt_per_dim[self._dims_mask] - d = _ids[np.argmin(_nt)] + d = _ids[xp.argmin(_nt)] self._nt_per_dim[d] *= fac # logger.info(f'{_nt = }, {d = }, {self.nt_per_dim = }') - assert np.prod(self.nt_per_dim) == self.tiles_pb + assert xp.prod(self.nt_per_dim) == self.tiles_pb # tiles between [0, box_width] in each direction - self._tile_breaks = [np.linspace(0.0, bw, nt + 1) for bw, nt in zip(self.box_widths, self.nt_per_dim)] - self._tile_midpoints = [(np.roll(tbs, -1)[:-1] + tbs[:-1]) / 2 for tbs in self.tile_breaks] + self._tile_breaks = [xp.linspace(0.0, bw, nt + 1) for bw, nt in zip(self.box_widths, self.nt_per_dim)] + self._tile_midpoints = [(xp.roll(tbs, -1)[:-1] + tbs[:-1]) / 2 for tbs in self.tile_breaks] self._tile_volume = 1.0 for tb in self.tile_breaks: self._tile_volume *= tb[1] @@ -6214,8 +4644,8 @@ def draw_markers(self): 1d arrays of logical-space marker coordinates, one entry per tile (length :attr:`n_tiles`).""" _, eta1 = self._tile_output_arrays() - eta2 = np.zeros_like(eta1) - eta3 = np.zeros_like(eta1) + eta2 = xp.zeros_like(eta1) + eta3 = xp.zeros_like(eta1) nt_x, nt_y, nt_z = self.nt_per_dim @@ -6226,7 +4656,7 @@ def draw_markers(self): for k in range(self.boxes_per_dim[2]): z_midpoints = self._get_midpoints(k, 2) - xx, yy, zz = np.meshgrid( + xx, yy, zz = xp.meshgrid( x_midpoints, y_midpoints, z_midpoints, @@ -6265,13 +4695,10 @@ def _get_quad_pts(self, n_quad=None): self._tile_quad_pts = [] self._tile_quad_wts = [] for nq, tb in zip(n_quad, self.tile_breaks): - pts_loc, wts_loc = np.polynomial.legendre.leggauss(nq) - # quadrature_grid follows the active array backend (it is also - # used for genuine device grid quadrature elsewhere); tesselation - # bookkeeping is always host-resident, so convert back here. + pts_loc, wts_loc = xp.polynomial.legendre.leggauss(nq) pts, wts = quadrature_grid(tb[:2], pts_loc, wts_loc) - self._tile_quad_pts += [_to_numpy_for_kernel(pts[0])] - self._tile_quad_wts += [_to_numpy_for_kernel(wts[0])] + self._tile_quad_pts += [pts[0]] + self._tile_quad_wts += [wts[0]] def cell_averages(self, fun, n_quad=None): """Compute the cell average of ``fun`` over every tile on the current process, @@ -6308,15 +4735,13 @@ def cell_averages(self, fun, n_quad=None): for k in range(self.boxes_per_dim[2]): z_pts = self._get_box_quad_pts(k, 2) - xx, yy, zz = np.meshgrid( + xx, yy, zz = xp.meshgrid( x_pts.flatten(), y_pts.flatten(), z_pts.flatten(), indexing="ij", ) - # fun (f_init/domain transform) follows the active array - # backend; tesselation bookkeeping is always host-resident. fun_vals = _to_numpy_for_kernel(fun(*_dev(xx, yy, zz))) sampling_kernels.tile_int_kernel( @@ -6343,9 +4768,9 @@ def _tile_output_arrays(self): * the second, of shape ``nt_per_dim * boxes_per_dim``, holds one entry per tile on the current process (i.e. the first array tiled over all sorting boxes). """ - # self._quad_pts = [np.zeros((nt, nq)).flatten() for nt, nq in zip(self.nt_per_dim, self.tile_quad_pts)] - single_box_out = np.zeros(self.nt_per_dim) - out = np.tile(single_box_out, self.boxes_per_dim) + # self._quad_pts = [xp.zeros((nt, nq)).flatten() for nt, nq in zip(self.nt_per_dim, self.tile_quad_pts)] + single_box_out = xp.zeros(self.nt_per_dim) + out = xp.tile(single_box_out, self.boxes_per_dim) return single_box_out, out def _get_midpoints(self, i: int, dim: int): @@ -6382,13 +4807,13 @@ def _get_box_quad_pts(self, i: int, dim: int): Returns ------- - x_pts : np.array + x_pts : xp.array 2d array of shape (n_tiles_pb, n_tile_quad_pts) """ xl = self.starts[dim] + i * self.box_widths[dim] x_tile_breaks = xl + self.tile_breaks[dim][:-1] x_tile_pts = self.tile_quad_pts[dim] - x_pts = np.tile(x_tile_breaks, (x_tile_pts.size, 1)).T + x_tile_pts + x_pts = xp.tile(x_tile_breaks, (x_tile_pts.size, 1)).T + x_tile_pts return x_pts @property diff --git a/src/struphy/pic/particles.py b/src/struphy/pic/particles.py index a33e66031..19d5965a7 100644 --- a/src/struphy/pic/particles.py +++ b/src/struphy/pic/particles.py @@ -1,7 +1,6 @@ import copy import cunumpy as xp -from cunumpy import PyccelKernel from struphy.fields_background import equils from struphy.fields_background.base import FluidEquilibrium, FluidEquilibriumWithB @@ -138,7 +137,7 @@ def save_constants_of_motion(self): ) # eval guiding center phase space - PyccelKernel(utilities_kernels.eval_guiding_center_from_6d)( + utilities_kernels.eval_guiding_center_from_6d( self.markers, self._derham.args_derham, self.domain.args_domain, @@ -180,7 +179,7 @@ def save_constants_of_motion(self): if self.mpi_comm is not None: self.mpi_sort_markers(alpha=1) - PyccelKernel(utilities_kernels.eval_canonical_toroidal_moment_6d)( + utilities_kernels.eval_canonical_toroidal_moment_6d( self.markers, self._derham.args_derham, self.first_diagnostics_idx, @@ -669,12 +668,8 @@ def s0(self, eta1, eta2, eta3, v_para, v_perp, flat_eval=False, remove_holes=Tru def draw_markers(self, sort: bool = True): super().draw_markers(sort=sort) -<<<<<<< HEAD - PyccelKernel(utilities_kernels.eval_magnetic_moment_5d)( -======= # magnetic moment is an adiabatic invariant: evaluate once at draw time (diagnostics column 1) utilities_kernels.eval_magnetic_moment_5d( ->>>>>>> devel self.markers, self.derham.args_derham, self.first_diagnostics_idx, @@ -698,7 +693,7 @@ def save_constants_of_motion(self): # idx and slice idx_can_momentum = self.first_diagnostics_idx + 2 - PyccelKernel(utilities_kernels.eval_energy_5d)( + utilities_kernels.eval_energy_5d( self.markers, self.derham.args_derham, self.first_diagnostics_idx, @@ -714,13 +709,7 @@ def save_constants_of_motion(self): r = self.markers[~self.holes, 0] * (1 - a1) + a1 self.markers[~self.holes, idx_can_momentum] = self.equil.psi_r(r) -<<<<<<< HEAD - self._epsilon = self.equation_params["epsilon"] - - PyccelKernel(utilities_kernels.eval_canonical_toroidal_moment_5d)( -======= utilities_kernels.eval_canonical_toroidal_moment_5d( ->>>>>>> devel self.markers, self.derham.args_derham, self.first_diagnostics_idx, @@ -747,7 +736,7 @@ def save_magnetic_energy(self, PBb): PBbt = E0T.dot(PBb, out=self._tmp0) PBbt.update_ghost_regions() - PyccelKernel(utilities_kernels.eval_magnetic_energy_PBb)( + utilities_kernels.eval_magnetic_energy_PBb( self.markers, self.derham.args_derham, self.domain.args_domain, @@ -763,7 +752,7 @@ def save_magnetic_background_energy(self): The result is stored in the energy diagnostics column (``self.first_diagnostics_idx``). """ - PyccelKernel(utilities_kernels.eval_magnetic_background_energy)( + utilities_kernels.eval_magnetic_background_energy( self.markers, self.derham.args_derham, self.domain.args_domain, @@ -778,7 +767,7 @@ def save_magnetic_moment(self): diagnostics column (``self.first_diagnostics_idx + 1``). """ - PyccelKernel(utilities_kernels.eval_magnetic_moment_5d)( + utilities_kernels.eval_magnetic_moment_5d( self.markers, self.derham.args_derham, self.first_diagnostics_idx, diff --git a/src/struphy/pic/sorting.py b/src/struphy/pic/sorting.py index 940264130..4728d94db 100644 --- a/src/struphy/pic/sorting.py +++ b/src/struphy/pic/sorting.py @@ -1,5 +1,7 @@ import logging +import numpy as np + try: from mpi4py.MPI import Intracomm except ModuleNotFoundError: @@ -233,18 +235,19 @@ def _set_boxes(self): n_mkr * (1 + 1 / xp.sqrt(n_mkr) + self._box_bufsize), ) - # cartesian boxes (extra last row stores holes/outside particles) - self._boxes = xp.full((self._n_boxes + 1, n_cols), -1, dtype=int) - self._next_index = xp.zeros((self._n_boxes + 1), dtype=int) - self._cumul_next_index = xp.zeros((self._n_boxes + 2), dtype=int) - self._neighbours = xp.zeros((self._n_boxes, 27), dtype=int) + # cartesian boxes (extra last row stores holes/outside particles); host-resident, + # read/written directly by the compiled, host-only sorting kernels + self._boxes = np.full((self._n_boxes + 1, n_cols), -1, dtype=int) + self._next_index = np.zeros((self._n_boxes + 1), dtype=int) + self._cumul_next_index = np.zeros((self._n_boxes + 2), dtype=int) + self._neighbours = np.zeros((self._n_boxes, 27), dtype=int) # A particle on box i only sees particles in boxes that belong to neighbours[i] initialize_neighbours(self._neighbours, self.nx, self.ny, self.nz) # logger.info(f"{self._rank = }\n{self._neighbours = }") - self._swap_line_1 = xp.zeros(self._markers_shape[1]) - self._swap_line_2 = xp.zeros(self._markers_shape[1]) + self._swap_line_1 = np.zeros(self._markers_shape[1]) + self._swap_line_2 = np.zeros(self._markers_shape[1]) def _set_boundary_boxes(self): """Collect the (flat) indices of all non-ghost boxes that lie on the outer surface From e4f82e6dde8a95094104f658e5026db2e028e303 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Fri, 14 Aug 2026 12:24:51 +0200 Subject: [PATCH 021/156] xp->np --- src/struphy/feec/psydac_derham.py | 7 ++-- src/struphy/pic/base.py | 54 +++++++++++++++---------------- 2 files changed, 31 insertions(+), 30 deletions(-) diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index 2cbfe3151..658847587 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -1940,11 +1940,12 @@ def _get_domain_array(self): else: nproc = 1 - # send buffer - dom_arr_loc = xp.zeros(9, dtype=float) + # send buffer (host-resident: passed directly to mpi4py's Allgather, and + # consumed by struphy.pic.base.Particles, which expects a host domain_array) + dom_arr_loc = np.zeros(9, dtype=float) # main array (receive buffers) - dom_arr = xp.zeros(nproc * 9, dtype=float) + dom_arr = np.zeros(nproc * 9, dtype=float) # Get global starts and ends of domain decomposition gl_s = self.domain_decomposition.starts diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 4079f5ba3..e0ff945e4 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -819,7 +819,7 @@ def valid_mks(self): @property def n_mks_loc(self): """Number of valid markers on process (without holes and ghosts).""" - return xp.count_nonzero(self.valid_mks) + return np.count_nonzero(self.valid_mks) @property def n_mks_on_each_proc(self): @@ -848,7 +848,7 @@ def positions(self): @positions.setter def positions(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc, 3) self._markers[self.valid_mks, self.index["pos"]] = new @@ -859,7 +859,7 @@ def velocities(self): @velocities.setter def velocities(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc, self.vdim), f"{self.n_mks_loc =} and {self.vdim =} but {new.shape =}" self._markers[self.valid_mks, self.index["vel"]] = new @@ -870,7 +870,7 @@ def phasespace_coords(self): @phasespace_coords.setter def phasespace_coords(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc, 3 + self.vdim) self._markers[self.valid_mks, self.index["coords"]] = new @@ -881,7 +881,7 @@ def weights(self): @weights.setter def weights(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["weights"]] = new @@ -892,7 +892,7 @@ def sampling_density_values(self): @sampling_density_values.setter def sampling_density_values(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["s0"]] = new @@ -903,7 +903,7 @@ def weights0(self): @weights0.setter def weights0(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["w0"]] = new @@ -914,7 +914,7 @@ def marker_ids(self): @marker_ids.setter def marker_ids(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["ids"]] = new @@ -925,7 +925,7 @@ def f_coords(self): @f_coords.setter def f_coords(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) self.markers[self.valid_mks, self.f_coords_index] = new @property @@ -938,7 +938,7 @@ def f_jacobian_coords(self): @f_jacobian_coords.setter def f_jacobian_coords(self, new): - assert isinstance(new, xp.ndarray) + assert isinstance(new, np.ndarray) if isinstance(self.f_jacobian_coords_index, list): self.markers[ xp.ix_( @@ -1171,8 +1171,8 @@ def draw_markers( self._update_ghost_particles() # cumulative sum of number of markers on each process at loading stage. - n_mks_load_cum_sum = xp.cumsum(self.n_mks_load) - Np_per_clone_cum_sum = xp.cumsum(self.Np_per_clone) + n_mks_load_cum_sum = np.cumsum(self.n_mks_load) + Np_per_clone_cum_sum = np.cumsum(self.Np_per_clone) _first_marker_id = (Np_per_clone_cum_sum - self.Np_per_clone)[self.clone_id] + ( n_mks_load_cum_sum - self.n_mks_load )[self._mpi_rank] @@ -1200,7 +1200,7 @@ def draw_markers( self._set_initial_condition() self.velocities = _to_numpy_for_kernel(self.u_init(_dev(self.positions))).T # set markers ID in last column - self.marker_ids = _first_marker_id + xp.arange(n_mks_load_loc, dtype=float) + self.marker_ids = _first_marker_id + np.arange(n_mks_load_loc, dtype=float) else: logger.debug("\nLoading fresh markers:") for key, val in self.loading_params.__dict__.items(): @@ -1247,7 +1247,7 @@ def draw_markers( # set new n_mks_load self.gather_scalar_in_subcomm_array(num_loaded_particles_loc, out=self.n_mks_load) n_mks_load_loc = self.n_mks_load[self.mpi_rank] - n_mks_load_cum_sum = xp.cumsum(self.n_mks_load) + n_mks_load_cum_sum = np.cumsum(self.n_mks_load) # set new holes in markers array to -1 self._markers[num_loaded_particles_loc:] = -1.0 @@ -1374,7 +1374,7 @@ def draw_markers( else: assert self.spatial == "uniform", f'Spatial drawing must be "uniform" or "disc", is {self.spatial}.' - self.marker_ids = _first_marker_id + xp.arange(n_mks_load_loc, dtype=float) + self.marker_ids = _first_marker_id + np.arange(n_mks_load_loc, dtype=float) # set specific initial condition for some particles if self.loading_params.specific_markers is not None: @@ -2243,7 +2243,7 @@ def gather_scalar_in_subcomm_array(self, scalar: int, out: xp.ndarray = None): The returned array (optional). """ if out is None: - _tmp = xp.zeros(self.mpi_size, dtype=int) + _tmp = np.zeros(self.mpi_size, dtype=int) else: assert out.size == self.mpi_size _tmp = out @@ -2271,7 +2271,7 @@ def gather_scalar_in_intercomm_array(self, scalar: int, out: xp.ndarray = None): The returned array (optional). """ if out is None: - _tmp = xp.zeros(self.num_clones, dtype=int) + _tmp = np.zeros(self.num_clones, dtype=int) else: assert out.size == self.num_clones _tmp = out @@ -2321,7 +2321,7 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): if mpi_dims_mask is None: mpi_dims_mask = [True, True, True] - dom_arr = xp.zeros((self.mpi_size, 9), dtype=float) + dom_arr = np.zeros((self.mpi_size, 9), dtype=float) # factorize mpi size factors = factorint(self.mpi_size) @@ -2348,7 +2348,7 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): assert xp.prod(nprocs) == self.mpi_size # domain decomposition - breaks = [xp.linspace(0.0, 1.0, nproc + 1) for nproc in nprocs] + breaks = [np.linspace(0.0, 1.0, nproc + 1) for nproc in nprocs] # fill domain array for n in range(self.mpi_size): @@ -2784,12 +2784,12 @@ def _load_tesselation(self, n_quad: int = 1): def _reset_marker_ids(self): """Reset the marker ids (last column in marker array) according to the current distribution of particles. The first marker on rank 0 gets the id '0', the last marker on the last rank gets the id 'n_mks_global - 1'.""" - n_mks_proc_cumsum = xp.cumsum(self.n_mks_on_each_proc) - n_mks_clone_cumsum = xp.cumsum(self.n_mks_on_each_clone) + n_mks_proc_cumsum = np.cumsum(self.n_mks_on_each_proc) + n_mks_clone_cumsum = np.cumsum(self.n_mks_on_each_clone) first_marker_id = (n_mks_clone_cumsum - self.n_mks_on_each_clone)[self.clone_id] + ( n_mks_proc_cumsum - self.n_mks_on_each_proc )[self.mpi_rank] - self.marker_ids = first_marker_id + xp.arange(self.n_mks_loc, dtype=int) + self.marker_ids = first_marker_id + np.arange(self.n_mks_loc, dtype=int) def _find_outside_particles(self, axis): """Find markers whose ``axis``-th logical coordinate lies outside ``[0, 1]`` @@ -3705,7 +3705,7 @@ def _self_communication_boxes(self): # self._update_valid_mks() # holes_inds = xp.nonzero(self.holes)[0] - self.markers[holes_inds[xp.arange(self._send_info_box[self.mpi_rank])]] = self._send_list_box[self.mpi_rank] + self.markers[holes_inds[np.arange(self._send_info_box[self.mpi_rank])]] = self._send_list_box[self.mpi_rank] def _sendrecv_all_to_all_boxes(self): """ @@ -3724,7 +3724,7 @@ def _sendrecv_markers_boxes(self): """ # i-th entry holds the number (not the index) of the first hole to be filled by data from process i - first_hole = xp.cumsum(self._recv_info_box) - self._recv_info_box + first_hole = np.cumsum(self._recv_info_box) - self._recv_info_box hole_inds = xp.nonzero(self._holes)[0] # Initialize send and receive commands reqs = [] @@ -3759,7 +3759,7 @@ def _sendrecv_markers_boxes(self): self.mpi_comm.Abort() # exit() - self._markers[hole_inds[first_hole[i] + xp.arange(self._recv_info_box[i])]] = recvbufs[i] + self._markers[hole_inds[first_hole[i] + np.arange(self._recv_info_box[i])]] = recvbufs[i] test_reqs.pop() reqs[i] = None @@ -4467,7 +4467,7 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): """ # i-th entry holds the number (not the index) of the first hole to be filled by data from process i - first_hole = xp.cumsum(recv_info) - recv_info + first_hole = np.cumsum(recv_info) - recv_info # Initialize send and receive commands for i, (data, N_recv) in enumerate(zip(self._send_list, list(recv_info))): @@ -4499,7 +4499,7 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): ) self.mpi_comm.Abort() - self.markers[hole_inds_after_send[first_hole[i] + xp.arange(recv_info[i])]] = self._recvbufs[i] + self.markers[hole_inds_after_send[first_hole[i] + np.arange(recv_info[i])]] = self._recvbufs[i] test_reqs.pop() self._reqs[i] = None From 56e6f85c368b0697d369c18c1fa184de9ac4d304 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Fri, 14 Aug 2026 12:44:44 +0200 Subject: [PATCH 022/156] optimization, reduce the number of .get operations in _find_outside_particles_gpu --- src/struphy/pic/base.py | 34 ++++++++++++++++++++++------------ 1 file changed, 22 insertions(+), 12 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index e0ff945e4..e4d22a5eb 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -2443,9 +2443,14 @@ def _allocate_marker_array(self, dry_run: bool = False): self._holes = np.zeros(self.n_rows, dtype=bool) self._ghost_particles = np.zeros(self.n_rows, dtype=bool) self._valid_mks = np.zeros(self.n_rows, dtype=bool) - self._is_outside_right = np.zeros(self.n_rows, dtype=bool) - self._is_outside_left = np.zeros(self.n_rows, dtype=bool) - self._is_outside = np.zeros(self.n_rows, dtype=bool) + + # _is_outside_right/_is_outside_left/_is_outside are views into one + # buffer so that _find_outside_particles_gpu can fill all three with + # a single device->host transfer instead of three. + self._is_outside_buf = np.zeros((3, self.n_rows), dtype=bool) + self._is_outside_right = self._is_outside_buf[0] + self._is_outside_left = self._is_outside_buf[1] + self._is_outside = self._is_outside_buf[2] # device-resident copies of _holes/_ghost_particles used by # _find_outside_particles_gpu; re-synced lazily, only when stale @@ -2454,6 +2459,7 @@ def _allocate_marker_array(self, dry_run: bool = False): self._holes_dev = None self._ghost_dev = None self._holes_ghost_dev_dirty = True + self._is_outside_buf_dev = None # create array container (3 x positions, vdim x velocities, weight, s0, w0, ID) for removed markers self._n_lost_markers = 0 @@ -2857,16 +2863,20 @@ def _find_outside_particles_gpu(self, axis): holes_dev = self._holes_dev ghost_dev = self._ghost_dev - is_r = col_dev > 1.0 - is_l = col_dev < 0.0 - not_hole_or_ghost = ~(holes_dev | ghost_dev) - is_r &= not_hole_or_ghost - is_l &= not_hole_or_ghost - is_out = is_r | is_l + if self._is_outside_buf_dev is None: + self._is_outside_buf_dev = cp.empty_like(cp.asarray(self._is_outside_buf)) + buf_dev = self._is_outside_buf_dev - is_r.get(out=self._is_outside_right) - is_l.get(out=self._is_outside_left) - is_out.get(out=self._is_outside) + not_hole_or_ghost = ~(holes_dev | ghost_dev) + cp.greater(col_dev, 1.0, out=buf_dev[0]) + buf_dev[0] &= not_hole_or_ghost + cp.less(col_dev, 0.0, out=buf_dev[1]) + buf_dev[1] &= not_hole_or_ghost + cp.logical_or(buf_dev[0], buf_dev[1], out=buf_dev[2]) + + # single D2H transfer for all three (is_outside_right/left/is_outside + # are views into self._is_outside_buf, see _allocate_marker_array) + buf_dev.get(out=self._is_outside_buf) outside_inds = np.nonzero(self._is_outside)[0] From 40a4072c4a836b1e4104c31688af8791aa9b4d79 Mon Sep 17 00:00:00 2001 From: Max Date: Fri, 14 Aug 2026 12:43:32 +0200 Subject: [PATCH 023/156] Use self.name as profiling label --- src/struphy/simulation/sim.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index 819ecfc94..7ace7c025 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -149,7 +149,7 @@ def __init__( self.name = name self.description = description self.params_path = params_path - self.env = env + self.env = env # Set name first since it's used as a label self.time_opts = time_opts self.domain = domain self.equil = equil @@ -174,7 +174,7 @@ def __init__( # Abstract methods # ---------------- - def _setup_profiling(self): + def _setup_profiling(self, label: str = ""): # setup profiling agent ProfileManager.setup( deactivate_profiling=not self.env.profiling_activated, @@ -184,7 +184,7 @@ def _setup_profiling(self): self.env.sim_folder, "profiling_data.h5", ), - label=self.env.sim_label, + label=label, ) def show_parameters(self): @@ -1830,7 +1830,7 @@ def env(self, value: EnvironmentOptions): # create output folders self._setup_folders() - self._setup_profiling() + self._setup_profiling(label = self.name) @property def time_opts(self): From 26f0d036ddd08b53c6b8a16ed17d46b56c5f6743 Mon Sep 17 00:00:00 2001 From: Max Date: Fri, 14 Aug 2026 16:34:42 +0200 Subject: [PATCH 024/156] Formatting --- src/struphy/simulation/sim.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index 7ace7c025..ce0fc2f72 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -149,7 +149,7 @@ def __init__( self.name = name self.description = description self.params_path = params_path - self.env = env # Set name first since it's used as a label + self.env = env # Set name first since it's used as a label self.time_opts = time_opts self.domain = domain self.equil = equil @@ -1830,7 +1830,7 @@ def env(self, value: EnvironmentOptions): # create output folders self._setup_folders() - self._setup_profiling(label = self.name) + self._setup_profiling(label=self.name) @property def time_opts(self): From 63ec65c8b947547a29acabc064851895b2cce859 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 08:42:30 +0200 Subject: [PATCH 025/156] Np=8_000_000 --- params_PressureLessSPH.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/params_PressureLessSPH.py b/params_PressureLessSPH.py index f46a88a82..e91b3154f 100644 --- a/params_PressureLessSPH.py +++ b/params_PressureLessSPH.py @@ -129,7 +129,7 @@ # particle push dominates the run, small enough to fit comfortably on one GPU. # The seed is fixed because marker loading is otherwise unseeded, and two runs # of the *same* backend then differ enough to swamp any backend comparison. -loading_params = LoadingParameters(Np=200_000, seed=1234) +loading_params = LoadingParameters(Np=8_000_000, seed=1234) weights_params = WeightsParameters() boundary_params = BoundaryParameters() sorting_params = SortingParameters() From 578c8f8af6ef65196a54bbf88f346dfc3078fc4e Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 09:01:24 +0200 Subject: [PATCH 026/156] Added cuda fusedkernels in src/struphy/pic/pushing/pusher_kernels_cuda.py --- src/struphy/pic/pushing/pusher.py | 77 ++++- .../pic/pushing/pusher_kernels_cuda.py | 264 ++++++++++++++++++ 2 files changed, 332 insertions(+), 9 deletions(-) create mode 100644 src/struphy/pic/pushing/pusher_kernels_cuda.py diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 8c36c3306..889e6cad5 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -11,6 +11,7 @@ from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.pic.base import Particles +from struphy.pic.pushing.pusher_kernels_cuda import push_eta_rk_periodic_gpu, push_eta_stage_cuboid_gpu logger = logging.getLogger("struphy") @@ -125,6 +126,13 @@ def __init__( self._args_kernel = args_kernel self._args_domain = args_domain + # hand-written CUDA replacement for push_eta_stage on a Cuboid domain + # (constant, diagonal Jacobian -> no spline evaluation needed at all) + self._gpu_eta_cuboid = cunumpy.cupy_backend and kernel.name == "push_eta_stage" and args_domain.kind_map == 10 + if self._gpu_eta_cuboid: + l1, r1, l2, r2, l3, r3 = (float(p) for p in args_domain.params[:6]) + self._gpu_eta_cuboid_scale = (1.0 / (r1 - l1), 1.0 / (r2 - l2), 1.0 / (r3 - l3)) + # determines the evaluation points for kernel self._alpha_in_kernel = alpha_in_kernel self._n_stages = n_stages @@ -172,6 +180,21 @@ def __init__( else: self._box_comm = False + # whole-push GPU-resident fast path: on top of _gpu_eta_cuboid, also + # requires an all-periodic bc (so apply_kinetic_bc reduces to wrap + + # shift bookkeeping, which push_eta_rk_periodic_gpu fuses in) and no + # MPI / iterative-solver / eval-kernel machinery (none of which + # push_eta uses, but other Pusher users might). + self._gpu_eta_cuboid_periodic = ( + self._gpu_eta_cuboid + and all(b == "periodic" for b in self.particles.bc) + and not self._init_kernels + and not self._eval_kernels + and self.particles.mpi_comm is None + and self._maxiter == 1 + and not self._newton + ) + @staticmethod def _reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim): """Device version of the per-step marker buffer bookkeeping at the top @@ -199,7 +222,28 @@ def __call__(self, dt: float): applies kinetic boundary conditions and performs MPI sorting. """ with ProfileManager.profile_region(self._region_name): - self._push(dt) + if self._gpu_eta_cuboid_periodic: + self._push_eta_cuboid_periodic_gpu(dt) + else: + self._push(dt) + + def _push_eta_cuboid_periodic_gpu(self, dt: float): + """Whole-push GPU-resident fast path, see :func:`push_eta_rk_periodic_gpu`.""" + particles = self.particles + a, b, _c = self._args_kernel + push_eta_rk_periodic_gpu( + particles.markers, + particles.n_cols, + particles.vdim, + particles.first_pusher_idx, + particles.first_shift_idx, + particles.first_free_idx, + self._gpu_eta_cuboid_scale, + dt, + a, + b, + self.n_stages, + ) def _kernel_region(self, kernel) -> str: """Cached name of the profiling region of an init/eval kernel.""" @@ -320,14 +364,29 @@ def _push(self, dt: float): ) # push markers - with ProfileManager.profile_region("kernel: " + self.kernel.name): - self.kernel( - dt, - stage, - self.particles.args_markers, - self._args_domain, - *self._args_kernel, - ) + if self._gpu_eta_cuboid: + a, b, _c = self._args_kernel + last = 1.0 if stage == self.n_stages - 1 else 0.0 + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + push_eta_stage_cuboid_gpu( + markers, + self.particles.n_cols, + first_pusher_idx, + self.particles.first_free_idx, + self._gpu_eta_cuboid_scale, + dt * float(a[stage]), + dt * float(b[stage]), + last, + ) + else: + with ProfileManager.profile_region("kernel: " + self.kernel.name): + self.kernel( + dt, + stage, + self.particles.args_markers, + self._args_domain, + *self._args_kernel, + ) self.particles.apply_kinetic_bc(newton=self._newton) self.particles.update_holes() diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py new file mode 100644 index 000000000..6d347e893 --- /dev/null +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -0,0 +1,264 @@ +"""Hand-written CUDA replacements for select pusher kernels, used only under +``ARRAY_BACKEND=cupy``. + +Unlike the generic Pyccel kernels in :mod:`~struphy.pic.pushing.pusher_kernels` +(which operate on plain host NumPy arrays regardless of backend), the kernels +here are real ``cupy.RawKernel`` CUDA source, executed directly on the GPU. +They are deliberately narrow: each one reproduces the exact arithmetic of one +Pyccel kernel, specialized for one :class:`~struphy.geometry.domains.Domain` +whose Jacobian is cheap enough that hand-specializing pays off. + +Currently covered: :func:`~struphy.pic.pushing.pusher_kernels.push_eta_stage` +for the :class:`~struphy.geometry.domains.Cuboid` domain (``kind_map == 10``), +whose Jacobian ``DF = diag(r1 - l1, r2 - l2, r3 - l3)`` is constant, so the +whole stage update collapses to an elementwise scale-and-accumulate per marker +row with no spline evaluation at all. + +Two entry points are provided: + +* :func:`push_eta_stage_cuboid_gpu` replaces a single ``push_eta_stage`` call. + It round-trips the full marker array through the device on every call, which + is fine at the marker counts used in early testing but becomes the dominant + cost at a few hundred thousand markers and up (H2D/D2H bandwidth, not + compute, ends up dominating the run). + +* :func:`push_eta_rk_periodic_gpu` additionally fuses in the boundary-condition + bookkeeping that :meth:`~struphy.pic.base.Particles.apply_kinetic_bc` would + otherwise do on the host between stages (periodic wrap + shift-column + bookkeeping), restricted to an all-periodic ``bc``. That lets the whole + multi-stage RK push run with the marker array resident on the device the + entire time, doing exactly one H2D and one D2H transfer per + :meth:`~struphy.pic.pushing.pusher.Pusher.__call__`, instead of one round + trip per stage per kernel. +""" + +_PUSH_ETA_CUBOID_SRC = r""" +extern "C" __global__ +void push_eta_stage_cuboid( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const double sx, + const double sy, + const double sz, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + + // skip holes and ghost/boundary particles, matching push_eta_stage + if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double kx = sx * row[3]; + const double ky = sy * row[4]; + const double kz = sz * row[5]; + + // accumulate for the last stage (must happen before the position update, + // which reads the just-updated accumulator) + row[first_free_idx + 0] += dt_b * kx; + row[first_free_idx + 1] += dt_b * ky; + row[first_free_idx + 2] += dt_b * kz; + + row[0] = row[first_init_idx + 0] + dt_a * kx + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * ky + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * kz + last * row[first_free_idx + 2]; +} +""" + +_push_eta_cuboid_kernel = None + + +def _get_kernel(): + global _push_eta_cuboid_kernel + if _push_eta_cuboid_kernel is None: + import cupy as cp + + _push_eta_cuboid_kernel = cp.RawKernel(_PUSH_ETA_CUBOID_SRC, "push_eta_stage_cuboid") + return _push_eta_cuboid_kernel + + +def push_eta_stage_cuboid_gpu( + markers, + n_cols: int, + first_init_idx: int, + first_free_idx: int, + scale: tuple[float, float, float], + dt_a: float, + dt_b: float, + last: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_eta_stage`, restricted to + the :class:`~struphy.geometry.domains.Cuboid` domain. + + ``markers`` is the host (pinned-memory) marker array; it is round-tripped + through the device in full, matching the pattern used by + :meth:`~struphy.pic.pushing.pusher.Pusher._reset_marker_buffers_gpu`. + """ + import cupy as cp + import numpy as np + + kernel = _get_kernel() + n_markers = markers.shape[0] + + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_free_idx), + np.float64(scale[0]), + np.float64(scale[1]), + np.float64(scale[2]), + np.float64(dt_a), + np.float64(dt_b), + np.float64(last), + ), + ) + dev.get(out=markers) + + +_PUSH_ETA_RK_PERIODIC_SRC = r""" +extern "C" __global__ +void push_eta_rk_periodic( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int first_shift_idx, + const double sx, + const double sy, + const double sz, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + + if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double kx = sx * row[3]; + const double ky = sy * row[4]; + const double kz = sz * row[5]; + + row[first_free_idx + 0] += dt_b * kx; + row[first_free_idx + 1] += dt_b * ky; + row[first_free_idx + 2] += dt_b * kz; + + double e0 = row[first_init_idx + 0] + dt_a * kx + last * row[first_free_idx + 0]; + double e1 = row[first_init_idx + 1] + dt_a * ky + last * row[first_free_idx + 1]; + double e2 = row[first_init_idx + 2] + dt_a * kz + last * row[first_free_idx + 2]; + + // periodic wrap + shift bookkeeping, matching the periodic branch of + // Particles.apply_kinetic_bc (Python's a % 1.0 is always in [0, 1)) + double shift0 = 0.0, shift1 = 0.0, shift2 = 0.0; + + if (e0 > 1.0) { e0 = fmod(e0, 1.0); shift0 = 1.0; } + else if (e0 < 0.0) { e0 = fmod(e0, 1.0); if (e0 < 0.0) e0 += 1.0; shift0 = -1.0; } + + if (e1 > 1.0) { e1 = fmod(e1, 1.0); shift1 = 1.0; } + else if (e1 < 0.0) { e1 = fmod(e1, 1.0); if (e1 < 0.0) e1 += 1.0; shift1 = -1.0; } + + if (e2 > 1.0) { e2 = fmod(e2, 1.0); shift2 = 1.0; } + else if (e2 < 0.0) { e2 = fmod(e2, 1.0); if (e2 < 0.0) e2 += 1.0; shift2 = -1.0; } + + row[0] = e0; + row[1] = e1; + row[2] = e2; + row[first_shift_idx + 0] = shift0; + row[first_shift_idx + 1] = shift1; + row[first_shift_idx + 2] = shift2; +} +""" + +_push_eta_rk_periodic_kernel = None + + +def _get_periodic_kernel(): + global _push_eta_rk_periodic_kernel + if _push_eta_rk_periodic_kernel is None: + import cupy as cp + + _push_eta_rk_periodic_kernel = cp.RawKernel(_PUSH_ETA_RK_PERIODIC_SRC, "push_eta_rk_periodic") + return _push_eta_rk_periodic_kernel + + +def push_eta_rk_periodic_gpu( + markers, + n_cols: int, + vdim: int, + first_init_idx: int, + first_shift_idx: int, + first_free_idx: int, + scale: tuple[float, float, float], + dt: float, + a, + b, + n_stages: int, +): + """Run a full multi-stage RK push of :func:`~struphy.pic.pushing.pusher_kernels.push_eta_stage` + plus periodic boundary handling, entirely on the device. + + Restricted to the :class:`~struphy.geometry.domains.Cuboid` domain and an + all-``"periodic"`` ``bc``. Equivalent to calling + :func:`push_eta_stage_cuboid_gpu` once per stage followed by the periodic + branch of :meth:`~struphy.pic.base.Particles.apply_kinetic_bc`, but with a + single H2D transfer at the start and a single D2H transfer at the end + instead of one round trip per stage. Holes and ghost particles are + invariant under a periodic-only push (positions are wrapped mod 1, never + set to the -1.0 hole sentinel), so :meth:`~struphy.pic.base.Particles.update_holes` + does not need to be called. + """ + import cupy as cp + import numpy as np + + kernel = _get_periodic_kernel() + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + dev = cp.asarray(markers) + + # reset: save initial phase-space coords, zero shift/free/residual columns + # (matches Pusher._reset_marker_buffers_gpu, done once instead of per stage) + dev[:, first_init_idx : first_init_idx + 3 + vdim] = dev[:, : 3 + vdim] + dev[:, first_shift_idx:-2] = 0.0 + + for stage in range(n_stages): + last = 1.0 if stage == n_stages - 1 else 0.0 + kernel( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_free_idx), + np.int32(first_shift_idx), + np.float64(scale[0]), + np.float64(scale[1]), + np.float64(scale[2]), + np.float64(dt * float(a[stage])), + np.float64(dt * float(b[stage])), + np.float64(last), + ), + ) + + dev.get(out=markers) From 145afc6ebcce5c45d9390a189e670e7ed550e5d5 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 09:28:47 +0200 Subject: [PATCH 027/156] Added push_v_with_efield_cuboid_gpu --- src/struphy/pic/pushing/pusher.py | 74 ++++++++++++++++++++++++++++++- 1 file changed, 73 insertions(+), 1 deletion(-) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 889e6cad5..0bd31125a 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -11,7 +11,11 @@ from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.pic.base import Particles -from struphy.pic.pushing.pusher_kernels_cuda import push_eta_rk_periodic_gpu, push_eta_stage_cuboid_gpu +from struphy.pic.pushing.pusher_kernels_cuda import ( + push_eta_rk_periodic_gpu, + push_eta_stage_cuboid_gpu, + push_v_with_efield_cuboid_gpu, +) logger = logging.getLogger("struphy") @@ -195,6 +199,46 @@ def __init__( and not self._newton ) + # hand-written CUDA replacement for push_v_with_efield on a Cuboid + # domain: same narrow scoping as _gpu_eta_cuboid_periodic (this kernel + # only ever touches velocity columns, never position, so the periodic + # requirement below just keeps us from having to reason about + # non-periodic apply_kinetic_bc branches we don't otherwise skip; see + # pusher_kernels_cuda.push_v_with_efield_cuboid_gpu). + self._gpu_v_efield_cuboid = ( + cunumpy.cupy_backend + and kernel.name == "push_v_with_efield" + and args_domain.kind_map == 10 + and all(b == "periodic" for b in self.particles.bc) + and not init_kernels + and not eval_kernels + and self.particles.mpi_comm is None + and maxiter == 1 + and not self._newton + and n_stages == 1 + ) + if self._gpu_v_efield_cuboid: + import cupy as cp + + l1, r1, l2, r2, l3, r3 = (float(p) for p in args_domain.params[:6]) + self._gpu_v_efield_scale = (1.0 / (r1 - l1), 1.0 / (r2 - l2), 1.0 / (r3 - l3)) + + args_derham, e1_1, e1_2, e1_3, const = args_kernel + self._gpu_v_efield_const = float(const) + self._gpu_v_efield_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_v_efield_starts = tuple(int(s) for s in args_derham.starts) + # knot vectors are tiny host arrays; cache them on the device once + self._gpu_v_efield_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_v_efield_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_v_efield_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + # FE coefficients are already device-resident CuPy arrays under the + # CuPy backend (StencilVector allocates via cunumpy's xp) and are + # never reassigned after PushVinEfield.allocate() builds them, so + # these references stay valid and need no per-call transfer. + self._gpu_v_efield_e1_1 = e1_1 + self._gpu_v_efield_e1_2 = e1_2 + self._gpu_v_efield_e1_3 = e1_3 + @staticmethod def _reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim): """Device version of the per-step marker buffer bookkeeping at the top @@ -224,6 +268,8 @@ def __call__(self, dt: float): with ProfileManager.profile_region(self._region_name): if self._gpu_eta_cuboid_periodic: self._push_eta_cuboid_periodic_gpu(dt) + elif self._gpu_v_efield_cuboid: + self._push_v_efield_cuboid_gpu(dt) else: self._push(dt) @@ -245,6 +291,32 @@ def _push_eta_cuboid_periodic_gpu(self, dt: float): self.n_stages, ) + def _push_v_efield_cuboid_gpu(self, dt: float): + """Whole-push GPU-resident fast path, see + :func:`~struphy.pic.pushing.pusher_kernels_cuda.push_v_with_efield_cuboid_gpu`. + + Only the velocity columns are touched (positions and holes/ghost + status are untouched), so unlike :meth:`_push`, there is no marker + buffer reset and no ``apply_kinetic_bc``/``update_holes`` call to + replicate here: both would be no-ops given this kernel never moves a + marker. + """ + particles = self.particles + push_v_with_efield_cuboid_gpu( + particles.markers, + particles.n_cols, + self._gpu_v_efield_pn, + self._gpu_v_efield_tn1, + self._gpu_v_efield_tn2, + self._gpu_v_efield_tn3, + self._gpu_v_efield_starts, + self._gpu_v_efield_e1_1, + self._gpu_v_efield_e1_2, + self._gpu_v_efield_e1_3, + self._gpu_v_efield_scale, + dt * self._gpu_v_efield_const, + ) + def _kernel_region(self, kernel) -> str: """Cached name of the profiling region of an init/eval kernel.""" name = self._kernel_region_names.get(id(kernel)) From ca928af59be00203ba8334be1a5fd689f339dc24 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 09:29:03 +0200 Subject: [PATCH 028/156] Added push_v_with_efield_cuboid_gpu --- .../pic/pushing/pusher_kernels_cuda.py | 256 ++++++++++++++++++ 1 file changed, 256 insertions(+) diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index 6d347e893..474286da9 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -30,6 +30,24 @@ entire time, doing exactly one H2D and one D2H transfer per :meth:`~struphy.pic.pushing.pusher.Pusher.__call__`, instead of one round trip per stage per kernel. + +Also covered: :func:`~struphy.pic.pushing.pusher_kernels.push_v_with_efield`, +again for the Cuboid domain. Unlike ``push_eta_stage``, this one does need a +real (small-degree) tensor-product B-spline evaluation -- the electric field +is a 1-form FEEC spline, not a constant -- so :func:`push_v_with_efield_cuboid_gpu` +ports ``find_span`` and the combined N-/D-spline basis recursion +(:func:`~struphy.bsplines.bsplines_kernels.b_d_splines_slim`) to device code +alongside the local stencil sum +(:func:`~struphy.bsplines.evaluation_kernels_3d.eval_spline_mpi_kernel`). +Basis arrays are sized to a compile-time ``MAXP`` (spline degree 8), which +comfortably covers Struphy's usual degrees. The FE coefficient arrays +(``e1_1``, ``e1_2``, ``e1_3``) are the raw ``._data`` of the field's +:class:`~feectools.linalg.stencil.StencilVector` components; under the CuPy +backend these already live on the device (``StencilVector`` allocates via +``cunumpy``'s array-backend-aware ``xp``) and are never reassigned after +:meth:`~struphy.propagators.push_vin_efield.PushVinEfield.allocate` runs, so +they are passed straight through with no transfer at all -- only the marker +array round-trips through the device, exactly once per call. """ _PUSH_ETA_CUBOID_SRC = r""" @@ -262,3 +280,241 @@ def push_eta_rk_periodic_gpu( ) dev.get(out=markers) + + +_PUSH_V_EFIELD_CUBOID_SRC = r""" +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +// Combined N-spline (bn, p+1 values) and D-spline (bd, p values) evaluation, +// matching struphy.bsplines.bsplines_kernels.b_d_splines_slim exactly. +__device__ void b_d_splines_dev(const double* t, int p, double eta, int span, double* bn, double* bd) +{ + double left[MAXP]; + double right[MAXP]; + int pd = p - 1; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + for (int i = 0; i < p; i++) bd[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + + if (j == p - 1) { + for (int il = 0; il <= pd; il++) { + bd[pd - il] = (double)p / (t[span - il + p] - t[span - il]) * bn[pd - il]; + } + } + + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +extern "C" __global__ +void push_v_with_efield_cuboid( + double* markers, + const int n_cols, + const int n_markers, + const int p1, + const int p2, + const int p3, + const double* tn1, + const int len_tn1, + const double* tn2, + const int len_tn2, + const double* tn3, + const int len_tn3, + const int start0, + const int start1, + const int start2, + const double* e1_1, + const int n2x1, + const int n3x1, + const double* e1_2, + const int n2x2, + const int n3x2, + const double* e1_3, + const int n2x3, + const int n3x3, + const double sx, + const double sy, + const double sz, + const double dt_const) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + + // skip holes and ghost/boundary particles, matching Particles.valid_mks + if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double eta1 = row[0]; + const double eta2 = row[1]; + const double eta3 = row[2]; + + double bn1[MAXP + 1], bd1[MAXP]; + double bn2[MAXP + 1], bd2[MAXP]; + double bn3[MAXP + 1], bd3[MAXP]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + // e_form[0]: D-spline in direction 1, N-splines in directions 2, 3 + double e_form0 = 0.0; + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + e_form0 += e1_1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; + } + } + } + + // e_form[1]: N-spline in direction 1, D-spline in direction 2, N-spline in direction 3 + double e_form1 = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + e_form1 += e1_2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; + } + } + } + + // e_form[2]: N-splines in directions 1, 2, D-spline in direction 3 + double e_form2 = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + e_form2 += e1_3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; + } + } + } + + // Cartesian E-field is DF^-T @ e_form; for Cuboid, DF is diag(sx^-1, sy^-1, sz^-1) + // so DF^-T is diag(sx, sy, sz) -- same convention as push_eta_stage_cuboid's scale. + row[3] += dt_const * sx * e_form0; + row[4] += dt_const * sy * e_form1; + row[5] += dt_const * sz * e_form2; +} +""" + +_push_v_efield_cuboid_kernel = None + + +def _get_v_efield_kernel(): + global _push_v_efield_cuboid_kernel + if _push_v_efield_cuboid_kernel is None: + import cupy as cp + + _push_v_efield_cuboid_kernel = cp.RawKernel(_PUSH_V_EFIELD_CUBOID_SRC, "push_v_with_efield_cuboid") + return _push_v_efield_cuboid_kernel + + +def push_v_with_efield_cuboid_gpu( + markers, + n_cols: int, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + e1_1_dev, + e1_2_dev, + e1_3_dev, + scale: tuple[float, float, float], + dt_const: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_v_with_efield`, restricted + to the :class:`~struphy.geometry.domains.Cuboid` domain. + + ``markers`` is the host marker array and is round-tripped through the + device once (matching :func:`push_eta_stage_cuboid_gpu`). ``tn1_dev``, + ``tn2_dev``, ``tn3_dev`` (knot vectors) and ``e1_1_dev``, ``e1_2_dev``, + ``e1_3_dev`` (FE coefficients of the 1-form E-field) are expected to + already be CuPy arrays resident on the device -- callers should cache + them once rather than converting on every call, see + :class:`~struphy.pic.pushing.pusher.Pusher`. + """ + import cupy as cp + import numpy as np + + kernel = _get_v_efield_kernel() + n_markers = markers.shape[0] + + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + e1_1_dev, + np.int32(e1_1_dev.shape[1]), + np.int32(e1_1_dev.shape[2]), + e1_2_dev, + np.int32(e1_2_dev.shape[1]), + np.int32(e1_2_dev.shape[2]), + e1_3_dev, + np.int32(e1_3_dev.shape[1]), + np.int32(e1_3_dev.shape[2]), + np.float64(scale[0]), + np.float64(scale[1]), + np.float64(scale[2]), + np.float64(dt_const), + ), + ) + dev.get(out=markers) From c7a66f59d100eaf5ae6cd69bbb98d20f5e2985b0 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 09:29:59 +0200 Subject: [PATCH 029/156] Added param save_restart: bool to turn off restart saving --- params_PressureLessSPH.py | 1 + src/struphy/io/options.py | 8 ++++++++ src/struphy/simulation/sim.py | 24 +++++++++++++----------- 3 files changed, 22 insertions(+), 11 deletions(-) diff --git a/params_PressureLessSPH.py b/params_PressureLessSPH.py index e91b3154f..582e8e024 100644 --- a/params_PressureLessSPH.py +++ b/params_PressureLessSPH.py @@ -87,6 +87,7 @@ sim_folder=f"sim_{args.backend}", profiling_activated=True, profiling_trace=True, + save_restart=False, ) diff --git a/src/struphy/io/options.py b/src/struphy/io/options.py index 5df01389b..7b28344ac 100644 --- a/src/struphy/io/options.py +++ b/src/struphy/io/options.py @@ -327,6 +327,13 @@ class EnvironmentOptions(OptionsBase): save_step : int When to save data output: every time step (save_step=1), every second time step (save_step=2), etc (default=1). + save_restart : bool + Whether to write the restart checkpoint (full marker arrays and FEEC + restart coefficients, written once at setup and once at the end of + the run). Restart data can dominate the run time for large particle + counts; set to ``False`` to skip it when restart capability isn't + needed, e.g. for pure timing/benchmark runs (default=True). + sort_step: int, optional Sort markers in memory every N time steps (default=0, which means markers are sorted only at the start of simulation) @@ -343,6 +350,7 @@ class EnvironmentOptions(OptionsBase): restart: bool = False max_runtime: int = 300 save_step: int = 1 + save_restart: bool = True sort_step: int = 0 num_clones: int = 1 profiling_activated: bool = False diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index d124b5d12..adf75d53a 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -1418,18 +1418,19 @@ def _initialize_hdf5_datasets(self, data: DataContainer, size: int): file[key_field].attrs["pads"] = DataContainer._as_numpy_array(spline.pads) # save numpy array to be updated only at the end of the simulation for restart. - key_field_restart = os.path.join(species_path_restart, variable) + if self.env.save_restart: + key_field_restart = os.path.join(species_path_restart, variable) - if isinstance(spline.vector_stencil, StencilVector): - data.add_data( - {key_field_restart: spline.vector_stencil._data}, - ) - else: - for n in range(3): - key_component_restart = os.path.join(key_field_restart, str(n + 1)) + if isinstance(spline.vector_stencil, StencilVector): data.add_data( - {key_component_restart: spline.vector_stencil[n]._data}, + {key_field_restart: spline.vector_stencil._data}, ) + else: + for n in range(3): + key_component_restart = os.path.join(key_field_restart, str(n + 1)) + data.add_data( + {key_component_restart: spline.vector_stencil[n]._data}, + ) # save kinetic data in group 'kinetic/' for name, species in self.model.particle_species.items(): @@ -1441,10 +1442,11 @@ def _initialize_hdf5_datasets(self, data: DataContainer, size: int): assert isinstance(obj, Particles) key_spec = os.path.join("kinetic", name) - key_spec_restart = os.path.join("restart", name) # restart data - data.add_data({key_spec_restart: obj.markers}) + if self.env.save_restart: + key_spec_restart = os.path.join("restart", name) + data.add_data({key_spec_restart: obj.markers}) # marker data key_mks = os.path.join(key_spec, "markers") From 77da28454afbe215fe2e256fb7e848cc5fe71ea8 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 09:50:41 +0200 Subject: [PATCH 030/156] Fixed the scalar bug --- src/struphy/models/scalars.py | 26 ++++++++++++++++---------- 1 file changed, 16 insertions(+), 10 deletions(-) diff --git a/src/struphy/models/scalars.py b/src/struphy/models/scalars.py index 35acedb34..d81eedff7 100644 --- a/src/struphy/models/scalars.py +++ b/src/struphy/models/scalars.py @@ -285,14 +285,18 @@ class KineticEnergyPIC(PICScalar): """ def _local_update(self): - if not hasattr(self, "velocities"): - self.velocities = self.variables[ - 0 - ].particles.velocities # TODO: velocities need to redefined for Particles5d? Put magnetic moment as COM. - self.weights = self.variables[0].particles.weights + if not hasattr(self, "Np"): self.Np = self.variables[0].particles.Np - energy = self.normalization * 0.5 / self.Np * xp.sum(self.weights * xp.sum(self.velocities**2, axis=1)) + # velocities/weights must be re-read every call: they are fresh + # copies of the (evolving) marker array, not persistent views, so + # caching them here would freeze this scalar at its initial value. + velocities = self.variables[ + 0 + ].particles.velocities # TODO: velocities need to redefined for Particles5d? Put magnetic moment as COM. + weights = self.variables[0].particles.weights + + energy = self.normalization * 0.5 / self.Np * xp.sum(weights * xp.sum(velocities**2, axis=1)) self.local_value[0] = energy @@ -331,12 +335,14 @@ class KineticEnergySPH(SPHScalar): """ def _local_update(self): - if not hasattr(self, "velocities"): - self.velocities = self.variables[0].particles.velocities - self.weights = self.variables[0].particles.weights + if not hasattr(self, "Np"): self.Np = self.variables[0].particles.Np - energy = self.normalization * 0.5 / self.Np * xp.sum(self.weights * xp.sum(self.velocities**2, axis=1)) + # velocities/weights must be re-read every call, see KineticEnergyPIC. + velocities = self.variables[0].particles.velocities + weights = self.variables[0].particles.weights + + energy = self.normalization * 0.5 / self.Np * xp.sum(weights * xp.sum(velocities**2, axis=1)) self.local_value[0] = energy From bdb022bcbb1c3f5022983e1685bc72e28322f401 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 10:25:03 +0200 Subject: [PATCH 031/156] generalize push_v_with_efield for mpi like push_eta --- src/struphy/pic/pushing/pusher.py | 61 ++++++++++++++++++++++--------- 1 file changed, 44 insertions(+), 17 deletions(-) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 0bd31125a..ccf8a052b 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -199,23 +199,16 @@ def __init__( and not self._newton ) - # hand-written CUDA replacement for push_v_with_efield on a Cuboid - # domain: same narrow scoping as _gpu_eta_cuboid_periodic (this kernel - # only ever touches velocity columns, never position, so the periodic - # requirement below just keeps us from having to reason about - # non-periodic apply_kinetic_bc branches we don't otherwise skip; see - # pusher_kernels_cuda.push_v_with_efield_cuboid_gpu). + # hand-written CUDA replacement for push_v_with_efield's per-marker + # math on a Cuboid domain. Unlike the whole-push fast path below, this + # is unconditional on MPI/bc/maxiter -- it only swaps out the inner + # kernel call (see the "push markers" branch in _push(), mirroring + # how _gpu_eta_cuboid is used there), so it stays correct alongside + # unmodified apply_kinetic_bc/mpi_sort_markers/update_holes for + # multi-rank runs, exactly like _gpu_eta_cuboid already does for + # push_eta_stage. self._gpu_v_efield_cuboid = ( - cunumpy.cupy_backend - and kernel.name == "push_v_with_efield" - and args_domain.kind_map == 10 - and all(b == "periodic" for b in self.particles.bc) - and not init_kernels - and not eval_kernels - and self.particles.mpi_comm is None - and maxiter == 1 - and not self._newton - and n_stages == 1 + cunumpy.cupy_backend and kernel.name == "push_v_with_efield" and args_domain.kind_map == 10 ) if self._gpu_v_efield_cuboid: import cupy as cp @@ -239,6 +232,24 @@ def __init__( self._gpu_v_efield_e1_2 = e1_2 self._gpu_v_efield_e1_3 = e1_3 + # whole-push GPU-resident fast path: on top of _gpu_v_efield_cuboid, + # additionally bypasses the per-call reset/apply_kinetic_bc/ + # update_holes machinery entirely (this kernel never touches position + # or holes/ghost columns, so that machinery is a no-op for it -- but + # only provably so under the same conditions as + # _gpu_eta_cuboid_periodic: no MPI, since mpi_sort_markers does real + # host-side communication we can't just skip). + self._gpu_v_efield_cuboid_wholepush = ( + self._gpu_v_efield_cuboid + and all(b == "periodic" for b in self.particles.bc) + and not init_kernels + and not eval_kernels + and self.particles.mpi_comm is None + and maxiter == 1 + and not self._newton + and n_stages == 1 + ) + @staticmethod def _reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim): """Device version of the per-step marker buffer bookkeeping at the top @@ -268,7 +279,7 @@ def __call__(self, dt: float): with ProfileManager.profile_region(self._region_name): if self._gpu_eta_cuboid_periodic: self._push_eta_cuboid_periodic_gpu(dt) - elif self._gpu_v_efield_cuboid: + elif self._gpu_v_efield_cuboid_wholepush: self._push_v_efield_cuboid_gpu(dt) else: self._push(dt) @@ -450,6 +461,22 @@ def _push(self, dt: float): dt * float(b[stage]), last, ) + elif self._gpu_v_efield_cuboid: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + push_v_with_efield_cuboid_gpu( + markers, + self.particles.n_cols, + self._gpu_v_efield_pn, + self._gpu_v_efield_tn1, + self._gpu_v_efield_tn2, + self._gpu_v_efield_tn3, + self._gpu_v_efield_starts, + self._gpu_v_efield_e1_1, + self._gpu_v_efield_e1_2, + self._gpu_v_efield_e1_3, + self._gpu_v_efield_scale, + dt * self._gpu_v_efield_const, + ) else: with ProfileManager.profile_region("kernel: " + self.kernel.name): self.kernel( From 3b64236203e3c1ae911ec8d68af291f4a40dec32 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 12:10:49 +0200 Subject: [PATCH 032/156] Fix MPI with cupy --- feectools | 2 +- src/struphy/feec/psydac_derham.py | 36 +++-- src/struphy/pic/base.py | 215 ++++++++++++++++++------------ src/struphy/pic/sorting.py | 5 +- 4 files changed, 158 insertions(+), 100 deletions(-) diff --git a/feectools b/feectools index 70e445f15..920695172 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 70e445f15c3fcd5e9d9981c51d9dce2e13394bff +Subproject commit 920695172104fa2ce225063ce9b9d1d73e14a839 diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index 658847587..7c1331188 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -1988,11 +1988,14 @@ def _get_index_array(self, decomposition): else: nproc = 1 - # send buffer - ind_arr_loc = xp.zeros(6, dtype=int) + # send/receive buffers for Allgather -- rank/topology bookkeeping + # (nproc * 6 ints), always host regardless of backend: mpi4py's + # uppercase buffer-protocol Allgather needs host-readable buffers, + # and there is no benefit to a device round trip for data this small. + ind_arr_loc = np.zeros(6, dtype=int) # main array (receive buffers) - ind_arr = xp.zeros(nproc * 6, dtype=int) + ind_arr = np.zeros(nproc * 6, dtype=int) # Get global starts and ends of cart OR domain decomposition gl_s = decomposition.starts @@ -2041,7 +2044,9 @@ def _get_neighbours(self): neighbours along the edges only have one 1, neighbours along the edges have no 1 in the index. """ - neighs = xp.empty((3, 3, 3), dtype=int) + # rank/topology bookkeeping (27 neighbour ranks), always host, see + # _get_index_array. + neighs = np.empty((3, 3, 3), dtype=int) for i in range(3): for j in range(3): @@ -2086,8 +2091,12 @@ def _get_neighbour_one_component(self, comp): if comp == [1, 1, 1]: return neigh_id - comp = xp.array(comp) - kinds = xp.array(kinds) + # rank/index bookkeeping, always host: neigh_inds below holds a mix + # of ints and None (compared against None further down), which is a + # NumPy object-dtype array and has no CuPy equivalent -- see + # _get_index_array. + comp = np.array(comp) + kinds = np.array(kinds) # if only one process: check if comp is neighbour in non-peridic directions, if this is not the case then return the rank as neighbour id if size == 1: @@ -2122,15 +2131,15 @@ def _get_neighbour_one_component(self, comp): "Wrong value for component; must be 0 or 1 or 2 !", ) - neigh_inds = xp.array(neigh_inds) + neigh_inds = np.array(neigh_inds) # only use indices where information is present to find the neighbours rank - inds = xp.where(xp.not_equal(neigh_inds, None)) + inds = np.where(np.not_equal(neigh_inds, None)) # find ranks (row index of domain_array) which agree in start/end indices - index_temp = xp.squeeze(self.index_array[:, inds]) - unique_ranks = xp.where( - xp.equal(index_temp, neigh_inds[inds]).all(1), + index_temp = np.squeeze(self.index_array[:, inds]) + unique_ranks = np.where( + np.equal(index_temp, neigh_inds[inds]).all(1), )[0] # if any row satisfies condition, return its index (=rank of neighbour) @@ -3617,7 +3626,10 @@ def get_pts_and_wts_quasi( n_quad = degree + 1 # Gauss - Legendre quadrature points and weights # products of basis functions are integrated exactly - pts_loc, wts_loc = xp.polynomial.legendre.leggauss(n_quad) + # cupy has no polynomial.legendre; this is tiny host-scale math, + # and quadrature_grid below converts to the active backend via + # xp.asarray, which (unlike the reverse direction) is always safe. + pts_loc, wts_loc = np.polynomial.legendre.leggauss(n_quad) x, wts = bsp.quadrature_grid(x_grid, pts_loc, wts_loc) pts = x % 1.0 diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index fd0453b8f..fef404501 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -434,10 +434,13 @@ def __init__( # if self.loading_params["moments"] is None and not isinstance(self, ParticlesSPH) and isinstance(self.bckgr_params, dict): self._generate_sampling_moments() - # create buffers for mpi_sort_markers - self._sorting_etas = xp.zeros((self.markers.shape[0], 3), dtype=float) - self._is_on_proc_domain = xp.zeros((self.markers.shape[0], 3), dtype=bool) - self._can_stay = xp.zeros(self.markers.shape[0], dtype=bool) + # create buffers for mpi_sort_markers -- marker-row-indexed, like + # markers itself always host-resident regardless of backend (see + # ISSUE_cupy_particles_never_pushed.md), and fed straight into mpi4py + # Alltoall/Isend/Irecv calls below, which need host buffers. + self._sorting_etas = np.zeros((self.markers.shape[0], 3), dtype=float) + self._is_on_proc_domain = np.zeros((self.markers.shape[0], 3), dtype=bool) + self._can_stay = np.zeros(self.markers.shape[0], dtype=bool) self._reqs = [None] * self.mpi_size self._recvbufs = [None] * self.mpi_size self._send_to_i = [None] * self.mpi_size @@ -1563,6 +1566,12 @@ def binning( The reconstructed delta-f distribution function. """ + # np.histogramdd below needs host bin edges, like the markers-derived + # sample/weights arrays it's called with (see ISSUE_cupy_particles_never_pushed.md); + # accept xp-typed input defensively so callers on the active backend + # don't need to know that. + bin_edges = tuple(_to_numpy_for_kernel(be) for be in bin_edges) + assert np.count_nonzero(components) == len(bin_edges) # volume of a bin @@ -2350,7 +2359,10 @@ def _get_domain_decomp(self, mpi_dims_mask: tuple | list = None): mm = (mm + 1) % 3 nprocs[mm] *= fac - assert xp.prod(nprocs) == self.mpi_size + # nprocs is a plain 3-element Python list (process counts, not + # physics data); np.prod handles it on both backends, unlike + # xp.prod which (on CuPy) requires an actual ndarray input. + assert np.prod(nprocs) == self.mpi_size # domain decomposition breaks = [np.linspace(0.0, 1.0, nproc + 1) for nproc in nprocs] @@ -2787,6 +2799,7 @@ def _load_tesselation(self, n_quad: int = 1): sorting_boxes=self.sorting_boxes, ) eta1, eta2, eta3 = self.tesselation.draw_markers() + eta1, eta2, eta3 = _to_numpy_for_kernel(eta1), _to_numpy_for_kernel(eta2), _to_numpy_for_kernel(eta3) self._markers[: eta1.size, 0] = eta1 self._markers[: eta2.size, 1] = eta2 self._markers[: eta3.size, 2] = eta3 @@ -3039,18 +3052,14 @@ def _check_and_assign_particles_to_boxes(self): """Check whether the box array has enough columns (detect load imbalance wrt to sorting boxes), and then assign the particles to boxes.""" - from cunumpy.xp import array_backend - - if array_backend.backend == "numpy": - bcount = xp.bincount(xp.int64(self.markers_wo_holes[:, -2])) - else: - import cupy as cp + # self.markers (and therefore markers_wo_holes) is always host-resident + # regardless of backend (see ISSUE_cupy_particles_never_pushed.md), so + # this is plain numpy unconditionally -- the previous backend branch + # predates that fix and fed a host array into cp.bincount, which + # (unlike xp.bincount on an actual CuPy array) does not accept one. + bcount = np.bincount(self.markers_wo_holes[:, -2].astype(np.int64)) - indices = self.markers_wo_holes[:, -2] - indices = indices.astype(cp.int64) - bcount = cp.bincount(indices) - - max_in_box = xp.max(bcount) + max_in_box = np.max(bcount) if max_in_box > self._sorting_boxes.boxes.shape[1]: warnings.warn( f'Strong load imbalance detected in sorting boxes: \ @@ -3527,7 +3536,8 @@ def _determine_markers_in_box(self, list_boxes): for i in list_boxes: indices += list(self._sorting_boxes._boxes[i][self._sorting_boxes._boxes[i] != -1]) - indices = xp.array(indices, dtype=int) + # row indices into self.markers, which is always host-resident + indices = np.array(indices, dtype=int) markers_in_box = self.markers[indices] return markers_in_box @@ -3537,156 +3547,164 @@ def _get_destinations_box(self): :meth:`_get_neighbouring_proc`), accumulating, per destination rank, the number of markers to send (:attr:`_send_info_box`) and the markers themselves (:attr:`_send_list_box`, used by :meth:`_self_communication_boxes` and - :meth:`_sendrecv_markers_boxes`).""" - self._send_info_box = xp.zeros(self.mpi_size, dtype=int) - self._send_list_box = [xp.zeros((0, self.n_cols))] * self.mpi_size + :meth:`_sendrecv_markers_boxes`). + + This method and the rest of the box-communication subsystem below are + host-resident throughout, like the analogous MPI marker-sort methods + (:meth:`_sendrecv_determine_mtbs` etc.): they operate on rows of + ``self.markers`` (always host, see ISSUE_cupy_particles_never_pushed.md) + and feed counts/buffers straight into mpi4py Alltoall/Isend/Irecv, + which need host-readable arguments regardless of backend. + """ + self._send_info_box = np.zeros(self.mpi_size, dtype=int) + self._send_list_box = [np.zeros((0, self.n_cols))] * self.mpi_size # Faces # if self._x_m_proc is not None: self._send_info_box[self._x_m_proc] += len(self._markers_x_m) - self._send_list_box[self._x_m_proc] = xp.concatenate((self._send_list_box[self._x_m_proc], self._markers_x_m)) + self._send_list_box[self._x_m_proc] = np.concatenate((self._send_list_box[self._x_m_proc], self._markers_x_m)) # if self._x_p_proc is not None: self._send_info_box[self._x_p_proc] += len(self._markers_x_p) - self._send_list_box[self._x_p_proc] = xp.concatenate((self._send_list_box[self._x_p_proc], self._markers_x_p)) + self._send_list_box[self._x_p_proc] = np.concatenate((self._send_list_box[self._x_p_proc], self._markers_x_p)) # if self._y_m_proc is not None: self._send_info_box[self._y_m_proc] += len(self._markers_y_m) - self._send_list_box[self._y_m_proc] = xp.concatenate((self._send_list_box[self._y_m_proc], self._markers_y_m)) + self._send_list_box[self._y_m_proc] = np.concatenate((self._send_list_box[self._y_m_proc], self._markers_y_m)) # if self._y_p_proc is not None: self._send_info_box[self._y_p_proc] += len(self._markers_y_p) - self._send_list_box[self._y_p_proc] = xp.concatenate((self._send_list_box[self._y_p_proc], self._markers_y_p)) + self._send_list_box[self._y_p_proc] = np.concatenate((self._send_list_box[self._y_p_proc], self._markers_y_p)) # if self._z_m_proc is not None: self._send_info_box[self._z_m_proc] += len(self._markers_z_m) - self._send_list_box[self._z_m_proc] = xp.concatenate((self._send_list_box[self._z_m_proc], self._markers_z_m)) + self._send_list_box[self._z_m_proc] = np.concatenate((self._send_list_box[self._z_m_proc], self._markers_z_m)) # if self._z_p_proc is not None: self._send_info_box[self._z_p_proc] += len(self._markers_z_p) - self._send_list_box[self._z_p_proc] = xp.concatenate((self._send_list_box[self._z_p_proc], self._markers_z_p)) + self._send_list_box[self._z_p_proc] = np.concatenate((self._send_list_box[self._z_p_proc], self._markers_z_p)) # x-y edges # if self._x_m_y_m_proc is not None: self._send_info_box[self._x_m_y_m_proc] += len(self._markers_x_m_y_m) - self._send_list_box[self._x_m_y_m_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_m_proc] = np.concatenate( (self._send_list_box[self._x_m_y_m_proc], self._markers_x_m_y_m), ) # if self._x_m_y_p_proc is not None: self._send_info_box[self._x_m_y_p_proc] += len(self._markers_x_m_y_p) - self._send_list_box[self._x_m_y_p_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_p_proc] = np.concatenate( (self._send_list_box[self._x_m_y_p_proc], self._markers_x_m_y_p), ) # if self._x_p_y_m_proc is not None: self._send_info_box[self._x_p_y_m_proc] += len(self._markers_x_p_y_m) - self._send_list_box[self._x_p_y_m_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_m_proc] = np.concatenate( (self._send_list_box[self._x_p_y_m_proc], self._markers_x_p_y_m), ) # if self._x_p_y_p_proc is not None: self._send_info_box[self._x_p_y_p_proc] += len(self._markers_x_p_y_p) - self._send_list_box[self._x_p_y_p_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_p_proc] = np.concatenate( (self._send_list_box[self._x_p_y_p_proc], self._markers_x_p_y_p), ) # x-z edges # if self._x_m_z_m_proc is not None: self._send_info_box[self._x_m_z_m_proc] += len(self._markers_x_m_z_m) - self._send_list_box[self._x_m_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_m_z_m_proc] = np.concatenate( (self._send_list_box[self._x_m_z_m_proc], self._markers_x_m_z_m), ) # if self._x_m_z_p_proc is not None: self._send_info_box[self._x_m_z_p_proc] += len(self._markers_x_m_z_p) - self._send_list_box[self._x_m_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_m_z_p_proc] = np.concatenate( (self._send_list_box[self._x_m_z_p_proc], self._markers_x_m_z_p), ) # if self._x_p_z_m_proc is not None: self._send_info_box[self._x_p_z_m_proc] += len(self._markers_x_p_z_m) - self._send_list_box[self._x_p_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_p_z_m_proc] = np.concatenate( (self._send_list_box[self._x_p_z_m_proc], self._markers_x_p_z_m), ) # if self._x_p_z_p_proc is not None: self._send_info_box[self._x_p_z_p_proc] += len(self._markers_x_p_z_p) - self._send_list_box[self._x_p_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_p_z_p_proc] = np.concatenate( (self._send_list_box[self._x_p_z_p_proc], self._markers_x_p_z_p), ) # y-z edges # if self._y_m_z_m_proc is not None: self._send_info_box[self._y_m_z_m_proc] += len(self._markers_y_m_z_m) - self._send_list_box[self._y_m_z_m_proc] = xp.concatenate( + self._send_list_box[self._y_m_z_m_proc] = np.concatenate( (self._send_list_box[self._y_m_z_m_proc], self._markers_y_m_z_m), ) # if self._y_m_z_p_proc is not None: self._send_info_box[self._y_m_z_p_proc] += len(self._markers_y_m_z_p) - self._send_list_box[self._y_m_z_p_proc] = xp.concatenate( + self._send_list_box[self._y_m_z_p_proc] = np.concatenate( (self._send_list_box[self._y_m_z_p_proc], self._markers_y_m_z_p), ) # if self._y_p_z_m_proc is not None: self._send_info_box[self._y_p_z_m_proc] += len(self._markers_y_p_z_m) - self._send_list_box[self._y_p_z_m_proc] = xp.concatenate( + self._send_list_box[self._y_p_z_m_proc] = np.concatenate( (self._send_list_box[self._y_p_z_m_proc], self._markers_y_p_z_m), ) # if self._y_p_z_p_proc is not None: self._send_info_box[self._y_p_z_p_proc] += len(self._markers_y_p_z_p) - self._send_list_box[self._y_p_z_p_proc] = xp.concatenate( + self._send_list_box[self._y_p_z_p_proc] = np.concatenate( (self._send_list_box[self._y_p_z_p_proc], self._markers_y_p_z_p), ) # corners # if self._x_m_y_m_z_m_proc is not None: self._send_info_box[self._x_m_y_m_z_m_proc] += len(self._markers_x_m_y_m_z_m) - self._send_list_box[self._x_m_y_m_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_m_z_m_proc] = np.concatenate( (self._send_list_box[self._x_m_y_m_z_m_proc], self._markers_x_m_y_m_z_m), ) # if self._x_m_y_m_z_p_proc is not None: self._send_info_box[self._x_m_y_m_z_p_proc] += len(self._markers_x_m_y_m_z_p) - self._send_list_box[self._x_m_y_m_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_m_z_p_proc] = np.concatenate( (self._send_list_box[self._x_m_y_m_z_p_proc], self._markers_x_m_y_m_z_p), ) # if self._x_m_y_p_z_m_proc is not None: self._send_info_box[self._x_m_y_p_z_m_proc] += len(self._markers_x_m_y_p_z_m) - self._send_list_box[self._x_m_y_p_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_p_z_m_proc] = np.concatenate( (self._send_list_box[self._x_m_y_p_z_m_proc], self._markers_x_m_y_p_z_m), ) # if self._x_m_y_p_z_p_proc is not None: self._send_info_box[self._x_m_y_p_z_p_proc] += len(self._markers_x_m_y_p_z_p) - self._send_list_box[self._x_m_y_p_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_m_y_p_z_p_proc] = np.concatenate( (self._send_list_box[self._x_m_y_p_z_p_proc], self._markers_x_m_y_p_z_p), ) # if self._x_p_y_m_z_m_proc is not None: self._send_info_box[self._x_p_y_m_z_m_proc] += len(self._markers_x_p_y_m_z_m) - self._send_list_box[self._x_p_y_m_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_m_z_m_proc] = np.concatenate( (self._send_list_box[self._x_p_y_m_z_m_proc], self._markers_x_p_y_m_z_m), ) # if self._x_p_y_m_z_p_proc is not None: self._send_info_box[self._x_p_y_m_z_p_proc] += len(self._markers_x_p_y_m_z_p) - self._send_list_box[self._x_p_y_m_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_m_z_p_proc] = np.concatenate( (self._send_list_box[self._x_p_y_m_z_p_proc], self._markers_x_p_y_m_z_p), ) # if self._x_p_y_p_z_m_proc is not None: self._send_info_box[self._x_p_y_p_z_m_proc] += len(self._markers_x_p_y_p_z_m) - self._send_list_box[self._x_p_y_p_z_m_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_p_z_m_proc] = np.concatenate( (self._send_list_box[self._x_p_y_p_z_m_proc], self._markers_x_p_y_p_z_m), ) # if self._x_p_y_p_z_p_proc is not None: self._send_info_box[self._x_p_y_p_z_p_proc] += len(self._markers_x_p_y_p_z_p) - self._send_list_box[self._x_p_y_p_z_p_proc] = xp.concatenate( + self._send_list_box[self._x_p_y_p_z_p_proc] = np.concatenate( (self._send_list_box[self._x_p_y_p_z_p_proc], self._markers_x_p_y_p_z_p), ) @@ -3696,7 +3714,7 @@ def _self_communication_boxes(self): if self._send_info_box[self.mpi_rank] > 0: self.update_holes() - holes_inds = xp.nonzero(self.holes)[0] + holes_inds = np.nonzero(self.holes)[0] if holes_inds.size < self._send_info_box[self.mpi_rank]: warnings.warn( @@ -3718,7 +3736,7 @@ def _self_communication_boxes(self): # self.update_holes() # self._update_ghost_particles() # self._update_valid_mks() - # holes_inds = xp.nonzero(self.holes)[0] + # holes_inds = np.nonzero(self.holes)[0] self.markers[holes_inds[np.arange(self._send_info_box[self.mpi_rank])]] = self._send_list_box[self.mpi_rank] @@ -3728,7 +3746,7 @@ def _sendrecv_all_to_all_boxes(self): for the communication of particles in boundary boxes. """ - self._recv_info_box = xp.zeros(self.mpi_comm.Get_size(), dtype=int) + self._recv_info_box = np.zeros(self.mpi_comm.Get_size(), dtype=int) self.mpi_comm.Alltoall(self._send_info_box, self._recv_info_box) @@ -3740,7 +3758,7 @@ def _sendrecv_markers_boxes(self): # i-th entry holds the number (not the index) of the first hole to be filled by data from process i first_hole = np.cumsum(self._recv_info_box) - self._recv_info_box - hole_inds = xp.nonzero(self._holes)[0] + hole_inds = np.nonzero(self._holes)[0] # Initialize send and receive commands reqs = [] recvbufs = [] @@ -3751,7 +3769,7 @@ def _sendrecv_markers_boxes(self): else: self.mpi_comm.Isend(data, dest=i, tag=self.mpi_comm.Get_rank()) - recvbufs += [xp.zeros((N_recv, self._markers.shape[1]), dtype=float)] + recvbufs += [np.zeros((N_recv, self._markers.shape[1]), dtype=float)] reqs += [self.mpi_comm.Irecv(recvbufs[-1], source=i, tag=i)] # Wait for buffer, then put markers into holes @@ -4384,12 +4402,15 @@ def _sendrecv_determine_mtbs( Eta-values of shape (n_send, :) according to which the sorting is performed. """ # position that determines the sorting (including periodic shift of boundary conditions) - if not isinstance(alpha, xp.ndarray): - alpha = xp.array(alpha, dtype=float) + # host throughout: self.markers is always host-resident (see + # ISSUE_cupy_particles_never_pushed.md), and alpha is a 3-element + # weighting, not physics data. + if not isinstance(alpha, np.ndarray): + alpha = np.array(alpha, dtype=float) assert alpha.size == 3 - assert xp.all(alpha >= 0.0) and xp.all(alpha <= 1.0) + assert np.all(alpha >= 0.0) and np.all(alpha <= 1.0) bi = self.first_pusher_idx - xp.mod( + np.mod( alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + (1.0 - alpha) * self.markers[:, bi : bi + 3], 1.0, @@ -4397,22 +4418,22 @@ def _sendrecv_determine_mtbs( ) # check which particles are on the current process domain - self._is_on_proc_domain = xp.logical_and( + self._is_on_proc_domain = np.logical_and( self._sorting_etas > self.domain_array[self.mpi_rank, 0::3], self._sorting_etas < self.domain_array[self.mpi_rank, 1::3], ) # to stay on the current process, all three columns must be True - self._can_stay = xp.all(self._is_on_proc_domain, axis=1) + self._can_stay = np.all(self._is_on_proc_domain, axis=1) # holes and ghosts can stay, too self._can_stay[self.holes] = True self._can_stay[self.ghost_particles] = True # True values can stay on the process, False must be sent, already empty rows (-1) cannot be sent - send_inds = xp.nonzero(~self._can_stay)[0] + send_inds = np.nonzero(~self._can_stay)[0] - hole_inds_after_send = xp.nonzero(xp.logical_or(~self._can_stay, self.holes))[0] + hole_inds_after_send = np.nonzero(np.logical_or(~self._can_stay, self.holes))[0] return hole_inds_after_send, send_inds @@ -4431,16 +4452,16 @@ def _sendrecv_get_destinations(self, send_inds): """ # One entry for each process - send_info = xp.zeros(self.mpi_size, dtype=int) + send_info = np.zeros(self.mpi_size, dtype=int) # TODO: do not loop over all processes, start with neighbours and work outwards (using while) for i in range(self.mpi_size): - conds = xp.logical_and( + conds = np.logical_and( self._sorting_etas[send_inds] > self.domain_array[i, 0::3], self._sorting_etas[send_inds] < self.domain_array[i, 1::3], ) - self._send_to_i[i] = xp.nonzero(xp.all(conds, axis=1))[0] + self._send_to_i[i] = np.nonzero(np.all(conds, axis=1))[0] send_info[i] = self._send_to_i[i].size self._send_list[i] = self.markers[send_inds][self._send_to_i[i]] @@ -4462,7 +4483,7 @@ def _sendrecv_all_to_all(self, send_info): Amount of marticles to be received from i-th process. """ - recv_info = xp.zeros(self.mpi_size, dtype=int) + recv_info = np.zeros(self.mpi_size, dtype=int) self.mpi_comm.Alltoall(send_info, recv_info) @@ -4492,7 +4513,7 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): else: self.mpi_comm.Isend(data, dest=i, tag=self.mpi_rank) - self._recvbufs[i] = xp.zeros((N_recv, self.markers.shape[1]), dtype=float) + self._recvbufs[i] = np.zeros((N_recv, self.markers.shape[1]), dtype=float) self._reqs[i] = self.mpi_comm.Irecv(self._recvbufs[i], source=i, tag=i) # Wait for buffer, then put markers into holes @@ -4576,9 +4597,11 @@ def __init__( self._rank = comm.Get_rank() assert domain_array is not None + # tile/box geometry bookkeeping (small, ndim-sized), always host -- + # domain_array (when given) is already host, see _get_domain_decomp. if domain_array is None: - self._starts = xp.zeros(3) - self._ends = xp.ones(3) + self._starts = np.zeros(3) + self._ends = np.ones(3) else: self._starts = domain_array[self.rank, 0::3] self._ends = domain_array[self.rank, 1::3] @@ -4601,9 +4624,9 @@ def __init__( if n_boxes == 1: self._dims_mask = [True] * 3 else: - self._dims_mask = xp.array(self.boxes_per_dim) > 1 + self._dims_mask = np.array(self.boxes_per_dim) > 1 - min_tiles = 2 ** xp.count_nonzero(self.dims_mask) + min_tiles = 2 ** np.count_nonzero(self.dims_mask) assert self.tiles_pb >= min_tiles, ( f"At least {min_tiles} tiles per sorting box is enforced, but you have {self.tiles_pb}!" ) @@ -4631,20 +4654,25 @@ def get_tiles(self): # logger.info(f'{factors_vec = }') # logger.info(f'{self.dims_mask = }') - # tiles in one sorting box - self._nt_per_dim = xp.array([1, 1, 1]) - _ids = xp.nonzero(self._dims_mask)[0] + # tiles in one sorting box -- geometry bookkeeping, always host (see + # note in __init__); nt below must be a plain int for xp.linspace's + # num= argument. + self._nt_per_dim = np.array([1, 1, 1]) + _ids = np.nonzero(self._dims_mask)[0] for fac in factors_vec: _nt = self.nt_per_dim[self._dims_mask] - d = _ids[xp.argmin(_nt)] + d = _ids[np.argmin(_nt)] self._nt_per_dim[d] *= fac # logger.info(f'{_nt = }, {d = }, {self.nt_per_dim = }') - assert xp.prod(self.nt_per_dim) == self.tiles_pb + assert np.prod(self.nt_per_dim) == self.tiles_pb - # tiles between [0, box_width] in each direction - self._tile_breaks = [xp.linspace(0.0, bw, nt + 1) for bw, nt in zip(self.box_widths, self.nt_per_dim)] - self._tile_midpoints = [(xp.roll(tbs, -1)[:-1] + tbs[:-1]) / 2 for tbs in self.tile_breaks] + # tiles between [0, box_width] in each direction -- geometry, always + # host (like the rest of this method); the only legitimate device + # computation in this class is fun() in cell_averages, which already + # crosses via _dev()/_to_numpy_for_kernel at that one boundary. + self._tile_breaks = [np.linspace(0.0, bw, int(nt) + 1) for bw, nt in zip(self.box_widths, self.nt_per_dim)] + self._tile_midpoints = [(np.roll(tbs, -1)[:-1] + tbs[:-1]) / 2 for tbs in self.tile_breaks] self._tile_volume = 1.0 for tb in self.tile_breaks: self._tile_volume *= tb[1] @@ -4659,8 +4687,8 @@ def draw_markers(self): 1d arrays of logical-space marker coordinates, one entry per tile (length :attr:`n_tiles`).""" _, eta1 = self._tile_output_arrays() - eta2 = xp.zeros_like(eta1) - eta3 = xp.zeros_like(eta1) + eta2 = np.zeros_like(eta1) + eta3 = np.zeros_like(eta1) nt_x, nt_y, nt_z = self.nt_per_dim @@ -4671,7 +4699,7 @@ def draw_markers(self): for k in range(self.boxes_per_dim[2]): z_midpoints = self._get_midpoints(k, 2) - xx, yy, zz = xp.meshgrid( + xx, yy, zz = np.meshgrid( x_midpoints, y_midpoints, z_midpoints, @@ -4710,10 +4738,17 @@ def _get_quad_pts(self, n_quad=None): self._tile_quad_pts = [] self._tile_quad_wts = [] for nq, tb in zip(n_quad, self.tile_breaks): - pts_loc, wts_loc = xp.polynomial.legendre.leggauss(nq) + # cupy has no polynomial.legendre; this is tiny host-scale math + # (n_quad eigenvalues), and quadrature_grid below converts the + # result to the active backend via xp.asarray, which (unlike + # the reverse direction) is always safe. + pts_loc, wts_loc = np.polynomial.legendre.leggauss(nq) + # quadrature_grid always converts its output to the active + # backend (xp.asarray internally); convert straight back so the + # rest of this class stays host, like tile_breaks above. pts, wts = quadrature_grid(tb[:2], pts_loc, wts_loc) - self._tile_quad_pts += [pts[0]] - self._tile_quad_wts += [wts[0]] + self._tile_quad_pts += [_to_numpy_for_kernel(pts[0])] + self._tile_quad_wts += [_to_numpy_for_kernel(wts[0])] def cell_averages(self, fun, n_quad=None): """Compute the cell average of ``fun`` over every tile on the current process, @@ -4750,7 +4785,12 @@ def cell_averages(self, fun, n_quad=None): for k in range(self.boxes_per_dim[2]): z_pts = self._get_box_quad_pts(k, 2) - xx, yy, zz = xp.meshgrid( + # meshgrid stays host (x_pts/y_pts/z_pts are host, see + # _get_box_quad_pts); fun() is evaluated on the active + # backend via _dev(), the one legitimate device boundary + # in this class, and converted straight back for + # tile_int_kernel (a compiled, host-only Pyccel kernel). + xx, yy, zz = np.meshgrid( x_pts.flatten(), y_pts.flatten(), z_pts.flatten(), @@ -4784,8 +4824,11 @@ def _tile_output_arrays(self): on the current process (i.e. the first array tiled over all sorting boxes). """ # self._quad_pts = [xp.zeros((nt, nq)).flatten() for nt, nq in zip(self.nt_per_dim, self.tile_quad_pts)] - single_box_out = xp.zeros(self.nt_per_dim) - out = xp.tile(single_box_out, self.boxes_per_dim) + # host, like the rest of this class (see get_tiles); feeds + # tile_int_kernel (cell_averages) and self._markers (draw_markers), + # both host-only. + single_box_out = np.zeros(self.nt_per_dim) + out = np.tile(single_box_out, self.boxes_per_dim) return single_box_out, out def _get_midpoints(self, i: int, dim: int): @@ -4828,7 +4871,7 @@ def _get_box_quad_pts(self, i: int, dim: int): xl = self.starts[dim] + i * self.box_widths[dim] x_tile_breaks = xl + self.tile_breaks[dim][:-1] x_tile_pts = self.tile_quad_pts[dim] - x_pts = xp.tile(x_tile_breaks, (x_tile_pts.size, 1)).T + x_tile_pts + x_pts = np.tile(x_tile_breaks, (x_tile_pts.size, 1)).T + x_tile_pts return x_pts @property diff --git a/src/struphy/pic/sorting.py b/src/struphy/pic/sorting.py index 4728d94db..f2a0b0a7d 100644 --- a/src/struphy/pic/sorting.py +++ b/src/struphy/pic/sorting.py @@ -231,8 +231,11 @@ def _set_boxes(self): n_particles = self._markers_shape[0] n_mkr = int(n_particles / n_box_in) + 1 + # scalar box-sizing estimate, not physics data; the rest of this box + # structure is host-resident (see below), and round() doesn't accept + # a CuPy 0-d array, so this must stay plain math regardless of backend. n_cols = round( - n_mkr * (1 + 1 / xp.sqrt(n_mkr) + self._box_bufsize), + n_mkr * (1 + 1 / np.sqrt(n_mkr) + self._box_bufsize), ) # cartesian boxes (extra last row stores holes/outside particles); host-resident, From 41022ecd120f4fef8d1d2e42ffb4e99de68c632a Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 15:43:19 +0200 Subject: [PATCH 033/156] Added cuda version of eval kernels! --- src/struphy/pic/base.py | 38 ++ src/struphy/pic/sph_eval_kernels_cuda.py | 526 ++++++++++++++++++ .../pic/tests/_bench_cuda_kernels_worker.py | 195 +++++++ src/struphy/pic/tests/bench_cuda_kernels.py | 74 +++ 4 files changed, 833 insertions(+) create mode 100644 src/struphy/pic/sph_eval_kernels_cuda.py create mode 100644 src/struphy/pic/tests/_bench_cuda_kernels_worker.py create mode 100644 src/struphy/pic/tests/bench_cuda_kernels.py diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index fef404501..791c241f6 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -64,6 +64,10 @@ class Intracomm: naive_evaluation_flat, naive_evaluation_meshgrid, ) +from struphy.pic.sph_eval_kernels_cuda import ( + box_based_evaluation_flat_gpu, + box_based_evaluation_meshgrid_gpu, +) from struphy.utils import utils from struphy.utils.clone_config import CloneConfig @@ -4320,6 +4324,40 @@ def _eval_sph( self.put_particles_in_boxes() if fast: + if xp.cupy_backend and len(_shp) in (1, 3): + # CUDA replacement for box_based_evaluation_flat/_meshgrid: + # one thread per evaluation point, see sph_eval_kernels_cuda. + if len(_shp) == 3: + if _shp[0] > 1: + assert eta1[0, 0, 0] != eta1[1, 0, 0], "Meshgrids must be obtained with indexing='ij'!" + if _shp[1] > 1: + assert eta2[0, 0, 0] != eta2[0, 1, 0], "Meshgrids must be obtained with indexing='ij'!" + if _shp[2] > 1: + assert eta3[0, 0, 0] != eta3[0, 0, 1], "Meshgrids must be obtained with indexing='ij'!" + gpu_func = box_based_evaluation_flat_gpu if len(_shp) == 1 else box_based_evaluation_meshgrid_gpu + gpu_func( + self.markers, + eta1, + eta2, + eta3, + self.sorting_boxes.nx, + self.sorting_boxes.ny, + self.sorting_boxes.nz, + self.domain_array[self.mpi_rank], + self.sorting_boxes.boxes, + self.sorting_boxes.neighbours, + self.holes, + periodic1, + periodic2, + periodic3, + index, + ker_id, + h1, + h2, + h3, + out, + ) + return out if len(_shp) == 1: func = PyccelKernel(box_based_evaluation_flat) elif len(_shp) == 3: diff --git a/src/struphy/pic/sph_eval_kernels_cuda.py b/src/struphy/pic/sph_eval_kernels_cuda.py new file mode 100644 index 000000000..fd1be6ae2 --- /dev/null +++ b/src/struphy/pic/sph_eval_kernels_cuda.py @@ -0,0 +1,526 @@ +"""Hand-written CUDA replacement for the box-based SPH kernel-density evaluation +in :mod:`~struphy.pic.sph_eval_kernels`, used only under ``ARRAY_BACKEND=cupy``. + +:func:`~struphy.pic.sph_eval_kernels.box_based_evaluation_flat` (called from +:meth:`~struphy.pic.base.Particles._eval_sph`, in turn used by +:meth:`~struphy.pic.base.Particles.eval_density` and +:meth:`~struphy.pic.base.Particles.eval_velocity`) is the actual SPH +kernel-density-estimation sum -- the defining operation of "smoothed particle +hydrodynamics": reconstruct a continuous field at a set of evaluation points +by summing a smoothing kernel over every marker in the 27 sorting boxes +neighbouring each point. It is embarrassingly parallel across evaluation +points (unlike the pusher kernels, there is no per-marker output to race on), +which makes it a clean fit for one CUDA thread per evaluation point. + +:func:`box_based_evaluation_flat_gpu` ports :func:`~struphy.pic.sorting_kernels.find_box`, +the 27-neighbour box loop of :func:`~struphy.pic.sph_eval_kernels.box_based_kernel`, +and every smoothing kernel in :mod:`~struphy.pic.sph_smoothing_kernels` (all of +them: they are cheap closed-form tensor products of one-dimensional +trigonometric/Gaussian/linear kernels, or -- for ``linear_isotropic_3d`` -- a +simple radial one, so there is no reason to port only the default kernel +type). ``markers``/``boxes``/``neighbours``/``holes`` are the same +host-resident arrays used everywhere else in this backend (see +``ISSUE_cupy_particles_never_pushed.md``); this function round-trips them +through the device once per call, matching :func:`push_v_with_efield_cuboid_gpu` +in :mod:`~struphy.pic.pushing.pusher_kernels_cuda` -- ``_eval_sph`` is a +diagnostics/reconstruction entry point, not a per-step hot loop, so there is +no benefit to caching device buffers across calls the way the pushers do. +""" + +_SPH_EVAL_FLAT_SRC = r""" +#define PI 3.14159265358979323846 + +__device__ double distance_dev(double x, double y, bool periodic) +{ + double d = x - y; + if (periodic) { + while (d > 0.5) d -= 1.0; + while (d < -0.5) d += 1.0; + } + return d; +} + +// --- uni-variate kernels (struphy.pic.sph_smoothing_kernels) --- + +__device__ double trigonometric_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return 0.785398163397448 / h * cos(x / h * PI / 2.0); + return 0.0; +} + +__device__ double grad_trigonometric_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return -(1.2337005501361697 / (h * h)) * sin(x / h * PI / 2.0); + return 0.0; +} + +__device__ double gaussian_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return 1.0 / (sqrt(PI) * h / 3.0) * exp(-(x * x) / ((h / 3.0) * (h / 3.0))); + return 0.0; +} + +__device__ double grad_gaussian_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return -54.0 * x / (h * h * h * sqrt(PI)) * exp(-(x * x) / ((h / 3.0) * (h / 3.0))); + return 0.0; +} + +__device__ double linear_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return (1.0 - fabs(x / h)) / h; + return 0.0; +} + +__device__ double grad_linear_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return (x > 0.0) ? -(1.0 / (h * h)) : (1.0 / (h * h)); + return 0.0; +} + +// --- kernel_type dispatch (struphy.pic.sph_smoothing_kernels.smoothing_kernel) --- + +__device__ double smoothing_kernel_dev( + int kernel_type, + double r1, double r2, double r3, + double h1, double h2, double h3) +{ + switch (kernel_type) { + // 1d + case 100: return trigonometric_uni(r1, h1); + case 101: return grad_trigonometric_uni(r1, h1); + case 110: return gaussian_uni(r1, h1); + case 111: return grad_gaussian_uni(r1, h1); + case 120: return linear_uni(r1, h1); + case 121: return grad_linear_uni(r1, h1); + + // 2d (tensor products) + case 340: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2); + case 341: return grad_trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2); + case 342: return trigonometric_uni(r1, h1) * grad_trigonometric_uni(r2, h2); + case 350: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2); + case 351: return grad_gaussian_uni(r1, h1) * gaussian_uni(r2, h2); + case 352: return gaussian_uni(r1, h1) * grad_gaussian_uni(r2, h2); + case 360: return linear_uni(r1, h1) * linear_uni(r2, h2); + case 361: return grad_linear_uni(r1, h1) * linear_uni(r2, h2); + case 362: return linear_uni(r1, h1) * grad_linear_uni(r2, h2); + + // 3d (tensor products) + case 670: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); + case 671: return grad_trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); + case 672: return trigonometric_uni(r1, h1) * grad_trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); + case 673: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * grad_trigonometric_uni(r3, h3); + case 680: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * gaussian_uni(r3, h3); + case 681: return grad_gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * gaussian_uni(r3, h3); + case 682: return gaussian_uni(r1, h1) * grad_gaussian_uni(r2, h2) * gaussian_uni(r3, h3); + case 683: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * grad_gaussian_uni(r3, h3); + case 700: return linear_uni(r1, h1) * linear_uni(r2, h2) * linear_uni(r3, h3); + case 701: return grad_linear_uni(r1, h1) * linear_uni(r2, h2) * linear_uni(r3, h3); + case 702: return linear_uni(r1, h1) * grad_linear_uni(r2, h2) * linear_uni(r3, h3); + case 703: return linear_uni(r1, h1) * linear_uni(r2, h2) * grad_linear_uni(r3, h3); + + // 3d, radially symmetric (linear_isotropic_3d and its gradient) + case 690: { + double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); + double h = h1; + if (r / h > 1.0) return 0.0; + return (1.0 - r / h) / (1.0471975512 * h * h * h); + } + case 691: { + double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); + double h = h1; + if (r / h > 1.0) return 0.0; + if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); + return -r1 / (r * h) / (1.0471975512 * h * h * h); + } + case 692: { + double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); + double h = h1; + if (r / h > 1.0) return 0.0; + if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); + return -r2 / (r * h) / (1.0471975512 * h * h * h); + } + case 693: { + double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); + double h = h1; + if (r / h > 1.0) return 0.0; + if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); + return -r3 / (r * h) / (1.0471975512 * h * h * h); + } + } + return 0.0; +} + +// --- box lookup (struphy.pic.sorting_kernels.find_box / flatten_index) --- + +__device__ int find_box_dev( + double eta1, double eta2, double eta3, + int nx, int ny, int nz, + const double* domain_array) +{ + if (eta1 == domain_array[0]) eta1 += 1e-8; + if (eta2 == domain_array[3]) eta2 += 1e-8; + if (eta3 == domain_array[6]) eta3 += 1e-8; + if (eta1 == domain_array[1]) eta1 -= 1e-8; + if (eta2 == domain_array[4]) eta2 -= 1e-8; + if (eta3 == domain_array[7]) eta3 -= 1e-8; + + double x_l = domain_array[0] - (domain_array[1] - domain_array[0]) / nx; + double x_r = domain_array[1] + (domain_array[1] - domain_array[0]) / nx; + double y_l = domain_array[3] - (domain_array[4] - domain_array[3]) / ny; + double y_r = domain_array[4] + (domain_array[4] - domain_array[3]) / ny; + double z_l = domain_array[6] - (domain_array[7] - domain_array[6]) / nz; + double z_r = domain_array[7] + (domain_array[7] - domain_array[6]) / nz; + + if (eta1 < x_l || eta1 > x_r || eta2 < y_l || eta2 > y_r || eta3 < z_l || eta3 > z_r) + return -1; + + int n1 = (int)floor((eta1 - x_l) / (x_r - x_l) * (nx + 2)); + int n2 = (int)floor((eta2 - y_l) / (y_r - y_l) * (ny + 2)); + int n3 = (int)floor((eta3 - z_l) / (z_r - z_l) * (nz + 2)); + + // flatten_index, fortran_ordering (the struphy default) + return n1 + n2 * (nx + 2) + n3 * (nx + 2) * (ny + 2); +} + +// --- entry point (struphy.pic.sph_eval_kernels.box_based_evaluation_flat) --- + +extern "C" __global__ +void box_based_evaluation_flat_cuda( + const double* markers, + const int n_cols, + const double* eta1, + const double* eta2, + const double* eta3, + const int n_eval, + const int nx, + const int ny, + const int nz, + const double* domain_array, + const int* boxes, + const int n_box_cols, + const int* neighbours, + const int* holes, + const int periodic1, + const int periodic2, + const int periodic3, + const int index, + const int kernel_type, + const double h1, + const double h2, + const double h3, + double* out) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n_eval) return; + + double e1 = eta1[i], e2 = eta2[i], e3 = eta3[i]; + + int loc_box = find_box_dev(e1, e2, e3, nx, ny, nz, domain_array); + if (loc_box == -1) { + out[i] = 0.0; + return; + } + + double acc = 0.0; + for (int neigh = 0; neigh < 27; neigh++) { + int box_to_search = neighbours[loc_box * 27 + neigh]; + int c = 0; + while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { + int p = boxes[(size_t)box_to_search * n_box_cols + c]; + c++; + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + } + out[i] = acc; +} + +// --- entry point (struphy.pic.sph_eval_kernels.box_based_evaluation_meshgrid) --- +// +// eta1/eta2/eta3 are the 3 distinct 1-D axis vectors of the meshgrid (the +// Pyccel kernel this ports only ever reads eta1[i,0,0]/eta2[0,j,0]/eta3[0,0,k], +// never the broadcast values, so the Python wrapper passes just the axes -- +// no reason to transfer the O(n1*n2*n3) redundant meshgrid). One CUDA thread +// per (i, j, k) evaluation point, flattened to match out's C-order layout. + +extern "C" __global__ +void box_based_evaluation_meshgrid_cuda( + const double* markers, + const int n_cols, + const double* eta1, + const double* eta2, + const double* eta3, + const int n1_eval, + const int n2_eval, + const int n3_eval, + const int nx, + const int ny, + const int nz, + const double* domain_array, + const int* boxes, + const int n_box_cols, + const int* neighbours, + const int* holes, + const int periodic1, + const int periodic2, + const int periodic3, + const int index, + const int kernel_type, + const double h1, + const double h2, + const double h3, + double* out) +{ + size_t idx = (size_t)blockIdx.x * blockDim.x + threadIdx.x; + size_t n_total = (size_t)n1_eval * n2_eval * n3_eval; + if (idx >= n_total) return; + + int i = idx / ((size_t)n2_eval * n3_eval); + int rem = idx % ((size_t)n2_eval * n3_eval); + int j = rem / n3_eval; + int k = rem % n3_eval; + + out[idx] = 0.0; + + double e1 = eta1[i]; + if (e1 < domain_array[0] || (e1 >= domain_array[1] && e1 != 1.0)) return; + + double e2 = eta2[j]; + if (e2 < domain_array[3] || (e2 >= domain_array[4] && e2 != 1.0)) return; + + double e3 = eta3[k]; + if (e3 < domain_array[6] || (e3 >= domain_array[7] && e3 != 1.0)) return; + + int loc_box = find_box_dev(e1, e2, e3, nx, ny, nz, domain_array); + if (loc_box == -1) return; + + double acc = 0.0; + for (int neigh = 0; neigh < 27; neigh++) { + int box_to_search = neighbours[loc_box * 27 + neigh]; + int c = 0; + while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { + int p = boxes[(size_t)box_to_search * n_box_cols + c]; + c++; + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + } + out[idx] = acc; +} +""" + +_box_based_evaluation_flat_kernel = None +_box_based_evaluation_meshgrid_kernel = None + + +def _get_kernel(): + global _box_based_evaluation_flat_kernel + if _box_based_evaluation_flat_kernel is None: + import cupy as cp + + _box_based_evaluation_flat_kernel = cp.RawKernel(_SPH_EVAL_FLAT_SRC, "box_based_evaluation_flat_cuda") + return _box_based_evaluation_flat_kernel + + +def _get_meshgrid_kernel(): + global _box_based_evaluation_meshgrid_kernel + if _box_based_evaluation_meshgrid_kernel is None: + import cupy as cp + + _box_based_evaluation_meshgrid_kernel = cp.RawKernel(_SPH_EVAL_FLAT_SRC, "box_based_evaluation_meshgrid_cuda") + return _box_based_evaluation_meshgrid_kernel + + +def box_based_evaluation_flat_gpu( + markers, + eta1, + eta2, + eta3, + nx: int, + ny: int, + nz: int, + domain_array, + boxes, + neighbours, + holes, + periodic1: bool, + periodic2: bool, + periodic3: bool, + index: int, + kernel_type: int, + h1: float, + h2: float, + h3: float, + out, +): + """GPU replacement for one call of + :func:`~struphy.pic.sph_eval_kernels.box_based_evaluation_flat`. + + All inputs are host arrays (``markers``, ``domain_array``, ``boxes``, + ``neighbours``, ``holes``, matching the rest of the CuPy backend) except + ``eta1``/``eta2``/``eta3``/``out``, which may already be device-resident + (the caller passes whatever backend it's using for evaluation points). + Everything is round-tripped through the device once for this call. + """ + import cupy as cp + import numpy as np + + kernel = _get_kernel() + n_cols = markers.shape[1] + n_eval = eta1.shape[0] + n_box_cols = boxes.shape[1] + + dev_markers = cp.asarray(markers) + # ascontiguousarray, not asarray: eta1/eta2/eta3 may be arbitrary (e.g. + # strided/sliced) views, and the kernel indexes them as dense 1-D buffers + # -- asarray is a no-op on an already-CuPy, already-float64 view and + # would silently pass the RawKernel a pointer with the wrong stride. + dev_eta1 = cp.ascontiguousarray(eta1, dtype=cp.float64) + dev_eta2 = cp.ascontiguousarray(eta2, dtype=cp.float64) + dev_eta3 = cp.ascontiguousarray(eta3, dtype=cp.float64) + dev_domain = cp.asarray(domain_array, dtype=cp.float64) + dev_boxes = cp.asarray(boxes, dtype=cp.int32) + dev_neighbours = cp.asarray(neighbours, dtype=cp.int32) + dev_holes = cp.asarray(holes, dtype=cp.int32) + dev_out = cp.zeros(n_eval, dtype=cp.float64) + + threads = 256 + blocks = (n_eval + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(n_cols), + dev_eta1, + dev_eta2, + dev_eta3, + np.int32(n_eval), + np.int32(nx), + np.int32(ny), + np.int32(nz), + dev_domain, + dev_boxes, + np.int32(n_box_cols), + dev_neighbours, + dev_holes, + np.int32(1 if periodic1 else 0), + np.int32(1 if periodic2 else 0), + np.int32(1 if periodic3 else 0), + np.int32(index), + np.int32(kernel_type), + np.float64(h1), + np.float64(h2), + np.float64(h3), + dev_out, + ), + ) + if isinstance(out, cp.ndarray): + out[:] = dev_out + else: + dev_out.get(out=out) + + +def box_based_evaluation_meshgrid_gpu( + markers, + eta1, + eta2, + eta3, + nx: int, + ny: int, + nz: int, + domain_array, + boxes, + neighbours, + holes, + periodic1: bool, + periodic2: bool, + periodic3: bool, + index: int, + kernel_type: int, + h1: float, + h2: float, + h3: float, + out, +): + """GPU replacement for one call of + :func:`~struphy.pic.sph_eval_kernels.box_based_evaluation_meshgrid`. + + ``eta1``, ``eta2``, ``eta3`` are the full 3-D meshgrid arrays (as produced + by ``xp.meshgrid(..., indexing="ij")``); only their distinct 1-D axis + vectors are transferred to the device, see the CUDA source. Otherwise + behaves like :func:`box_based_evaluation_flat_gpu`. + """ + import cupy as cp + import numpy as np + + kernel = _get_meshgrid_kernel() + n_cols = markers.shape[1] + n1_eval, n2_eval, n3_eval = eta1.shape[0], eta2.shape[1], eta3.shape[2] + n_box_cols = boxes.shape[1] + + dev_markers = cp.asarray(markers) + # ascontiguousarray, not asarray: eta1[:,0,0] etc. are strided views into + # the full meshgrid (stride = the *other* axes' extents, not 1 element), + # and the kernel indexes them as dense 1-D buffers -- asarray is a no-op + # on an already-CuPy view and would silently pass the RawKernel a pointer + # with the wrong stride (this was a real bug: mismatched against the + # already-validated flat kernel on identical points until fixed). + dev_eta1 = cp.ascontiguousarray(eta1[:, 0, 0], dtype=cp.float64) + dev_eta2 = cp.ascontiguousarray(eta2[0, :, 0], dtype=cp.float64) + dev_eta3 = cp.ascontiguousarray(eta3[0, 0, :], dtype=cp.float64) + dev_domain = cp.asarray(domain_array, dtype=cp.float64) + dev_boxes = cp.asarray(boxes, dtype=cp.int32) + dev_neighbours = cp.asarray(neighbours, dtype=cp.int32) + dev_holes = cp.asarray(holes, dtype=cp.int32) + dev_out = cp.zeros((n1_eval, n2_eval, n3_eval), dtype=cp.float64) + + n_total = n1_eval * n2_eval * n3_eval + threads = 256 + blocks = (n_total + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(n_cols), + dev_eta1, + dev_eta2, + dev_eta3, + np.int32(n1_eval), + np.int32(n2_eval), + np.int32(n3_eval), + np.int32(nx), + np.int32(ny), + np.int32(nz), + dev_domain, + dev_boxes, + np.int32(n_box_cols), + dev_neighbours, + dev_holes, + np.int32(1 if periodic1 else 0), + np.int32(1 if periodic2 else 0), + np.int32(1 if periodic3 else 0), + np.int32(index), + np.int32(kernel_type), + np.float64(h1), + np.float64(h2), + np.float64(h3), + dev_out, + ), + ) + if isinstance(out, cp.ndarray): + out[:] = dev_out + else: + dev_out.get(out=out) diff --git a/src/struphy/pic/tests/_bench_cuda_kernels_worker.py b/src/struphy/pic/tests/_bench_cuda_kernels_worker.py new file mode 100644 index 000000000..608eb5b67 --- /dev/null +++ b/src/struphy/pic/tests/_bench_cuda_kernels_worker.py @@ -0,0 +1,195 @@ +""" +Worker script for :mod:`bench_cuda_kernels`. + +Times one of the CUDA-RawKernel-ported operations (see +:mod:`struphy.pic.pushing.pusher_kernels_cuda` and +:mod:`struphy.pic.sph_eval_kernels_cuda`) in isolation, on a single marker +set, and prints the median wall time per call (in seconds) as the last line +on stdout. Runs in a fresh subprocess per (backend, op, Np) combination +because ``ARRAY_BACKEND`` is read once at import time (by ``cunumpy``) and +cannot be changed within a running process. + +Usage:: + + ARRAY_BACKEND= python _bench_cuda_kernels_worker.py + +``op`` is one of: push_eta, push_v, eval_density_flat, eval_density_mesh +""" + +import statistics +import sys +import time + +N_WARMUP = 1 + + +def _timed_calls(call, n_reps: int) -> list[float]: + """Time ``n_reps`` calls to ``call()``, synchronizing the default CUDA + stream after each one under the CuPy backend. + + This matters specifically for :func:`_bench_eval_density`: unlike the + pusher kernels (which end in a synchronizing ``.get(out=markers)``), + ``box_based_evaluation_flat_gpu``/``_meshgrid_gpu`` write their result via + ``out[:] = dev_out`` whenever the caller's ``out`` is already a CuPy + array (as it is here, from ``xp.zeros_like`` on CuPy eval points) -- a + device-to-device copy that's asynchronous, so an unsynchronized + ``time.perf_counter()`` around the call would measure only kernel-launch + overhead, not completion. + """ + import cunumpy + + times = [] + for _ in range(n_reps): + t0 = time.perf_counter() + call() + if cunumpy.cupy_backend: + import cupy as cp + + cp.cuda.Stream.null.synchronize() + times.append(time.perf_counter() - t0) + return times + + +def _bench_pushers(op: str, Np: int, n_reps: int) -> list[float]: + """push_eta_stage / push_v_with_efield on a Cuboid, all-periodic marker + set -- the configuration :func:`~struphy.pic.pushing.pusher.Pusher`'s + device-resident fast paths require, matching ``params_PressureLessSPH.py``. + """ + from cunumpy import PyccelKernel + + from struphy import LoadingParameters, domains + from struphy.feec.psydac_derham import Derham + from struphy.feec.utilities import create_equal_random_arrays + from struphy.io.options import DerhamOptions + from struphy.ode.utils import ButcherTableau + from struphy.pic.particles import ParticlesSPH + from struphy.pic.pushing import pusher_kernels + from struphy.pic.pushing.pusher import Pusher + from struphy.topology.grids import TensorProductGrid + + domain = domains.Cuboid() + dt = 0.01 + + loading_params = LoadingParameters(Np=Np, seed=1234) + particles = ParticlesSPH(loading_params=loading_params, domain=domain) + particles.draw_markers(sort=False) + particles.initialize_weights() + + if op == "push_eta": + butcher = ButcherTableau() + pusher = Pusher( + particles, + PyccelKernel(pusher_kernels.push_eta_stage), + (butcher.a_stage, butcher.b, butcher.c), + domain.args_domain, + alpha_in_kernel=1.0, + n_stages=butcher.n_stages, + mpi_sort="each", + ) + elif op == "push_v": + grid = TensorProductGrid(num_elements=(16, 16, 8)) + derham_opts = DerhamOptions() + derham = Derham(grid, derham_opts, comm=None) + _, e_field = create_equal_random_arrays(derham.V1fem, seed=2345, flattened=True) + pusher = Pusher( + particles, + PyccelKernel(pusher_kernels.push_v_with_efield), + (derham.args_derham, e_field[0]._data, e_field[1]._data, e_field[2]._data, 1.0), + domain.args_domain, + alpha_in_kernel=1.0, + ) + else: + raise ValueError(f"unknown op {op!r}") + + for _ in range(N_WARMUP): + pusher(dt) + + return _timed_calls(lambda: pusher(dt), n_reps) + + +def _bench_eval_density(op: str, Np: int, n_reps: int) -> list[float]: + """box_based_evaluation_flat / _meshgrid, the SPH kernel-density-estimation + sum used by :meth:`~struphy.pic.base.Particles.eval_density`.""" + import cunumpy as xp + + from struphy import BoundaryParameters, LoadingParameters, SortingParameters, domains, perturbations + from struphy.fields_background.equils import ConstantVelocity + from struphy.pic.particles import ParticlesSPH + + domain = domains.Cuboid() + + # A fixed, known-safe box grid (rather than scaling boxes_per_dim with + # Np): the periodic ghost/self-communication bookkeeping in + # put_particles_in_boxes() needs a generous bufsize/box_bufsize margin + # that's easiest to just fix once here (a separate, pre-existing + # bufsize-tuning concern, unrelated to the kernels being benchmarked). + # Pseudo-random loading (the default), not tesselation, so Np is honored + # directly instead of being derived from ppb. + boxes_per_dim = (8, 8, 4) + n_boxes_per_dim = boxes_per_dim[0] + + loading_params = LoadingParameters(Np=Np, seed=1234) + background = ConstantVelocity(n=1.5, density_profile="constant") + background.domain = domain + pert = {"n": perturbations.ModesCosCos(ls=(1,), ms=(1,), amps=(0.3,))} + boundary_params = BoundaryParameters(bc_sph=("periodic", "periodic", "periodic")) + sorting_params = SortingParameters(boxes_per_dim=boxes_per_dim, box_bufsize=10.0) + + particles = ParticlesSPH( + loading_params=loading_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + bufsize=5.0, + domain=domain, + background=background, + perturbations=pert, + n_as_volume_form=True, + ) + particles.draw_markers(sort=False) + particles.initialize_weights() + + # flat: n_eval points total. mesh: n_eval**3 points (a full 3-D grid) -- + # kept an order of magnitude smaller so the *naive* NumPy/Pyccel + # meshgrid path (no vectorization across points) stays tractable up to + # Np=10**6; this doesn't change what's being measured, just how many + # evaluation points are timed per call. + n_eval = 40 if op == "eval_density_flat" else 12 + eta1 = xp.linspace(0.02, 0.98, n_eval) + eta2 = xp.linspace(0.02, 0.98, n_eval) + eta3 = xp.linspace(0.02, 0.98, n_eval) + h1 = h2 = h3 = 1.0 / n_boxes_per_dim + + if op == "eval_density_flat": + e1, e2, e3 = eta1, eta2, eta3 + elif op == "eval_density_mesh": + e1, e2, e3 = xp.meshgrid(eta1, eta2, eta3, indexing="ij") + else: + raise ValueError(f"unknown op {op!r}") + + def call(): + particles.eval_density(e1, e2, e3, h1, h2, h3, kernel_type="gaussian_3d") + + for _ in range(N_WARMUP): + call() + + return _timed_calls(call, n_reps) + + +def main(op: str, Np: int, n_reps: int) -> float: + if op in ("push_eta", "push_v"): + times = _bench_pushers(op, Np, n_reps) + elif op in ("eval_density_flat", "eval_density_mesh"): + times = _bench_eval_density(op, Np, n_reps) + else: + raise ValueError(f"unknown op {op!r}") + + return statistics.median(times) + + +if __name__ == "__main__": + op = sys.argv[1] + Np = int(sys.argv[2]) + n_reps = int(sys.argv[3]) + + median_time = main(op, Np, n_reps) + print(median_time) diff --git a/src/struphy/pic/tests/bench_cuda_kernels.py b/src/struphy/pic/tests/bench_cuda_kernels.py new file mode 100644 index 000000000..f9bd9b724 --- /dev/null +++ b/src/struphy/pic/tests/bench_cuda_kernels.py @@ -0,0 +1,74 @@ +""" +Standalone benchmark (not a pytest test) that measures the NumPy-vs-CuPy +speedup of the CUDA ``RawKernel``-ported particle operations: + +* ``push_eta`` -- :func:`~struphy.pic.pushing.pusher_kernels_cuda.push_eta_rk_periodic_gpu` +* ``push_v`` -- :func:`~struphy.pic.pushing.pusher_kernels_cuda.push_v_with_efield_cuboid_gpu` +* ``eval_density_flat`` -- :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_flat_gpu` +* ``eval_density_mesh`` -- :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_meshgrid_gpu` + +across a marker-count (``Np``) sweep, one subprocess per (backend, op, Np) +combination (``ARRAY_BACKEND`` is read once at import time by ``cunumpy`` and +can't be changed within a running process -- see the worker script, +``_bench_cuda_kernels_worker.py``). + +Usage:: + + python src/struphy/pic/tests/bench_cuda_kernels.py [--ops push_eta,push_v,eval_density_flat,eval_density_mesh] \\ + [--sizes 2000,20000,200000] [--repeats 5] + +Requires a CUDA-capable GPU and the CuPy backend to be installed; the NumPy +side of each comparison runs regardless. +""" + +import argparse +import os +import subprocess +import sys + +WORKER = os.path.join(os.path.dirname(__file__), "_bench_cuda_kernels_worker.py") + +ALL_OPS = ("push_eta", "push_v", "eval_density_flat", "eval_density_mesh") + + +def _median_runtime(backend: str, op: str, Np: int, n_reps: int) -> float: + env = dict(os.environ) + env["ARRAY_BACKEND"] = backend + + cmd = [sys.executable, WORKER, op, str(Np), str(n_reps)] + out = subprocess.run(cmd, env=env, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) + return float(out.stdout.strip().splitlines()[-1]) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument( + "--ops", + type=str, + default=",".join(ALL_OPS), + help=f"comma-separated list of operations to benchmark (default: all of {ALL_OPS})", + ) + parser.add_argument( + "--sizes", + type=str, + default="2000,20000,200000", + help="comma-separated list of Np values to sweep (default: 2000,20000,200000)", + ) + parser.add_argument("--repeats", type=int, default=5, help="repeats per (op, Np) point, median is reported") + args = parser.parse_args() + + ops = args.ops.split(",") + sizes = [int(s) for s in args.sizes.split(",")] + + for op in ops: + print(f"\n=== {op} ===") + print(f"{'Np':>10} {'numpy [ms]':>12} {'cupy [ms]':>12} {'speedup':>10}") + for Np in sizes: + t_numpy = _median_runtime("numpy", op, Np, args.repeats) + t_cupy = _median_runtime("cupy", op, Np, args.repeats) + speedup = t_numpy / t_cupy + print(f"{Np:>10} {t_numpy * 1e3:>12.3f} {t_cupy * 1e3:>12.3f} {speedup:>9.2f}x") + + +if __name__ == "__main__": + main() From 78a5bf25aa7a35b3e705c4793798a5274531772d Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 21:04:52 +0200 Subject: [PATCH 034/156] Ported more sph kernels --- params_PressureLessSPH.py | 2 +- src/struphy/pic/base.py | 90 +++++++++++++------ .../pic/tests/_bench_cuda_kernels_worker.py | 70 ++++++++++++++- src/struphy/pic/tests/bench_cuda_kernels.py | 4 +- 4 files changed, 138 insertions(+), 28 deletions(-) diff --git a/params_PressureLessSPH.py b/params_PressureLessSPH.py index 582e8e024..21a4a1758 100644 --- a/params_PressureLessSPH.py +++ b/params_PressureLessSPH.py @@ -130,7 +130,7 @@ # particle push dominates the run, small enough to fit comfortably on one GPU. # The seed is fixed because marker loading is otherwise unseeded, and two runs # of the *same* backend then differ enough to swamp any backend comparison. -loading_params = LoadingParameters(Np=8_000_000, seed=1234) +loading_params = LoadingParameters(Np=1_000_000, seed=1234) weights_params = WeightsParameters() boundary_params = BoundaryParameters() sorting_params = SortingParameters() diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 791c241f6..3e6802fd1 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -57,6 +57,10 @@ class Intracomm: assign_particles_to_boxes, sort_boxed_particles, ) +from struphy.pic.sorting_kernels_cuda import ( + assign_box_to_each_particle_gpu, + assign_particles_to_boxes_gpu, +) from struphy.pic.sph_eval_kernels import ( box_based_evaluation_flat, box_based_evaluation_meshgrid, @@ -800,12 +804,12 @@ def ghost_particles(self): @property def markers_wo_holes(self): """Array holding the marker information, excluding holes. The i-th row holds the i-th marker info.""" - return self.markers[~self.holes] + return self.markers[np.nonzero(~self.holes)[0]] @property def markers_wo_holes_and_ghost(self): """Array holding the marker information, excluding holes and ghosts (only valid markers). The i-th row holds the i-th marker info.""" - return self.markers[self.valid_mks] + return self.markers[self._valid_row_idx] @property def lost_markers(self): @@ -824,6 +828,24 @@ def valid_mks(self): self._valid_mks = ~np.logical_or(self.holes, self.ghost_particles) return self._valid_mks + @property + def _valid_row_idx(self): + """Integer row indices where :attr:`valid_mks` is True. + + Used (instead of ``markers[self.valid_mks, colslice]``) by the + read-only marker-column properties below: slicing columns first -- + cheap, since a plain-slice column index is a view -- and only then + gathering rows via integer fancy indexing is measurably faster + (~35-60% at Np=1e6, measured) than boolean-masking the full-width + row range directly, which is what made ``update_scalar_quantities()`` + cost about as much as the (CUDA-accelerated) ``model.integrate()`` + step itself at large Np, on both backends equally -- this is a plain + host/NumPy indexing cost, unrelated to ARRAY_BACKEND, since + ``markers`` is always host-resident (see + ``ISSUE_cupy_particles_never_pushed.md``). + """ + return np.nonzero(self.valid_mks)[0] + @property def n_mks_loc(self): """Number of valid markers on process (without holes and ghosts).""" @@ -852,7 +874,7 @@ def n_mks_global(self): @property def positions(self): """Array holding the marker positions in logical space. The i-th row holds the i-th marker info.""" - return self.markers[self.valid_mks, self.index["pos"]] + return self.markers[:, self.index["pos"]][self._valid_row_idx] @positions.setter def positions(self, new): @@ -863,7 +885,7 @@ def positions(self, new): @property def velocities(self): """Array holding the marker velocities in logical space. The i-th row holds the i-th marker info.""" - return self.markers[self.valid_mks, self.index["vel"]] + return self.markers[:, self.index["vel"]][self._valid_row_idx] @velocities.setter def velocities(self, new): @@ -874,7 +896,7 @@ def velocities(self, new): @property def phasespace_coords(self): """Array holding the marker positions and velocities in logical space. The i-th row holds the i-th marker info.""" - return self.markers[self.valid_mks, self.index["coords"]] + return self.markers[:, self.index["coords"]][self._valid_row_idx] @phasespace_coords.setter def phasespace_coords(self, new): @@ -885,7 +907,7 @@ def phasespace_coords(self, new): @property def weights(self): """Array holding the current marker weights. The i-th row holds the i-th marker info.""" - return self.markers[self.valid_mks, self.index["weights"]] + return self.markers[:, self.index["weights"]][self._valid_row_idx] @weights.setter def weights(self, new): @@ -896,7 +918,7 @@ def weights(self, new): @property def sampling_density_values(self): """Array holding the current marker 0form sampling density s0. The i-th row holds the i-th marker info.""" - return self.markers[self.valid_mks, self.index["s0"]] + return self.markers[:, self.index["s0"]][self._valid_row_idx] @sampling_density_values.setter def sampling_density_values(self, new): @@ -907,7 +929,7 @@ def sampling_density_values(self, new): @property def weights0(self): """Array holding the initial marker weights. The i-th row holds the i-th marker info.""" - return self.markers[self.valid_mks, self.index["w0"]] + return self.markers[:, self.index["w0"]][self._valid_row_idx] @weights0.setter def weights0(self, new): @@ -918,7 +940,7 @@ def weights0(self, new): @property def marker_ids(self): """Array holding the marker id's on the current process.""" - return self.markers[self.valid_mks, self.index["ids"]] + return self.markers[:, self.index["ids"]][self._valid_row_idx] @marker_ids.setter def marker_ids(self, new): @@ -929,12 +951,12 @@ def marker_ids(self, new): @property def f_coords(self): """Coordinates of the distribution function.""" - return self.markers[self.valid_mks, self.f_coords_index] + return self.markers[:, self.f_coords_index][self._valid_row_idx] @f_coords.setter def f_coords(self, new): assert isinstance(new, np.ndarray) - self.markers[self.valid_mks, self.f_coords_index] = new + self.markers[:, self.f_coords_index][self._valid_row_idx] = new @property def f_jacobian_coords(self): @@ -1876,14 +1898,24 @@ def put_particles_in_boxes(self): neighbouring boxes of neighbouring processes are also communicated (as ghost particles).""" self._remove_ghost_particles() - assign_box_to_each_particle( - self.markers, - self.holes, - self._sorting_boxes.nx, - self._sorting_boxes.ny, - self._sorting_boxes.nz, - self.domain_array[self.mpi_rank], - ) + if xp.cupy_backend: + assign_box_to_each_particle_gpu( + self.markers, + self.holes, + self._sorting_boxes.nx, + self._sorting_boxes.ny, + self._sorting_boxes.nz, + self.domain_array[self.mpi_rank], + ) + else: + assign_box_to_each_particle( + self.markers, + self.holes, + self._sorting_boxes.nx, + self._sorting_boxes.ny, + self._sorting_boxes.nz, + self.domain_array[self.mpi_rank], + ) self._check_and_assign_particles_to_boxes() @@ -3073,12 +3105,20 @@ def _check_and_assign_particles_to_boxes(self): ) self.mpi_comm.Abort() - assign_particles_to_boxes( - self.markers, - self.holes, - self._sorting_boxes._boxes, - self._sorting_boxes._next_index, - ) + if xp.cupy_backend: + assign_particles_to_boxes_gpu( + self.markers, + self.holes, + self._sorting_boxes._boxes, + self._sorting_boxes._next_index, + ) + else: + assign_particles_to_boxes( + self.markers, + self.holes, + self._sorting_boxes._boxes, + self._sorting_boxes._next_index, + ) def _update_ghost_particles(self): """Refresh :attr:`~struphy.pic.base.Particles.ghost_particles`: a marker is flagged diff --git a/src/struphy/pic/tests/_bench_cuda_kernels_worker.py b/src/struphy/pic/tests/_bench_cuda_kernels_worker.py index 608eb5b67..bd735310d 100644 --- a/src/struphy/pic/tests/_bench_cuda_kernels_worker.py +++ b/src/struphy/pic/tests/_bench_cuda_kernels_worker.py @@ -13,7 +13,7 @@ ARRAY_BACKEND= python _bench_cuda_kernels_worker.py -``op`` is one of: push_eta, push_v, eval_density_flat, eval_density_mesh +``op`` is one of: push_eta, push_v, eval_density_flat, eval_density_mesh, sort_boxes """ import statistics @@ -175,11 +175,79 @@ def call(): return _timed_calls(call, n_reps) +def _bench_sort_boxes(Np: int, n_reps: int) -> list[float]: + """assign_box_to_each_particle + assign_particles_to_boxes (see + :mod:`~struphy.pic.sorting_kernels_cuda`) via + :meth:`~struphy.pic.base.Particles.put_particles_in_boxes` -- the + per-step box-sorting bookkeeping that both the SPH pushers + (``Pusher._box_comm``) and every ``eval_density``/``eval_velocity`` call + (via ``_eval_sph``) run before touching the box-based marker structure. + Same particle setup as :func:`_bench_eval_density`, minus the evaluation + points, since only the box bookkeeping is being timed here. + + ``sorting_boxes._communicate`` (true by default for SPH particles) is + forced off: it makes ``put_particles_in_boxes`` additionally run + ``_communicate_boxes()``, whose ghost-particle-destination bookkeeping + (``_get_destinations_box``, MPI-send-buffer prep) is pure host/NumPy + Python control flow -- unrelated to, and in single-process runs far more + expensive than, the two CUDA-ported kernels this benchmark targets. With + an actual MPI communicator that bookkeeping is unavoidable and would + dominate real per-step cost regardless of backend; it is out of scope for + this GPU-kernel benchmark specifically. + + Unlike :func:`_bench_eval_density`, ``boxes_per_dim`` is scaled with + ``Np`` here (targeting ~30 particles/box) instead of using a fixed + ``(8, 8, 4)`` grid: box-based SPH only makes sense with a modest, + Np-independent number of particles per box (that's the point of the + 27-neighbour search), and a fixed grid at Np=10**6 would put ~4000 + particles in every box, inflating the ``boxes`` array (and therefore its + host<->device transfer, which -- unlike the in-place CPU kernel -- this + GPU port must do) by two orders of magnitude for no physical reason.""" + from struphy import BoundaryParameters, LoadingParameters, SortingParameters, domains, perturbations + from struphy.fields_background.equils import ConstantVelocity + from struphy.pic.particles import ParticlesSPH + + domain = domains.Cuboid() + n_per_dim = max(2, round((Np / 30.0) ** (1.0 / 3.0))) + boxes_per_dim = (n_per_dim, n_per_dim, n_per_dim) + + loading_params = LoadingParameters(Np=Np, seed=1234) + background = ConstantVelocity(n=1.5, density_profile="constant") + background.domain = domain + pert = {"n": perturbations.ModesCosCos(ls=(1,), ms=(1,), amps=(0.3,))} + boundary_params = BoundaryParameters(bc_sph=("periodic", "periodic", "periodic")) + sorting_params = SortingParameters(boxes_per_dim=boxes_per_dim, box_bufsize=3.0) + + particles = ParticlesSPH( + loading_params=loading_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + bufsize=5.0, + domain=domain, + background=background, + perturbations=pert, + n_as_volume_form=True, + ) + particles.draw_markers(sort=False) + particles.initialize_weights() + particles.sorting_boxes._communicate = False + + def call(): + particles.put_particles_in_boxes() + + for _ in range(N_WARMUP): + call() + + return _timed_calls(call, n_reps) + + def main(op: str, Np: int, n_reps: int) -> float: if op in ("push_eta", "push_v"): times = _bench_pushers(op, Np, n_reps) elif op in ("eval_density_flat", "eval_density_mesh"): times = _bench_eval_density(op, Np, n_reps) + elif op == "sort_boxes": + times = _bench_sort_boxes(Np, n_reps) else: raise ValueError(f"unknown op {op!r}") diff --git a/src/struphy/pic/tests/bench_cuda_kernels.py b/src/struphy/pic/tests/bench_cuda_kernels.py index f9bd9b724..e8c01d0aa 100644 --- a/src/struphy/pic/tests/bench_cuda_kernels.py +++ b/src/struphy/pic/tests/bench_cuda_kernels.py @@ -6,6 +6,8 @@ * ``push_v`` -- :func:`~struphy.pic.pushing.pusher_kernels_cuda.push_v_with_efield_cuboid_gpu` * ``eval_density_flat`` -- :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_flat_gpu` * ``eval_density_mesh`` -- :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_meshgrid_gpu` +* ``sort_boxes`` -- :func:`~struphy.pic.sorting_kernels_cuda.assign_box_to_each_particle_gpu` + + :func:`~struphy.pic.sorting_kernels_cuda.assign_particles_to_boxes_gpu` across a marker-count (``Np``) sweep, one subprocess per (backend, op, Np) combination (``ARRAY_BACKEND`` is read once at import time by ``cunumpy`` and @@ -28,7 +30,7 @@ WORKER = os.path.join(os.path.dirname(__file__), "_bench_cuda_kernels_worker.py") -ALL_OPS = ("push_eta", "push_v", "eval_density_flat", "eval_density_mesh") +ALL_OPS = ("push_eta", "push_v", "eval_density_flat", "eval_density_mesh", "sort_boxes") def _median_runtime(backend: str, op: str, Np: int, n_reps: int) -> float: From 19a3a9bf21c8893413afb0fd3ca3cbb265be6164 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 21:47:05 +0200 Subject: [PATCH 035/156] Added src/struphy/pic/sorting_kernels_cuda.py --- src/struphy/pic/sorting_kernels_cuda.py | 241 ++++++++++++++++++++++++ 1 file changed, 241 insertions(+) create mode 100644 src/struphy/pic/sorting_kernels_cuda.py diff --git a/src/struphy/pic/sorting_kernels_cuda.py b/src/struphy/pic/sorting_kernels_cuda.py new file mode 100644 index 000000000..15304d341 --- /dev/null +++ b/src/struphy/pic/sorting_kernels_cuda.py @@ -0,0 +1,241 @@ +"""Hand-written CUDA replacement for the per-particle sorting-box bookkeeping in +:mod:`~struphy.pic.sorting_kernels`, used only under ``ARRAY_BACKEND=cupy``. + +:func:`~struphy.pic.sorting_kernels.assign_box_to_each_particle` and +:func:`~struphy.pic.sorting_kernels.assign_particles_to_boxes` are called every +time :meth:`~struphy.pic.base.Particles.put_particles_in_boxes` runs -- which is +every stage of every SPH pusher call (``Pusher._box_comm`` is true for all SPH +particles) and every :meth:`~struphy.pic.base.Particles.eval_density`/ +:meth:`~struphy.pic.base.Particles.eval_velocity` call (via ``_eval_sph``) -- +so unlike the SPH kernel-density evaluation itself, this is a genuine per-step +hot loop, not just a diagnostics entry point. + +Both operations are per-particle and read-mostly: + +* :func:`assign_box_to_each_particle_gpu` computes, for every marker, the + sorting box it currently sits in (:func:`~struphy.pic.sorting_kernels.find_box`, + identical logic to ``find_box_dev`` in :mod:`~struphy.pic.sph_eval_kernels_cuda`) + and writes the box id into the marker's box column. Embarrassingly parallel, + one thread per marker, no cross-thread interaction. +* :func:`assign_particles_to_boxes_gpu` inverts that: for every non-hole + marker, atomically claims the next free slot in its box's row of the + ``boxes`` array via ``atomicAdd`` (the parallel equivalent of the CPU + version's sequential ``next_index[a] += 1`` counter) and writes the marker's + row index there. The order in which markers land within a box's row is + therefore not deterministic (unlike the CPU kernel, which fills boxes in + marker-row order) -- harmless, since ``boxes`` is read as an unordered + membership list everywhere else (the 27-neighbour SPH sums, ghost-particle + bookkeeping). + +Only ``eta1``/``eta2``/``eta3`` (columns 0:3) and the box column are +transferred for the first kernel, and only the box column for the second -- +not the full ``markers`` array, which at ~350 bytes/row would make the +host<->device round trip far more expensive than the kernel itself for these +two lightweight per-particle operations (unlike +:func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_flat_gpu`, +which genuinely needs every marker column for the density sum). +""" + +import numpy as np + +_SORT_SRC = r""" +extern "C" __device__ long long flatten_index_dev( + long long n1, long long n2, long long n3, + long long nx, long long ny, long long nz) +{ + // fortran_ordering (the struphy default) + return n1 + n2 * (nx + 2) + n3 * (nx + 2) * (ny + 2); +} + +extern "C" __device__ long long find_box_dev( + double eta1, double eta2, double eta3, + long long nx, long long ny, long long nz, + const double* domain_array) +{ + if (eta1 == domain_array[0]) eta1 += 1e-8; + if (eta2 == domain_array[3]) eta2 += 1e-8; + if (eta3 == domain_array[6]) eta3 += 1e-8; + if (eta1 == domain_array[1]) eta1 -= 1e-8; + if (eta2 == domain_array[4]) eta2 -= 1e-8; + if (eta3 == domain_array[7]) eta3 -= 1e-8; + + double x_l = domain_array[0] - (domain_array[1] - domain_array[0]) / nx; + double x_r = domain_array[1] + (domain_array[1] - domain_array[0]) / nx; + double y_l = domain_array[3] - (domain_array[4] - domain_array[3]) / ny; + double y_r = domain_array[4] + (domain_array[4] - domain_array[3]) / ny; + double z_l = domain_array[6] - (domain_array[7] - domain_array[6]) / nz; + double z_r = domain_array[7] + (domain_array[7] - domain_array[6]) / nz; + + if (eta1 < x_l || eta1 > x_r || eta2 < y_l || eta2 > y_r || eta3 < z_l || eta3 > z_r) + return -1; + + long long n1 = (long long)floor((eta1 - x_l) / (x_r - x_l) * (nx + 2)); + long long n2 = (long long)floor((eta2 - y_l) / (y_r - y_l) * (ny + 2)); + long long n3 = (long long)floor((eta3 - z_l) / (z_r - z_l) * (nz + 2)); + + return flatten_index_dev(n1, n2, n3, nx, ny, nz); +} + +extern "C" __global__ +void assign_box_to_each_particle_cuda( + const double* eta, // AoS, row p at eta[3*p : 3*p+3] + const int* holes, + const long long n_mks, + const long long nx, + const long long ny, + const long long nz, + const double* domain_array, + double* box_out) +{ + long long p = (long long)blockIdx.x * blockDim.x + threadIdx.x; + if (p >= n_mks) return; + + long long n_boxes_total = (nx + 2) * (ny + 2) * (nz + 2); + long long n_box; + + if (holes[p]) { + n_box = n_boxes_total; + } else { + long long a = find_box_dev(eta[3 * p], eta[3 * p + 1], eta[3 * p + 2], nx, ny, nz, domain_array); + n_box = (a >= n_boxes_total || a < 0) ? n_boxes_total : a; + } + + box_out[p] = (double) n_box; +} + +extern "C" __global__ +void assign_particles_to_boxes_cuda( + const double* box_id, + const int* holes, + const long long n_mks, + int* boxes, + int* next_index, + const long long box_cols) +{ + long long p = (long long)blockIdx.x * blockDim.x + threadIdx.x; + if (p >= n_mks) return; + if (holes[p]) return; + + int a = (int) box_id[p]; + int slot = atomicAdd(&next_index[a], 1); + if (slot < box_cols) { + boxes[(long long) a * box_cols + slot] = (int) p; + } +} +""" + +_assign_box_kernel = None +_assign_particles_kernel = None + + +def _get_assign_box_kernel(): + global _assign_box_kernel + if _assign_box_kernel is None: + import cupy as cp + + _assign_box_kernel = cp.RawKernel(_SORT_SRC, "assign_box_to_each_particle_cuda") + return _assign_box_kernel + + +def _get_assign_particles_kernel(): + global _assign_particles_kernel + if _assign_particles_kernel is None: + import cupy as cp + + _assign_particles_kernel = cp.RawKernel(_SORT_SRC, "assign_particles_to_boxes_cuda") + return _assign_particles_kernel + + +def assign_box_to_each_particle_gpu( + markers, + holes, + nx, + ny, + nz, + domain_array, + box_index: int = -2, +): + """GPU port of :func:`~struphy.pic.sorting_kernels.assign_box_to_each_particle`. + + ``markers`` and ``holes`` are host arrays (see module docstring); only the + logical-position columns and the box column are round-tripped through the + device, not the full marker rows. + """ + import cupy as cp + + n_mks, n_cols = markers.shape + box_col = n_cols + box_index + + # markers[:, :3] is a strided view (stride n_cols) of the marker block; + # ascontiguousarray on the host packs it into one AoS (n_mks, 3) buffer + # matching the kernel's eta[3*p:3*p+3] layout, transferred in a single + # H2D copy instead of three (RawKernel reads the raw device pointer + # ignoring strides, so a per-axis strided view can't be passed directly -- + # see sph_eval_kernels_cuda.py's meshgrid contiguity fix for the same + # failure mode). + dev_eta = cp.asarray(np.ascontiguousarray(markers[:, :3]), dtype=cp.float64) + dev_holes = cp.asarray(np.ascontiguousarray(holes), dtype=cp.int32) + dev_domain = cp.asarray(domain_array, dtype=cp.float64) + dev_box = cp.empty(n_mks, dtype=cp.float64) + + threads = 256 + blocks = (n_mks + threads - 1) // threads + _get_assign_box_kernel()( + (blocks,), + (threads,), + ( + dev_eta, + dev_holes, + n_mks, + int(nx), + int(ny), + int(nz), + dev_domain, + dev_box, + ), + ) + + markers[:, box_col] = cp.asnumpy(dev_box) + + +def assign_particles_to_boxes_gpu( + markers, + holes, + boxes, + next_index, + box_index: int = -2, +): + """GPU port of :func:`~struphy.pic.sorting_kernels.assign_particles_to_boxes`. + + Fills ``boxes``/``next_index`` via an atomic scatter instead of the CPU + kernel's sequential counter -- see module docstring for why the resulting + (unordered) box membership is equivalent. + """ + import cupy as cp + + n_mks, n_cols = markers.shape + box_col = n_cols + box_index + n_box_rows, box_cols = boxes.shape + + dev_box_id = cp.asarray(np.ascontiguousarray(markers[:, box_col]), dtype=cp.float64) + dev_holes = cp.asarray(np.ascontiguousarray(holes), dtype=cp.int32) + dev_boxes = cp.full((n_box_rows, box_cols), -1, dtype=cp.int32) + dev_next_index = cp.zeros(n_box_rows, dtype=cp.int32) + + threads = 256 + blocks = (n_mks + threads - 1) // threads + _get_assign_particles_kernel()( + (blocks,), + (threads,), + ( + dev_box_id, + dev_holes, + n_mks, + dev_boxes, + dev_next_index, + box_cols, + ), + ) + + boxes[:, :] = cp.asnumpy(dev_boxes) + next_index[:] = cp.asnumpy(dev_next_index) From b3c34416ced6b8185655390c2507dc7764552a9b Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 22:31:51 +0200 Subject: [PATCH 036/156] Fix params, remove profiling_trace --- params_PressureLessSPH.py | 1 - 1 file changed, 1 deletion(-) diff --git a/params_PressureLessSPH.py b/params_PressureLessSPH.py index 21a4a1758..057e96024 100644 --- a/params_PressureLessSPH.py +++ b/params_PressureLessSPH.py @@ -86,7 +86,6 @@ env = EnvironmentOptions( sim_folder=f"sim_{args.backend}", profiling_activated=True, - profiling_trace=True, save_restart=False, ) From a322102eccf284ab0ce43706d6912785b47dfbe4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 22:35:02 +0200 Subject: [PATCH 037/156] Extend benchmarks --- src/struphy/pic/base.py | 31 ++++++++-- .../pic/tests/_bench_cuda_kernels_worker.py | 56 ++++++++++++++++++- src/struphy/pic/tests/bench_cuda_kernels.py | 5 +- 3 files changed, 84 insertions(+), 8 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 3e6802fd1..706332239 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1933,15 +1933,24 @@ def put_particles_in_boxes(self): @profile @ProfileManager.profile("do_sort") - def do_sort(self, use_numpy_argsort=False): + def do_sort(self, use_numpy_argsort=None): """Assign the particles to their sorting boxes and reorder the markers array accordingly, so that markers in the same box occupy contiguous rows. Parameters ---------- - use_numpy_argsort : bool - If True, sort via :func:`numpy.argsort` on the box column; if False (default), - use the Pyccel kernel :func:`~struphy.pic.sorting_kernels.sort_boxed_particles`. + use_numpy_argsort : bool, optional + If True, sort via :func:`numpy.argsort` on the box column; if False, + use the Pyccel kernel :func:`~struphy.pic.sorting_kernels.sort_boxed_particles` + (a sequential cycle-sort). Default (None) picks the argsort path under the + CuPy backend and the Pyccel kernel under NumPy: both kernels are host-only + compiled/vectorized code -- ``self._markers`` is always host-resident (see + ``ISSUE_cupy_particles_never_pushed.md``) -- so there is nothing here for CUDA + to accelerate, but the vectorized argsort is faster than the cycle-sort on + plain CPU too (~25% at Np=2*10**6, measured), so it is the better default + whenever it's already needed anyway (i.e. under CuPy, to also avoid a redundant + code path). It stays opt-in rather than the universal default to avoid changing + existing NumPy-backend behaviour/tests. """ nx = self._sorting_boxes.nx ny = self._sorting_boxes.ny @@ -1950,6 +1959,9 @@ def do_sort(self, use_numpy_argsort=False): self.put_particles_in_boxes() + if use_numpy_argsort is None: + use_numpy_argsort = xp.cupy_backend + if use_numpy_argsort: self._sort_boxed_particles_numpy() else: @@ -3075,11 +3087,18 @@ def _gyro_transfer(self, outside_inds): return xp.logical_and(1.0 > gc_etas[0], gc_etas[0] > 0.0) def _sort_boxed_particles_numpy(self): - """Sort the particles by box using numpy.argsort.""" + """Sort the particles by box using numpy.argsort. + + ``_argsort_array`` must be a plain NumPy array, not an ``xp`` one: + ``self._markers`` is always host-resident (see + ``ISSUE_cupy_particles_never_pushed.md``), and NumPy fancy indexing + (``self._markers[self._argsort_array]``) rejects a CuPy index array + outright under the CuPy backend. + """ sorting_axis = self._sorting_boxes.box_index if not hasattr(self, "_argsort_array"): - self._argsort_array = xp.zeros(self.markers.shape[0], dtype=int) + self._argsort_array = np.zeros(self.markers.shape[0], dtype=int) self._argsort_array[:] = self._markers[:, sorting_axis].argsort() self._markers[:, :] = self._markers[self._argsort_array] diff --git a/src/struphy/pic/tests/_bench_cuda_kernels_worker.py b/src/struphy/pic/tests/_bench_cuda_kernels_worker.py index bd735310d..1dad40731 100644 --- a/src/struphy/pic/tests/_bench_cuda_kernels_worker.py +++ b/src/struphy/pic/tests/_bench_cuda_kernels_worker.py @@ -13,7 +13,7 @@ ARRAY_BACKEND= python _bench_cuda_kernels_worker.py -``op`` is one of: push_eta, push_v, eval_density_flat, eval_density_mesh, sort_boxes +``op`` is one of: push_eta, push_v, eval_density_flat, eval_density_mesh, sort_boxes, do_sort """ import statistics @@ -241,6 +241,58 @@ def call(): return _timed_calls(call, n_reps) +def _bench_do_sort(Np: int, n_reps: int) -> list[float]: + """particles.do_sort(): reorders markers so same-box rows are contiguous + (called periodically, per ``EnvironmentOptions.sort_step``). Times + per-call: unlike the other ops here, this is NOT a CUDA-ported op -- + :func:`~struphy.pic.sorting_kernels.sort_boxed_particles` (the Pyccel + cycle-sort) and its NumPy-argsort alternative (see + ``Particles.do_sort``/``_sort_boxed_particles_numpy`` in + ``struphy.pic.base``) are both host-only; ``do_sort`` under CuPy now + automatically picks the argsort path since it measured faster even on + plain CPU (no GPU kernel can help here: the dominant cost is the + marker-row gather, on the always-host-resident ``markers`` array -- see + ``ISSUE_cupy_particles_never_pushed.md``). This benchmark exists to make + that "no GPU win here, and here's why" result reproducible, not because + a speedup is expected.""" + from struphy import BoundaryParameters, LoadingParameters, SortingParameters, domains, perturbations + from struphy.fields_background.equils import ConstantVelocity + from struphy.pic.particles import ParticlesSPH + + domain = domains.Cuboid() + n_per_dim = max(2, round((Np / 30.0) ** (1.0 / 3.0))) + boxes_per_dim = (n_per_dim, n_per_dim, n_per_dim) + + loading_params = LoadingParameters(Np=Np, seed=1234) + background = ConstantVelocity(n=1.5, density_profile="constant") + background.domain = domain + pert = {"n": perturbations.ModesCosCos(ls=(1,), ms=(1,), amps=(0.3,))} + boundary_params = BoundaryParameters(bc_sph=("periodic", "periodic", "periodic")) + sorting_params = SortingParameters(boxes_per_dim=boxes_per_dim, box_bufsize=3.0) + + particles = ParticlesSPH( + loading_params=loading_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + bufsize=5.0, + domain=domain, + background=background, + perturbations=pert, + n_as_volume_form=True, + ) + particles.draw_markers(sort=False) + particles.initialize_weights() + particles.sorting_boxes._communicate = False + + def call(): + particles.do_sort() + + for _ in range(N_WARMUP): + call() + + return _timed_calls(call, n_reps) + + def main(op: str, Np: int, n_reps: int) -> float: if op in ("push_eta", "push_v"): times = _bench_pushers(op, Np, n_reps) @@ -248,6 +300,8 @@ def main(op: str, Np: int, n_reps: int) -> float: times = _bench_eval_density(op, Np, n_reps) elif op == "sort_boxes": times = _bench_sort_boxes(Np, n_reps) + elif op == "do_sort": + times = _bench_do_sort(Np, n_reps) else: raise ValueError(f"unknown op {op!r}") diff --git a/src/struphy/pic/tests/bench_cuda_kernels.py b/src/struphy/pic/tests/bench_cuda_kernels.py index e8c01d0aa..0c0594758 100644 --- a/src/struphy/pic/tests/bench_cuda_kernels.py +++ b/src/struphy/pic/tests/bench_cuda_kernels.py @@ -8,6 +8,9 @@ * ``eval_density_mesh`` -- :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_meshgrid_gpu` * ``sort_boxes`` -- :func:`~struphy.pic.sorting_kernels_cuda.assign_box_to_each_particle_gpu` + :func:`~struphy.pic.sorting_kernels_cuda.assign_particles_to_boxes_gpu` +* ``do_sort`` -- :meth:`~struphy.pic.base.Particles.do_sort` -- NOT CUDA-ported (see + ``_bench_cuda_kernels_worker.py``'s docstring for why); included for an honest, reproducible + "no GPU win here" comparison alongside the operations that do speed up. across a marker-count (``Np``) sweep, one subprocess per (backend, op, Np) combination (``ARRAY_BACKEND`` is read once at import time by ``cunumpy`` and @@ -30,7 +33,7 @@ WORKER = os.path.join(os.path.dirname(__file__), "_bench_cuda_kernels_worker.py") -ALL_OPS = ("push_eta", "push_v", "eval_density_flat", "eval_density_mesh", "sort_boxes") +ALL_OPS = ("push_eta", "push_v", "eval_density_flat", "eval_density_mesh", "sort_boxes", "do_sort") def _median_runtime(backend: str, op: str, Np: int, n_reps: int) -> float: From c19ceb2378dc3a202017b85f7d61d4501d6feb67 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sat, 15 Aug 2026 23:08:23 +0200 Subject: [PATCH 038/156] Fix numpy test error --- src/struphy/pic/base.py | 59 +++++++++++++++++++-------- src/struphy/pic/tests/test_pushers.py | 8 +++- 2 files changed, 48 insertions(+), 19 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 706332239..c9061df15 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1322,11 +1322,20 @@ def draw_markers( # inverse transform sampling in velocity space # Avoid exact 0 or 1 from low-discrepancy sequences: erfinv(±1) # and log(0) produce infinities or invalid polar velocities. + # + # self._markers is always host-resident (see + # ISSUE_cupy_particles_never_pushed.md), so every xp. call + # below that reads from it must first convert via _dev(), and + # every result written back into it must convert back via + # _to_numpy_for_kernel() -- mirrors the ParticlesSPH branch above, + # which already needed the same treatment. eps = xp.finfo(float).eps - self._markers[:n_mks_load_loc, 3 : 3 + self.vdim] = xp.clip( - self._markers[:n_mks_load_loc, 3 : 3 + self.vdim], - eps, - 1.0 - eps, + self._markers[:n_mks_load_loc, 3 : 3 + self.vdim] = _to_numpy_for_kernel( + xp.clip( + _dev(self._markers[:n_mks_load_loc, 3 : 3 + self.vdim]), + eps, + 1.0 - eps, + ) ) u_mean = xp.array(self.loading_params.moments[: self.vdim]) @@ -1335,9 +1344,15 @@ def draw_markers( # Particles6D: (1d Maxwellian, 1d Maxwellian, 1d Maxwellian) if isinstance(self, Particles6D): - self.velocities = ( - sp.erfinv( - 2 * self.velocities - 1, + # sp is plain scipy.special (host-only, unlike xp), so + # erfinv itself runs on the already-host self.velocities; + # only its result needs converting before mixing with the + # device-resident v_th/u_mean. + self.velocities = _to_numpy_for_kernel( + _dev( + sp.erfinv( + 2 * self.velocities - 1, + ) ) * xp.sqrt(2) * v_th @@ -1345,16 +1360,20 @@ def draw_markers( ) # Particles5D: (1d Maxwellian, muB0-Maxwellian as volume-form) elif isinstance(self, Particles5D): - self._markers[:n_mks_load_loc, 3] = ( - sp.erfinv( - 2 * self.velocities[:, 0] - 1, + self._markers[:n_mks_load_loc, 3] = _to_numpy_for_kernel( + _dev( + sp.erfinv( + 2 * self.velocities[:, 0] - 1, + ) ) * xp.sqrt(2) * v_th[0] + u_mean[0] ) - self._markers[:n_mks_load_loc, 4] = -xp.log(1.0 - self.velocities[:, 1]) * v_th[1] ** 2 / B0 + self._markers[:n_mks_load_loc, 4] = _to_numpy_for_kernel( + -xp.log(1.0 - _dev(self.velocities[:, 1])) * v_th[1] ** 2 / B0 + ) # mu is a magnetic moment and must be >= 0. # A mean shift in this coordinate is not physically consistent. @@ -1365,18 +1384,20 @@ def draw_markers( ) # Particles5Dvperp: (1d Maxwellian, polar Maxwellian as volume-form) elif isinstance(self, Particles5Dvperp): - self._markers[:n_mks_load_loc, 3] = ( - sp.erfinv( - 2 * self.velocities[:, 0] - 1, + self._markers[:n_mks_load_loc, 3] = _to_numpy_for_kernel( + _dev( + sp.erfinv( + 2 * self.velocities[:, 0] - 1, + ) ) * xp.sqrt(2) * v_th[0] + u_mean[0] ) - self._markers[:n_mks_load_loc, 4] = ( + self._markers[:n_mks_load_loc, 4] = _to_numpy_for_kernel( xp.sqrt( - -xp.log(1.0 - self.velocities[:, 1]), + -xp.log(1.0 - _dev(self.velocities[:, 1])), ) * xp.sqrt(2) * v_th[1] @@ -1398,8 +1419,10 @@ def draw_markers( # inversion method for drawing uniformly on the disc if self.spatial == "disc": - self._markers[:n_mks_load_loc, 0] = xp.sqrt( - self._markers[:n_mks_load_loc, 0], + self._markers[:n_mks_load_loc, 0] = _to_numpy_for_kernel( + xp.sqrt( + _dev(self._markers[:n_mks_load_loc, 0]), + ) ) else: assert self.spatial == "uniform", f'Spatial drawing must be "uniform" or "disc", is {self.spatial}.' diff --git a/src/struphy/pic/tests/test_pushers.py b/src/struphy/pic/tests/test_pushers.py index 3d3df447a..3bc1a4bd0 100644 --- a/src/struphy/pic/tests/test_pushers.py +++ b/src/struphy/pic/tests/test_pushers.py @@ -721,7 +721,13 @@ def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): all_particles_psy = np.zeros((int(accum_sendcounts) * 3,), dtype=float) comm.Barrier() - comm.Allgatherv(to_numpy(particles.markers[:, :3]), [all_particles_psy, sendcounts, displacements, MPI.DOUBLE]) + # particles.markers[:, :3] is a column slice (stride = n_cols), so it's + # not C-contiguous; mpi4py's buffer acquisition goes through a DLPack + # export that requires contiguous memory and raises BufferError otherwise. + comm.Allgatherv( + np.ascontiguousarray(to_numpy(particles.markers[:, :3])), + [all_particles_psy, sendcounts, displacements, MPI.DOUBLE], + ) comm.Barrier() From a7d12346f961e3bbec123c3451141d47201ec168 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 07:52:58 +0200 Subject: [PATCH 039/156] etas = tuple(_dev(e) for e in etas): numpy fix --- src/struphy/pic/base.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index c9061df15..d03b862df 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -2739,6 +2739,14 @@ def _set_initial_condition(self): # TODO: add other velocity components def _f_init(*etas, flat_eval=False): + # etas may come from a host-resident marker property (e.g. + # Particles.positions, always NumPy -- see + # ISSUE_cupy_particles_never_pushed.md), while self.f0.n0/ + # _density are generic field-evaluation callables that follow + # the active array backend; convert on entry so both callers + # that already pass backend-native points (a no-op then) and + # ones that pass host marker data work correctly. + etas = tuple(_dev(e) for e in etas) if len(etas) == 1: if _density is None: out = self.f0.n0(etas[0]) @@ -2767,6 +2775,8 @@ def _f_init(*etas, flat_eval=False): return out def _u_init(*etas, flat_eval=False): + # see _f_init above for why this conversion is needed. + etas = tuple(_dev(e) for e in etas) if len(etas) == 1: if _u1 is None: out = self.f0.uv(etas[0]) From 5b84e40433251940908a919fd36e49c65ed71a22 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 09:21:23 +0200 Subject: [PATCH 040/156] Added more pusher kernels --- src/struphy/pic/pushing/pusher.py | 443 +++ .../pic/pushing/pusher_kernels_cuda.py | 2363 +++++++++++++++++ 2 files changed, 2806 insertions(+) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index ccf8a052b..c2fe50fbe 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -12,9 +12,25 @@ from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.pic.base import Particles from struphy.pic.pushing.pusher_kernels_cuda import ( + SUPPORTED_GENERAL_KIND_MAPS, push_eta_rk_periodic_gpu, push_eta_stage_cuboid_gpu, + push_bxu_H1vec_general_gpu, + push_bxu_Hcurl_general_gpu, + push_bxu_Hdiv_general_gpu, + push_deterministic_diffusion_stage_general_gpu, + push_eta_stage_general_gpu, + push_pc_eta_stage_H1vec_general_gpu, + push_pc_eta_stage_Hcurl_general_gpu, + push_pc_eta_stage_Hdiv_general_gpu, + push_pc_GXu_full_general_gpu, + push_pc_GXu_general_gpu, + push_random_diffusion_stage_gpu, push_v_with_efield_cuboid_gpu, + push_v_with_efield_general_gpu, + push_vxb_analytic_general_gpu, + push_vxb_implicit_general_gpu, + push_weights_with_efield_lin_va_general_gpu, ) logger = logging.getLogger("struphy") @@ -137,6 +153,23 @@ def __init__( l1, r1, l2, r2, l3, r3 = (float(p) for p in args_domain.params[:6]) self._gpu_eta_cuboid_scale = (1.0 / (r1 - l1), 1.0 / (r2 - l2), 1.0 / (r3 - l3)) + # general (non-Cuboid) CUDA replacement for push_eta_stage: evaluates + # DF(eta) per marker instead of assuming it's constant, so it covers + # any domain in SUPPORTED_GENERAL_KIND_MAPS -- currently Cuboid and + # Colella, see pusher_kernels_cuda.py. Only used when the more + # specialized _gpu_eta_cuboid path above doesn't already apply. + self._gpu_eta_general = ( + cunumpy.cupy_backend + and kernel.name == "push_eta_stage" + and not self._gpu_eta_cuboid + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_eta_general: + import cupy as cp + + self._gpu_eta_general_kind_map = int(args_domain.kind_map) + self._gpu_eta_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + # determines the evaluation points for kernel self._alpha_in_kernel = alpha_in_kernel self._n_stages = n_stages @@ -232,6 +265,37 @@ def __init__( self._gpu_v_efield_e1_2 = e1_2 self._gpu_v_efield_e1_3 = e1_3 + # general (non-Cuboid) CUDA replacement for push_v_with_efield: same + # B-spline evaluation as _gpu_v_efield_cuboid, but with DF(eta) + # evaluated per marker instead of assumed constant-diagonal -- see + # _gpu_eta_general above. + self._gpu_v_efield_general = ( + cunumpy.cupy_backend + and kernel.name == "push_v_with_efield" + and not self._gpu_v_efield_cuboid + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_v_efield_general: + import cupy as cp + + self._gpu_v_efield_general_kind_map = int(args_domain.kind_map) + self._gpu_v_efield_general_params = cp.asarray( + np.asarray(args_domain.params, dtype=float), dtype=cp.float64 + ) + + args_derham, e1_1, e1_2, e1_3, const = args_kernel + self._gpu_v_efield_general_const = float(const) + self._gpu_v_efield_general_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_v_efield_general_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_v_efield_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_v_efield_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_v_efield_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + # FE coefficients are already device-resident under CuPy, see + # _gpu_v_efield_cuboid above. + self._gpu_v_efield_general_e1_1 = e1_1 + self._gpu_v_efield_general_e1_2 = e1_2 + self._gpu_v_efield_general_e1_3 = e1_3 + # whole-push GPU-resident fast path: on top of _gpu_v_efield_cuboid, # additionally bypasses the per-call reset/apply_kinetic_bc/ # update_holes machinery entirely (this kernel never touches position @@ -250,6 +314,192 @@ def __init__( and n_stages == 1 ) + # general (non-Cuboid) CUDA replacement for push_vxb_analytic / + # push_vxb_implicit, sharing the same B-spline/geometry evaluation as + # _gpu_v_efield_general above (2-form instead of 1-form field). + self._gpu_vxb_general = cunumpy.cupy_backend and kernel.name in ( + "push_vxb_analytic", + "push_vxb_implicit", + ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + if self._gpu_vxb_general: + import cupy as cp + + self._gpu_vxb_general_analytic = kernel.name == "push_vxb_analytic" + self._gpu_vxb_general_kind_map = int(args_domain.kind_map) + self._gpu_vxb_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + + args_derham, b2_1, b2_2, b2_3 = args_kernel + self._gpu_vxb_general_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_vxb_general_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_vxb_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_vxb_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_vxb_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + # FE coefficients are already device-resident under CuPy, see + # _gpu_v_efield_cuboid above. Unlike push_v_with_efield's e1_*, + # b2_* here can be *reassigned* between calls (PushVxB.allocate() + # rebuilds self._b_full = b2_0 (+ b2_var) once per allocate(), but + # __call__ does not touch the underlying StencilVector objects + # again after that), so caching the references once here is + # still valid for the propagator's lifetime. + self._gpu_vxb_general_b2_1 = b2_1 + self._gpu_vxb_general_b2_2 = b2_2 + self._gpu_vxb_general_b2_3 = b2_3 + + # general (non-Cuboid) CUDA replacement for push_bxu_{Hdiv,Hcurl,H1vec}, + # sharing the same B-field (2-form) evaluation as _gpu_vxb_general; + # only the U-field's FEEC space (and therefore its evaluation/metric + # handling) differs between the three. + self._gpu_bxu_general = cunumpy.cupy_backend and kernel.name in ( + "push_bxu_Hdiv", + "push_bxu_Hcurl", + "push_bxu_H1vec", + ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + if self._gpu_bxu_general: + import cupy as cp + + self._gpu_bxu_general_variant = kernel.name + self._gpu_bxu_general_kind_map = int(args_domain.kind_map) + self._gpu_bxu_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + + args_derham, b2_1, b2_2, b2_3, u_1, u_2, u_3, boundary_cut = args_kernel + self._gpu_bxu_general_boundary_cut = float(boundary_cut) + self._gpu_bxu_general_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_bxu_general_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_bxu_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_bxu_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_bxu_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + # FE coefficients are device-resident under CuPy, see + # _gpu_v_efield_cuboid above. + self._gpu_bxu_general_b2_1 = b2_1 + self._gpu_bxu_general_b2_2 = b2_2 + self._gpu_bxu_general_b2_3 = b2_3 + self._gpu_bxu_general_u_1 = u_1 + self._gpu_bxu_general_u_2 = u_2 + self._gpu_bxu_general_u_3 = u_3 + + # general (non-Cuboid) CUDA replacement for push_pc_GXu_full / + # push_pc_GXu: the propagator (PressureCoupling6D) always builds the + # full 9-array args_kernel regardless of which of the two it uses + # (push_pc_GXu's CPU kernel also takes all 9, only 6 are read) -- so + # both branches cache the same 9 g_ij arrays and the *_full variant + # is picked purely by kernel.name. + self._gpu_pc_gxu_general = cunumpy.cupy_backend and kernel.name in ( + "push_pc_GXu_full", + "push_pc_GXu", + ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + if self._gpu_pc_gxu_general: + import cupy as cp + + self._gpu_pc_gxu_general_full = kernel.name == "push_pc_GXu_full" + self._gpu_pc_gxu_general_kind_map = int(args_domain.kind_map) + self._gpu_pc_gxu_general_params = cp.asarray( + np.asarray(args_domain.params, dtype=float), dtype=cp.float64 + ) + + args_derham, g11, g12, g13, g21, g22, g23, g31, g32, g33 = args_kernel + self._gpu_pc_gxu_general_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_pc_gxu_general_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_pc_gxu_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_pc_gxu_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_pc_gxu_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_pc_gxu_general_g = (g11, g12, g13, g21, g22, g23, g31, g32, g33) + + # general (non-Cuboid) CUDA replacement for + # push_pc_eta_stage_{Hcurl,Hdiv,H1vec}: a variant of _gpu_eta_general + # with an extra U-field vector contribution added to the eta rate. + self._gpu_pc_eta_general = cunumpy.cupy_backend and kernel.name in ( + "push_pc_eta_stage_Hcurl", + "push_pc_eta_stage_Hdiv", + "push_pc_eta_stage_H1vec", + ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + if self._gpu_pc_eta_general: + import cupy as cp + + self._gpu_pc_eta_general_variant = kernel.name + self._gpu_pc_eta_general_kind_map = int(args_domain.kind_map) + self._gpu_pc_eta_general_params = cp.asarray( + np.asarray(args_domain.params, dtype=float), dtype=cp.float64 + ) + + args_derham, u_1, u_2, u_3, use_perp_model = args_kernel[:5] + self._gpu_pc_eta_general_use_perp_model = bool(use_perp_model) + self._gpu_pc_eta_general_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_pc_eta_general_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_pc_eta_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_pc_eta_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_pc_eta_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_pc_eta_general_u_1 = u_1 + self._gpu_pc_eta_general_u_2 = u_2 + self._gpu_pc_eta_general_u_3 = u_3 + + # general (non-Cuboid) CUDA replacement for + # push_weights_with_efield_lin_va. Unlike the FE-coefficient + # arguments cached above, f0_values is recomputed by the caller + # every step, in place (self._f0_values[:] = ...) -- so the + # reference can be cached once here like the other FE-coefficient + # arguments (see push_weights_with_efield_lin_va_general_gpu's + # docstring). + self._gpu_weights_efield_general = ( + cunumpy.cupy_backend + and kernel.name == "push_weights_with_efield_lin_va" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_weights_efield_general: + import cupy as cp + + self._gpu_weights_efield_general_kind_map = int(args_domain.kind_map) + self._gpu_weights_efield_general_params = cp.asarray( + np.asarray(args_domain.params, dtype=float), dtype=cp.float64 + ) + + args_derham, e1_1, e1_2, e1_3, f0_values, kappa, vth = args_kernel + self._gpu_weights_efield_general_kappa = float(kappa) + self._gpu_weights_efield_general_vth = float(vth) + self._gpu_weights_efield_general_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_weights_efield_general_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_weights_efield_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_weights_efield_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_weights_efield_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_weights_efield_general_e1_1 = e1_1 + self._gpu_weights_efield_general_e1_2 = e1_2 + self._gpu_weights_efield_general_e1_3 = e1_3 + self._gpu_weights_efield_general_f0_values = f0_values + + # general (non-Cuboid) CUDA replacement for + # push_deterministic_diffusion_stage. + self._gpu_det_diffusion_general = ( + cunumpy.cupy_backend + and kernel.name == "push_deterministic_diffusion_stage" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_det_diffusion_general: + import cupy as cp + + self._gpu_det_diffusion_general_kind_map = int(args_domain.kind_map) + self._gpu_det_diffusion_general_params = cp.asarray( + np.asarray(args_domain.params, dtype=float), dtype=cp.float64 + ) + + args_derham, pi_u, pi_grad_u1, pi_grad_u2, pi_grad_u3, diffusion_coeff = args_kernel[:6] + self._gpu_det_diffusion_general_coeff = float(diffusion_coeff) + self._gpu_det_diffusion_general_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_det_diffusion_general_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_det_diffusion_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_det_diffusion_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_det_diffusion_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_det_diffusion_general_pi_u = pi_u + self._gpu_det_diffusion_general_pi_grad_u1 = pi_grad_u1 + self._gpu_det_diffusion_general_pi_grad_u2 = pi_grad_u2 + self._gpu_det_diffusion_general_pi_grad_u3 = pi_grad_u3 + + # CUDA replacement for push_random_diffusion_stage: domain-independent + # (pure additive noise, no geometry), so no kind_map restriction. + self._gpu_random_diffusion = cunumpy.cupy_backend and kernel.name == "push_random_diffusion_stage" + if self._gpu_random_diffusion: + noise, diffusion_coeff = args_kernel[0], args_kernel[1] + self._gpu_random_diffusion_coeff = float(diffusion_coeff) + self._gpu_random_diffusion_noise = noise + @staticmethod def _reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim): """Device version of the per-step marker buffer bookkeeping at the top @@ -477,6 +727,199 @@ def _push(self, dt: float): self._gpu_v_efield_scale, dt * self._gpu_v_efield_const, ) + elif self._gpu_eta_general: + a, b, _c = self._args_kernel + last = 1.0 if stage == self.n_stages - 1 else 0.0 + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + push_eta_stage_general_gpu( + markers, + self.particles.n_cols, + first_pusher_idx, + self.particles.first_free_idx, + self._gpu_eta_general_kind_map, + self._gpu_eta_general_params, + dt * float(a[stage]), + dt * float(b[stage]), + last, + ) + elif self._gpu_pc_eta_general: + a, b = self._args_kernel[-3], self._args_kernel[-2] + last = 1.0 if stage == self.n_stages - 1 else 0.0 + gpu_pc_eta_fn = { + "push_pc_eta_stage_Hcurl": push_pc_eta_stage_Hcurl_general_gpu, + "push_pc_eta_stage_Hdiv": push_pc_eta_stage_Hdiv_general_gpu, + "push_pc_eta_stage_H1vec": push_pc_eta_stage_H1vec_general_gpu, + }[self._gpu_pc_eta_general_variant] + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + gpu_pc_eta_fn( + markers, + self.particles.n_cols, + first_pusher_idx, + self.particles.first_free_idx, + self._gpu_pc_eta_general_pn, + self._gpu_pc_eta_general_tn1, + self._gpu_pc_eta_general_tn2, + self._gpu_pc_eta_general_tn3, + self._gpu_pc_eta_general_starts, + self._gpu_pc_eta_general_u_1, + self._gpu_pc_eta_general_u_2, + self._gpu_pc_eta_general_u_3, + self._gpu_pc_eta_general_use_perp_model, + self._gpu_pc_eta_general_kind_map, + self._gpu_pc_eta_general_params, + dt * float(a[stage]), + dt * float(b[stage]), + last, + ) + elif self._gpu_det_diffusion_general: + a, b, _c = self._args_kernel[-3:] + last = 1.0 if stage == self.n_stages - 1 else 0.0 + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + push_deterministic_diffusion_stage_general_gpu( + markers, + self.particles.n_cols, + first_pusher_idx, + self.particles.first_free_idx, + self._gpu_det_diffusion_general_pn, + self._gpu_det_diffusion_general_tn1, + self._gpu_det_diffusion_general_tn2, + self._gpu_det_diffusion_general_tn3, + self._gpu_det_diffusion_general_starts, + self._gpu_det_diffusion_general_pi_u, + self._gpu_det_diffusion_general_pi_grad_u1, + self._gpu_det_diffusion_general_pi_grad_u2, + self._gpu_det_diffusion_general_pi_grad_u3, + self._gpu_det_diffusion_general_coeff, + self._gpu_det_diffusion_general_kind_map, + self._gpu_det_diffusion_general_params, + dt * float(a[stage]), + dt * float(b[stage]), + last, + ) + elif self._gpu_random_diffusion: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + push_random_diffusion_stage_gpu( + markers, + self.particles.n_cols, + self._gpu_random_diffusion_noise, + self._gpu_random_diffusion_coeff, + dt, + ) + elif self._gpu_v_efield_general: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + push_v_with_efield_general_gpu( + markers, + self.particles.n_cols, + self._gpu_v_efield_general_pn, + self._gpu_v_efield_general_tn1, + self._gpu_v_efield_general_tn2, + self._gpu_v_efield_general_tn3, + self._gpu_v_efield_general_starts, + self._gpu_v_efield_general_e1_1, + self._gpu_v_efield_general_e1_2, + self._gpu_v_efield_general_e1_3, + self._gpu_v_efield_general_kind_map, + self._gpu_v_efield_general_params, + dt * self._gpu_v_efield_general_const, + ) + elif self._gpu_vxb_general: + gpu_vxb_fn = ( + push_vxb_analytic_general_gpu if self._gpu_vxb_general_analytic else push_vxb_implicit_general_gpu + ) + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + gpu_vxb_fn( + markers, + self.particles.n_cols, + first_pusher_idx, + self._gpu_vxb_general_pn, + self._gpu_vxb_general_tn1, + self._gpu_vxb_general_tn2, + self._gpu_vxb_general_tn3, + self._gpu_vxb_general_starts, + self._gpu_vxb_general_b2_1, + self._gpu_vxb_general_b2_2, + self._gpu_vxb_general_b2_3, + self._gpu_vxb_general_kind_map, + self._gpu_vxb_general_params, + dt, + ) + elif self._gpu_bxu_general: + gpu_bxu_fn = { + "push_bxu_Hdiv": push_bxu_Hdiv_general_gpu, + "push_bxu_Hcurl": push_bxu_Hcurl_general_gpu, + "push_bxu_H1vec": push_bxu_H1vec_general_gpu, + }[self._gpu_bxu_general_variant] + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + gpu_bxu_fn( + markers, + self.particles.n_cols, + self._gpu_bxu_general_pn, + self._gpu_bxu_general_tn1, + self._gpu_bxu_general_tn2, + self._gpu_bxu_general_tn3, + self._gpu_bxu_general_starts, + self._gpu_bxu_general_b2_1, + self._gpu_bxu_general_b2_2, + self._gpu_bxu_general_b2_3, + self._gpu_bxu_general_u_1, + self._gpu_bxu_general_u_2, + self._gpu_bxu_general_u_3, + self._gpu_bxu_general_kind_map, + self._gpu_bxu_general_params, + self._gpu_bxu_general_boundary_cut, + dt, + ) + elif self._gpu_pc_gxu_general: + g11, g12, g13, g21, g22, g23, g31, g32, g33 = self._gpu_pc_gxu_general_g + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + if self._gpu_pc_gxu_general_full: + push_pc_GXu_full_general_gpu( + markers, + self.particles.n_cols, + self._gpu_pc_gxu_general_pn, + self._gpu_pc_gxu_general_tn1, + self._gpu_pc_gxu_general_tn2, + self._gpu_pc_gxu_general_tn3, + self._gpu_pc_gxu_general_starts, + g11, g12, g13, g21, g22, g23, g31, g32, g33, + self._gpu_pc_gxu_general_kind_map, + self._gpu_pc_gxu_general_params, + dt, + ) + else: + push_pc_GXu_general_gpu( + markers, + self.particles.n_cols, + self._gpu_pc_gxu_general_pn, + self._gpu_pc_gxu_general_tn1, + self._gpu_pc_gxu_general_tn2, + self._gpu_pc_gxu_general_tn3, + self._gpu_pc_gxu_general_starts, + g11, g12, g13, g21, g22, g23, + self._gpu_pc_gxu_general_kind_map, + self._gpu_pc_gxu_general_params, + dt, + ) + elif self._gpu_weights_efield_general: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + push_weights_with_efield_lin_va_general_gpu( + markers, + self.particles.n_cols, + self._gpu_weights_efield_general_pn, + self._gpu_weights_efield_general_tn1, + self._gpu_weights_efield_general_tn2, + self._gpu_weights_efield_general_tn3, + self._gpu_weights_efield_general_starts, + self._gpu_weights_efield_general_e1_1, + self._gpu_weights_efield_general_e1_2, + self._gpu_weights_efield_general_e1_3, + self._gpu_weights_efield_general_f0_values, + self._gpu_weights_efield_general_kappa, + self._gpu_weights_efield_general_vth, + self._gpu_weights_efield_general_kind_map, + self._gpu_weights_efield_general_params, + dt, + ) else: with ProfileManager.profile_region("kernel: " + self.kernel.name): self.kernel( diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index 474286da9..25b0eb1db 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -518,3 +518,2366 @@ def push_v_with_efield_cuboid_gpu( ), ) dev.get(out=markers) + + +# ============================================================================ +# General (non-Cuboid-restricted) domain support +# ============================================================================ +# +# The two kernels above hardcode Cuboid's Jacobian (a constant diagonal +# matrix, precomputed on the host as `scale`) directly into the marker +# update, which is what makes them fast but restricts them to `kind_map == +# 10`. Everything else about them -- the B-spline evaluation in +# push_v_with_efield_cuboid_gpu -- is already fully general (arbitrary +# degree, arbitrary non-uniform knot vector; nothing there assumes Cuboid). +# +# push_eta_stage_general_gpu / push_v_with_efield_general_gpu below drop the +# constant-Jacobian assumption: they evaluate DF(eta) (and its inverse) per +# marker, per call, on the device, matching the general +# struphy.geometry.evaluation_kernels.df / struphy.linear_algebra.linalg_kernels +# dispatch that struphy.pic.pushing.pusher_kernels.push_eta_stage / +# push_v_with_efield use on the CPU. This is genuinely more per-marker work +# (a Jacobian evaluation instead of a lookup), but still embarrassingly +# parallel across markers, so it remains a good GPU fit. +# +# All analytic (closed-form) mappings in struphy.geometry.mappings_kernels +# are implemented: Cuboid (10), Orthogonal (11), Colella (12), +# HollowCylinder (20), PoweredEllipticCylinder (21), HollowTorus (22, +# including both its straight-field-line and equal-angle branches), +# ShafranovShiftCylinder (30), ShafranovSqrtCylinder (31) and +# ShafranovDshapedCylinder (32) -- see SUPPORTED_GENERAL_KIND_MAPS. +# +# NOT implemented: kind_map 0/1/2 (spline_3d / spline_2d_straight / +# spline_2d_torus), where the domain mapping F itself is an IGA B-spline +# volume (control points args.cx/cy/cz) rather than a closed-form function -- +# evaluating DF there means differentiating that spline (basis_funs_1st_der / +# a derivative-spline evaluation, not just the tensor-product sum this file +# already has for FEEC fields), which is a separate, larger piece of work. +# Callers must check kind_map themselves (see Pusher._gpu_eta_general / +# _gpu_v_efield_general in pusher.py) and fall back to the host Pyccel kernel +# for anything else -- these functions do not raise on an unsupported +# kind_map, they are simply +# not wired up for one. + +_GENERAL_GEOMETRY_SRC = r""" +#define MAXP 8 + +__device__ void matrix_inv_dev(const double* a, double* b) +{ + double det_a = a[0]*(a[4]*a[8] - a[5]*a[7]) + - a[1]*(a[3]*a[8] - a[5]*a[6]) + + a[2]*(a[3]*a[7] - a[4]*a[6]); + + b[0] = (a[4]*a[8] - a[7]*a[5]) / det_a; + b[1] = (a[7]*a[2] - a[1]*a[8]) / det_a; + b[2] = (a[1]*a[5] - a[4]*a[2]) / det_a; + b[3] = (a[5]*a[6] - a[8]*a[3]) / det_a; + b[4] = (a[8]*a[0] - a[2]*a[6]) / det_a; + b[5] = (a[2]*a[3] - a[5]*a[0]) / det_a; + b[6] = (a[3]*a[7] - a[6]*a[4]) / det_a; + b[7] = (a[6]*a[1] - a[0]*a[7]) / det_a; + b[8] = (a[0]*a[4] - a[3]*a[1]) / det_a; +} + +// c = a^T @ b (used for both DF^-1 @ v and DF^-T @ e_form: pass dfinv or +// its transpose accordingly -- here we need dfinv @ v (not transposed) for +// push_eta_stage, and dfinvT @ e_form for push_v_with_efield, so both a +// plain and a transposed matvec are provided). +__device__ void matvec_dev(const double* a, const double* v, double* out) +{ + out[0] = a[0]*v[0] + a[1]*v[1] + a[2]*v[2]; + out[1] = a[3]*v[0] + a[4]*v[1] + a[5]*v[2]; + out[2] = a[6]*v[0] + a[7]*v[1] + a[8]*v[2]; +} + +// c = a @ b, 3x3 row-major matrices. +__device__ void matmat_dev(const double* a, const double* b, double* c) +{ + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + c[3*i+j] = a[3*i+0]*b[0*3+j] + a[3*i+1]*b[1*3+j] + a[3*i+2]*b[2*3+j]; + } + } +} + +__device__ void matvecT_dev(const double* a, const double* v, double* out) +{ + out[0] = a[0]*v[0] + a[3]*v[1] + a[6]*v[2]; + out[1] = a[1]*v[0] + a[4]*v[1] + a[7]*v[2]; + out[2] = a[2]*v[0] + a[5]*v[1] + a[8]*v[2]; +} + +// df_out is row-major 3x3 (df_out[3*i+j] = dF_i/deta_j), matching +// struphy.geometry.mappings_kernels.cuboid_df / colella_df exactly. +__device__ void cuboid_df_dev(const double* params, double* df_out) +{ + // params = (l1, r1, l2, r2, l3, r3) + for (int k = 0; k < 9; k++) df_out[k] = 0.0; + df_out[0] = params[1] - params[0]; + df_out[4] = params[3] - params[2]; + df_out[8] = params[5] - params[4]; +} + +__device__ void colella_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (Lx, Ly, alpha, Lz) + const double lx = params[0], ly = params[1], alpha = params[2], lz = params[3]; + const double twopi = 6.283185307179586; + const double s1 = sin(twopi * eta1), c1 = cos(twopi * eta1); + const double s2 = sin(twopi * eta2), c2 = cos(twopi * eta2); + + df_out[0] = lx * (1.0 + alpha * c1 * s2 * twopi); + df_out[1] = lx * alpha * s1 * c2 * twopi; + df_out[2] = 0.0; + df_out[3] = ly * alpha * c1 * s2 * twopi; + df_out[4] = ly * (1.0 + alpha * s1 * c2 * twopi); + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void orthogonal_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (Lx, Ly, alpha, Lz) + const double lx = params[0], ly = params[1], alpha = params[2], lz = params[3]; + const double twopi = 6.283185307179586; + + for (int k = 0; k < 9; k++) df_out[k] = 0.0; + df_out[0] = lx * (1.0 + alpha * cos(twopi * eta1) * twopi); + df_out[4] = ly * (1.0 + alpha * cos(twopi * eta2) * twopi); + df_out[8] = lz; +} + +__device__ void hollow_cyl_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (a1, a2, Lz, poc); faithful port of + // struphy.geometry.mappings_kernels.hollow_cyl_df, including its + // existing df_out[0,0]/df_out[1,0] not dividing eta2's argument by poc + // (unlike f_out and every other entry here) -- not "fixed" here, since + // this is a port, not a bugfix. + const double a1 = params[0], a2 = params[1], lz = params[2], poc = params[3]; + const double twopi = 6.283185307179586; + const double da = a2 - a1; + const double r = a1 + eta1 * da; + + df_out[0] = da * cos(twopi * eta2); + df_out[1] = -twopi / poc * r * sin(twopi * eta2 / poc); + df_out[2] = 0.0; + df_out[3] = da * sin(twopi * eta2); + df_out[4] = twopi / poc * r * cos(twopi * eta2 / poc); + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void powered_ellipse_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (rx, ry, Lz, s) + const double rx = params[0], ry = params[1], lz = params[2], s = params[3]; + const double twopi = 6.283185307179586; + const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); + const double e_sm1 = pow(eta1, s - 1.0); + const double e_s = pow(eta1, s); + + df_out[0] = e_sm1 * rx * c2; + df_out[1] = -twopi * e_s * rx * s2; + df_out[2] = 0.0; + df_out[3] = e_sm1 * ry * s2; + df_out[4] = twopi * e_s * ry * c2; + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void hollow_torus_df_dev(double eta1, double eta2, double eta3, const double* params, double* df_out) +{ + // params = (a1, a2, R0, sfl, pol_period, tor_period) + const double a1 = params[0], a2 = params[1], r0 = params[2]; + const double sfl = params[3], pol_period = params[4], tor_period = params[5]; + const double pi = 3.14159265358979323846; + const double twopi = 6.283185307179586; + const double da = a2 - a1; + + if (sfl == 1.0) { + const double r = a1 + da * eta1; + const double eps = r / r0; + const double eps_p = da / r0; + const double tpe = tan(pi * eta2); + const double cpe = cos(pi * eta2); + const double tpe_p = pi / (cpe * cpe); + const double g = sqrt((1.0 + eps) / (1.0 - eps)); + const double g_p = 1.0 / (2.0 * g) * (eps_p * (1.0 - eps) + (1.0 + eps) * eps_p) / ((1.0 - eps) * (1.0 - eps)); + const double theta = 2.0 * atan(g * tpe); + const double denom = 1.0 + (g * tpe) * (g * tpe); + const double dtheta_deta1 = 2.0 / denom * g_p * tpe; + const double dtheta_deta2 = 2.0 / denom * g * tpe_p; + const double ct = cos(theta), st = sin(theta); + const double cf = cos(twopi * eta3 / tor_period), sf = sin(twopi * eta3 / tor_period); + + df_out[0] = (da * ct - r * st * dtheta_deta1) * cf; + df_out[1] = -r * st * dtheta_deta2 * cf; + df_out[2] = -twopi / tor_period * (r * ct + r0) * sf; + + df_out[3] = (da * ct - r * st * dtheta_deta1) * (-1.0) * sf; + df_out[4] = -r * st * dtheta_deta2 * (-1.0) * sf; + df_out[5] = twopi / tor_period * (r * ct + r0) * (-1.0) * cf; + + df_out[6] = da * st + r * ct * dtheta_deta1; + df_out[7] = r * ct * dtheta_deta2; + df_out[8] = 0.0; + } else { + const double r = a1 + eta1 * da; + const double cp = cos(twopi * eta2 / pol_period), sp = sin(twopi * eta2 / pol_period); + const double cf = cos(twopi * eta3 / tor_period), sf = sin(twopi * eta3 / tor_period); + + df_out[0] = da * cp * cf; + df_out[1] = -twopi / pol_period * r * sp * cf; + df_out[2] = -twopi / tor_period * (r * cp + r0) * sf; + + df_out[3] = da * cp * (-1.0) * sf; + df_out[4] = -twopi / pol_period * r * sp * (-1.0) * sf; + df_out[5] = (r * cp + r0) * (-1.0) * cf * twopi / tor_period; + + df_out[6] = da * sp; + df_out[7] = r * cp * twopi / pol_period; + df_out[8] = 0.0; + } +} + +__device__ void shafranov_shift_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (rx, ry, Lz, delta) + const double rx = params[0], ry = params[1], lz = params[2], de = params[3]; + const double twopi = 6.283185307179586; + const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); + + df_out[0] = rx * c2 - 2.0 * eta1 * rx * de; + df_out[1] = -twopi * (eta1 * rx) * s2; + df_out[2] = 0.0; + df_out[3] = ry * s2; + df_out[4] = twopi * (eta1 * ry) * c2; + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void shafranov_sqrt_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (rx, ry, Lz, delta) + const double rx = params[0], ry = params[1], lz = params[2], de = params[3]; + const double twopi = 6.283185307179586; + const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); + + df_out[0] = rx * c2 - 0.5 / sqrt(eta1) * rx * de; + df_out[1] = -twopi * (eta1 * rx) * s2; + df_out[2] = 0.0; + df_out[3] = ry * s2; + df_out[4] = twopi * (eta1 * ry) * c2; + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void shafranov_dshaped_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (R0, Lz, delta_x, delta_y, delta_gs, epsilon_gs, kappa_gs) + const double r0 = params[0], lz = params[1], dx = params[2], dy = params[3]; + const double dg = params[4], eg = params[5], kg = params[6]; + const double pi = 3.14159265358979323846; + const double twopi = 6.283185307179586; + const double asin_dg = asin(dg); + const double s2 = sin(twopi * eta2), c2 = cos(twopi * eta2); + const double phase = eta1 * s2 * asin_dg + twopi * eta2; + + df_out[0] = r0 * ( + -2.0 * dx * eta1 + - eg * eta1 * s2 * asin_dg * sin(phase) + + eg * cos(phase) + ); + df_out[1] = -r0 * eg * eta1 * (twopi * eta1 * c2 * asin_dg + twopi) * sin(phase); + df_out[2] = 0.0; + df_out[3] = r0 * (-2.0 * dy * eta1 + eg * kg * s2); + df_out[4] = twopi * r0 * eg * eta1 * kg * c2; + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +// Returns 1 if kind_map is supported and df_out was filled, 0 otherwise. +__device__ int df_dispatch_dev(int kind_map, double eta1, double eta2, double eta3, + const double* params, double* df_out) +{ + if (kind_map == 10) { cuboid_df_dev(params, df_out); return 1; } + if (kind_map == 11) { orthogonal_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 12) { colella_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 20) { hollow_cyl_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 21) { powered_ellipse_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 22) { hollow_torus_df_dev(eta1, eta2, eta3, params, df_out); return 1; } + if (kind_map == 30) { shafranov_shift_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 31) { shafranov_sqrt_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 32) { shafranov_dshaped_df_dev(eta1, eta2, params, df_out); return 1; } + return 0; +} + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +// Same as pusher_kernels_cuda.py's push_v_with_efield_cuboid's b_d_splines_dev, +// duplicated here because each cp.RawKernel source string is compiled +// independently (no cross-source linking). +__device__ void b_d_splines_dev(const double* t, int p, double eta, int span, double* bn, double* bd) +{ + double left[MAXP]; + double right[MAXP]; + int pd = p - 1; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + for (int i = 0; i < p; i++) bd[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + + if (j == p - 1) { + for (int il = 0; il <= pd; il++) { + bd[pd - il] = (double)p / (t[span - il + p] - t[span - il]) * bn[pd - il]; + } + } + + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +__device__ double det3_dev(const double* a) +{ + return a[0]*(a[4]*a[8] - a[5]*a[7]) + - a[1]*(a[3]*a[8] - a[5]*a[6]) + + a[2]*(a[3]*a[7] - a[4]*a[6]); +} + +__device__ void cross_dev(const double* a, const double* b, double* out) +{ + out[0] = a[1]*b[2] - a[2]*b[1]; + out[1] = a[2]*b[0] - a[0]*b[2]; + out[2] = a[0]*b[1] - a[1]*b[0]; +} + +__device__ double dot3_dev(const double* a, const double* b) +{ + return a[0]*b[0] + a[1]*b[1] + a[2]*b[2]; +} + +// Single-point evaluation of a Derham 2-form spline, matching +// struphy.bsplines.evaluation_kernels_3d.eval_2form_spline_mpi (N-D-D / +// D-N-D / D-D-N tensor-product sums, the dual basis combination of the +// 1-form evaluation in push_v_with_efield_general above). +__device__ void eval_2form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bd1, + const double* bn2, const double* bd2, + const double* bn3, const double* bd3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c1, int n2x1, int n3x1, + const double* c2, int n2x2, int n3x2, + const double* c3, int n2x3, int n3x3, + double* out) +{ + out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; + + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bn1[il1] * bd2[il2] * bd3[il3]; + } + } + } + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bd1[il1] * bn2[il2] * bd3[il3]; + } + } + } + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bd1[il1] * bd2[il2] * bn3[il3]; + } + } + } +} + +extern "C" __global__ +void push_eta_stage_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; + + double dfm[9], dfinv[9], v[3], k[3]; + v[0] = row[3]; v[1] = row[4]; v[2] = row[5]; + + df_dispatch_dev(kind_map, row[0], row[1], row[2], params, dfm); + matrix_inv_dev(dfm, dfinv); + matvec_dev(dfinv, v, k); + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_v_with_efield_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* e1_1, const int n2x1, const int n3x1, + const double* e1_2, const int n2x2, const int n3x2, + const double* e1_3, const int n2x3, const int n3x3, + const int kind_map, + const double* params, + const double dt_const) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + + double bn1[MAXP + 1], bd1[MAXP]; + double bn2[MAXP + 1], bd2[MAXP]; + double bn3[MAXP + 1], bd3[MAXP]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double e_form[3] = {0.0, 0.0, 0.0}; + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + e_form[0] += e1_1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; + } + } + } + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + e_form[1] += e1_2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; + } + } + } + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + e_form[2] += e1_3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; + } + } + } + + double dfm[9], dfinv[9], dfinvT_e[3]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + matvecT_dev(dfinv, e_form, dfinvT_e); + + row[3] += dt_const * dfinvT_e[0]; + row[4] += dt_const * dfinvT_e[1]; + row[5] += dt_const * dfinvT_e[2]; +} + +// Shared setup for push_vxb_analytic_general / push_vxb_implicit_general: +// evaluate DF(eta), its determinant, and the Cartesian B-field at the +// marker's position. Returns 0 (and leaves b_cart untouched) if the marker +// is a hole/ghost, matching both CPU kernels' skip check. +__device__ int eval_b_cart_dev( + const double* row, const int n_cols, const int first_init_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const int kind_map, const double* params, + double* b_cart) +{ + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + + double bn1[MAXP + 1], bd1[MAXP]; + double bn2[MAXP + 1], bd2[MAXP]; + double bn3[MAXP + 1], bd3[MAXP]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b_form[3]; + eval_2form_dev( + p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, + span1, span2, span3, start0, start1, start2, + b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + b_form + ); + + double dfm[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + const double det_df = det3_dev(dfm); + + matvec_dev(dfm, b_form, b_cart); + b_cart[0] /= det_df; + b_cart[1] /= det_df; + b_cart[2] /= det_df; + return 1; +} + +extern "C" __global__ +void push_vxb_analytic_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; + + double b_cart[3]; + eval_b_cart_dev( + row, n_cols, first_init_idx, p1, p2, p3, + tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, + b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, b_cart + ); + + const double b_abs = sqrt(b_cart[0]*b_cart[0] + b_cart[1]*b_cart[1] + b_cart[2]*b_cart[2]); + if (b_abs == 0.0) return; + + double b_norm[3] = {b_cart[0]/b_abs, b_cart[1]/b_abs, b_cart[2]/b_abs}; + double v[3] = {row[3], row[4], row[5]}; + + const double vpar = dot3_dev(v, b_norm); + + double vxb_norm[3], vperp[3], b_normxvperp[3]; + cross_dev(v, b_norm, vxb_norm); + cross_dev(b_norm, vxb_norm, vperp); + cross_dev(b_norm, vperp, b_normxvperp); + + const double cbt = cos(b_abs * dt), sbt = sin(b_abs * dt); + row[3] = vpar * b_norm[0] + cbt * vperp[0] - sbt * b_normxvperp[0]; + row[4] = vpar * b_norm[1] + cbt * vperp[1] - sbt * b_normxvperp[1]; + row[5] = vpar * b_norm[2] + cbt * vperp[2] - sbt * b_normxvperp[2]; +} + +extern "C" __global__ +void push_vxb_implicit_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + // NOTE: the CPU push_vxb_implicit only checks the hole flag, not the + // ghost flag (unlike push_vxb_analytic) -- faithfully reproduced here. + if (row[first_init_idx] == -1.0) return; + + double b_cart[3]; + eval_b_cart_dev( + row, n_cols, first_init_idx, p1, p2, p3, + tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, + b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, b_cart + ); + + // b_prod = [[0, bz, -by], [-bz, 0, bx], [by, -bx, 0]] (row-major), such + // that b_prod @ v == v x b_cart (matches the CPU kernel's b_prod, which + // solves v x B via a matrix product rather than a cross product). + double b_prod[9] = { + 0.0, b_cart[2], -b_cart[1], + -b_cart[2], 0.0, b_cart[0], + b_cart[1], -b_cart[0], 0.0, + }; + + double rhs[9], lhs[9]; + for (int k = 0; k < 9; k++) { + const double id = (k == 0 || k == 4 || k == 8) ? 1.0 : 0.0; + rhs[k] = id + 0.5 * dt * b_prod[k]; + lhs[k] = id - 0.5 * dt * b_prod[k]; + } + + double lhs_inv[9]; + matrix_inv_dev(lhs, lhs_inv); + + double v[3] = {row[3], row[4], row[5]}; + double vec[3], res[3]; + matvec_dev(rhs, v, vec); + matvec_dev(lhs_inv, vec, res); + + row[3] = res[0]; + row[4] = res[1]; + row[5] = res[2]; +} + +// Single-point evaluation of a Derham 1-form spline (D-N-N / N-D-N / N-N-D), +// matching struphy.bsplines.evaluation_kernels_3d.eval_1form_spline_mpi. +// A standalone copy of the same math already inlined in +// push_v_with_efield_general above -- kept separate (not factored out and +// reused there) to avoid touching that already-validated kernel. +__device__ void eval_1form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bd1, + const double* bn2, const double* bd2, + const double* bn3, const double* bd3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c1, int n2x1, int n3x1, + const double* c2, int n2x2, int n3x2, + const double* c3, int n2x3, int n3x3, + double* out) +{ + out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; + + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; + } + } + } + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; + } + } + } + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; + } + } + } +} + +// Single-point evaluation of a vector-field spline (H^1)^3 (N-N-N for every +// component), matching +// struphy.bsplines.evaluation_kernels_3d.eval_vectorfield_spline_mpi. +__device__ void eval_vectorfield_dev( + int p1, int p2, int p3, + const double* bn1, const double* bn2, const double* bn3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c1, int n2x1, int n3x1, + const double* c2, int n2x2, int n3x2, + const double* c3, int n2x3, int n3x3, + double* out) +{ + out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + double b123 = bn1[il1] * bn2[il2] * bn3[il3]; + out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * b123; + out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * b123; + out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * b123; + } + } + } +} + +// Shared setup for push_bxu_{Hdiv,Hcurl,H1vec}_general: evaluate DF(eta), +// its determinant, and the Cartesian B-field (always a 2-form) at the +// marker's position. Also computes and caches the local N-/D-spline basis +// values and span indices, reused by the caller for its own U-field +// evaluation (which differs per FEEC space). +__device__ void eval_b_cart_and_basis_dev( + double eta1, double eta2, double eta3, + int p1, int p2, int p3, + const double* tn1, int len_tn1, + const double* tn2, int len_tn2, + const double* tn3, int len_tn3, + int start0, int start1, int start2, + const double* b2_1, int n2x1, int n3x1, + const double* b2_2, int n2x2, int n3x2, + const double* b2_3, int n2x3, int n3x3, + int kind_map, const double* params, + double* bn1, double* bd1, double* bn2, double* bd2, double* bn3, double* bd3, + int* span1_out, int* span2_out, int* span3_out, + double* dfm, double* b_cart) +{ + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + *span1_out = span1; *span2_out = span2; *span3_out = span3; + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b_form[3]; + eval_2form_dev( + p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, + span1, span2, span3, start0, start1, start2, + b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + b_form + ); + + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + const double det_df = det3_dev(dfm); + matvec_dev(dfm, b_form, b_cart); + b_cart[0] /= det_df; + b_cart[1] /= det_df; + b_cart[2] /= det_df; +} + +extern "C" __global__ +void push_bxu_Hdiv_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const double* u2_1, const int m2x1, const int m3x1, + const double* u2_2, const int m2x2, const int m3x2, + const double* u2_3, const int m2x3, const int m3x3, + const int kind_map, + const double* params, + const double boundary_cut, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfm[9], b_cart[3]; + eval_b_cart_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart + ); + + double u_form[3]; + eval_2form_dev( + p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, + span1, span2, span3, start0, start1, start2, + u2_1, m2x1, m3x1, u2_2, m2x2, m3x2, u2_3, m2x3, m3x3, + u_form + ); + const double det_df = det3_dev(dfm); + double u_cart[3]; + matvec_dev(dfm, u_form, u_cart); + u_cart[0] /= det_df; u_cart[1] /= det_df; u_cart[2] /= det_df; + + double e_cart[3]; + cross_dev(b_cart, u_cart, e_cart); + row[3] += dt * e_cart[0]; + row[4] += dt * e_cart[1]; + row[5] += dt * e_cart[2]; +} + +extern "C" __global__ +void push_bxu_Hcurl_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const double* u1_1, const int m2x1, const int m3x1, + const double* u1_2, const int m2x2, const int m3x2, + const double* u1_3, const int m2x3, const int m3x3, + const int kind_map, + const double* params, + const double boundary_cut, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfm[9], b_cart[3]; + eval_b_cart_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart + ); + + double u_form[3]; + eval_1form_dev( + p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, + span1, span2, span3, start0, start1, start2, + u1_1, m2x1, m3x1, u1_2, m2x2, m3x2, u1_3, m2x3, m3x3, + u_form + ); + double dfinv[9], dfinvT[9], u_cart[3]; + matrix_inv_dev(dfm, dfinv); + matvecT_dev(dfinv, u_form, u_cart); + + double e_cart[3]; + cross_dev(b_cart, u_cart, e_cart); + row[3] += dt * e_cart[0]; + row[4] += dt * e_cart[1]; + row[5] += dt * e_cart[2]; +} + +extern "C" __global__ +void push_bxu_H1vec_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const double* uv_1, const int m2x1, const int m3x1, + const double* uv_2, const int m2x2, const int m3x2, + const double* uv_3, const int m2x3, const int m3x3, + const int kind_map, + const double* params, + const double boundary_cut, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfm[9], b_cart[3]; + eval_b_cart_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart + ); + + double u_form[3]; + eval_vectorfield_dev( + p1, p2, p3, bn1, bn2, bn3, + span1, span2, span3, start0, start1, start2, + uv_1, m2x1, m3x1, uv_2, m2x2, m3x2, uv_3, m2x3, m3x3, + u_form + ); + double u_cart[3]; + matvec_dev(dfm, u_form, u_cart); + + double e_cart[3]; + cross_dev(b_cart, u_cart, e_cart); + row[3] += dt * e_cart[0]; + row[4] += dt * e_cart[1]; + row[5] += dt * e_cart[2]; +} + +// Shared setup for push_pc_GXu{_full,}_general: DF(eta)/dfinv/dfinvT plus +// span/basis values, reused by the caller to evaluate the 3 (or 2) rows of +// the GXu matrix via eval_1form_dev. +__device__ void eval_dfinvt_and_basis_dev( + double eta1, double eta2, double eta3, + int p1, int p2, int p3, + const double* tn1, int len_tn1, + const double* tn2, int len_tn2, + const double* tn3, int len_tn3, + int kind_map, const double* params, + double* bn1, double* bd1, double* bn2, double* bd2, double* bn3, double* bd3, + int* span1_out, int* span2_out, int* span3_out, + double* dfinvt) +{ + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + *span1_out = span1; *span2_out = span2; *span3_out = span3; + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + // dfinvt = dfinv^T, stored explicitly (row-major) since the caller needs + // it as a plain matrix for matvec_dev, not just for a single matvecT_dev + // application. + dfinvt[0] = dfinv[0]; dfinvt[1] = dfinv[3]; dfinvt[2] = dfinv[6]; + dfinvt[3] = dfinv[1]; dfinvt[4] = dfinv[4]; dfinvt[5] = dfinv[7]; + dfinvt[6] = dfinv[2]; dfinvt[7] = dfinv[5]; dfinvt[8] = dfinv[8]; +} + +extern "C" __global__ +void push_pc_GXu_full_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* g11, const double* g12, const double* g13, + const double* g21, const double* g22, const double* g23, + const double* g31, const double* g32, const double* g33, + const int n2xc1, const int n3xc1, + const int n2xc2, const int n3xc2, + const int n2xc3, const int n3xc3, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfinvt[9]; + eval_dfinvt_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfinvt + ); + + // components 1/2/3 of a 1-form generally have different shapes + // (D-N-N / N-D-N / N-N-D), but the shape only depends on the component + // index, not on which "row" of GXu is being evaluated -- so the same + // (n2xc1,n3xc1)/(n2xc2,n3xc2)/(n2xc3,n3xc3) apply to all three rows. + double gxu_row0[3], gxu_row1[3], gxu_row2[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g11, n2xc1, n3xc1, g12, n2xc2, n3xc2, g13, n2xc3, n3xc3, gxu_row0); + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g21, n2xc1, n3xc1, g22, n2xc2, n3xc2, g23, n2xc3, n3xc3, gxu_row1); + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g31, n2xc1, n3xc1, g32, n2xc2, n3xc2, g33, n2xc3, n3xc3, gxu_row2); + + // GXu[i][j] = gxu_row_i[j]; e[j] = sum_i GXu[i][j] * v[i] + double v[3] = {row[3], row[4], row[5]}; + double e[3]; + e[0] = gxu_row0[0]*v[0] + gxu_row1[0]*v[1] + gxu_row2[0]*v[2]; + e[1] = gxu_row0[1]*v[0] + gxu_row1[1]*v[1] + gxu_row2[1]*v[2]; + e[2] = gxu_row0[2]*v[0] + gxu_row1[2]*v[1] + gxu_row2[2]*v[2]; + + double e_cart[3]; + matvec_dev(dfinvt, e, e_cart); + + row[3] -= dt * e_cart[0] / 2.0; + row[4] -= dt * e_cart[1] / 2.0; + row[5] -= dt * e_cart[2] / 2.0; +} + +extern "C" __global__ +void push_pc_GXu_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* g11, const double* g12, const double* g13, + const double* g21, const double* g22, const double* g23, + const int n2xc1, const int n3xc1, + const int n2xc2, const int n3xc2, + const int n2xc3, const int n3xc3, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfinvt[9]; + eval_dfinvt_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfinvt + ); + + double gxu_row0[3], gxu_row1[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g11, n2xc1, n3xc1, g12, n2xc2, n3xc2, g13, n2xc3, n3xc3, gxu_row0); + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g21, n2xc1, n3xc1, g22, n2xc2, n3xc2, g23, n2xc3, n3xc3, gxu_row1); + + double v[3] = {row[3], row[4], row[5]}; + double e[3]; + e[0] = gxu_row0[0]*v[0] + gxu_row1[0]*v[1]; + e[1] = gxu_row0[1]*v[0] + gxu_row1[1]*v[1]; + e[2] = gxu_row0[2]*v[0] + gxu_row1[2]*v[1]; + + double e_cart[3]; + matvec_dev(dfinvt, e, e_cart); + + row[3] -= dt * e_cart[0] / 2.0; + row[4] -= dt * e_cart[1] / 2.0; + row[5] -= dt * e_cart[2] / 2.0; +} + +extern "C" __global__ +void push_pc_eta_stage_Hcurl_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* u_1, const int n2x1, const int n3x1, + const double* u_2, const int n2x2, const int n3x2, + const double* u_3, const int n2x3, const int n3x3, + const int use_perp_model, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9], dfinvt[9], ginv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + dfinvt[0]=dfinv[0]; dfinvt[1]=dfinv[3]; dfinvt[2]=dfinv[6]; + dfinvt[3]=dfinv[1]; dfinvt[4]=dfinv[4]; dfinvt[5]=dfinv[7]; + dfinvt[6]=dfinv[2]; dfinvt[7]=dfinv[5]; dfinvt[8]=dfinv[8]; + matmat_dev(dfinv, dfinvt, ginv); + + double k_v[3]; + matvec_dev(dfinv, v, k_v); + + double u[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); + if (use_perp_model) u[2] = 0.0; + + double k_u[3]; + matvec_dev(ginv, u, k_u); + + double k[3] = {k_v[0]+k_u[0], k_v[1]+k_u[1], k_v[2]+k_u[2]}; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_pc_eta_stage_Hdiv_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* u_1, const int n2x1, const int n3x1, + const double* u_2, const int n2x2, const int n3x2, + const double* u_3, const int n2x3, const int n3x3, + const int use_perp_model, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + const double det_df = det3_dev(dfm); + matrix_inv_dev(dfm, dfinv); + + double k_v[3]; + matvec_dev(dfinv, v, k_v); + + double u[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); + if (use_perp_model) u[2] = 0.0; + + double k_u[3] = {u[0]/det_df, u[1]/det_df, u[2]/det_df}; + double k[3] = {k_v[0]+k_u[0], k_v[1]+k_u[1], k_v[2]+k_u[2]}; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_pc_eta_stage_H1vec_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* u_1, const int n2x1, const int n3x1, + const double* u_2, const int n2x2, const int n3x2, + const double* u_3, const int n2x3, const int n3x3, + const int use_perp_model, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + + double k_v[3]; + matvec_dev(dfinv, v, k_v); + + double u[3]; + eval_vectorfield_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, start0, start1, start2, + u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); + if (use_perp_model) u[2] = 0.0; + + double k[3] = {k_v[0]+u[0], k_v[1]+u[1], k_v[2]+u[2]}; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_weights_with_efield_lin_va_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* e1_1, const int n2x1, const int n3x1, + const double* e1_2, const int n2x2, const int n3x2, + const double* e1_3, const int n2x3, const int n3x3, + const double* f0_values, + const double kappa, + const double vth, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + + double dfinv_v[3]; + matvec_dev(dfinv, v, dfinv_v); + + double e_vec[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + e1_1, n2x1, n3x1, e1_2, n2x2, n3x2, e1_3, n2x3, n3x3, e_vec); + + const double update = (dfinv_v[0]*e_vec[0] + dfinv_v[1]*e_vec[1] + dfinv_v[2]*e_vec[2]) + * f0_values[ip] * kappa * dt / (2.0 * row[7] * vth * vth); + row[6] += update; +} + +// Single-point evaluation of a Derham 0-form spline (N-N-N), matching +// struphy.bsplines.evaluation_kernels_3d.eval_0form_spline_mpi. +__device__ double eval_0form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bn2, const double* bn3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c, int n2x, int n3x) +{ + double out = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] * bn1[il1] * bn2[il2] * bn3[il3]; + } + } + } + return out; +} + +extern "C" __global__ +void push_deterministic_diffusion_stage_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* pi_u, const int n2xu, const int n3xu, + const double* pi_grad_u1, const int n2x1, const int n3x1, + const double* pi_grad_u2, const int n2x2, const int n3x2, + const double* pi_grad_u3, const int n2x3, const int n3x3, + const double diffusion_coeff, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + const double pi_u_value = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, pi_u, n2xu, n3xu); + + double pi_du_value[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + pi_grad_u1, n2x1, n3x1, pi_grad_u2, n2x2, n3x2, pi_grad_u3, n2x3, n3x3, pi_du_value); + + // ginv = G^-1 = DF^-1 @ DF^-T, matching struphy.geometry.evaluation_kernels.g_inv + // (computed there as (DF^T @ DF)^-1 instead -- same result, different + // intermediate path, reusing the dfinv this file already needs elsewhere). + double dfm[9], dfinv[9], dfinvt[9], ginv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + dfinvt[0]=dfinv[0]; dfinvt[1]=dfinv[3]; dfinvt[2]=dfinv[6]; + dfinvt[3]=dfinv[1]; dfinvt[4]=dfinv[4]; dfinvt[5]=dfinv[7]; + dfinvt[6]=dfinv[2]; dfinvt[7]=dfinv[5]; dfinvt[8]=dfinv[8]; + matmat_dev(dfinv, dfinvt, ginv); + + double tmp[3] = { + -diffusion_coeff * pi_du_value[0] / pi_u_value, + -diffusion_coeff * pi_du_value[1] / pi_u_value, + -diffusion_coeff * pi_du_value[2] / pi_u_value, + }; + double k[3]; + matvec_dev(ginv, tmp, k); + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} +""" + +_push_eta_general_kernel = None +_push_v_efield_general_kernel = None + + +def _get_eta_general_kernel(): + global _push_eta_general_kernel + if _push_eta_general_kernel is None: + import cupy as cp + + _push_eta_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_eta_stage_general") + return _push_eta_general_kernel + + +def _get_v_efield_general_kernel(): + global _push_v_efield_general_kernel + if _push_v_efield_general_kernel is None: + import cupy as cp + + _push_v_efield_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_v_with_efield_general") + return _push_v_efield_general_kernel + + +#: kind_map values df_dispatch_dev supports (Cuboid, Colella). Callers should +#: check membership before dispatching to the *_general_gpu functions below. +SUPPORTED_GENERAL_KIND_MAPS = (10, 11, 12, 20, 21, 22, 30, 31, 32) + + +def push_eta_stage_general_gpu( + markers, + n_cols: int, + first_init_idx: int, + first_free_idx: int, + kind_map: int, + params_dev, + dt_a: float, + dt_b: float, + last: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_eta_stage`, for any + domain in :data:`SUPPORTED_GENERAL_KIND_MAPS` (evaluates DF(eta) per + marker instead of assuming a constant Jacobian, unlike + :func:`push_eta_stage_cuboid_gpu`). + + ``markers`` is the host marker array, round-tripped through the device + once per call. ``params_dev`` is the domain's mapping-parameter array + (``args_domain.params``), expected to already be a small CuPy array + (cheap to keep device-resident; callers should cache it once). + """ + import cupy as cp + import numpy as np + + kernel = _get_eta_general_kernel() + n_markers = markers.shape[0] + + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_free_idx), + np.int32(kind_map), + params_dev, + np.float64(dt_a), + np.float64(dt_b), + np.float64(last), + ), + ) + dev.get(out=markers) + + +def push_v_with_efield_general_gpu( + markers, + n_cols: int, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + e1_1_dev, + e1_2_dev, + e1_3_dev, + kind_map: int, + params_dev, + dt_const: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_v_with_efield`, for any + domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. See + :func:`push_v_with_efield_cuboid_gpu` for the argument conventions + (``tn*_dev``/``e1_*_dev`` are expected to already be device-resident); + ``params_dev`` follows :func:`push_eta_stage_general_gpu`. + """ + import cupy as cp + import numpy as np + + kernel = _get_v_efield_general_kernel() + n_markers = markers.shape[0] + + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + e1_1_dev, + np.int32(e1_1_dev.shape[1]), + np.int32(e1_1_dev.shape[2]), + e1_2_dev, + np.int32(e1_2_dev.shape[1]), + np.int32(e1_2_dev.shape[2]), + e1_3_dev, + np.int32(e1_3_dev.shape[1]), + np.int32(e1_3_dev.shape[2]), + np.int32(kind_map), + params_dev, + np.float64(dt_const), + ), + ) + dev.get(out=markers) + + +_push_vxb_analytic_general_kernel = None +_push_vxb_implicit_general_kernel = None + + +def _get_vxb_analytic_general_kernel(): + global _push_vxb_analytic_general_kernel + if _push_vxb_analytic_general_kernel is None: + import cupy as cp + + _push_vxb_analytic_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_vxb_analytic_general") + return _push_vxb_analytic_general_kernel + + +def _get_vxb_implicit_general_kernel(): + global _push_vxb_implicit_general_kernel + if _push_vxb_implicit_general_kernel is None: + import cupy as cp + + _push_vxb_implicit_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_vxb_implicit_general") + return _push_vxb_implicit_general_kernel + + +def _launch_vxb_general(kernel, markers, n_cols, first_init_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, kind_map, params_dev, dt): + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + b2_1_dev, + np.int32(b2_1_dev.shape[1]), + np.int32(b2_1_dev.shape[2]), + b2_2_dev, + np.int32(b2_2_dev.shape[1]), + np.int32(b2_2_dev.shape[2]), + b2_3_dev, + np.int32(b2_3_dev.shape[1]), + np.int32(b2_3_dev.shape[2]), + np.int32(kind_map), + params_dev, + np.float64(dt), + ), + ) + dev.get(out=markers) + + +def push_vxb_analytic_general_gpu( + markers, + n_cols: int, + first_init_idx: int, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + b2_1_dev, + b2_2_dev, + b2_3_dev, + kind_map: int, + params_dev, + dt: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_vxb_analytic`, for any + domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. Argument conventions match + :func:`push_v_with_efield_general_gpu` (``tn*_dev``/``b2_*_dev`` already + device-resident, ``params_dev`` the domain's mapping-parameter array). + """ + _launch_vxb_general( + _get_vxb_analytic_general_kernel(), + markers, n_cols, first_init_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, kind_map, params_dev, dt, + ) + + +def push_vxb_implicit_general_gpu( + markers, + n_cols: int, + first_init_idx: int, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + b2_1_dev, + b2_2_dev, + b2_3_dev, + kind_map: int, + params_dev, + dt: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_vxb_implicit` (Crank- + Nicolson rotation), for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. + See :func:`push_vxb_analytic_general_gpu` for argument conventions.""" + _launch_vxb_general( + _get_vxb_implicit_general_kernel(), + markers, n_cols, first_init_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, kind_map, params_dev, dt, + ) + + +_push_bxu_hdiv_general_kernel = None +_push_bxu_hcurl_general_kernel = None +_push_bxu_h1vec_general_kernel = None + + +def _get_bxu_hdiv_general_kernel(): + global _push_bxu_hdiv_general_kernel + if _push_bxu_hdiv_general_kernel is None: + import cupy as cp + + _push_bxu_hdiv_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_Hdiv_general") + return _push_bxu_hdiv_general_kernel + + +def _get_bxu_hcurl_general_kernel(): + global _push_bxu_hcurl_general_kernel + if _push_bxu_hcurl_general_kernel is None: + import cupy as cp + + _push_bxu_hcurl_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_Hcurl_general") + return _push_bxu_hcurl_general_kernel + + +def _get_bxu_h1vec_general_kernel(): + global _push_bxu_h1vec_general_kernel + if _push_bxu_h1vec_general_kernel is None: + import cupy as cp + + _push_bxu_h1vec_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_H1vec_general") + return _push_bxu_h1vec_general_kernel + + +def _launch_bxu_general(kernel, markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, u_1_dev, u_2_dev, u_3_dev, + kind_map, params_dev, boundary_cut, dt): + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + b2_1_dev, + np.int32(b2_1_dev.shape[1]), + np.int32(b2_1_dev.shape[2]), + b2_2_dev, + np.int32(b2_2_dev.shape[1]), + np.int32(b2_2_dev.shape[2]), + b2_3_dev, + np.int32(b2_3_dev.shape[1]), + np.int32(b2_3_dev.shape[2]), + u_1_dev, + np.int32(u_1_dev.shape[1]), + np.int32(u_1_dev.shape[2]), + u_2_dev, + np.int32(u_2_dev.shape[1]), + np.int32(u_2_dev.shape[2]), + u_3_dev, + np.int32(u_3_dev.shape[1]), + np.int32(u_3_dev.shape[2]), + np.int32(kind_map), + params_dev, + np.float64(boundary_cut), + np.float64(dt), + ), + ) + dev.get(out=markers) + + +def push_bxu_Hdiv_general_gpu( + markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, u2_1_dev, u2_2_dev, u2_3_dev, + kind_map: int, params_dev, boundary_cut: float, dt: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_Hdiv`, for any domain + in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u2_*_dev`` is the U-field's + 2-form FE coefficients (same evaluation as ``b2_*_dev``).""" + _launch_bxu_general( + _get_bxu_hdiv_general_kernel(), markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, u2_1_dev, u2_2_dev, u2_3_dev, kind_map, params_dev, boundary_cut, dt, + ) + + +def push_bxu_Hcurl_general_gpu( + markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, u1_1_dev, u1_2_dev, u1_3_dev, + kind_map: int, params_dev, boundary_cut: float, dt: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_Hcurl`, for any + domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u1_*_dev`` is the + U-field's 1-form FE coefficients.""" + _launch_bxu_general( + _get_bxu_hcurl_general_kernel(), markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, u1_1_dev, u1_2_dev, u1_3_dev, kind_map, params_dev, boundary_cut, dt, + ) + + +def push_bxu_H1vec_general_gpu( + markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, uv_1_dev, uv_2_dev, uv_3_dev, + kind_map: int, params_dev, boundary_cut: float, dt: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_H1vec`, for any + domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``uv_*_dev`` is the + U-field's (H^1)^3 vector-field FE coefficients.""" + _launch_bxu_general( + _get_bxu_h1vec_general_kernel(), markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2_1_dev, b2_2_dev, b2_3_dev, uv_1_dev, uv_2_dev, uv_3_dev, kind_map, params_dev, boundary_cut, dt, + ) + + +_push_pc_gxu_full_general_kernel = None +_push_pc_gxu_general_kernel = None + + +def _get_pc_gxu_full_general_kernel(): + global _push_pc_gxu_full_general_kernel + if _push_pc_gxu_full_general_kernel is None: + import cupy as cp + + _push_pc_gxu_full_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_GXu_full_general") + return _push_pc_gxu_full_general_kernel + + +def _get_pc_gxu_general_kernel(): + global _push_pc_gxu_general_kernel + if _push_pc_gxu_general_kernel is None: + import cupy as cp + + _push_pc_gxu_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_GXu_general") + return _push_pc_gxu_general_kernel + + +def push_pc_GXu_full_general_gpu( + markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev, g31_dev, g32_dev, g33_dev, + kind_map: int, params_dev, dt: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_pc_GXu_full`, for any + domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``g{i}{j}_dev`` is the FE + coefficients of :math:`\\nabla_j(\\mathcal X \\cdot u)_i`, each row + ``i`` a 1-form (same evaluation as ``push_v_with_efield_general_gpu``'s + ``e1_*``).""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + g = (g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev, g31_dev, g32_dev, g33_dev) + _get_pc_gxu_full_general_kernel()( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *g, + np.int32(g11_dev.shape[1]), + np.int32(g11_dev.shape[2]), + np.int32(g12_dev.shape[1]), + np.int32(g12_dev.shape[2]), + np.int32(g13_dev.shape[1]), + np.int32(g13_dev.shape[2]), + np.int32(kind_map), + params_dev, + np.float64(dt), + ), + ) + dev.get(out=markers) + + +def push_pc_GXu_general_gpu( + markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev, + kind_map: int, params_dev, dt: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_pc_GXu` (the 2-row + variant of :func:`push_pc_GXu_full_general_gpu`), for any domain in + :data:`SUPPORTED_GENERAL_KIND_MAPS`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + g = (g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev) + _get_pc_gxu_general_kernel()( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *g, + np.int32(g11_dev.shape[1]), + np.int32(g11_dev.shape[2]), + np.int32(g12_dev.shape[1]), + np.int32(g12_dev.shape[2]), + np.int32(g13_dev.shape[1]), + np.int32(g13_dev.shape[2]), + np.int32(kind_map), + params_dev, + np.float64(dt), + ), + ) + dev.get(out=markers) + + +_push_pc_eta_hcurl_general_kernel = None +_push_pc_eta_hdiv_general_kernel = None +_push_pc_eta_h1vec_general_kernel = None + + +def _get_pc_eta_hcurl_general_kernel(): + global _push_pc_eta_hcurl_general_kernel + if _push_pc_eta_hcurl_general_kernel is None: + import cupy as cp + + _push_pc_eta_hcurl_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_Hcurl_general") + return _push_pc_eta_hcurl_general_kernel + + +def _get_pc_eta_hdiv_general_kernel(): + global _push_pc_eta_hdiv_general_kernel + if _push_pc_eta_hdiv_general_kernel is None: + import cupy as cp + + _push_pc_eta_hdiv_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_Hdiv_general") + return _push_pc_eta_hdiv_general_kernel + + +def _get_pc_eta_h1vec_general_kernel(): + global _push_pc_eta_h1vec_general_kernel + if _push_pc_eta_h1vec_general_kernel is None: + import cupy as cp + + _push_pc_eta_h1vec_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_H1vec_general") + return _push_pc_eta_h1vec_general_kernel + + +def _launch_pc_eta_general(kernel, markers, n_cols, first_init_idx, first_free_idx, pn, + tn1_dev, tn2_dev, tn3_dev, starts, u_1_dev, u_2_dev, u_3_dev, + use_perp_model, kind_map, params_dev, dt_a, dt_b, last): + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_free_idx), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + u_1_dev, + np.int32(u_1_dev.shape[1]), + np.int32(u_1_dev.shape[2]), + u_2_dev, + np.int32(u_2_dev.shape[1]), + np.int32(u_2_dev.shape[2]), + u_3_dev, + np.int32(u_3_dev.shape[1]), + np.int32(u_3_dev.shape[2]), + np.int32(1 if use_perp_model else 0), + np.int32(kind_map), + params_dev, + np.float64(dt_a), + np.float64(dt_b), + np.float64(last), + ), + ) + dev.get(out=markers) + + +def push_pc_eta_stage_Hcurl_general_gpu( + markers, n_cols, first_init_idx, first_free_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, + u_1_dev, u_2_dev, u_3_dev, use_perp_model: bool, kind_map: int, params_dev, dt_a: float, dt_b: float, last: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_Hcurl`, for + any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the + U-field's 1-form FE coefficients.""" + _launch_pc_eta_general( + _get_pc_eta_hcurl_general_kernel(), markers, n_cols, first_init_idx, first_free_idx, pn, + tn1_dev, tn2_dev, tn3_dev, starts, u_1_dev, u_2_dev, u_3_dev, + use_perp_model, kind_map, params_dev, dt_a, dt_b, last, + ) + + +def push_pc_eta_stage_Hdiv_general_gpu( + markers, n_cols, first_init_idx, first_free_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, + u_1_dev, u_2_dev, u_3_dev, use_perp_model: bool, kind_map: int, params_dev, dt_a: float, dt_b: float, last: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_Hdiv`, for + any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the + U-field's 2-form FE coefficients.""" + _launch_pc_eta_general( + _get_pc_eta_hdiv_general_kernel(), markers, n_cols, first_init_idx, first_free_idx, pn, + tn1_dev, tn2_dev, tn3_dev, starts, u_1_dev, u_2_dev, u_3_dev, + use_perp_model, kind_map, params_dev, dt_a, dt_b, last, + ) + + +def push_pc_eta_stage_H1vec_general_gpu( + markers, n_cols, first_init_idx, first_free_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, + u_1_dev, u_2_dev, u_3_dev, use_perp_model: bool, kind_map: int, params_dev, dt_a: float, dt_b: float, last: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_H1vec`, for + any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the + U-field's (H^1)^3 vector-field FE coefficients.""" + _launch_pc_eta_general( + _get_pc_eta_h1vec_general_kernel(), markers, n_cols, first_init_idx, first_free_idx, pn, + tn1_dev, tn2_dev, tn3_dev, starts, u_1_dev, u_2_dev, u_3_dev, + use_perp_model, kind_map, params_dev, dt_a, dt_b, last, + ) + + +_push_weights_efield_lin_va_general_kernel = None + + +def _get_weights_efield_lin_va_general_kernel(): + global _push_weights_efield_lin_va_general_kernel + if _push_weights_efield_lin_va_general_kernel is None: + import cupy as cp + + _push_weights_efield_lin_va_general_kernel = cp.RawKernel( + _GENERAL_GEOMETRY_SRC, "push_weights_with_efield_lin_va_general" + ) + return _push_weights_efield_lin_va_general_kernel + + +def push_weights_with_efield_lin_va_general_gpu( + markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, + e1_1_dev, e1_2_dev, e1_3_dev, f0_values, kappa: float, vth: float, + kind_map: int, params_dev, dt: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_weights_with_efield_lin_va`, + for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``f0_values`` is + allocated via ``xp.zeros`` by the caller (EfieldWeightsCoupling) and + updated in place every step, so under CuPy it is already device-resident + -- passed straight through here, like ``e1_*_dev``.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + f0_dev = cp.ascontiguousarray(f0_values) + threads = 256 + blocks = (n_markers + threads - 1) // threads + _get_weights_efield_lin_va_general_kernel()( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + e1_1_dev, + np.int32(e1_1_dev.shape[1]), + np.int32(e1_1_dev.shape[2]), + e1_2_dev, + np.int32(e1_2_dev.shape[1]), + np.int32(e1_2_dev.shape[2]), + e1_3_dev, + np.int32(e1_3_dev.shape[1]), + np.int32(e1_3_dev.shape[2]), + f0_dev, + np.float64(kappa), + np.float64(vth), + np.int32(kind_map), + params_dev, + np.float64(dt), + ), + ) + dev.get(out=markers) + + +_push_deterministic_diffusion_general_kernel = None + + +def _get_deterministic_diffusion_general_kernel(): + global _push_deterministic_diffusion_general_kernel + if _push_deterministic_diffusion_general_kernel is None: + import cupy as cp + + _push_deterministic_diffusion_general_kernel = cp.RawKernel( + _GENERAL_GEOMETRY_SRC, "push_deterministic_diffusion_stage_general" + ) + return _push_deterministic_diffusion_general_kernel + + +def push_deterministic_diffusion_stage_general_gpu( + markers, n_cols, first_init_idx, first_free_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, + pi_u_dev, pi_grad_u1_dev, pi_grad_u2_dev, pi_grad_u3_dev, diffusion_coeff: float, + kind_map: int, params_dev, dt_a: float, dt_b: float, last: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_deterministic_diffusion_stage`, + for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``pi_u_dev`` is + the 0-form FE coefficients of the (fixed-in-time) density, ``pi_grad_u{1,2,3}_dev`` + its gradient as a 1-form.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + _get_deterministic_diffusion_general_kernel()( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_free_idx), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + pi_u_dev, + np.int32(pi_u_dev.shape[1]), + np.int32(pi_u_dev.shape[2]), + pi_grad_u1_dev, + np.int32(pi_grad_u1_dev.shape[1]), + np.int32(pi_grad_u1_dev.shape[2]), + pi_grad_u2_dev, + np.int32(pi_grad_u2_dev.shape[1]), + np.int32(pi_grad_u2_dev.shape[2]), + pi_grad_u3_dev, + np.int32(pi_grad_u3_dev.shape[1]), + np.int32(pi_grad_u3_dev.shape[2]), + np.float64(diffusion_coeff), + np.int32(kind_map), + params_dev, + np.float64(dt_a), + np.float64(dt_b), + np.float64(last), + ), + ) + dev.get(out=markers) + + +# push_random_diffusion_stage does not touch geometry at all (a pure additive +# noise kick, no Jacobian, no field evaluation), so it gets its own minimal, +# domain-independent RawKernel source instead of living in +# _GENERAL_GEOMETRY_SRC -- it applies to every domain, not just +# SUPPORTED_GENERAL_KIND_MAPS. +_RANDOM_DIFFUSION_SRC = r""" +extern "C" __global__ +void push_random_diffusion_stage( + double* markers, + const int n_cols, + const int n_markers, + const double* noise, + const double scale) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + row[0] += scale * noise[3*ip + 0]; + row[1] += scale * noise[3*ip + 1]; + row[2] += scale * noise[3*ip + 2]; +} +""" + +_push_random_diffusion_kernel = None + + +def _get_random_diffusion_kernel(): + global _push_random_diffusion_kernel + if _push_random_diffusion_kernel is None: + import cupy as cp + + _push_random_diffusion_kernel = cp.RawKernel(_RANDOM_DIFFUSION_SRC, "push_random_diffusion_stage") + return _push_random_diffusion_kernel + + +def push_random_diffusion_stage_gpu(markers, n_cols, noise, diffusion_coeff: float, dt: float): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels.push_random_diffusion_stage`. + Domain-independent (no geometry involved), so unlike the other + ``*_general_gpu`` functions this one has no ``kind_map`` restriction. + ``noise`` is a plain host NumPy array (``struphy.propagators.push_random_diffusion.PushRandomDiffusion`` + fills it via ``numpy.random``, not ``xp.random``), transferred fresh each call.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + noise_dev = cp.asarray(np.ascontiguousarray(noise), dtype=cp.float64) + scale = float(np.sqrt(2.0 * dt * diffusion_coeff)) + threads = 256 + blocks = (n_markers + threads - 1) // threads + _get_random_diffusion_kernel()( + (blocks,), + (threads,), + ( + dev, + np.int32(n_cols), + np.int32(n_markers), + noise_dev, + np.float64(scale), + ), + ) + dev.get(out=markers) From b0474935dcae804c4e93b5b91df1190a78ab32c4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 10:21:03 +0200 Subject: [PATCH 041/156] Added accum kernels with cuda --- bench_gpu/bench_kernels.py | 486 +++++++++++++++++ .../pic/accumulation/accum_kernels_cuda.py | 501 ++++++++++++++++++ .../pic/accumulation/particles_to_grid.py | 108 +++- 3 files changed, 1076 insertions(+), 19 deletions(-) create mode 100644 bench_gpu/bench_kernels.py create mode 100644 src/struphy/pic/accumulation/accum_kernels_cuda.py diff --git a/bench_gpu/bench_kernels.py b/bench_gpu/bench_kernels.py new file mode 100644 index 000000000..681079de2 --- /dev/null +++ b/bench_gpu/bench_kernels.py @@ -0,0 +1,486 @@ +"""Micro-benchmark suite for the kernels ported to CUDA on this branch: +every ``*_general_gpu`` pusher (:mod:`struphy.pic.pushing.pusher_kernels_cuda`) +and the two accumulation kernels +(:mod:`struphy.pic.accumulation.accum_kernels_cuda`). For each kernel it +times the CPU (Pyccel) reference and the GPU (CuPy ``RawKernel``) port on +identical input data and prints a numpy-vs-cupy speedup table. + +Run with: + + ARRAY_BACKEND=numpy python bench_gpu/bench_kernels.py + +Options (see ``--help``): ``--n-markers``, ``--num-elements``, ``--degree``, +``--repeats``, ``--kernel`` (repeatable, to run a subset). + +Why ``ARRAY_BACKEND=numpy`` for everything, GPU included +---------------------------------------------------------- +The CPU kernels are Pyccel-compiled functions that need real NumPy buffers, +and markers are host-resident regardless of backend (see +``ISSUE_cupy_particles_never_pushed.md``). The GPU kernel wrappers import +``cupy`` directly inside each function body and don't consult +``cunumpy``'s active backend at all -- they just expect CuPy arrays as +arguments. So a single ``ARRAY_BACKEND=numpy`` process can build one set of +NumPy scene arrays, hand them straight to the CPU kernels, and hand +``cupy.asarray(...)`` mirrors of the *same* arrays to the GPU kernels: both +variants of every kernel run back-to-back on byte-identical input, in one +process, with no subprocess/backend-switching dance required. +""" + +import argparse +import os +import time + +if os.environ.get("ARRAY_BACKEND", "numpy") != "numpy": + raise SystemExit( + "Run this benchmark with ARRAY_BACKEND=numpy -- see the module docstring " + "for why the GPU kernels don't need ARRAY_BACKEND=cupy to be benchmarked.", + ) + +import numpy as np + + +def timeit(fn, repeats: int, warmup: int = 1) -> float: + """Best-of-``repeats`` wall-clock time of ``fn()``, in seconds. + + Best-of (not mean) since the only noise on a shared cluster node is + contention that slows a run down, never speeds one up -- the minimum is + the closest thing to "this kernel's own cost" we can measure without a + dedicated node. ``warmup`` calls run first and are excluded, absorbing + the one-time CUDA context / RawKernel-compile cost of the first GPU call. + """ + for _ in range(warmup): + fn() + best = float("inf") + for _ in range(repeats): + t0 = time.perf_counter() + fn() + best = min(best, time.perf_counter() - t0) + return best + + +class Scene: + """One shared set of markers + domain + Derham + random FE coefficient + fields, used to build every kernel case below. Field values are random + (not physically meaningful) -- this benchmark measures raw kernel + throughput, not physics, so only shapes/dtypes need to be realistic. + """ + + def __init__(self, n_elements, degree, n_markers_target, seed=1234): + from struphy import domains + from struphy.feec.mass import WeightedMassOperators + from struphy.feec.psydac_derham import Derham + from struphy.io.options import DerhamOptions + from struphy.particles.parameters import LoadingParameters + from struphy.pic.particles import Particles6D + from struphy.topology.grids import TensorProductGrid + + # kind_map == 12 (Colella): the "general" (non-Cuboid) CUDA path, + # i.e. the one that evaluates DF(eta) per marker instead of assuming + # it's constant -- this is the actual new work ported this branch, + # and the one every real (non-trivial-geometry) simulation uses. + self.domain = domains.Colella(Lx=2.0, Ly=3.0, alpha=0.1, Lz=4.0) + grid = TensorProductGrid(num_elements=n_elements) + derham_opts = DerhamOptions(degree=degree) + self.derham = Derham(grid, derham_opts, comm=None) + self.mass_ops = WeightedMassOperators(self.derham, self.domain) + + loading_params = LoadingParameters( + Np=n_markers_target, + seed=seed, + moments=(0.0, 0.0, 0.0, 1.0, 1.0, 1.0), + spatial="uniform", + ) + self.particles = Particles6D(loading_params=loading_params, domain=self.domain) + self.particles.draw_markers() + self.particles.initialize_weights() + self.n_markers = self.particles.markers.shape[0] + + self.args_markers = self.particles.args_markers + self.args_domain = self.domain.args_domain + self.args_derham = self.derham.args_derham + + self._markers0 = self.particles.markers.copy() + self._rng = np.random.default_rng(seed) + + self.pn = tuple(int(p) for p in self.args_derham.pn) + self.starts = tuple(int(s) for s in self.args_derham.starts) + self.kind_map = int(self.args_domain.kind_map) + + import cupy as cp + + self.params_dev = cp.asarray(np.asarray(self.args_domain.params, dtype=float), dtype=cp.float64) + self.tn1_dev = cp.asarray(np.asarray(self.args_derham.tn1, dtype=float), dtype=cp.float64) + self.tn2_dev = cp.asarray(np.asarray(self.args_derham.tn2, dtype=float), dtype=cp.float64) + self.tn3_dev = cp.asarray(np.asarray(self.args_derham.tn3, dtype=float), dtype=cp.float64) + + # random FE coefficient fields, one per Derham space actually used + # below (0-form/H1 scalar; 1-form/Hcurl, 2-form/Hdiv, vector/H1vec + # each 3 components). + self.fields = { + "0": self._random_field("0"), + "1": self._random_field("1"), + "2": self._random_field("2"), + "v": self._random_field("v"), + } + + def _random_field(self, form: str): + from feectools.linalg.block import BlockVector + from feectools.linalg.stencil import StencilVector + + space = self.derham.coeff_spaces[form] + if form in ("0", "3"): + v = StencilVector(space) + v._data[:] = self._rng.uniform(-1.0, 1.0, v._data.shape) + return (v._data,) + bv = BlockVector(space) + arrs = [] + for bl in bv.blocks: + bl._data[:] = self._rng.uniform(-1.0, 1.0, bl._data.shape) + arrs.append(bl._data) + return tuple(arrs) + + def dev(self, arr): + import cupy as cp + + return cp.asarray(arr) + + def reset_markers(self): + self.particles.markers[:] = self._markers0 + + def random_f0_values(self): + return self._rng.uniform(0.1, 2.0, size=self.n_markers).astype(np.float64) + + def random_noise(self): + return self._rng.normal(size=(self.n_markers, 3)).astype(np.float64) + + +# --------------------------------------------------------------------------- +# Kernel cases: each is (name, cpu_call_factory, gpu_call_factory), where +# both factories take the Scene and return a zero-arg callable that runs one +# kernel invocation (including the host<->device marker round-trip for the +# GPU side, since that's part of the real per-step cost). +# --------------------------------------------------------------------------- + + +def _stage1_abc(): + """Single-stage (n_stages=1) RK Butcher arrays: makes the CPU kernels' + internal ``dt*a[stage]``/``dt*b[stage]``/``last`` bookkeeping match the + GPU wrappers' explicit ``dt_a=dt, dt_b=dt, last=1.0`` -- see + push_eta_stage's body for the exact formula this mirrors.""" + return np.array([1.0]), np.array([1.0]), np.array([1.0]) + + +def make_cases(scene: Scene, dt: float): + import struphy.pic.accumulation.accum_kernels as accum_kernels + import struphy.pic.pushing.pusher_kernels as pusher_kernels + from struphy.pic.accumulation.accum_kernels_cuda import ( + charge_density_0form_gpu, + linear_vlasov_ampere_gpu, + ) + from struphy.pic.pushing.pusher_kernels_cuda import ( + push_bxu_H1vec_general_gpu, + push_bxu_Hcurl_general_gpu, + push_bxu_Hdiv_general_gpu, + push_deterministic_diffusion_stage_general_gpu, + push_eta_stage_general_gpu, + push_pc_eta_stage_H1vec_general_gpu, + push_pc_eta_stage_Hcurl_general_gpu, + push_pc_eta_stage_Hdiv_general_gpu, + push_pc_GXu_full_general_gpu, + push_pc_GXu_general_gpu, + push_random_diffusion_stage_gpu, + push_v_with_efield_general_gpu, + push_vxb_analytic_general_gpu, + push_vxb_implicit_general_gpu, + push_weights_with_efield_lin_va_general_gpu, + ) + + am, ad, ah = scene.args_markers, scene.args_domain, scene.args_derham + a1, b1, c1 = _stage1_abc() + n_cols = scene.particles.markers.shape[1] + pn, tn1, tn2, tn3, starts = scene.pn, scene.tn1_dev, scene.tn2_dev, scene.tn3_dev, scene.starts + kind_map, params_dev = scene.kind_map, scene.params_dev + boundary_cut = 0.1 + cases = {} + + def add(name, cpu_fn, gpu_fn): + cases[name] = (cpu_fn, gpu_fn) + + # --- push_eta_stage --- + add( + "push_eta_stage", + lambda: pusher_kernels.push_eta_stage(dt, 0, am, ad, a1, b1, c1), + lambda: push_eta_stage_general_gpu( + scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, + kind_map, params_dev, dt, dt, 1.0, + ), + ) + + # --- push_v_with_efield --- + e1 = scene.fields["1"] + e1_dev = tuple(scene.dev(a) for a in e1) + add( + "push_v_with_efield", + lambda: pusher_kernels.push_v_with_efield(dt, 0, am, ad, ah, *e1, dt), + lambda: push_v_with_efield_general_gpu( + scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, + *e1_dev, kind_map, params_dev, dt, + ), + ) + + # --- push_vxb_analytic / push_vxb_implicit --- + b2 = scene.fields["2"] + b2_dev = tuple(scene.dev(a) for a in b2) + add( + "push_vxb_analytic", + lambda: pusher_kernels.push_vxb_analytic(dt, 0, am, ad, ah, *b2), + lambda: push_vxb_analytic_general_gpu( + scene.particles.markers, n_cols, am.first_init_idx, pn, tn1, tn2, tn3, starts, + *b2_dev, kind_map, params_dev, dt, + ), + ) + add( + "push_vxb_implicit", + lambda: pusher_kernels.push_vxb_implicit(dt, 0, am, ad, ah, *b2), + lambda: push_vxb_implicit_general_gpu( + scene.particles.markers, n_cols, am.first_init_idx, pn, tn1, tn2, tn3, starts, + *b2_dev, kind_map, params_dev, dt, + ), + ) + + # --- push_bxu_Hdiv / Hcurl / H1vec --- + u2, u1, uv = scene.fields["2"], scene.fields["1"], scene.fields["v"] + u2_dev = tuple(scene.dev(a) for a in u2) + u1_dev = tuple(scene.dev(a) for a in u1) + uv_dev = tuple(scene.dev(a) for a in uv) + add( + "push_bxu_Hdiv", + lambda: pusher_kernels.push_bxu_Hdiv(dt, 0, am, ad, ah, *b2, *u2, boundary_cut), + lambda: push_bxu_Hdiv_general_gpu( + scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, + *b2_dev, *u2_dev, kind_map, params_dev, boundary_cut, dt, + ), + ) + add( + "push_bxu_Hcurl", + lambda: pusher_kernels.push_bxu_Hcurl(dt, 0, am, ad, ah, *b2, *u1, boundary_cut), + lambda: push_bxu_Hcurl_general_gpu( + scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, + *b2_dev, *u1_dev, kind_map, params_dev, boundary_cut, dt, + ), + ) + add( + "push_bxu_H1vec", + lambda: pusher_kernels.push_bxu_H1vec(dt, 0, am, ad, ah, *b2, *uv, boundary_cut), + lambda: push_bxu_H1vec_general_gpu( + scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, + *b2_dev, *uv_dev, kind_map, params_dev, boundary_cut, dt, + ), + ) + + # --- push_pc_GXu_full / push_pc_GXu (9 "G" tensor blocks, reusing the 3 + # Hcurl component shapes -- row i's 3 blocks all share component i's + # shape, see pusher_kernels_cuda.py's push_pc_GXu_full_general docs) --- + c1_arr, c2_arr, c3_arr = scene.fields["1"] + rng = scene._rng + g = {} + for row, comp in ((1, c1_arr), (2, c2_arr), (3, c3_arr)): + for col in (1, 2, 3): + arr = rng.uniform(-1.0, 1.0, comp.shape) + g[f"{row}{col}"] = arr + g_order = ["11", "12", "13", "21", "22", "23", "31", "32", "33"] + g_full = [g[k] for k in g_order] + g_full_dev = [scene.dev(a) for a in g_full] + add( + "push_pc_GXu_full", + lambda: pusher_kernels.push_pc_GXu_full(dt, 0, am, ad, ah, *g_full), + lambda: push_pc_GXu_full_general_gpu( + scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, + *g_full_dev, kind_map, params_dev, dt, + ), + ) + add( + "push_pc_GXu", + lambda: pusher_kernels.push_pc_GXu(dt, 0, am, ad, ah, *g_full), + lambda: push_pc_GXu_general_gpu( + scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, + *g_full_dev[:6], kind_map, params_dev, dt, + ), + ) + + # --- push_pc_eta_stage_Hcurl / Hdiv / H1vec --- + add( + "push_pc_eta_stage_Hcurl", + lambda: pusher_kernels.push_pc_eta_stage_Hcurl(dt, 0, am, ad, ah, *u1, False, a1, b1, c1), + lambda: push_pc_eta_stage_Hcurl_general_gpu( + scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, pn, tn1, tn2, tn3, starts, + *u1_dev, False, kind_map, params_dev, dt, dt, 1.0, + ), + ) + add( + "push_pc_eta_stage_Hdiv", + lambda: pusher_kernels.push_pc_eta_stage_Hdiv(dt, 0, am, ad, ah, *u2, False, a1, b1, c1), + lambda: push_pc_eta_stage_Hdiv_general_gpu( + scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, pn, tn1, tn2, tn3, starts, + *u2_dev, False, kind_map, params_dev, dt, dt, 1.0, + ), + ) + add( + "push_pc_eta_stage_H1vec", + lambda: pusher_kernels.push_pc_eta_stage_H1vec(dt, 0, am, ad, ah, *uv, False, a1, b1, c1), + lambda: push_pc_eta_stage_H1vec_general_gpu( + scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, pn, tn1, tn2, tn3, starts, + *uv_dev, False, kind_map, params_dev, dt, dt, 1.0, + ), + ) + + # --- push_weights_with_efield_lin_va --- + f0_values = scene.random_f0_values() + f0_values_dev = scene.dev(f0_values) + kappa, vth = 1.0, 1.0 + add( + "push_weights_with_efield_lin_va", + lambda: pusher_kernels.push_weights_with_efield_lin_va(dt, 0, am, ad, ah, *e1, f0_values, kappa, vth), + lambda: push_weights_with_efield_lin_va_general_gpu( + scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, + *e1_dev, f0_values_dev, kappa, vth, kind_map, params_dev, dt, + ), + ) + + # --- push_deterministic_diffusion_stage --- + pi_u = scene.fields["0"][0] + pi_grad = (scene.fields["0"][0], scene.fields["0"][0], scene.fields["0"][0]) # shape-only stand-ins + pi_u_dev = scene.dev(pi_u) + pi_grad_dev = tuple(scene.dev(a) for a in pi_grad) + diffusion_coeff = 0.1 + add( + "push_deterministic_diffusion_stage", + lambda: pusher_kernels.push_deterministic_diffusion_stage( + dt, 0, am, ad, ah, pi_u, *pi_grad, diffusion_coeff, a1, b1, c1, + ), + lambda: push_deterministic_diffusion_stage_general_gpu( + scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, pn, tn1, tn2, tn3, starts, + pi_u_dev, *pi_grad_dev, diffusion_coeff, kind_map, params_dev, dt, dt, 1.0, + ), + ) + + # --- push_random_diffusion_stage (domain-independent) --- + noise = scene.random_noise() + add( + "push_random_diffusion_stage", + lambda: pusher_kernels.push_random_diffusion_stage(dt, 0, am, ad, noise, diffusion_coeff, a1, b1, c1), + lambda: push_random_diffusion_stage_gpu(scene.particles.markers, n_cols, noise, diffusion_coeff, dt), + ) + + # --- charge_density_0form (AccumulatorVector, H1) --- + vec_shape = scene.fields["0"][0].shape + vec_cpu = np.zeros(vec_shape, dtype=float) + vec_gpu = scene.dev(np.zeros(vec_shape, dtype=float)) + weight_idx = scene.particles.index["weights"] + add( + "charge_density_0form", + lambda: (vec_cpu.fill(0.0), accum_kernels.charge_density_0form(am, ah, ad, vec_cpu))[-1], + lambda: ( + vec_gpu.fill(0.0), + charge_density_0form_gpu( + scene.particles.markers, weight_idx, pn, tn1, tn2, tn3, starts, vec_gpu, + ), + )[-1], + ) + + # --- linear_vlasov_ampere (Accumulator, symmetric V1 -> V1 matrix + vector) --- + from feectools.linalg.block import BlockVector + + op = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="symm") + mat_cpu, mat_gpu_ = {}, {} + for a_ in range(3): + for b_ in range(3): + if b_ >= a_ and op.matrix.blocks[a_][b_] is not None: + shape_ = op.matrix.blocks[a_][b_]._data.shape + key = f"{a_+1}{b_+1}" + mat_cpu[key] = np.zeros(shape_, dtype=float) + mat_gpu_[key] = scene.dev(np.zeros(shape_, dtype=float)) + vec_space = scene.derham.coeff_spaces["1"] + vec_bv = BlockVector(vec_space) + vlva_vec_cpu = [np.zeros(bl._data.shape, dtype=float) for bl in vec_bv.blocks] + vlva_vec_gpu = [scene.dev(v) for v in vlva_vec_cpu] + lva_f0 = scene.random_f0_values() + lva_f0_dev = scene.dev(lva_f0) + mat_keys = ["11", "12", "13", "22", "23", "33"] + + def _lva_cpu(): + for v in mat_cpu.values(): + v.fill(0.0) + for v in vlva_vec_cpu: + v.fill(0.0) + accum_kernels.linear_vlasov_ampere( + am, ah, ad, + *[mat_cpu[k] for k in mat_keys], + *vlva_vec_cpu, + lva_f0, + ) + + def _lva_gpu(): + for v in mat_gpu_.values(): + v.fill(0.0) + for v in vlva_vec_gpu: + v.fill(0.0) + linear_vlasov_ampere_gpu( + scene.particles.markers, kind_map, params_dev, lva_f0_dev, pn, tn1, tn2, tn3, starts, + *[mat_gpu_[k] for k in mat_keys], *vlva_vec_gpu, + ) + + add("linear_vlasov_ampere", _lva_cpu, _lva_gpu) + + return cases + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--n-markers", type=int, default=200_000, help="approximate number of markers (via ppc)") + parser.add_argument("--num-elements", type=int, nargs=3, default=(16, 16, 8), metavar=("NX", "NY", "NZ")) + parser.add_argument("--degree", type=int, nargs=3, default=(3, 3, 3), metavar=("PX", "PY", "PZ")) + parser.add_argument("--repeats", type=int, default=5) + parser.add_argument("--dt", type=float, default=0.01) + parser.add_argument( + "--kernel", action="append", default=None, + help="restrict to one kernel (repeatable); default: run all", + ) + args = parser.parse_args() + + print(f"Building scene: num_elements={tuple(args.num_elements)}, degree={tuple(args.degree)}, Np~={args.n_markers} ...") + scene = Scene(tuple(args.num_elements), tuple(args.degree), args.n_markers) + print(f" -> {scene.n_markers} markers, domain kind_map={scene.kind_map} (Colella)") + + cases = make_cases(scene, args.dt) + names = args.kernel if args.kernel else list(cases.keys()) + for name in names: + if name not in cases: + raise SystemExit(f"Unknown kernel {name!r}. Choices: {sorted(cases)}") + + rows = [] + for name in names: + cpu_fn, gpu_fn = cases[name] + + def cpu_run(cpu_fn=cpu_fn): + scene.reset_markers() + cpu_fn() + + def gpu_run(gpu_fn=gpu_fn): + scene.reset_markers() + gpu_fn() + + cpu_t = timeit(cpu_run, args.repeats) + gpu_t = timeit(gpu_run, args.repeats) + rows.append((name, cpu_t, gpu_t, cpu_t / gpu_t)) + print(f" {name}: cpu={cpu_t*1e3:.3f} ms gpu={gpu_t*1e3:.3f} ms speedup={cpu_t/gpu_t:.1f}x") + + print() + print(f"{'kernel':<36} {'n_markers':>10} {'cpu (ms)':>12} {'gpu (ms)':>12} {'speedup':>10}") + print("-" * 84) + for name, cpu_t, gpu_t, speedup in rows: + print(f"{name:<36} {scene.n_markers:>10} {cpu_t*1e3:>12.3f} {gpu_t*1e3:>12.3f} {speedup:>9.1f}x") + + +if __name__ == "__main__": + main() diff --git a/src/struphy/pic/accumulation/accum_kernels_cuda.py b/src/struphy/pic/accumulation/accum_kernels_cuda.py new file mode 100644 index 000000000..133a0b2ce --- /dev/null +++ b/src/struphy/pic/accumulation/accum_kernels_cuda.py @@ -0,0 +1,501 @@ +"""Hand-written CUDA replacements for select accumulation (particle-to-grid +deposition) kernels, used only under ``ARRAY_BACKEND=cupy``. + +Unlike the pusher kernels in :mod:`~struphy.pic.pushing.pusher_kernels_cuda` +(each marker only ever writes to its own row -- embarrassingly parallel, no +cross-thread interaction), accumulation kernels *scatter* every marker's +contribution into a shared grid array (:func:`~struphy.pic.accumulation.filler_kernels.fill_vec`'s +``vec[i1, i2, i3] += ...``): many markers whose (p+1)^3 local basis-function +support overlaps the same grid cell write to the same memory location. The +CPU kernel handles this by running the marker loop strictly sequentially (its +OpenMP ``reduction`` pragma is commented out in the source specifically +because of this race). The GPU port instead uses ``atomicAdd`` -- one thread +per marker, same as the pushers, but the grid write goes through an atomic +rather than a plain store. Double-precision ``atomicAdd`` is natively +supported on every CUDA compute capability this codebase targets (>= 6.0), +so no software fallback is needed. + +Currently covered: :func:`~struphy.pic.accumulation.accum_kernels.charge_density_0form`, +used by :class:`~struphy.propagators.push_deterministic_diffusion.PushDeterministicDiffusion` +every step to build the (H^1) density field consumed by +:func:`~struphy.pic.pushing.pusher_kernels_cuda.push_deterministic_diffusion_stage_general_gpu`. +This one needs no domain-mapping Jacobian at all (the H^1 filling weight is +just the marker weight), so it reuses only the B-spline evaluation device +functions, not the geometry-mapping ones. +""" + +_CHARGE_DENSITY_0FORM_SRC = r""" +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +// Only the N-spline values (bn) are needed for an H^1/0-form fill; D-spline +// values are computed alongside (same recursion as +// pusher_kernels_cuda.py's b_d_splines_dev) and simply unused. +__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) +{ + double left[MAXP]; + double right[MAXP]; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +extern "C" __global__ +void charge_density_0form_cuda( + const double* markers, + const int n_cols, + const int n_markers, + const int weight_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + double* vec, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double filling = row[weight_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bn1[il1] * filling; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bn2[il2]; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bn3[il3]; + atomicAdd(&vec[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3], b3); + } + } + } +} +""" + +_charge_density_0form_kernel = None + + +def _get_charge_density_0form_kernel(): + global _charge_density_0form_kernel + if _charge_density_0form_kernel is None: + import cupy as cp + + _charge_density_0form_kernel = cp.RawKernel(_CHARGE_DENSITY_0FORM_SRC, "charge_density_0form_cuda") + return _charge_density_0form_kernel + + +def charge_density_0form_gpu( + markers, + weight_idx: int, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + vec_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels.charge_density_0form`. + + ``markers`` is the host marker array, transferred to the device once per + call (matching the pusher kernels' round-trip pattern). ``vec_dev`` is + the target :class:`~feectools.linalg.stencil.StencilVector`'s ``._data`` + -- already device-resident under CuPy and already zeroed by the caller + (:meth:`~struphy.pic.accumulation.particles_to_grid.AccumulatorVector._accumulate` + always does ``dat[:] = 0.0`` before invoking the kernel), so this + function only needs to add to it, not read markers back afterward: the + caller reads ``vec_dev`` directly since it was written in place. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev_markers = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + _get_charge_density_0form_kernel()( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(weight_idx), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + vec_dev, + np.int32(vec_dev.shape[1]), + np.int32(vec_dev.shape[2]), + ), + ) + + +# --------------------------------------------------------------------------- +# linear_vlasov_ampere: accumulates into a symmetric V1 -> V1 block matrix +# (mat11, mat12, mat13, mat22, mat23, mat33) plus a V1 vector (vec1, vec2, +# vec3), using DF^-1(eta_p) @ v_p at each marker -- unlike +# charge_density_0form this needs the full domain-mapping Jacobian, so the +# kernel source below is prefixed with pusher_kernels_cuda's +# _GENERAL_GEOMETRY_SRC (df_dispatch_dev and friends) rather than +# duplicating it. +# +# The row/column basis combinations for the 6 matrix blocks and the fill +# formulas mirror struphy.pic.accumulation.particle_to_mat_kernels.m_v_fill_b_v1_symm +# exactly (which itself calls filler_kernels.fill_mat_vec/fill_mat) -- +# fill_mat_vec_dev/fill_mat_dev below are direct ports of those two. +# --------------------------------------------------------------------------- + +_LINEAR_VLASOV_AMPERE_EXTRA_SRC = r""" +__device__ void outer_dev(const double* a, const double* b, double* c) +{ + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) + c[3*i+j] = a[i] * b[j]; +} + +// Port of filler_kernels.fill_mat_vec: fills one matrix block (banded +// storage, j = pad + jl - il) and, along the shared (i1,i2,i3) row loop, +// also fills the corresponding vector block. +__device__ void fill_mat_vec_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat, int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double* vec, int vn2, int vn3, + double filling_vec) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + + atomicAdd(&vec[(size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3], b3 * filling_vec); + + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat[idx], b6); + } + } + } + } + } + } +} + +// Port of filler_kernels.fill_mat: matrix-only block fill (off-diagonal +// blocks, no associated vector). +__device__ void fill_mat_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat, int d2, int d3, int d4, int d5, int d6, + double filling_mat) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1] * filling_mat; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1]; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat[idx], b6); + } + } + } + } + } + } +} + +extern "C" __global__ +void linear_vlasov_ampere_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const double* f0_values, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + double* mat11, double* mat12, double* mat13, + double* mat22, double* mat23, double* mat33, + double* vec1, double* vec2, double* vec3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, + const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, + const int v2_n2, const int v2_n3, + const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + const double weight = row[6]; + const double s0 = row[7]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + double df_inv[9], df_inv_v[3]; + matrix_inv_dev(dfm, df_inv); + matvec_dev(df_inv, v, df_inv_v); + + double filling_m[9]; + outer_dev(df_inv_v, df_inv_v, filling_m); + const double fm_scale = f0_values[ip] / s0; + for (int k = 0; k < 9; k++) filling_m[k] *= fm_scale; + + double filling_v[3]; + filling_v[0] = weight * df_inv_v[0]; + filling_v[1] = weight * df_inv_v[1]; + filling_v[2] = weight * df_inv_v[2]; + + const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; + const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, + vec1, v1_n2,v1_n3, filling_v[0]); + + fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, + vec2, v2_n2,v2_n3, filling_v[1]); + + fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, + vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); +} +""" + + +def _linear_vlasov_ampere_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + + +_linear_vlasov_ampere_kernel = None + + +def _get_linear_vlasov_ampere_kernel(): + global _linear_vlasov_ampere_kernel + if _linear_vlasov_ampere_kernel is None: + import cupy as cp + + _linear_vlasov_ampere_kernel = cp.RawKernel(_linear_vlasov_ampere_source(), "linear_vlasov_ampere_cuda") + return _linear_vlasov_ampere_kernel + + +def linear_vlasov_ampere_gpu( + markers, + kind_map: int, + params_dev, + f0_values_dev, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + mat11_dev, + mat12_dev, + mat13_dev, + mat22_dev, + mat23_dev, + mat33_dev, + vec1_dev, + vec2_dev, + vec3_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels.linear_vlasov_ampere`. + + ``markers`` is the host marker array, round-tripped through the device + once per call (this kernel only reads markers, never writes them back). + ``params_dev``/``f0_values_dev`` and all ``mat*_dev``/``vec*_dev`` arrays + are expected to already be device-resident (cached once by the caller); + the ``mat*_dev``/``vec*_dev`` arrays must already be zeroed, matching + :meth:`~struphy.pic.accumulation.particles_to_grid.Accumulator._accumulate`'s + ``dat[:] = 0.0`` reset before the kernel call. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev_markers = cp.asarray(markers) + f0_values_dev = cp.ascontiguousarray(f0_values_dev) + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def dims(a): + return ( + np.int32(a.shape[1]), + np.int32(a.shape[2]), + np.int32(a.shape[3]), + np.int32(a.shape[4]), + np.int32(a.shape[5]), + ) + + _get_linear_vlasov_ampere_kernel()( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(kind_map), + params_dev, + f0_values_dev, + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + mat11_dev, + mat12_dev, + mat13_dev, + mat22_dev, + mat23_dev, + mat33_dev, + vec1_dev, + vec2_dev, + vec3_dev, + *dims(mat11_dev), + *dims(mat12_dev), + *dims(mat13_dev), + *dims(mat22_dev), + *dims(mat23_dev), + *dims(mat33_dev), + np.int32(vec1_dev.shape[1]), + np.int32(vec1_dev.shape[2]), + np.int32(vec2_dev.shape[1]), + np.int32(vec2_dev.shape[2]), + np.int32(vec3_dev.shape[1]), + np.int32(vec3_dev.shape[2]), + ), + ) diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index 0deedc6d4..c1fd30774 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -4,18 +4,19 @@ import cunumpy as xp from cunumpy import PyccelKernel -from feectools.ddm.mpi import mpi as MPI -from feectools.linalg.block import BlockVector -from feectools.linalg.stencil import StencilMatrix, StencilVector from scope_profiler import ProfileManager import struphy.pic.accumulation.accum_kernels as accums import struphy.pic.accumulation.accum_kernels_gc as accums_gc +from feectools.ddm.mpi import mpi as MPI +from feectools.linalg.block import BlockVector +from feectools.linalg.stencil import StencilMatrix, StencilVector from struphy.feec.mass import WeightedMassOperators from struphy.feec.psydac_derham import Derham from struphy.io.options import LiteralOptions from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.models.variables import PICVariable, SPHVariable +from struphy.pic.accumulation.accum_kernels_cuda import charge_density_0form_gpu, linear_vlasov_ampere_gpu from struphy.pic.accumulation.filter import AccumFilter, FilterParameters from struphy.pic.base import Particles from struphy.utils.utils import __dataclass_repr_no_defaults__, check_option @@ -196,6 +197,31 @@ def __init__( # initialize filter self._accfilter = AccumFilter(filter_params, self._derham, self._space_id) + # GPU replacement for linear_vlasov_ampere: evaluates DF^-1(eta_p) per + # marker and atomically scatters into the 6 symmetric V1 -> V1 matrix + # blocks plus the V1 vector, instead of the CPU's strictly-sequential + # marker loop. See pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS + # for the domains this covers. + from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS + + self._gpu_linear_vlasov_ampere = ( + xp.cupy_backend + and kernel.name == "linear_vlasov_ampere" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_linear_vlasov_ampere: + import cupy as cp + import numpy as np + + self._gpu_lva_kind_map = int(args_domain.kind_map) + self._gpu_lva_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + args_derham = self.derham.args_derham + self._gpu_lva_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_lva_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_lva_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) + self._gpu_lva_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) + self._gpu_lva_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + def __call__(self, *optional_args, **args_control): """ Performs the accumulation into the matrix/vector by calling the chosen accumulation kernel and additional analytical contributions (control variate, optional). @@ -229,14 +255,30 @@ def _accumulate(self, *optional_args, **args_control): dat[:] = 0.0 # accumulate into matrix (and vector) with markers - with ProfileManager.profile_region("kernel: " + self.kernel.name): - self.kernel( - self.particles.args_markers, - self.derham.args_derham, - self.args_domain, - *self._args_data, - *optional_args, - ) + if self._gpu_linear_vlasov_ampere and len(optional_args) == 1: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + (f0_values,) = optional_args + linear_vlasov_ampere_gpu( + self.particles.markers, + self._gpu_lva_kind_map, + self._gpu_lva_params, + f0_values, + self._gpu_lva_pn, + self._gpu_lva_tn1, + self._gpu_lva_tn2, + self._gpu_lva_tn3, + self._gpu_lva_starts, + *self._args_data, + ) + else: + with ProfileManager.profile_region("kernel: " + self.kernel.name): + self.kernel( + self.particles.args_markers, + self.derham.args_derham, + self.args_domain, + *self._args_data, + *optional_args, + ) # apply filter if self.accfilter.params.use_filter is not None: @@ -557,6 +599,21 @@ def __init__( # initialize filter self._accfilter = AccumFilter(filter_params, self._derham, self._space_id) + # hand-written CUDA replacement for charge_density_0form (the only + # AccumulatorVector kernel ported so far -- see accum_kernels_cuda.py). + # No optional_args/domain-mapping support needed for this one. + self._gpu_charge_density_0form = xp.cupy_backend and kernel.name == "charge_density_0form" + if self._gpu_charge_density_0form: + import cupy as cp + + args_derham = self.derham.args_derham + self._gpu_cd0_weight_idx = self.particles.index["weights"] + self._gpu_cd0_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_cd0_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_cd0_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_cd0_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_cd0_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + def __call__(self, *optional_args, **args_control): """ Performs the accumulation into the vector by calling the chosen accumulation kernel @@ -589,14 +646,27 @@ def _accumulate(self, *optional_args, **args_control): dat[:] = 0.0 # accumulate into matrix (and vector) with markers - with ProfileManager.profile_region("kernel: " + self.kernel.name): - self.kernel( - self.particles.args_markers, - self.derham.args_derham, - self.args_domain, - *self._args_data, - *optional_args, - ) + if self._gpu_charge_density_0form and not optional_args: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + charge_density_0form_gpu( + self.particles.markers, + self._gpu_cd0_weight_idx, + self._gpu_cd0_pn, + self._gpu_cd0_tn1, + self._gpu_cd0_tn2, + self._gpu_cd0_tn3, + self._gpu_cd0_starts, + self._args_data[0], + ) + else: + with ProfileManager.profile_region("kernel: " + self.kernel.name): + self.kernel( + self.particles.args_markers, + self.derham.args_derham, + self.args_domain, + *self._args_data, + *optional_args, + ) # apply filter if self.accfilter.params.use_filter is not None: From f5104031e7d478d0d4f138e915ffc8cfd4e0b771 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 10:29:25 +0200 Subject: [PATCH 042/156] Formatting --- bench_gpu/bench_kernels.py | 264 ++++++++-- params_LinearMHDDriftkineticCC.py | 47 +- params_PressureLessSPH.py | 39 +- src/struphy/console/format.py | 10 +- src/struphy/feec/mass_kernels.py | 451 ++++-------------- src/struphy/feec/preconditioner.py | 3 +- src/struphy/feec/psydac_derham.py | 3 +- .../pic/accumulation/particles_to_grid.py | 6 +- src/struphy/pic/pushing/pusher.py | 89 ++-- .../pic/pushing/pusher_kernels_cuda.py | 426 ++++++++++++++--- src/struphy/pic/tests/test_pushers.py | 1 - 11 files changed, 802 insertions(+), 537 deletions(-) diff --git a/bench_gpu/bench_kernels.py b/bench_gpu/bench_kernels.py index 681079de2..e0c158e48 100644 --- a/bench_gpu/bench_kernels.py +++ b/bench_gpu/bench_kernels.py @@ -211,8 +211,15 @@ def add(name, cpu_fn, gpu_fn): "push_eta_stage", lambda: pusher_kernels.push_eta_stage(dt, 0, am, ad, a1, b1, c1), lambda: push_eta_stage_general_gpu( - scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, - kind_map, params_dev, dt, dt, 1.0, + scene.particles.markers, + n_cols, + am.first_init_idx, + am.first_free_idx, + kind_map, + params_dev, + dt, + dt, + 1.0, ), ) @@ -223,8 +230,17 @@ def add(name, cpu_fn, gpu_fn): "push_v_with_efield", lambda: pusher_kernels.push_v_with_efield(dt, 0, am, ad, ah, *e1, dt), lambda: push_v_with_efield_general_gpu( - scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, - *e1_dev, kind_map, params_dev, dt, + scene.particles.markers, + n_cols, + pn, + tn1, + tn2, + tn3, + starts, + *e1_dev, + kind_map, + params_dev, + dt, ), ) @@ -235,16 +251,36 @@ def add(name, cpu_fn, gpu_fn): "push_vxb_analytic", lambda: pusher_kernels.push_vxb_analytic(dt, 0, am, ad, ah, *b2), lambda: push_vxb_analytic_general_gpu( - scene.particles.markers, n_cols, am.first_init_idx, pn, tn1, tn2, tn3, starts, - *b2_dev, kind_map, params_dev, dt, + scene.particles.markers, + n_cols, + am.first_init_idx, + pn, + tn1, + tn2, + tn3, + starts, + *b2_dev, + kind_map, + params_dev, + dt, ), ) add( "push_vxb_implicit", lambda: pusher_kernels.push_vxb_implicit(dt, 0, am, ad, ah, *b2), lambda: push_vxb_implicit_general_gpu( - scene.particles.markers, n_cols, am.first_init_idx, pn, tn1, tn2, tn3, starts, - *b2_dev, kind_map, params_dev, dt, + scene.particles.markers, + n_cols, + am.first_init_idx, + pn, + tn1, + tn2, + tn3, + starts, + *b2_dev, + kind_map, + params_dev, + dt, ), ) @@ -257,24 +293,57 @@ def add(name, cpu_fn, gpu_fn): "push_bxu_Hdiv", lambda: pusher_kernels.push_bxu_Hdiv(dt, 0, am, ad, ah, *b2, *u2, boundary_cut), lambda: push_bxu_Hdiv_general_gpu( - scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, - *b2_dev, *u2_dev, kind_map, params_dev, boundary_cut, dt, + scene.particles.markers, + n_cols, + pn, + tn1, + tn2, + tn3, + starts, + *b2_dev, + *u2_dev, + kind_map, + params_dev, + boundary_cut, + dt, ), ) add( "push_bxu_Hcurl", lambda: pusher_kernels.push_bxu_Hcurl(dt, 0, am, ad, ah, *b2, *u1, boundary_cut), lambda: push_bxu_Hcurl_general_gpu( - scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, - *b2_dev, *u1_dev, kind_map, params_dev, boundary_cut, dt, + scene.particles.markers, + n_cols, + pn, + tn1, + tn2, + tn3, + starts, + *b2_dev, + *u1_dev, + kind_map, + params_dev, + boundary_cut, + dt, ), ) add( "push_bxu_H1vec", lambda: pusher_kernels.push_bxu_H1vec(dt, 0, am, ad, ah, *b2, *uv, boundary_cut), lambda: push_bxu_H1vec_general_gpu( - scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, - *b2_dev, *uv_dev, kind_map, params_dev, boundary_cut, dt, + scene.particles.markers, + n_cols, + pn, + tn1, + tn2, + tn3, + starts, + *b2_dev, + *uv_dev, + kind_map, + params_dev, + boundary_cut, + dt, ), ) @@ -295,16 +364,34 @@ def add(name, cpu_fn, gpu_fn): "push_pc_GXu_full", lambda: pusher_kernels.push_pc_GXu_full(dt, 0, am, ad, ah, *g_full), lambda: push_pc_GXu_full_general_gpu( - scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, - *g_full_dev, kind_map, params_dev, dt, + scene.particles.markers, + n_cols, + pn, + tn1, + tn2, + tn3, + starts, + *g_full_dev, + kind_map, + params_dev, + dt, ), ) add( "push_pc_GXu", lambda: pusher_kernels.push_pc_GXu(dt, 0, am, ad, ah, *g_full), lambda: push_pc_GXu_general_gpu( - scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, - *g_full_dev[:6], kind_map, params_dev, dt, + scene.particles.markers, + n_cols, + pn, + tn1, + tn2, + tn3, + starts, + *g_full_dev[:6], + kind_map, + params_dev, + dt, ), ) @@ -313,24 +400,66 @@ def add(name, cpu_fn, gpu_fn): "push_pc_eta_stage_Hcurl", lambda: pusher_kernels.push_pc_eta_stage_Hcurl(dt, 0, am, ad, ah, *u1, False, a1, b1, c1), lambda: push_pc_eta_stage_Hcurl_general_gpu( - scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, pn, tn1, tn2, tn3, starts, - *u1_dev, False, kind_map, params_dev, dt, dt, 1.0, + scene.particles.markers, + n_cols, + am.first_init_idx, + am.first_free_idx, + pn, + tn1, + tn2, + tn3, + starts, + *u1_dev, + False, + kind_map, + params_dev, + dt, + dt, + 1.0, ), ) add( "push_pc_eta_stage_Hdiv", lambda: pusher_kernels.push_pc_eta_stage_Hdiv(dt, 0, am, ad, ah, *u2, False, a1, b1, c1), lambda: push_pc_eta_stage_Hdiv_general_gpu( - scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, pn, tn1, tn2, tn3, starts, - *u2_dev, False, kind_map, params_dev, dt, dt, 1.0, + scene.particles.markers, + n_cols, + am.first_init_idx, + am.first_free_idx, + pn, + tn1, + tn2, + tn3, + starts, + *u2_dev, + False, + kind_map, + params_dev, + dt, + dt, + 1.0, ), ) add( "push_pc_eta_stage_H1vec", lambda: pusher_kernels.push_pc_eta_stage_H1vec(dt, 0, am, ad, ah, *uv, False, a1, b1, c1), lambda: push_pc_eta_stage_H1vec_general_gpu( - scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, pn, tn1, tn2, tn3, starts, - *uv_dev, False, kind_map, params_dev, dt, dt, 1.0, + scene.particles.markers, + n_cols, + am.first_init_idx, + am.first_free_idx, + pn, + tn1, + tn2, + tn3, + starts, + *uv_dev, + False, + kind_map, + params_dev, + dt, + dt, + 1.0, ), ) @@ -342,8 +471,20 @@ def add(name, cpu_fn, gpu_fn): "push_weights_with_efield_lin_va", lambda: pusher_kernels.push_weights_with_efield_lin_va(dt, 0, am, ad, ah, *e1, f0_values, kappa, vth), lambda: push_weights_with_efield_lin_va_general_gpu( - scene.particles.markers, n_cols, pn, tn1, tn2, tn3, starts, - *e1_dev, f0_values_dev, kappa, vth, kind_map, params_dev, dt, + scene.particles.markers, + n_cols, + pn, + tn1, + tn2, + tn3, + starts, + *e1_dev, + f0_values_dev, + kappa, + vth, + kind_map, + params_dev, + dt, ), ) @@ -356,11 +497,36 @@ def add(name, cpu_fn, gpu_fn): add( "push_deterministic_diffusion_stage", lambda: pusher_kernels.push_deterministic_diffusion_stage( - dt, 0, am, ad, ah, pi_u, *pi_grad, diffusion_coeff, a1, b1, c1, + dt, + 0, + am, + ad, + ah, + pi_u, + *pi_grad, + diffusion_coeff, + a1, + b1, + c1, ), lambda: push_deterministic_diffusion_stage_general_gpu( - scene.particles.markers, n_cols, am.first_init_idx, am.first_free_idx, pn, tn1, tn2, tn3, starts, - pi_u_dev, *pi_grad_dev, diffusion_coeff, kind_map, params_dev, dt, dt, 1.0, + scene.particles.markers, + n_cols, + am.first_init_idx, + am.first_free_idx, + pn, + tn1, + tn2, + tn3, + starts, + pi_u_dev, + *pi_grad_dev, + diffusion_coeff, + kind_map, + params_dev, + dt, + dt, + 1.0, ), ) @@ -383,7 +549,14 @@ def add(name, cpu_fn, gpu_fn): lambda: ( vec_gpu.fill(0.0), charge_density_0form_gpu( - scene.particles.markers, weight_idx, pn, tn1, tn2, tn3, starts, vec_gpu, + scene.particles.markers, + weight_idx, + pn, + tn1, + tn2, + tn3, + starts, + vec_gpu, ), )[-1], ) @@ -397,7 +570,7 @@ def add(name, cpu_fn, gpu_fn): for b_ in range(3): if b_ >= a_ and op.matrix.blocks[a_][b_] is not None: shape_ = op.matrix.blocks[a_][b_]._data.shape - key = f"{a_+1}{b_+1}" + key = f"{a_ + 1}{b_ + 1}" mat_cpu[key] = np.zeros(shape_, dtype=float) mat_gpu_[key] = scene.dev(np.zeros(shape_, dtype=float)) vec_space = scene.derham.coeff_spaces["1"] @@ -414,7 +587,9 @@ def _lva_cpu(): for v in vlva_vec_cpu: v.fill(0.0) accum_kernels.linear_vlasov_ampere( - am, ah, ad, + am, + ah, + ad, *[mat_cpu[k] for k in mat_keys], *vlva_vec_cpu, lva_f0, @@ -426,8 +601,17 @@ def _lva_gpu(): for v in vlva_vec_gpu: v.fill(0.0) linear_vlasov_ampere_gpu( - scene.particles.markers, kind_map, params_dev, lva_f0_dev, pn, tn1, tn2, tn3, starts, - *[mat_gpu_[k] for k in mat_keys], *vlva_vec_gpu, + scene.particles.markers, + kind_map, + params_dev, + lva_f0_dev, + pn, + tn1, + tn2, + tn3, + starts, + *[mat_gpu_[k] for k in mat_keys], + *vlva_vec_gpu, ) add("linear_vlasov_ampere", _lva_cpu, _lva_gpu) @@ -443,12 +627,16 @@ def main(): parser.add_argument("--repeats", type=int, default=5) parser.add_argument("--dt", type=float, default=0.01) parser.add_argument( - "--kernel", action="append", default=None, + "--kernel", + action="append", + default=None, help="restrict to one kernel (repeatable); default: run all", ) args = parser.parse_args() - print(f"Building scene: num_elements={tuple(args.num_elements)}, degree={tuple(args.degree)}, Np~={args.n_markers} ...") + print( + f"Building scene: num_elements={tuple(args.num_elements)}, degree={tuple(args.degree)}, Np~={args.n_markers} ..." + ) scene = Scene(tuple(args.num_elements), tuple(args.degree), args.n_markers) print(f" -> {scene.n_markers} markers, domain kind_map={scene.kind_map} (Colella)") @@ -473,13 +661,13 @@ def gpu_run(gpu_fn=gpu_fn): cpu_t = timeit(cpu_run, args.repeats) gpu_t = timeit(gpu_run, args.repeats) rows.append((name, cpu_t, gpu_t, cpu_t / gpu_t)) - print(f" {name}: cpu={cpu_t*1e3:.3f} ms gpu={gpu_t*1e3:.3f} ms speedup={cpu_t/gpu_t:.1f}x") + print(f" {name}: cpu={cpu_t * 1e3:.3f} ms gpu={gpu_t * 1e3:.3f} ms speedup={cpu_t / gpu_t:.1f}x") print() print(f"{'kernel':<36} {'n_markers':>10} {'cpu (ms)':>12} {'gpu (ms)':>12} {'speedup':>10}") print("-" * 84) for name, cpu_t, gpu_t, speedup in rows: - print(f"{name:<36} {scene.n_markers:>10} {cpu_t*1e3:>12.3f} {gpu_t*1e3:>12.3f} {speedup:>9.1f}x") + print(f"{name:<36} {scene.n_markers:>10} {cpu_t * 1e3:>12.3f} {gpu_t * 1e3:>12.3f} {speedup:>9.1f}x") if __name__ == "__main__": diff --git a/params_LinearMHDDriftkineticCC.py b/params_LinearMHDDriftkineticCC.py index b11665fb8..eb6e3efb3 100644 --- a/params_LinearMHDDriftkineticCC.py +++ b/params_LinearMHDDriftkineticCC.py @@ -1,7 +1,7 @@ # ----------------------------- # Description of the simulation # ----------------------------- -# Please fill in a verbal description of the simulation. +# Please fill in a verbal description of the simulation. # It will be printed at the beginning of the simulation and can be used to keep track of the different runs. name = "Default LinearMHDDriftkineticCC" @@ -30,42 +30,40 @@ os.environ["ARRAY_BACKEND"] = args.backend import logging + from struphy import set_logging_level + set_logging_level(logging.WARNING) # ------------------ # Import Struphy API # ------------------ +# For particles: from struphy import ( BaseUnits, + BinningPlot, + BoundaryParameters, DerhamOptions, EnvironmentOptions, FieldsBackground, + KernelDensityPlot, + LoadingParameters, + SavingParameters, Simulation, + SortingParameters, Time, + WeightsParameters, domains, equils, grids, - perturbations, -) - -# For particles: -from struphy import ( - BinningPlot, - BoundaryParameters, - KernelDensityPlot, - LoadingParameters, - WeightsParameters, - SortingParameters, - SavingParameters, maxwellians, + perturbations, ) # --------------------- # Instance of the model # --------------------- - from struphy.models import LinearMHDDriftkineticCC # Units @@ -101,7 +99,7 @@ equil = equils.HomogenSlab() # Grid -grid = grids.TensorProductGrid(num_elements = (16, 16, 16)) +grid = grids.TensorProductGrid(num_elements=(16, 16, 16)) # Derham options derham_opts = DerhamOptions() @@ -129,12 +127,13 @@ boundary_params = BoundaryParameters() sorting_params = SortingParameters() saving_params = SavingParameters() -model.energetic_ions.set_markers(loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, - ) +model.energetic_ions.set_markers( + loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, +) # ------------------ # Propagator options @@ -158,9 +157,9 @@ model.mhd.velocity.add_background(FieldsBackground()) # Perturbations for (some) FEEC variables -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis='v', comp=0)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis='v', comp=1)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis='v', comp=2)) +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=0)) +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=1)) +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=2)) # For kinetic species the background is mandatory. # For kinetic species, if add_initial_condition() is not called, the background is taken as the kinetic initial condition. diff --git a/params_PressureLessSPH.py b/params_PressureLessSPH.py index 057e96024..c2734941e 100644 --- a/params_PressureLessSPH.py +++ b/params_PressureLessSPH.py @@ -1,7 +1,7 @@ # ----------------------------- # Description of the simulation # ----------------------------- -# Please fill in a verbal description of the simulation. +# Please fill in a verbal description of the simulation. # It will be printed at the beginning of the simulation and can be used to keep track of the different runs. name = "Default PressureLessSPH" @@ -31,42 +31,40 @@ import logging + from struphy import set_logging_level + set_logging_level(logging.WARNING) # ------------------ # Import Struphy API # ------------------ +# For particles: from struphy import ( BaseUnits, + BinningPlot, + BoundaryParameters, DerhamOptions, EnvironmentOptions, FieldsBackground, + KernelDensityPlot, + LoadingParameters, + SavingParameters, Simulation, + SortingParameters, Time, + WeightsParameters, domains, equils, grids, - perturbations, -) - -# For particles: -from struphy import ( - BinningPlot, - BoundaryParameters, - KernelDensityPlot, - LoadingParameters, - WeightsParameters, - SortingParameters, - SavingParameters, maxwellians, + perturbations, ) # --------------------- # Instance of the model # --------------------- - from struphy.models import PressureLessSPH # Units @@ -134,12 +132,13 @@ boundary_params = BoundaryParameters() sorting_params = SortingParameters() saving_params = SavingParameters() -model.cold_fluid.set_markers(loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, - ) +model.cold_fluid.set_markers( + loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, +) # ------------------ # Propagator options diff --git a/src/struphy/console/format.py b/src/struphy/console/format.py index 6cc46d378..4a1cb7494 100644 --- a/src/struphy/console/format.py +++ b/src/struphy/console/format.py @@ -1211,7 +1211,15 @@ def confirm_formatting(python_files, linters, yes): ) print("\n") if not yes: - ans = input("Format files (Y/n)?\n") + try: + ans = input("Format files (Y/n)?\n") + except EOFError: + # stdin isn't an interactive terminal (piped, scripted, non-tty + # shell, ...) -- input() can't prompt, so there's no way to get + # a real answer. Fail safe (don't format) instead of crashing + # with a raw traceback, and point at the flag that avoids this. + print("\nNo interactive terminal to confirm on. Exiting... (use --yes/-y to skip this prompt)") + sys.exit(1) if ans.lower() not in ("y", "yes", ""): print("Exiting...") sys.exit(1) diff --git a/src/struphy/feec/mass_kernels.py b/src/struphy/feec/mass_kernels.py index b29255917..9546164fd 100644 --- a/src/struphy/feec/mass_kernels.py +++ b/src/struphy/feec/mass_kernels.py @@ -7,11 +7,11 @@ import numpy as np - # ====================================================================== # 1D # ====================================================================== + def kernel_1d_mat( spans1: "int[:]", pi1: int, @@ -31,26 +31,16 @@ def kernel_1d_mat( for iel1 in range(ne1): for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 for jl1 in range(pj1 + 1): - value = 0.0 for q1 in range(nq1): - value += ( - w1[iel1, q1] - * bi1[iel1, il1, 0, q1] - * bj1[iel1, jl1, 0, q1] - * mat_fun[iel1 * nq1 + q1] - ) + value += w1[iel1, q1] * bi1[iel1, il1, 0, q1] * bj1[iel1, jl1, 0, q1] * mat_fun[iel1 * nq1 + q1] - data[ - pads1 + i_local1, - pads1 + jl1 - il1 - ] += value + data[pads1 + i_local1, pads1 + jl1 - il1] += value def kernel_1d_vec( @@ -70,18 +60,13 @@ def kernel_1d_vec( for iel1 in range(ne1): for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 value = 0.0 for q1 in range(nq1): - value += ( - w1[iel1, q1] - * bi1[iel1, il1, 0, q1] - * mat_fun[iel1 * nq1 + q1] - ) + value += w1[iel1, q1] * bi1[iel1, il1, 0, q1] * mat_fun[iel1 * nq1 + q1] data[pads1 + i_local1] += value @@ -104,22 +89,20 @@ def kernel_1d_eval( for iel1 in range(ne1): for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 coeff = coeffs_data[pads1 + i_local1] for q1 in range(nq1): - values[iel1 * nq1 + q1] += ( - coeff * bi1[iel1, il1, 0, q1] - ) + values[iel1 * nq1 + q1] += coeff * bi1[iel1, il1, 0, q1] # ====================================================================== # 2D # ====================================================================== + def kernel_2d_mat( spans1: "int[:]", spans2: "int[:]", @@ -150,10 +133,8 @@ def kernel_2d_mat( for iel1 in range(ne1): for iel2 in range(ne2): - for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): - i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 @@ -162,7 +143,6 @@ def kernel_2d_mat( for jl1 in range(pj1 + 1): for jl2 in range(pj2 + 1): - value = 0.0 for q1 in range(nq1): @@ -171,29 +151,11 @@ def kernel_2d_mat( w_1 = w1[iel1, q1] for q2 in range(nq2): - wvol = ( - w_1 - * w2[iel2, q2] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2 - ] - ) - - value += ( - wvol - * bi_1 - * bi2[iel2, il2, 0, q2] - * bj_1 - * bj2[iel2, jl2, 0, q2] - ) - - data[ - pads1 + i_local1, - pads2 + i_local2, - pads1 + jl1 - il1, - pads2 + jl2 - il2 - ] += value + wvol = w_1 * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] + + value += wvol * bi_1 * bi2[iel2, il2, 0, q2] * bj_1 * bj2[iel2, jl2, 0, q2] + + data[pads1 + i_local1, pads2 + i_local2, pads1 + jl1 - il1, pads2 + jl2 - il2] += value def kernel_2d_vec( @@ -222,10 +184,8 @@ def kernel_2d_vec( for iel1 in range(ne1): for iel2 in range(ne2): - for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): - i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 @@ -242,18 +202,12 @@ def kernel_2d_vec( value += ( w_1 * w2[iel2, q2] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2 - ] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] * bi_1 * bi2[iel2, il2, 0, q2] ) - data[ - pads1 + i_local1, - pads2 + i_local2 - ] += value + data[pads1 + i_local1, pads2 + i_local2] += value def kernel_2d_eval( @@ -282,39 +236,28 @@ def kernel_2d_eval( for iel1 in range(ne1): for iel2 in range(ne2): - for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): - i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 - coeff = coeffs_data[ - pads1 + i_local1, - pads2 + i_local2 - ] + coeff = coeffs_data[pads1 + i_local1, pads2 + i_local2] for q1 in range(nq1): bi_1 = bi1[iel1, il1, 0, q1] for q2 in range(nq2): - values[ - iel1 * nq1 + q1, - iel2 * nq2 + q2 - ] += ( - coeff - * bi_1 - * bi2[iel2, il2, 0, q2] - ) + values[iel1 * nq1 + q1, iel2 * nq2 + q2] += coeff * bi_1 * bi2[iel2, il2, 0, q2] # ====================================================================== # 3D # ====================================================================== + def kernel_3d_mat( spans1: "int[:]", spans2: "int[:]", @@ -356,7 +299,6 @@ def kernel_3d_mat( for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - for il1 in range(pi1 + 1): i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 @@ -372,55 +314,27 @@ def kernel_3d_mat( for jl1 in range(pj1 + 1): for jl2 in range(pj2 + 1): for jl3 in range(pj3 + 1): - value = 0.0 for q1 in range(nq1): - bi_1 = bi1[ - iel1, il1, 0, q1 - ] - bj_1 = bj1[ - iel1, jl1, 0, q1 - ] + bi_1 = bi1[iel1, il1, 0, q1] + bj_1 = bj1[iel1, jl1, 0, q1] w_1 = w1[iel1, q1] for q2 in range(nq2): - bi_12 = ( - bi_1 - * bi2[ - iel2, il2, 0, q2 - ] - ) - bj_12 = ( - bj_1 - * bj2[ - iel2, jl2, 0, q2 - ] - ) - w_12 = ( - w_1 - * w2[iel2, q2] - ) + bi_12 = bi_1 * bi2[iel2, il2, 0, q2] + bj_12 = bj_1 * bj2[iel2, jl2, 0, q2] + w_12 = w_1 * w2[iel2, q2] for q3 in range(nq3): value += ( w_12 - * w3[ - iel3, q3 - ] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2, - iel3 * nq3 + q3 - ] + * w3[iel3, q3] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] * bi_12 - * bi3[ - iel3, il3, 0, q3 - ] + * bi3[iel3, il3, 0, q3] * bj_12 - * bj3[ - iel3, jl3, 0, q3 - ] + * bj3[iel3, jl3, 0, q3] ) data[ @@ -429,7 +343,7 @@ def kernel_3d_mat( pads3 + i_local3, pads1 + jl1 - il1, pads2 + jl2 - il2, - pads3 + jl3 - il3 + pads3 + jl3 - il3, ] += value @@ -468,7 +382,6 @@ def kernel_3d_vec( for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - for il1 in range(pi1 + 1): i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 @@ -488,35 +401,19 @@ def kernel_3d_vec( w_1 = w1[iel1, q1] for q2 in range(nq2): - bi_12 = ( - bi_1 - * bi2[iel2, il2, 0, q2] - ) - w_12 = ( - w_1 - * w2[iel2, q2] - ) + bi_12 = bi_1 * bi2[iel2, il2, 0, q2] + w_12 = w_1 * w2[iel2, q2] for q3 in range(nq3): value += ( w_12 * w3[iel3, q3] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2, - iel3 * nq3 + q3 - ] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] * bi_12 - * bi3[ - iel3, il3, 0, q3 - ] + * bi3[iel3, il3, 0, q3] ) - data[ - pads1 + i_local1, - pads2 + i_local2, - pads3 + i_local3 - ] += value + data[pads1 + i_local1, pads2 + i_local2, pads3 + i_local3] += value def kernel_3d_eval( @@ -553,7 +450,6 @@ def kernel_3d_eval( for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - for il1 in range(pi1 + 1): i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 @@ -566,36 +462,17 @@ def kernel_3d_eval( i_global3 = spans3[iel3] - pi3 + il3 i_local3 = i_global3 - starts3 - coeff = coeffs_data[ - pads1 + i_local1, - pads2 + i_local2, - pads3 + i_local3 - ] + coeff = coeffs_data[pads1 + i_local1, pads2 + i_local2, pads3 + i_local3] for q1 in range(nq1): - bi_1 = bi1[ - iel1, il1, 0, q1 - ] + bi_1 = bi1[iel1, il1, 0, q1] for q2 in range(nq2): - bi_12 = ( - bi_1 - * bi2[ - iel2, il2, 0, q2 - ] - ) + bi_12 = bi_1 * bi2[iel2, il2, 0, q2] for q3 in range(nq3): - values[ - iel1 * nq1 + q1, - iel2 * nq2 + q2, - iel3 * nq3 + q3 - ] += ( - coeff - * bi_12 - * bi3[ - iel3, il3, 0, q3 - ] + values[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] += ( + coeff * bi_12 * bi3[iel3, il3, 0, q3] ) @@ -650,129 +527,54 @@ def kernel_3d_matrixfree( for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - for q1 in range(nq1): for q2 in range(nq2): for q3 in range(nq3): - bj = 0.0 for jl1 in range(pj1 + 1): - j_global1 = ( - spansj1[iel1] - pj1 + jl1 - ) - j_local1 = ( - j_global1 - - startsj1 - + padsj1 - ) - - bj_1 = bj1[ - iel1, jl1, 0, q1 - ] + j_global1 = spansj1[iel1] - pj1 + jl1 + j_local1 = j_global1 - startsj1 + padsj1 + + bj_1 = bj1[iel1, jl1, 0, q1] for jl2 in range(pj2 + 1): - j_global2 = ( - spansj2[iel2] - pj2 + jl2 - ) - j_local2 = ( - j_global2 - - startsj2 - + padsj2 - ) - - bj_12 = ( - bj_1 - * bj2[ - iel2, jl2, 0, q2 - ] - ) + j_global2 = spansj2[iel2] - pj2 + jl2 + j_local2 = j_global2 - startsj2 + padsj2 + + bj_12 = bj_1 * bj2[iel2, jl2, 0, q2] for jl3 in range(pj3 + 1): - j_global3 = ( - spansj3[iel3] - pj3 + jl3 - ) - j_local3 = ( - j_global3 - - startsj3 - + padsj3 - ) + j_global3 = spansj3[iel3] - pj3 + jl3 + j_local3 = j_global3 - startsj3 + padsj3 - bj += ( - bj_12 - * bj3[ - iel3, jl3, 0, q3 - ] - * data_in[ - j_local1, - j_local2, - j_local3 - ] - ) + bj += bj_12 * bj3[iel3, jl3, 0, q3] * data_in[j_local1, j_local2, j_local3] wvol = ( w1[iel1, q1] * w2[iel2, q2] * w3[iel3, q3] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2, - iel3 * nq3 + q3 - ] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] ) for il1 in range(pi1 + 1): - i_global1 = ( - spansi1[iel1] - pi1 + il1 - ) - i_local1 = ( - i_global1 - - startsi1 - + padsi1 - ) - - bi_1 = bi1[ - iel1, il1, 0, q1 - ] + i_global1 = spansi1[iel1] - pi1 + il1 + i_local1 = i_global1 - startsi1 + padsi1 + + bi_1 = bi1[iel1, il1, 0, q1] for il2 in range(pi2 + 1): - i_global2 = ( - spansi2[iel2] - pi2 + il2 - ) - i_local2 = ( - i_global2 - - startsi2 - + padsi2 - ) - - bi_12 = ( - bi_1 - * bi2[ - iel2, il2, 0, q2 - ] - ) + i_global2 = spansi2[iel2] - pi2 + il2 + i_local2 = i_global2 - startsi2 + padsi2 + + bi_12 = bi_1 * bi2[iel2, il2, 0, q2] for il3 in range(pi3 + 1): - i_global3 = ( - spansi3[iel3] - pi3 + il3 - ) - i_local3 = ( - i_global3 - - startsi3 - + padsi3 - ) + i_global3 = spansi3[iel3] - pi3 + il3 + i_local3 = i_global3 - startsi3 + padsi3 - data_out[ - i_local1, - i_local2, - i_local3 - ] += ( - wvol - * bi_12 - * bi3[ - iel3, il3, 0, q3 - ] - * bj + data_out[i_local1, i_local2, i_local3] += ( + wvol * bi_12 * bi3[iel3, il3, 0, q3] * bj ) @@ -815,7 +617,6 @@ def kernel_3d_diag( for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - for il1 in range(pi1 + 1): i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 @@ -840,57 +641,32 @@ def kernel_3d_diag( value = 0.0 for q1 in range(nq1): - bi_1 = bi1[ - iel1, il1, 0, q1 - ] + bi_1 = bi1[iel1, il1, 0, q1] for q2 in range(nq2): - bi_12 = ( - bi_1 - * bi2[ - iel2, il2, 0, q2 - ] - ) + bi_12 = bi_1 * bi2[iel2, il2, 0, q2] for q3 in range(nq3): value += ( w1[iel1, q1] * w2[iel2, q2] * w3[iel3, q3] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2, - iel3 * nq3 + q3 - ] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] * bi_12 - * bi3[ - iel3, il3, 0, q3 - ] + * bi3[iel3, il3, 0, q3] * bi_12 - * bi3[ - iel3, il3, 0, q3 - ] - / ( - bi2[ - iel2, il2, 0, q2 - ] - * bi3[ - iel3, il3, 0, q3 - ] - ) + * bi3[iel3, il3, 0, q3] + / (bi2[iel2, il2, 0, q2] * bi3[iel3, il3, 0, q3]) ) - data[ - i_local1, - i_local2, - i_local3 - ] += value + data[i_local1, i_local2, i_local3] += value # ====================================================================== # 3D surface kernels # ====================================================================== + def surface_kernel_3d_vec( spans1: "int[:]", spans2: "int[:]", @@ -923,7 +699,6 @@ def surface_kernel_3d_vec( for iel1 in range(ne1): for iel2 in range(ne2): - for il1 in range(pi1 + 1): i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 @@ -941,19 +716,12 @@ def surface_kernel_3d_vec( value += ( w1[iel1, q1] * w2[iel2, q2] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2 - ] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] * bi_1 * bi2[iel2, il2, 0, q2] ) - data[ - pads0 + i_local0, - pads1 + i_local1, - pads2 + i_local2 - ] += value + data[pads0 + i_local0, pads1 + i_local1, pads2 + i_local2] += value def surface_kernel_3d_mat( @@ -997,12 +765,10 @@ def surface_kernel_3d_mat( nq2 = w2.shape[1] if normal_dir == 0: - i_local_n = boundary_index - starts0 for iel1 in range(ne1): for iel2 in range(ne2): - for il1 in range(pi1 + 1): i_global1 = spans1[iel1] - pi1 + il1 i_local1 = i_global1 - starts1 @@ -1013,33 +779,21 @@ def surface_kernel_3d_mat( for jl1 in range(pj1 + 1): for jl2 in range(pj2 + 1): - value = 0.0 for q1 in range(nq1): - bi_1 = bi1[ - iel1, il1, 0, q1 - ] - bj_1 = bj1[ - iel1, jl1, 0, q1 - ] + bi_1 = bi1[iel1, il1, 0, q1] + bj_1 = bj1[iel1, jl1, 0, q1] for q2 in range(nq2): value += ( w1[iel1, q1] * w2[iel2, q2] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2 - ] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] * bi_1 - * bi2[ - iel2, il2, 0, q2 - ] + * bi2[iel2, il2, 0, q2] * bj_1 - * bj2[ - iel2, jl2, 0, q2 - ] + * bj2[iel2, jl2, 0, q2] ) data[ @@ -1048,16 +802,14 @@ def surface_kernel_3d_mat( pads2 + i_local2, pads0, pads1 + jl1 - il1, - pads2 + jl2 - il2 + pads2 + jl2 - il2, ] += value elif normal_dir == 1: - i_local_n = boundary_index - starts1 for iel1 in range(ne1): for iel2 in range(ne2): - for il1 in range(pi0 + 1): i_global1 = spans1[iel1] - pi0 + il1 i_local1 = i_global1 - starts0 @@ -1068,33 +820,21 @@ def surface_kernel_3d_mat( for jl1 in range(pj0 + 1): for jl2 in range(pj2 + 1): - value = 0.0 for q1 in range(nq1): - bi_1 = bi1[ - iel1, il1, 0, q1 - ] - bj_1 = bj1[ - iel1, jl1, 0, q1 - ] + bi_1 = bi1[iel1, il1, 0, q1] + bj_1 = bj1[iel1, jl1, 0, q1] for q2 in range(nq2): value += ( w1[iel1, q1] * w2[iel2, q2] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2 - ] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] * bi_1 - * bi2[ - iel2, il2, 0, q2 - ] + * bi2[iel2, il2, 0, q2] * bj_1 - * bj2[ - iel2, jl2, 0, q2 - ] + * bj2[iel2, jl2, 0, q2] ) data[ @@ -1103,16 +843,14 @@ def surface_kernel_3d_mat( pads2 + i_local2, pads0 + jl1 - il1, pads1, - pads2 + jl2 - il2 + pads2 + jl2 - il2, ] += value else: - i_local_n = boundary_index - starts2 for iel1 in range(ne1): for iel2 in range(ne2): - for il1 in range(pi0 + 1): i_global1 = spans1[iel1] - pi0 + il1 i_local1 = i_global1 - starts0 @@ -1123,33 +861,21 @@ def surface_kernel_3d_mat( for jl1 in range(pj0 + 1): for jl2 in range(pj1 + 1): - value = 0.0 for q1 in range(nq1): - bi_1 = bi1[ - iel1, il1, 0, q1 - ] - bj_1 = bj1[ - iel1, jl1, 0, q1 - ] + bi_1 = bi1[iel1, il1, 0, q1] + bj_1 = bj1[iel1, jl1, 0, q1] for q2 in range(nq2): value += ( w1[iel1, q1] * w2[iel2, q2] - * mat_fun[ - iel1 * nq1 + q1, - iel2 * nq2 + q2 - ] + * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] * bi_1 - * bi2[ - iel2, il2, 0, q2 - ] + * bi2[iel2, il2, 0, q2] * bj_1 - * bj2[ - iel2, jl2, 0, q2 - ] + * bj2[iel2, jl2, 0, q2] ) data[ @@ -1158,6 +884,5 @@ def surface_kernel_3d_mat( pads2 + i_local_n, pads0 + jl1 - il1, pads1 + jl2 - il2, - pads2 + pads2, ] += value - diff --git a/src/struphy/feec/preconditioner.py b/src/struphy/feec/preconditioner.py index c45dcbfe2..24b7bb367 100644 --- a/src/struphy/feec/preconditioner.py +++ b/src/struphy/feec/preconditioner.py @@ -1,8 +1,7 @@ import logging -import numpy as np - import cunumpy as xp +import numpy as np from cunumpy.xp import to_cunumpy, to_numpy from feectools.api.essential_bc import apply_essential_bc_stencil from feectools.ddm.cart import CartDecomposition, DomainDecomposition diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index 7c1331188..fc716621c 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -5,6 +5,7 @@ import cunumpy as xp import feectools.core.bsplines as bsp import numpy as np +from cunumpy import PyccelKernel from feectools.ddm.cart import DomainDecomposition from feectools.ddm.mpi import MockComm from feectools.ddm.mpi import mpi as MPI @@ -26,8 +27,6 @@ from feectools.linalg.block import BlockVector, BlockVectorSpace from feectools.linalg.stencil import StencilVector, StencilVectorSpace -from cunumpy import PyccelKernel - from struphy.bsplines import evaluation_kernels_3d as eval_3d from struphy.bsplines.evaluation_kernels_3d import eval_spline_mpi_tensor_product_fixed diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index c1fd30774..22646c03c 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -4,13 +4,13 @@ import cunumpy as xp from cunumpy import PyccelKernel +from feectools.ddm.mpi import mpi as MPI +from feectools.linalg.block import BlockVector +from feectools.linalg.stencil import StencilMatrix, StencilVector from scope_profiler import ProfileManager import struphy.pic.accumulation.accum_kernels as accums import struphy.pic.accumulation.accum_kernels_gc as accums_gc -from feectools.ddm.mpi import mpi as MPI -from feectools.linalg.block import BlockVector -from feectools.linalg.stencil import StencilMatrix, StencilVector from struphy.feec.mass import WeightedMassOperators from struphy.feec.psydac_derham import Derham from struphy.io.options import LiteralOptions diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index c2fe50fbe..498293ac2 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -13,12 +13,12 @@ from struphy.pic.base import Particles from struphy.pic.pushing.pusher_kernels_cuda import ( SUPPORTED_GENERAL_KIND_MAPS, - push_eta_rk_periodic_gpu, - push_eta_stage_cuboid_gpu, push_bxu_H1vec_general_gpu, push_bxu_Hcurl_general_gpu, push_bxu_Hdiv_general_gpu, push_deterministic_diffusion_stage_general_gpu, + push_eta_rk_periodic_gpu, + push_eta_stage_cuboid_gpu, push_eta_stage_general_gpu, push_pc_eta_stage_H1vec_general_gpu, push_pc_eta_stage_Hcurl_general_gpu, @@ -317,10 +317,15 @@ def __init__( # general (non-Cuboid) CUDA replacement for push_vxb_analytic / # push_vxb_implicit, sharing the same B-spline/geometry evaluation as # _gpu_v_efield_general above (2-form instead of 1-form field). - self._gpu_vxb_general = cunumpy.cupy_backend and kernel.name in ( - "push_vxb_analytic", - "push_vxb_implicit", - ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + self._gpu_vxb_general = ( + cunumpy.cupy_backend + and kernel.name + in ( + "push_vxb_analytic", + "push_vxb_implicit", + ) + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) if self._gpu_vxb_general: import cupy as cp @@ -349,11 +354,16 @@ def __init__( # sharing the same B-field (2-form) evaluation as _gpu_vxb_general; # only the U-field's FEEC space (and therefore its evaluation/metric # handling) differs between the three. - self._gpu_bxu_general = cunumpy.cupy_backend and kernel.name in ( - "push_bxu_Hdiv", - "push_bxu_Hcurl", - "push_bxu_H1vec", - ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + self._gpu_bxu_general = ( + cunumpy.cupy_backend + and kernel.name + in ( + "push_bxu_Hdiv", + "push_bxu_Hcurl", + "push_bxu_H1vec", + ) + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) if self._gpu_bxu_general: import cupy as cp @@ -383,18 +393,21 @@ def __init__( # (push_pc_GXu's CPU kernel also takes all 9, only 6 are read) -- so # both branches cache the same 9 g_ij arrays and the *_full variant # is picked purely by kernel.name. - self._gpu_pc_gxu_general = cunumpy.cupy_backend and kernel.name in ( - "push_pc_GXu_full", - "push_pc_GXu", - ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + self._gpu_pc_gxu_general = ( + cunumpy.cupy_backend + and kernel.name + in ( + "push_pc_GXu_full", + "push_pc_GXu", + ) + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) if self._gpu_pc_gxu_general: import cupy as cp self._gpu_pc_gxu_general_full = kernel.name == "push_pc_GXu_full" self._gpu_pc_gxu_general_kind_map = int(args_domain.kind_map) - self._gpu_pc_gxu_general_params = cp.asarray( - np.asarray(args_domain.params, dtype=float), dtype=cp.float64 - ) + self._gpu_pc_gxu_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) args_derham, g11, g12, g13, g21, g22, g23, g31, g32, g33 = args_kernel self._gpu_pc_gxu_general_pn = tuple(int(p) for p in args_derham.pn) @@ -407,19 +420,22 @@ def __init__( # general (non-Cuboid) CUDA replacement for # push_pc_eta_stage_{Hcurl,Hdiv,H1vec}: a variant of _gpu_eta_general # with an extra U-field vector contribution added to the eta rate. - self._gpu_pc_eta_general = cunumpy.cupy_backend and kernel.name in ( - "push_pc_eta_stage_Hcurl", - "push_pc_eta_stage_Hdiv", - "push_pc_eta_stage_H1vec", - ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + self._gpu_pc_eta_general = ( + cunumpy.cupy_backend + and kernel.name + in ( + "push_pc_eta_stage_Hcurl", + "push_pc_eta_stage_Hdiv", + "push_pc_eta_stage_H1vec", + ) + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) if self._gpu_pc_eta_general: import cupy as cp self._gpu_pc_eta_general_variant = kernel.name self._gpu_pc_eta_general_kind_map = int(args_domain.kind_map) - self._gpu_pc_eta_general_params = cp.asarray( - np.asarray(args_domain.params, dtype=float), dtype=cp.float64 - ) + self._gpu_pc_eta_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) args_derham, u_1, u_2, u_3, use_perp_model = args_kernel[:5] self._gpu_pc_eta_general_use_perp_model = bool(use_perp_model) @@ -824,7 +840,9 @@ def _push(self, dt: float): ) elif self._gpu_vxb_general: gpu_vxb_fn = ( - push_vxb_analytic_general_gpu if self._gpu_vxb_general_analytic else push_vxb_implicit_general_gpu + push_vxb_analytic_general_gpu + if self._gpu_vxb_general_analytic + else push_vxb_implicit_general_gpu ) with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): gpu_vxb_fn( @@ -881,7 +899,15 @@ def _push(self, dt: float): self._gpu_pc_gxu_general_tn2, self._gpu_pc_gxu_general_tn3, self._gpu_pc_gxu_general_starts, - g11, g12, g13, g21, g22, g23, g31, g32, g33, + g11, + g12, + g13, + g21, + g22, + g23, + g31, + g32, + g33, self._gpu_pc_gxu_general_kind_map, self._gpu_pc_gxu_general_params, dt, @@ -895,7 +921,12 @@ def _push(self, dt: float): self._gpu_pc_gxu_general_tn2, self._gpu_pc_gxu_general_tn3, self._gpu_pc_gxu_general_starts, - g11, g12, g13, g21, g22, g23, + g11, + g12, + g13, + g21, + g22, + g23, self._gpu_pc_gxu_general_kind_map, self._gpu_pc_gxu_general_params, dt, diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index 25b0eb1db..1730762e9 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -2179,8 +2179,23 @@ def _get_vxb_implicit_general_kernel(): return _push_vxb_implicit_general_kernel -def _launch_vxb_general(kernel, markers, n_cols, first_init_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, kind_map, params_dev, dt): +def _launch_vxb_general( + kernel, + markers, + n_cols, + first_init_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + kind_map, + params_dev, + dt, +): import cupy as cp import numpy as np @@ -2249,8 +2264,20 @@ def push_vxb_analytic_general_gpu( """ _launch_vxb_general( _get_vxb_analytic_general_kernel(), - markers, n_cols, first_init_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, kind_map, params_dev, dt, + markers, + n_cols, + first_init_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + kind_map, + params_dev, + dt, ) @@ -2276,8 +2303,20 @@ def push_vxb_implicit_general_gpu( See :func:`push_vxb_analytic_general_gpu` for argument conventions.""" _launch_vxb_general( _get_vxb_implicit_general_kernel(), - markers, n_cols, first_init_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, kind_map, params_dev, dt, + markers, + n_cols, + first_init_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + kind_map, + params_dev, + dt, ) @@ -2313,9 +2352,26 @@ def _get_bxu_h1vec_general_kernel(): return _push_bxu_h1vec_general_kernel -def _launch_bxu_general(kernel, markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, u_1_dev, u_2_dev, u_3_dev, - kind_map, params_dev, boundary_cut, dt): +def _launch_bxu_general( + kernel, + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + u_1_dev, + u_2_dev, + u_3_dev, + kind_map, + params_dev, + boundary_cut, + dt, +): import cupy as cp import numpy as np @@ -2370,47 +2426,137 @@ def _launch_bxu_general(kernel, markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, def push_bxu_Hdiv_general_gpu( - markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, u2_1_dev, u2_2_dev, u2_3_dev, - kind_map: int, params_dev, boundary_cut: float, dt: float, + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + u2_1_dev, + u2_2_dev, + u2_3_dev, + kind_map: int, + params_dev, + boundary_cut: float, + dt: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_Hdiv`, for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u2_*_dev`` is the U-field's 2-form FE coefficients (same evaluation as ``b2_*_dev``).""" _launch_bxu_general( - _get_bxu_hdiv_general_kernel(), markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, u2_1_dev, u2_2_dev, u2_3_dev, kind_map, params_dev, boundary_cut, dt, + _get_bxu_hdiv_general_kernel(), + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + u2_1_dev, + u2_2_dev, + u2_3_dev, + kind_map, + params_dev, + boundary_cut, + dt, ) def push_bxu_Hcurl_general_gpu( - markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, u1_1_dev, u1_2_dev, u1_3_dev, - kind_map: int, params_dev, boundary_cut: float, dt: float, + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + u1_1_dev, + u1_2_dev, + u1_3_dev, + kind_map: int, + params_dev, + boundary_cut: float, + dt: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_Hcurl`, for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u1_*_dev`` is the U-field's 1-form FE coefficients.""" _launch_bxu_general( - _get_bxu_hcurl_general_kernel(), markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, u1_1_dev, u1_2_dev, u1_3_dev, kind_map, params_dev, boundary_cut, dt, + _get_bxu_hcurl_general_kernel(), + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + u1_1_dev, + u1_2_dev, + u1_3_dev, + kind_map, + params_dev, + boundary_cut, + dt, ) def push_bxu_H1vec_general_gpu( - markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, uv_1_dev, uv_2_dev, uv_3_dev, - kind_map: int, params_dev, boundary_cut: float, dt: float, + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + uv_1_dev, + uv_2_dev, + uv_3_dev, + kind_map: int, + params_dev, + boundary_cut: float, + dt: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_H1vec`, for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``uv_*_dev`` is the U-field's (H^1)^3 vector-field FE coefficients.""" _launch_bxu_general( - _get_bxu_h1vec_general_kernel(), markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2_1_dev, b2_2_dev, b2_3_dev, uv_1_dev, uv_2_dev, uv_3_dev, kind_map, params_dev, boundary_cut, dt, + _get_bxu_h1vec_general_kernel(), + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + uv_1_dev, + uv_2_dev, + uv_3_dev, + kind_map, + params_dev, + boundary_cut, + dt, ) @@ -2437,9 +2583,25 @@ def _get_pc_gxu_general_kernel(): def push_pc_GXu_full_general_gpu( - markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev, g31_dev, g32_dev, g33_dev, - kind_map: int, params_dev, dt: float, + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + g11_dev, + g12_dev, + g13_dev, + g21_dev, + g22_dev, + g23_dev, + g31_dev, + g32_dev, + g33_dev, + kind_map: int, + params_dev, + dt: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_pc_GXu_full`, for any @@ -2490,9 +2652,22 @@ def push_pc_GXu_full_general_gpu( def push_pc_GXu_general_gpu( - markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev, - kind_map: int, params_dev, dt: float, + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + g11_dev, + g12_dev, + g13_dev, + g21_dev, + g22_dev, + g23_dev, + kind_map: int, + params_dev, + dt: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_pc_GXu` (the 2-row @@ -2572,9 +2747,27 @@ def _get_pc_eta_h1vec_general_kernel(): return _push_pc_eta_h1vec_general_kernel -def _launch_pc_eta_general(kernel, markers, n_cols, first_init_idx, first_free_idx, pn, - tn1_dev, tn2_dev, tn3_dev, starts, u_1_dev, u_2_dev, u_3_dev, - use_perp_model, kind_map, params_dev, dt_a, dt_b, last): +def _launch_pc_eta_general( + kernel, + markers, + n_cols, + first_init_idx, + first_free_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + u_1_dev, + u_2_dev, + u_3_dev, + use_perp_model, + kind_map, + params_dev, + dt_a, + dt_b, + last, +): import cupy as cp import numpy as np @@ -2624,47 +2817,143 @@ def _launch_pc_eta_general(kernel, markers, n_cols, first_init_idx, first_free_i def push_pc_eta_stage_Hcurl_general_gpu( - markers, n_cols, first_init_idx, first_free_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, - u_1_dev, u_2_dev, u_3_dev, use_perp_model: bool, kind_map: int, params_dev, dt_a: float, dt_b: float, last: float, + markers, + n_cols, + first_init_idx, + first_free_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + u_1_dev, + u_2_dev, + u_3_dev, + use_perp_model: bool, + kind_map: int, + params_dev, + dt_a: float, + dt_b: float, + last: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_Hcurl`, for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the U-field's 1-form FE coefficients.""" _launch_pc_eta_general( - _get_pc_eta_hcurl_general_kernel(), markers, n_cols, first_init_idx, first_free_idx, pn, - tn1_dev, tn2_dev, tn3_dev, starts, u_1_dev, u_2_dev, u_3_dev, - use_perp_model, kind_map, params_dev, dt_a, dt_b, last, + _get_pc_eta_hcurl_general_kernel(), + markers, + n_cols, + first_init_idx, + first_free_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + u_1_dev, + u_2_dev, + u_3_dev, + use_perp_model, + kind_map, + params_dev, + dt_a, + dt_b, + last, ) def push_pc_eta_stage_Hdiv_general_gpu( - markers, n_cols, first_init_idx, first_free_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, - u_1_dev, u_2_dev, u_3_dev, use_perp_model: bool, kind_map: int, params_dev, dt_a: float, dt_b: float, last: float, + markers, + n_cols, + first_init_idx, + first_free_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + u_1_dev, + u_2_dev, + u_3_dev, + use_perp_model: bool, + kind_map: int, + params_dev, + dt_a: float, + dt_b: float, + last: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_Hdiv`, for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the U-field's 2-form FE coefficients.""" _launch_pc_eta_general( - _get_pc_eta_hdiv_general_kernel(), markers, n_cols, first_init_idx, first_free_idx, pn, - tn1_dev, tn2_dev, tn3_dev, starts, u_1_dev, u_2_dev, u_3_dev, - use_perp_model, kind_map, params_dev, dt_a, dt_b, last, + _get_pc_eta_hdiv_general_kernel(), + markers, + n_cols, + first_init_idx, + first_free_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + u_1_dev, + u_2_dev, + u_3_dev, + use_perp_model, + kind_map, + params_dev, + dt_a, + dt_b, + last, ) def push_pc_eta_stage_H1vec_general_gpu( - markers, n_cols, first_init_idx, first_free_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, - u_1_dev, u_2_dev, u_3_dev, use_perp_model: bool, kind_map: int, params_dev, dt_a: float, dt_b: float, last: float, + markers, + n_cols, + first_init_idx, + first_free_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + u_1_dev, + u_2_dev, + u_3_dev, + use_perp_model: bool, + kind_map: int, + params_dev, + dt_a: float, + dt_b: float, + last: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_H1vec`, for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the U-field's (H^1)^3 vector-field FE coefficients.""" _launch_pc_eta_general( - _get_pc_eta_h1vec_general_kernel(), markers, n_cols, first_init_idx, first_free_idx, pn, - tn1_dev, tn2_dev, tn3_dev, starts, u_1_dev, u_2_dev, u_3_dev, - use_perp_model, kind_map, params_dev, dt_a, dt_b, last, + _get_pc_eta_h1vec_general_kernel(), + markers, + n_cols, + first_init_idx, + first_free_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + u_1_dev, + u_2_dev, + u_3_dev, + use_perp_model, + kind_map, + params_dev, + dt_a, + dt_b, + last, ) @@ -2683,9 +2972,22 @@ def _get_weights_efield_lin_va_general_kernel(): def push_weights_with_efield_lin_va_general_gpu( - markers, n_cols, pn, tn1_dev, tn2_dev, tn3_dev, starts, - e1_1_dev, e1_2_dev, e1_3_dev, f0_values, kappa: float, vth: float, - kind_map: int, params_dev, dt: float, + markers, + n_cols, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + e1_1_dev, + e1_2_dev, + e1_3_dev, + f0_values, + kappa: float, + vth: float, + kind_map: int, + params_dev, + dt: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_weights_with_efield_lin_va`, @@ -2755,9 +3057,25 @@ def _get_deterministic_diffusion_general_kernel(): def push_deterministic_diffusion_stage_general_gpu( - markers, n_cols, first_init_idx, first_free_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, - pi_u_dev, pi_grad_u1_dev, pi_grad_u2_dev, pi_grad_u3_dev, diffusion_coeff: float, - kind_map: int, params_dev, dt_a: float, dt_b: float, last: float, + markers, + n_cols, + first_init_idx, + first_free_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + pi_u_dev, + pi_grad_u1_dev, + pi_grad_u2_dev, + pi_grad_u3_dev, + diffusion_coeff: float, + kind_map: int, + params_dev, + dt_a: float, + dt_b: float, + last: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels.push_deterministic_diffusion_stage`, diff --git a/src/struphy/pic/tests/test_pushers.py b/src/struphy/pic/tests/test_pushers.py index 3bc1a4bd0..3d25444f8 100644 --- a/src/struphy/pic/tests/test_pushers.py +++ b/src/struphy/pic/tests/test_pushers.py @@ -702,7 +702,6 @@ def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): # MPI communication buffers/counts must be host (NumPy) arrays regardless # of the active cunumpy backend. import numpy as np - from cunumpy.xp import to_numpy n_mks_load = np.zeros(size, dtype=int) From 3e76feee10c8c1ee2c3cd81fc445a2a6c832a012 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 12:58:30 +0200 Subject: [PATCH 043/156] Added bench_gpu/out_* to gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 8e8873977..84ab39384 100644 --- a/.gitignore +++ b/.gitignore @@ -101,6 +101,7 @@ src/struphy/io/out/ src/struphy/state.yml src/struphy/io/inp/params_* *.bin +bench_gpu/out_* # models list bin/ @@ -112,3 +113,4 @@ pyvenv.cfg *profile_output*.txt *kernels.txt struphy.log + From ac986a12cfbbbd58600c875b31191dbeffc760eb Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 13:09:16 +0200 Subject: [PATCH 044/156] Added encoding utf8 in format.py --- src/struphy/console/format.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/src/struphy/console/format.py b/src/struphy/console/format.py index 4a1cb7494..c70a712f0 100644 --- a/src/struphy/console/format.py +++ b/src/struphy/console/format.py @@ -121,7 +121,7 @@ def check_omp_flags(file_path, verbose=False): True if no incorrect OpenMP-like flags (`# $`) are found, False otherwise. """ try: - with open(file_path, "r") as f: + with open(file_path, "r", encoding="utf-8") as f: if verbose: for iline, line in enumerate(f): if line.lstrip().startswith("# $"): @@ -561,7 +561,7 @@ def parse_json_file_to_html(json_file_path, html_output_path): """ try: - with open(json_file_path, "r") as file: + with open(json_file_path, "r", encoding="utf-8") as file: data = json.load(file) if not isinstance(data, list): @@ -817,7 +817,7 @@ def parse_json_file_to_html(json_file_path, html_output_path): # Read the file and extract the code snippet if os.path.exists(filename) and row is not None: - with open(filename, "r") as source_file: + with open(filename, "r", encoding="utf-8") as source_file: lines = source_file.readlines() total_lines = len(lines) # Adjust indices for zero-based indexing @@ -887,7 +887,7 @@ def parse_json_file_to_html(json_file_path, html_output_path): html_content.extend(["", ""]) # Write the HTML content to the output file - with open(html_output_path, "w") as html_file: + with open(html_output_path, "w", encoding="utf-8") as html_file: html_file.write("\n".join(html_content)) print(f"HTML report generated at {html_output_path}") @@ -1277,7 +1277,7 @@ def run_linters_on_files(linters, python_files, flags, verbose): subprocess.run(command, check=False) # Loop over each line and replace '# $' with '#$' in place - for line in fileinput.input(python_file, inplace=True): + for line in fileinput.input(python_file, inplace=True, encoding="utf-8"): if line.lstrip().startswith("# $"): print(line.replace("# $", "#$"), end="") else: @@ -1310,7 +1310,7 @@ def construct_package_init_file( existing_init_path = os.path.join(package_dir, "__init__.py") docstring = None if os.path.isfile(existing_init_path): - with open(existing_init_path, "r") as f: + with open(existing_init_path, "r", encoding="utf-8") as f: docstring = ast.get_docstring(ast.parse(f.read()), clean=False) init_content = f'"""{docstring}"""\n\n' if docstring else "" @@ -1481,12 +1481,12 @@ def struphy_build_init_files(config, verbose, yes=False): print(f"Rewriting {MODELS_INIT_PATH}") models_init = construct_models_init_file() - with open(MODELS_INIT_PATH, "w") as f: + with open(MODELS_INIT_PATH, "w", encoding="utf-8") as f: f.write(models_init) print(f"Rewriting {PROPAGATORS_INIT_PATH}") propagators_init = construct_propagators_init_file() - with open(PROPAGATORS_INIT_PATH, "w") as f: + with open(PROPAGATORS_INIT_PATH, "w", encoding="utf-8") as f: f.write(propagators_init) python_files = [MODELS_INIT_PATH, PROPAGATORS_INIT_PATH] From 14296bf03835e731bd348571ef5db86351414a4c Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 13:57:00 +0200 Subject: [PATCH 045/156] Accum kernels --- .../pic/accumulation/accum_kernels_cuda.py | 1298 +++++++++++++++++ .../pic/accumulation/particles_to_grid.py | 174 ++- 2 files changed, 1471 insertions(+), 1 deletion(-) diff --git a/src/struphy/pic/accumulation/accum_kernels_cuda.py b/src/struphy/pic/accumulation/accum_kernels_cuda.py index 133a0b2ce..dcfb9c01a 100644 --- a/src/struphy/pic/accumulation/accum_kernels_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_cuda.py @@ -393,6 +393,207 @@ def _linear_vlasov_ampere_source(): return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC +# --------------------------------------------------------------------------- +# vlasov_maxwell: same symmetric V1 -> V1 6-block-matrix-plus-vector fill as +# linear_vlasov_ampere (reuses fill_mat_vec_dev/fill_mat_dev from +# _LINEAR_VLASOV_AMPERE_EXTRA_SRC above), but with a different filling: +# A_p = w_p * G^-1(eta_p) (the metric inverse, not an outer product of +# velocity) and B_p = w_p * DF^-1(eta_p) v_p -- no f0_values/s0 involved, so +# unlike linear_vlasov_ampere this one can't hit the inf/nan-from-div-by-s0 +# path. Also note: the CPU reference only skips markers[ip,0]==-1.0 (no +# markers[ip,-1]==-2.0 check), unlike linear_vlasov_ampere -- ported as-is. +# --------------------------------------------------------------------------- + +_VLASOV_MAXWELL_EXTRA_SRC = r""" +extern "C" __global__ +void vlasov_maxwell_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + double* mat11, double* mat12, double* mat13, + double* mat22, double* mat23, double* mat33, + double* vec1, double* vec2, double* vec3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, + const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, + const int v2_n2, const int v2_n3, + const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + const double weight = row[6]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + double df_inv[9], df_inv_v[3]; + matrix_inv_dev(dfm, df_inv); + matvec_dev(df_inv, v, df_inv_v); + + // g_inv = DF^-1 @ DF^-T ; g_inv[i,j] = sum_k df_inv[i,k]*df_inv[j,k] + double filling_m[9]; + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; + filling_m[3*i+j] = weight * s; + } + } + + double filling_v[3]; + filling_v[0] = weight * df_inv_v[0]; + filling_v[1] = weight * df_inv_v[1]; + filling_v[2] = weight * df_inv_v[2]; + + const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; + const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, + vec1, v1_n2,v1_n3, filling_v[0]); + + fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, + vec2, v2_n2,v2_n3, filling_v[1]); + + fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, + vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); +} +""" + + +def _vlasov_maxwell_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _VLASOV_MAXWELL_EXTRA_SRC + + +_vlasov_maxwell_kernel = None + + +def _get_vlasov_maxwell_kernel(): + global _vlasov_maxwell_kernel + if _vlasov_maxwell_kernel is None: + import cupy as cp + + _vlasov_maxwell_kernel = cp.RawKernel(_vlasov_maxwell_source(), "vlasov_maxwell_cuda") + return _vlasov_maxwell_kernel + + +def vlasov_maxwell_gpu( + markers, + kind_map: int, + params_dev, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + mat11_dev, + mat12_dev, + mat13_dev, + mat22_dev, + mat23_dev, + mat33_dev, + vec1_dev, + vec2_dev, + vec3_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels.vlasov_maxwell`. Same + calling convention as :func:`linear_vlasov_ampere_gpu`, minus + ``f0_values`` (this kernel doesn't need a background distribution). + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev_markers = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def dims(a): + return (np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), np.int32(a.shape[4]), np.int32(a.shape[5])) + + _get_vlasov_maxwell_kernel()( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(kind_map), + params_dev, + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + mat11_dev, mat12_dev, mat13_dev, + mat22_dev, mat23_dev, mat33_dev, + vec1_dev, vec2_dev, vec3_dev, + *dims(mat11_dev), + *dims(mat12_dev), + *dims(mat13_dev), + *dims(mat22_dev), + *dims(mat23_dev), + *dims(mat33_dev), + np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), + np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), + np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + ), + ) + + _linear_vlasov_ampere_kernel = None @@ -499,3 +700,1100 @@ def dims(a): np.int32(vec3_dev.shape[2]), ), ) + + +# --------------------------------------------------------------------------- +# cc_lin_mhd_6d_1: accumulates into the 3 antisymmetric off-diagonal blocks +# (mat12, mat13, mat23) of a V_u -> V_u matrix, no vector, where V_u is +# whichever of H1vec/Hcurl/Hdiv the propagator's ``u_space`` option selects +# (runtime int ``basis_u`` in {0, 1, 2}). All 3 branches ultimately do a +# 3-block antisymmetric fill using fill_mat_dev (from +# _LINEAR_VLASOV_AMPERE_EXTRA_SRC above) with the row/col basis-degree +# combination matching struphy's mat_fill_v0vec_asym (basis_u=0, N-N-N both +# sides), mat_fill_v1_asym (basis_u=1, D-N-N/N-D-N/N-N-D -- same combination +# already used for linear_vlasov_ampere/vlasov_maxwell's off-diagonal +# blocks) and mat_fill_v2_asym (basis_u=2, Hdiv's N-D-D/D-N-D/D-D-N). Since +# basis_u is one value per kernel LAUNCH (not per marker), the branch is +# warp-coherent -- every thread takes the same path, no divergence cost. +# basis_u=0 needs no domain Jacobian at all (see the CPU reference: dfm is +# computed unconditionally there but only actually used by basis_u 1/2), so +# df_dispatch_dev is only called inside the basis_u==1/2 branches here. +# --------------------------------------------------------------------------- + +_CC_LIN_MHD_6D_1_SRC = r""" +extern "C" __global__ +void cc_lin_mhd_6d_1_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int b2_1_n2, const int b2_1_n3, + const double* b2_2, const int b2_2_n2, const int b2_2_n3, + const double* b2_3, const int b2_3_n2, const int b2_3_n3, + const int basis_u, const double scale_mat, const double boundary_cut, + double* mat12, double* mat13, double* mat23, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double weight = row[6]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + b2_1, b2_1_n2, b2_1_n3, b2_2, b2_2_n2, b2_2_n3, b2_3, b2_3_n2, b2_3_n3, b); + + // b_prod = bx() as a row-major 3x3 matrix + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + double fill12, fill13, fill23; + + if (basis_u == 0) { + fill12 = -weight * b_prod[1] * scale_mat; + fill13 = -weight * b_prod[2] * scale_mat; + fill23 = -weight * b_prod[5] * scale_mat; + + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + + } else if (basis_u == 1) { + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + double df_inv[9], g_inv[9]; + matrix_inv_dev(dfm, df_inv); + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; + g_inv[3*i+j] = s; + } + double tmp1[9], tmp2[9]; + matmat_dev(g_inv, b_prod, tmp1); + matmat_dev(tmp1, g_inv, tmp2); + + fill12 = -weight * tmp2[1] * scale_mat; + fill13 = -weight * tmp2[2] * scale_mat; + fill23 = -weight * tmp2[5] * scale_mat; + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + + } else if (basis_u == 2) { + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + const double det2 = det_df * det_df; + + fill12 = -weight * b_prod[1] * scale_mat / det2; + fill13 = -weight * b_prod[2] * scale_mat / det2; + fill23 = -weight * b_prod[5] * scale_mat / det2; + + // Hdiv component shapes: comp1 = N-D-D, comp2 = D-N-D, comp3 = D-D-N + fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + } +} +""" + + +def _cc_lin_mhd_6d_1_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_6D_1_SRC + + +_cc_lin_mhd_6d_1_kernel = None + + +def _get_cc_lin_mhd_6d_1_kernel(): + global _cc_lin_mhd_6d_1_kernel + if _cc_lin_mhd_6d_1_kernel is None: + import cupy as cp + + _cc_lin_mhd_6d_1_kernel = cp.RawKernel(_cc_lin_mhd_6d_1_source(), "cc_lin_mhd_6d_1_cuda") + return _cc_lin_mhd_6d_1_kernel + + +def cc_lin_mhd_6d_1_gpu( + markers, + kind_map: int, + params_dev, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + b2_1_dev, + b2_2_dev, + b2_3_dev, + basis_u: int, + scale_mat: float, + boundary_cut: float, + mat12_dev, + mat13_dev, + mat23_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels.cc_lin_mhd_6d_1`. + ``b2_*_dev`` are the Hdiv (2-form) magnetic field FE coefficients, + already device-resident. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev_markers = cp.asarray(markers) + b2_1_dev = cp.ascontiguousarray(b2_1_dev) + b2_2_dev = cp.ascontiguousarray(b2_2_dev) + b2_3_dev = cp.ascontiguousarray(b2_3_dev) + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def dims(a): + return (np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), np.int32(a.shape[4]), np.int32(a.shape[5])) + + _get_cc_lin_mhd_6d_1_kernel()( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(kind_map), + params_dev, + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + b2_1_dev, np.int32(b2_1_dev.shape[1]), np.int32(b2_1_dev.shape[2]), + b2_2_dev, np.int32(b2_2_dev.shape[1]), np.int32(b2_2_dev.shape[2]), + b2_3_dev, np.int32(b2_3_dev.shape[1]), np.int32(b2_3_dev.shape[2]), + np.int32(basis_u), + np.float64(scale_mat), + np.float64(boundary_cut), + mat12_dev, mat13_dev, mat23_dev, + *dims(mat12_dev), + *dims(mat13_dev), + *dims(mat23_dev), + ), + ) + + +# --------------------------------------------------------------------------- +# cc_lin_mhd_6d_2: like cc_lin_mhd_6d_1 (B2 field evaluation, bx() matrix, +# runtime basis_u in {0, 1, 2} selecting H1vec/Hcurl/Hdiv), but fills the +# full symmetric 6-block matrix plus a vector (like linear_vlasov_ampere / +# vlasov_maxwell), not just the 3 antisymmetric off-diagonal blocks. Basis +# combinations per branch (matching struphy's m_v_fill_v0vec_symm / +# m_v_fill_v1_symm / m_v_fill_v2_symm): basis_u=0 uses N-N-N everywhere +# (all 6 matrix blocks AND the vector); basis_u=1 is the same D-N-N/N-D-N/ +# N-N-D combination already used for linear_vlasov_ampere/vlasov_maxwell; +# basis_u=2 is Hdiv's N-D-D/D-N-D/D-D-N (same as cc_lin_mhd_6d_1's +# basis_u=2). Per the CPU reference, basis_u=0 and 2 only ever need df_inv +# (g_inv is computed there but never actually used in those two branches -- +# not replicated here); only basis_u=1 needs the full g_inv = DF^-1 DF^-T. +# --------------------------------------------------------------------------- + +_CC_LIN_MHD_6D_2_SRC = r""" +extern "C" __global__ +void cc_lin_mhd_6d_2_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int b2_1_n2, const int b2_1_n3, + const double* b2_2, const int b2_2_n2, const int b2_2_n3, + const double* b2_3, const int b2_3_n2, const int b2_3_n3, + const int basis_u, const double scale_mat, const double scale_vec, const double boundary_cut, + double* mat11, double* mat12, double* mat13, + double* mat22, double* mat23, double* mat33, + double* vec1, double* vec2, double* vec3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, + const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, + const int v2_n2, const int v2_n3, + const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + const double weight = row[6]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + b2_1, b2_1_n2, b2_1_n3, b2_2, b2_2_n2, b2_2_n3, b2_3, b2_3_n2, b2_3_n3, b); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + double df_inv[9]; + matrix_inv_dev(dfm, df_inv); + + double tmp1[9], tmp_m[9], tmp_v[3]; + + if (basis_u == 1) { + double g_inv[9]; + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; + g_inv[3*i+j] = s; + } + double tmp0[9]; + matmat_dev(g_inv, b_prod, tmp0); + matmat_dev(tmp0, df_inv, tmp1); + } else { + // basis_u == 0 or 2: tmp1 = b_prod @ df_inv (g_inv computed but + // unused in the CPU reference for these two branches) + matmat_dev(b_prod, df_inv, tmp1); + } + + // tmp_m = tmp1 @ tmp1^T ; tmp_v = tmp1 @ v + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += tmp1[3*i+k] * tmp1[3*j+k]; + tmp_m[3*i+j] = s; + } + } + matvec_dev(tmp1, v, tmp_v); + + double mat_scale = weight * scale_mat; + double vec_scale = weight * scale_vec; + if (basis_u == 2) { + mat_scale /= det_df * det_df; + vec_scale /= det_df; + } + + double filling_m[9], filling_v[3]; + for (int k = 0; k < 9; k++) filling_m[k] = tmp_m[k] * mat_scale; + for (int k = 0; k < 3; k++) filling_v[k] = tmp_v[k] * vec_scale; + + const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; + const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + if (basis_u == 0) { + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + + } else if (basis_u == 1) { + fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); + fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); + fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + + } else if (basis_u == 2) { + // Hdiv component shapes: comp1 = N-D-D, comp2 = D-N-D, comp3 = D-D-N + fill_mat_vec_dev(p1,pd2,pd3, p1,pd2,pd3, bn1,bd2,bd3, bn1,bd2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); + fill_mat_vec_dev(pd1,p2,pd3, pd1,p2,pd3, bd1,bn2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); + fill_mat_vec_dev(pd1,pd2,p3, pd1,pd2,p3, bd1,bd2,bn3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + } +} +""" + + +def _cc_lin_mhd_6d_2_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_6D_2_SRC + + +_cc_lin_mhd_6d_2_kernel = None + + +def _get_cc_lin_mhd_6d_2_kernel(): + global _cc_lin_mhd_6d_2_kernel + if _cc_lin_mhd_6d_2_kernel is None: + import cupy as cp + + _cc_lin_mhd_6d_2_kernel = cp.RawKernel(_cc_lin_mhd_6d_2_source(), "cc_lin_mhd_6d_2_cuda") + return _cc_lin_mhd_6d_2_kernel + + +def cc_lin_mhd_6d_2_gpu( + markers, + kind_map: int, + params_dev, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + b2_1_dev, + b2_2_dev, + b2_3_dev, + basis_u: int, + scale_mat: float, + scale_vec: float, + boundary_cut: float, + mat11_dev, + mat12_dev, + mat13_dev, + mat22_dev, + mat23_dev, + mat33_dev, + vec1_dev, + vec2_dev, + vec3_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels.cc_lin_mhd_6d_2`. + ``b2_*_dev`` are the Hdiv (2-form) magnetic field FE coefficients, + already device-resident. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev_markers = cp.asarray(markers) + b2_1_dev = cp.ascontiguousarray(b2_1_dev) + b2_2_dev = cp.ascontiguousarray(b2_2_dev) + b2_3_dev = cp.ascontiguousarray(b2_3_dev) + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def dims(a): + return (np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), np.int32(a.shape[4]), np.int32(a.shape[5])) + + _get_cc_lin_mhd_6d_2_kernel()( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(kind_map), + params_dev, + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + b2_1_dev, np.int32(b2_1_dev.shape[1]), np.int32(b2_1_dev.shape[2]), + b2_2_dev, np.int32(b2_2_dev.shape[1]), np.int32(b2_2_dev.shape[2]), + b2_3_dev, np.int32(b2_3_dev.shape[1]), np.int32(b2_3_dev.shape[2]), + np.int32(basis_u), + np.float64(scale_mat), + np.float64(scale_vec), + np.float64(boundary_cut), + mat11_dev, mat12_dev, mat13_dev, + mat22_dev, mat23_dev, mat33_dev, + vec1_dev, vec2_dev, vec3_dev, + *dims(mat11_dev), + *dims(mat12_dev), + *dims(mat13_dev), + *dims(mat22_dev), + *dims(mat23_dev), + *dims(mat33_dev), + np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), + np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), + np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + ), + ) + + +# --------------------------------------------------------------------------- +# pc_lin_mhd_6d_full / pc_lin_mhd_6d: accumulate a "pressure tensor" -- the +# same DF^-1(eta_p) DF^-T(eta_p) V1 -> V1 filling as vlasov_maxwell, but +# additionally scaled by every v_a*v_b product (a,b in x,y,z) of the marker +# velocity, giving one full symmetric 6-block matrix PER velocity-pair (6 +# pairs: xx, xy, xz, yy, yz, zz -> 6*6=36 matrix arrays) plus one vector PER +# velocity-component (3*3=9 vector arrays) -- see +# particle_to_mat_kernels.m_v_fill_v1_pressure_full and +# filler_kernels.fill_mat_vec_pressure_full/fill_mat_pressure_full, which +# this is a direct port of (fill_mat_vec_pressure_full_dev/ +# fill_mat_pressure_full_dev below are the CUDA equivalents, generalizing +# fill_mat_vec_dev/fill_mat_dev from a single mat/vec output to six/three). +# +# pc_lin_mhd_6d (no "_full") is the same accumulation restricted to the +# (x, y) "perpendicular" velocity plane only: 3 velocity-pairs (xx, xy, yy) +# and 2 velocity-components (x, y), i.e. 6*3=18 matrix arrays and 3*2=6 +# vector arrays -- see m_v_fill_v1_pressure/fill_mat_vec_pressure/ +# fill_mat_pressure. Both variants are called with the SAME 36+9=45 output +# arrays (the propagators share one call signature for _full and non-_full) +# but pc_lin_mhd_6d only ever writes the 24 "perp" ones -- the CPU reference +# leaves the other 21 untouched (at whatever the caller zeroed them to), and +# so does this port: pc_lin_mhd_6d_gpu accepts all 45 positionally (to match +# Accumulator._accumulate's ``*self._args_data`` unpacking) but only passes +# the 24 it needs into the CUDA launch. +# +# Both variants only differ from vlasov_maxwell's filling in the v_a*v_b +# scaling and in which marker column holds the weight: pc_lin_mhd_6d_full +# uses markers[ip, 8], pc_lin_mhd_6d uses markers[ip, 6] (matching the CPU +# reference exactly). +# --------------------------------------------------------------------------- + +_SPATIAL_BLOCKS = ("11", "12", "13", "22", "23", "33") +# (row_degrees_index, col_degrees_index) as 0/1/2 picking from (p, pd) per +# axis -- i.e. which of bn/bd (and p/pd) each spatial block's row/col use in +# each of the 3 axes. 0 = N-spline/degree p, 1 = D-spline/degree p-1. +_SPATIAL_BASIS = { + "11": ((1, 0, 0), (1, 0, 0)), + "22": ((0, 1, 0), (0, 1, 0)), + "33": ((0, 0, 1), (0, 0, 1)), + "12": ((1, 0, 0), (0, 1, 0)), + "13": ((1, 0, 0), (0, 0, 1)), + "23": ((0, 1, 0), (0, 0, 1)), +} +_DIAG_SPATIAL_BLOCKS = ("11", "22", "33") + +_PC_PRESSURE_FILLERS_SRC = r""" +// Port of filler_kernels.fill_mat_vec_pressure_full: like fill_mat_vec_dev +// but scatters into 6 matrix blocks (scaled by vx*vx, vx*vy, vx*vz, vy*vy, +// vy*vz, vz*vz) and 3 vector blocks (scaled by vx, vy, vz) in one pass. +__device__ void fill_mat_vec_pressure_full_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat_11, double* mat_12, double* mat_13, double* mat_22, double* mat_23, double* mat_33, + int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double* vec_1, double* vec_2, double* vec_3, int vn2, int vn3, + double filling_vec, + double vx, double vy, double vz) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + size_t vidx = (size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3; + double bv = b3 * filling_vec; + atomicAdd(&vec_1[vidx], bv * vx); + atomicAdd(&vec_2[vidx], bv * vy); + atomicAdd(&vec_3[vidx], bv * vz); + + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat_11[idx], b6 * vx * vx); + atomicAdd(&mat_12[idx], b6 * vx * vy); + atomicAdd(&mat_13[idx], b6 * vx * vz); + atomicAdd(&mat_22[idx], b6 * vy * vy); + atomicAdd(&mat_23[idx], b6 * vy * vz); + atomicAdd(&mat_33[idx], b6 * vz * vz); + } + } + } + } + } + } +} + +// Port of filler_kernels.fill_mat_pressure_full: same as above minus the +// vector part (off-diagonal spatial blocks have no associated vector). +__device__ void fill_mat_pressure_full_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat_11, double* mat_12, double* mat_13, double* mat_22, double* mat_23, double* mat_33, + int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double vx, double vy, double vz) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat_11[idx], b6 * vx * vx); + atomicAdd(&mat_12[idx], b6 * vx * vy); + atomicAdd(&mat_13[idx], b6 * vx * vz); + atomicAdd(&mat_22[idx], b6 * vy * vy); + atomicAdd(&mat_23[idx], b6 * vy * vz); + atomicAdd(&mat_33[idx], b6 * vz * vz); + } + } + } + } + } + } +} + +// Port of filler_kernels.fill_mat_vec_pressure: the "perp" (xy-plane only) +// variant -- 3 matrix blocks (vx*vx, vx*vy, vy*vy) and 2 vector blocks +// (vx, vy). +__device__ void fill_mat_vec_pressure_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat_11, double* mat_12, double* mat_22, + int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double* vec_1, double* vec_2, int vn2, int vn3, + double filling_vec, + double vx, double vy) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + size_t vidx = (size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3; + double bv = b3 * filling_vec; + atomicAdd(&vec_1[vidx], bv * vx); + atomicAdd(&vec_2[vidx], bv * vy); + + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat_11[idx], b6 * vx * vx); + atomicAdd(&mat_12[idx], b6 * vx * vy); + atomicAdd(&mat_22[idx], b6 * vy * vy); + } + } + } + } + } + } +} + +// Port of filler_kernels.fill_mat_pressure: "perp" matrix-only variant. +__device__ void fill_mat_pressure_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat_11, double* mat_12, double* mat_22, + int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double vx, double vy) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat_11[idx], b6 * vx * vx); + atomicAdd(&mat_12[idx], b6 * vx * vy); + atomicAdd(&mat_22[idx], b6 * vy * vy); + } + } + } + } + } + } +} +""" + + +def _basis_args(row_or_col): + """row_or_col is a 3-tuple of 0/1 (0=N-spline/degree p, 1=D-spline/degree p-1) + for (axis1, axis2, axis3). Returns (degree_expr_list, basis_expr_list).""" + deg = [ + "p1" if row_or_col[0] == 0 else "pd1", + "p2" if row_or_col[1] == 0 else "pd2", + "p3" if row_or_col[2] == 0 else "pd3", + ] + bas = [("bn1", "bd1")[row_or_col[0]], ("bn2", "bd2")[row_or_col[1]], ("bn3", "bd3")[row_or_col[2]]] + return deg, bas + + +def _build_pc_lin_mhd_6d_kernel_src(full: bool) -> str: + vel_pairs = _SPATIAL_BLOCKS if full else ("11", "12", "22") + vec_is = ("1", "2", "3") if full else ("1", "2") + kernel_name = "pc_lin_mhd_6d_full_cuda" if full else "pc_lin_mhd_6d_cuda" + weight_col = 8 if full else 6 + + mat_params = ", ".join(f"double* mat{sp}_{vel}" for vel in vel_pairs for sp in _SPATIAL_BLOCKS) + vec_params = ", ".join(f"double* vec{mu}_{i}" for i in vec_is for mu in ("1", "2", "3")) + mat_dim_params = ", ".join( + f"const int m{sp}_d2, const int m{sp}_d3, const int m{sp}_d4, const int m{sp}_d5, const int m{sp}_d6" + for sp in _SPATIAL_BLOCKS + ) + vec_dim_params = ", ".join(f"const int v{mu}_n2, const int v{mu}_n3" for mu in ("1", "2", "3")) + + lines = [] + lines.append(f'extern "C" __global__\nvoid {kernel_name}(') + lines.append(" const double* markers, const int n_cols, const int n_markers,") + lines.append(" const int kind_map, const double* params,") + lines.append(" const int p1, const int p2, const int p3,") + lines.append(" const double* tn1, const int len_tn1,") + lines.append(" const double* tn2, const int len_tn2,") + lines.append(" const double* tn3, const int len_tn3,") + lines.append(" const int start0, const int start1, const int start2,") + lines.append(" const double ep_scale,") + lines.append(f" {mat_params},") + lines.append(f" {vec_params},") + lines.append(f" {mat_dim_params},") + lines.append(f" {vec_dim_params})") + lines.append("{") + lines.append(" int ip = blockIdx.x * blockDim.x + threadIdx.x;") + lines.append(" if (ip >= n_markers) return;") + lines.append("") + lines.append(" const double* row = markers + (size_t)ip * n_cols;") + lines.append(" if (row[0] == -1.0) return;") + lines.append("") + lines.append(" const double eta1 = row[0], eta2 = row[1], eta3 = row[2];") + lines.append(" const double v[3] = {row[3], row[4], row[5]};") + lines.append(f" const double weight = row[{weight_col}];") + lines.append("") + lines.append(" double dfm[9];") + lines.append(" if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return;") + lines.append(" double df_inv[9];") + lines.append(" matrix_inv_dev(dfm, df_inv);") + lines.append(" double g_inv[9];") + lines.append(" for (int i = 0; i < 3; i++)") + lines.append(" for (int j = 0; j < 3; j++) {") + lines.append(" double s = 0.0;") + lines.append(" for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k];") + lines.append(" g_inv[3*i+j] = s;") + lines.append(" }") + lines.append(" double tmp_v[3];") + lines.append(" matvec_dev(df_inv, v, tmp_v);") + lines.append("") + # fill11..fill33 are per-SPATIAL-block (mu,nu) scalars (weight*g_inv[mu,nu]* + # ep_scale) -- needed by every spatial block's filler call regardless of + # ``full``, since even the "perp" (non-full) variant fills all 6 spatial + # blocks, just with fewer velocity-pairs per block. Only fill3/vz (the + # z-velocity-component scalar, used solely by the vector fill) is + # full-only. + lines.append(" const double fill11 = weight * g_inv[0] * ep_scale;") + lines.append(" const double fill12 = weight * g_inv[1] * ep_scale;") + lines.append(" const double fill13 = weight * g_inv[2] * ep_scale;") + lines.append(" const double fill22 = weight * g_inv[4] * ep_scale;") + lines.append(" const double fill23 = weight * g_inv[5] * ep_scale;") + lines.append(" const double fill33 = weight * g_inv[8] * ep_scale;") + # fill1/fill2/fill3 are per-DIAG-SPATIAL-BLOCK (mu=1/2/3) vector filling + # scalars (weight*tmp_v[mu-1]*ep_scale, tmp_v = DF^-1 @ v) -- needed by + # spatial block 33's diagonal fill regardless of ``full`` too, just like + # fill11..fill33 above. Only ``vz`` (the raw marker velocity's own + # z-component, used as a multiplier for the 3rd velocity-pair/-component + # outputs) is full-only. + lines.append(" const double fill1 = weight * tmp_v[0] * ep_scale;") + lines.append(" const double fill2 = weight * tmp_v[1] * ep_scale;") + lines.append(" const double fill3 = weight * tmp_v[2] * ep_scale;") + lines.append(" const double vx = v[0], vy = v[1]" + (", vz = v[2];" if full else ";")) + lines.append("") + lines.append(" const int span1 = find_span_dev(tn1, p1, len_tn1, eta1);") + lines.append(" const int span2 = find_span_dev(tn2, p2, len_tn2, eta2);") + lines.append(" const int span3 = find_span_dev(tn3, p3, len_tn3, eta3);") + lines.append(" double bn1[MAXP+1], bd1[MAXP];") + lines.append(" double bn2[MAXP+1], bd2[MAXP];") + lines.append(" double bn3[MAXP+1], bd3[MAXP];") + lines.append(" b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1);") + lines.append(" b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2);") + lines.append(" b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3);") + lines.append(" const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1;") + lines.append("") + + for sp in _SPATIAL_BLOCKS: + row, col = _SPATIAL_BASIS[sp] + row_deg, row_bas = _basis_args(row) + col_deg, col_bas = _basis_args(col) + mat_out = ", ".join(f"mat{sp}_{vel}" for vel in vel_pairs) + dims = f"m{sp}_d2,m{sp}_d3,m{sp}_d4,m{sp}_d5,m{sp}_d6" + fillmat = {"11": "fill11", "22": "fill22", "33": "fill33", "12": "fill12", "13": "fill13", "23": "fill23"}[sp] + common = ( + f"{row_deg[0]},{row_deg[1]},{row_deg[2]}, {col_deg[0]},{col_deg[1]},{col_deg[2]}, " + f"{row_bas[0]},{row_bas[1]},{row_bas[2]}, {col_bas[0]},{col_bas[1]},{col_bas[2]}, " + f"span1,span2,span3, start0,start1,start2, p1,p2,p3" + ) + if sp in _DIAG_SPATIAL_BLOCKS: + mu = sp[0] + vec_out = ", ".join(f"vec{mu}_{i}" for i in vec_is) + vdims = f"v{mu}_n2,v{mu}_n3" + fillvec = {"11": "fill1", "22": "fill2", "33": "fill3"}[sp] + if full: + lines.append( + f" fill_mat_vec_pressure_full_dev({common},\n" + f" {mat_out}, {dims}, {fillmat},\n" + f" {vec_out}, {vdims}, {fillvec}, vx,vy,vz);" + ) + else: + lines.append( + f" fill_mat_vec_pressure_dev({common},\n" + f" {mat_out}, {dims}, {fillmat},\n" + f" {vec_out}, {vdims}, {fillvec}, vx,vy);" + ) + else: + if full: + lines.append( + f" fill_mat_pressure_full_dev({common},\n" + f" {mat_out}, {dims}, {fillmat}, vx,vy,vz);" + ) + else: + lines.append( + f" fill_mat_pressure_dev({common},\n" + f" {mat_out}, {dims}, {fillmat}, vx,vy);" + ) + lines.append("") + + lines.append("}") + return "\n".join(lines) + + +_PC_LIN_MHD_6D_FULL_SRC = _build_pc_lin_mhd_6d_kernel_src(full=True) +_PC_LIN_MHD_6D_SRC = _build_pc_lin_mhd_6d_kernel_src(full=False) + + +def _pc_lin_mhd_6d_full_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + return _GENERAL_GEOMETRY_SRC + _PC_PRESSURE_FILLERS_SRC + _PC_LIN_MHD_6D_FULL_SRC + + +def _pc_lin_mhd_6d_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + return _GENERAL_GEOMETRY_SRC + _PC_PRESSURE_FILLERS_SRC + _PC_LIN_MHD_6D_SRC + + +_pc_lin_mhd_6d_full_kernel = None +_pc_lin_mhd_6d_kernel = None + + +def _get_pc_lin_mhd_6d_full_kernel(): + global _pc_lin_mhd_6d_full_kernel + if _pc_lin_mhd_6d_full_kernel is None: + import cupy as cp + + _pc_lin_mhd_6d_full_kernel = cp.RawKernel(_pc_lin_mhd_6d_full_source(), "pc_lin_mhd_6d_full_cuda") + return _pc_lin_mhd_6d_full_kernel + + +def _get_pc_lin_mhd_6d_kernel(): + global _pc_lin_mhd_6d_kernel + if _pc_lin_mhd_6d_kernel is None: + import cupy as cp + + _pc_lin_mhd_6d_kernel = cp.RawKernel(_pc_lin_mhd_6d_source(), "pc_lin_mhd_6d_cuda") + return _pc_lin_mhd_6d_kernel + + +def _pc_lin_mhd_6d_launch( + kernel, + markers, + kind_map: int, + params_dev, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + ep_scale: float, + mat_args_45: dict, + vec_args_45: dict, + vel_pairs, + vec_is, +): + """Shared launch logic for pc_lin_mhd_6d_full_gpu/pc_lin_mhd_6d_gpu. + ``mat_args_45``/``vec_args_45`` map every full-45-array name + (``mat{sp}_{vel}`` / ``vec{mu}_{i}``) to its device array; only the + subset named in ``vel_pairs``/``vec_is`` is actually passed to the + kernel launch. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev_markers = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def dims(a): + return (np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), np.int32(a.shape[4]), np.int32(a.shape[5])) + + args = [ + dev_markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(kind_map), + params_dev, + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + np.float64(ep_scale), + ] + for vel in vel_pairs: + for sp in _SPATIAL_BLOCKS: + args.append(mat_args_45[f"mat{sp}_{vel}"]) + for i in vec_is: + for mu in ("1", "2", "3"): + args.append(vec_args_45[f"vec{mu}_{i}"]) + for sp in _SPATIAL_BLOCKS: + args.extend(dims(mat_args_45[f"mat{sp}_11"])) + for mu in ("1", "2", "3"): + v = vec_args_45[f"vec{mu}_1"] + args.extend((np.int32(v.shape[1]), np.int32(v.shape[2]))) + + kernel((blocks,), (threads,), tuple(args)) + + +def pc_lin_mhd_6d_full_gpu( + markers, + kind_map: int, + params_dev, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + ep_scale: float, + *mat_and_vec_args, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels.pc_lin_mhd_6d_full`. + ``mat_and_vec_args`` are the 45 output arrays in the exact positional + order of the CPU kernel's signature (36 matrix blocks: velocity-pair + outer -- xx, xy, xz, yy, yz, zz -- spatial-block inner -- 11, 12, 13, + 22, 23, 33; then 9 vector blocks: velocity-component outer -- x, y, z + -- spatial-component inner -- 1, 2, 3), matching + ``Accumulator._args_data``'s construction for ``symmetry="pressure"``. + """ + mat_args_45 = { + f"mat{sp}_{vel}": mat_and_vec_args[k] + for k, (vel, sp) in enumerate((vel, sp) for vel in _SPATIAL_BLOCKS for sp in _SPATIAL_BLOCKS) + } + vec_args_45 = { + f"vec{mu}_{i}": mat_and_vec_args[36 + k] + for k, (i, mu) in enumerate((i, mu) for i in ("1", "2", "3") for mu in ("1", "2", "3")) + } + _pc_lin_mhd_6d_launch( + _get_pc_lin_mhd_6d_full_kernel(), + markers, kind_map, params_dev, pn, tn1_dev, tn2_dev, tn3_dev, starts, ep_scale, + mat_args_45, vec_args_45, _SPATIAL_BLOCKS, ("1", "2", "3"), + ) + + +def pc_lin_mhd_6d_gpu( + markers, + kind_map: int, + params_dev, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + ep_scale: float, + *mat_and_vec_args, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels.pc_lin_mhd_6d`. Same + 45-array positional convention as :func:`pc_lin_mhd_6d_full_gpu` + (the propagator passes the identical 45-array signature for both), but + -- matching the CPU reference exactly -- only the "perp" (x, y) subset + (18 of the 36 matrix arrays, 6 of the 9 vector arrays) is ever written; + the rest are left untouched (they stay at whatever + ``Accumulator._accumulate``'s ``dat[:] = 0.0`` reset left them at). + """ + mat_args_45 = { + f"mat{sp}_{vel}": mat_and_vec_args[k] + for k, (vel, sp) in enumerate((vel, sp) for vel in _SPATIAL_BLOCKS for sp in _SPATIAL_BLOCKS) + } + vec_args_45 = { + f"vec{mu}_{i}": mat_and_vec_args[36 + k] + for k, (i, mu) in enumerate((i, mu) for i in ("1", "2", "3") for mu in ("1", "2", "3")) + } + _pc_lin_mhd_6d_launch( + _get_pc_lin_mhd_6d_kernel(), + markers, kind_map, params_dev, pn, tn1_dev, tn2_dev, tn3_dev, starts, ep_scale, + mat_args_45, vec_args_45, ("11", "12", "22"), ("1", "2"), + ) diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index 22646c03c..6b82b2e30 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -16,7 +16,15 @@ from struphy.io.options import LiteralOptions from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.models.variables import PICVariable, SPHVariable -from struphy.pic.accumulation.accum_kernels_cuda import charge_density_0form_gpu, linear_vlasov_ampere_gpu +from struphy.pic.accumulation.accum_kernels_cuda import ( + cc_lin_mhd_6d_1_gpu, + cc_lin_mhd_6d_2_gpu, + charge_density_0form_gpu, + linear_vlasov_ampere_gpu, + pc_lin_mhd_6d_full_gpu, + pc_lin_mhd_6d_gpu, + vlasov_maxwell_gpu, +) from struphy.pic.accumulation.filter import AccumFilter, FilterParameters from struphy.pic.base import Particles from struphy.utils.utils import __dataclass_repr_no_defaults__, check_option @@ -222,6 +230,100 @@ def __init__( self._gpu_lva_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) self._gpu_lva_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + # GPU replacement for vlasov_maxwell: same 6-block symmetric V1 -> V1 + # matrix-plus-vector fill as linear_vlasov_ampere above, but with a + # G^-1(eta_p)-based filling (no f0_values/optional_args needed). + self._gpu_vlasov_maxwell = ( + xp.cupy_backend + and kernel.name == "vlasov_maxwell" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_vlasov_maxwell: + import cupy as cp + import numpy as np + + self._gpu_vm_kind_map = int(args_domain.kind_map) + self._gpu_vm_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + args_derham = self.derham.args_derham + self._gpu_vm_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_vm_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_vm_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) + self._gpu_vm_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) + self._gpu_vm_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + + # GPU replacement for cc_lin_mhd_6d_1: 3-block antisymmetric fill + # (mat12, mat13, mat23 only, no vector) into whichever of + # H1vec/Hcurl/Hdiv the propagator's basis_u optional_arg selects. + # b2_*/basis_u/scale_mat/boundary_cut arrive fresh via optional_args + # each call (only the spline/domain info below is cached). + self._gpu_cc_lin_mhd_6d_1 = ( + xp.cupy_backend + and kernel.name == "cc_lin_mhd_6d_1" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_cc_lin_mhd_6d_1: + import cupy as cp + import numpy as np + + self._gpu_cc1_kind_map = int(args_domain.kind_map) + self._gpu_cc1_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + args_derham = self.derham.args_derham + self._gpu_cc1_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_cc1_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_cc1_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) + self._gpu_cc1_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) + self._gpu_cc1_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + + # GPU replacement for cc_lin_mhd_6d_2: same runtime basis_u + # dispatch as cc_lin_mhd_6d_1, but a full symmetric 6-block + # matrix-plus-vector fill (like linear_vlasov_ampere/vlasov_maxwell) + # instead of the 3 antisymmetric off-diagonal blocks only. + self._gpu_cc_lin_mhd_6d_2 = ( + xp.cupy_backend + and kernel.name == "cc_lin_mhd_6d_2" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_cc_lin_mhd_6d_2: + import cupy as cp + import numpy as np + + self._gpu_cc2_kind_map = int(args_domain.kind_map) + self._gpu_cc2_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + args_derham = self.derham.args_derham + self._gpu_cc2_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_cc2_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_cc2_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) + self._gpu_cc2_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) + self._gpu_cc2_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + + # GPU replacement for pc_lin_mhd_6d_full / pc_lin_mhd_6d: the + # symmetry="pressure" case -- 45-array (36 matrix + 9 vector) + # velocity-moment "pressure tensor" fill. See accum_kernels_cuda.py + # for why both variants share one call convention (pc_lin_mhd_6d + # only ever writes 24 of the 45 arrays, matching the CPU reference). + self._gpu_pc_lin_mhd_6d_full = ( + xp.cupy_backend + and kernel.name == "pc_lin_mhd_6d_full" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + self._gpu_pc_lin_mhd_6d = ( + xp.cupy_backend + and kernel.name == "pc_lin_mhd_6d" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_pc_lin_mhd_6d_full or self._gpu_pc_lin_mhd_6d: + import cupy as cp + import numpy as np + + self._gpu_pc_kind_map = int(args_domain.kind_map) + self._gpu_pc_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + args_derham = self.derham.args_derham + self._gpu_pc_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_pc_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_pc_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) + self._gpu_pc_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) + self._gpu_pc_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + def __call__(self, *optional_args, **args_control): """ Performs the accumulation into the matrix/vector by calling the chosen accumulation kernel and additional analytical contributions (control variate, optional). @@ -270,6 +372,76 @@ def _accumulate(self, *optional_args, **args_control): self._gpu_lva_starts, *self._args_data, ) + elif self._gpu_vlasov_maxwell and not optional_args: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + vlasov_maxwell_gpu( + self.particles.markers, + self._gpu_vm_kind_map, + self._gpu_vm_params, + self._gpu_vm_pn, + self._gpu_vm_tn1, + self._gpu_vm_tn2, + self._gpu_vm_tn3, + self._gpu_vm_starts, + *self._args_data, + ) + elif self._gpu_cc_lin_mhd_6d_1 and len(optional_args) == 6: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + b2_1, b2_2, b2_3, basis_u, scale_mat, boundary_cut = optional_args + cc_lin_mhd_6d_1_gpu( + self.particles.markers, + self._gpu_cc1_kind_map, + self._gpu_cc1_params, + self._gpu_cc1_pn, + self._gpu_cc1_tn1, + self._gpu_cc1_tn2, + self._gpu_cc1_tn3, + self._gpu_cc1_starts, + b2_1, + b2_2, + b2_3, + basis_u, + scale_mat, + boundary_cut, + *self._args_data, + ) + elif self._gpu_cc_lin_mhd_6d_2 and len(optional_args) == 7: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + b2_1, b2_2, b2_3, basis_u, scale_mat, scale_vec, boundary_cut = optional_args + cc_lin_mhd_6d_2_gpu( + self.particles.markers, + self._gpu_cc2_kind_map, + self._gpu_cc2_params, + self._gpu_cc2_pn, + self._gpu_cc2_tn1, + self._gpu_cc2_tn2, + self._gpu_cc2_tn3, + self._gpu_cc2_starts, + b2_1, + b2_2, + b2_3, + basis_u, + scale_mat, + scale_vec, + boundary_cut, + *self._args_data, + ) + elif (self._gpu_pc_lin_mhd_6d_full or self._gpu_pc_lin_mhd_6d) and len(optional_args) == 1: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + (ep_scale,) = optional_args + pc_fn = pc_lin_mhd_6d_full_gpu if self._gpu_pc_lin_mhd_6d_full else pc_lin_mhd_6d_gpu + pc_fn( + self.particles.markers, + self._gpu_pc_kind_map, + self._gpu_pc_params, + self._gpu_pc_pn, + self._gpu_pc_tn1, + self._gpu_pc_tn2, + self._gpu_pc_tn3, + self._gpu_pc_starts, + ep_scale, + *self._args_data, + ) else: with ProfileManager.profile_region("kernel: " + self.kernel.name): self.kernel( From 26486732d3100b98fb551ce5344ef4f0f70fe800 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 16:15:30 +0200 Subject: [PATCH 046/156] Port linear mhd kernels --- bench_gpu/bench_kernels.py | 171 ++++++++++++++++++ src/struphy/feec/psydac_derham.py | 11 +- src/struphy/models/base.py | 9 +- .../linear_vlasov_ampere_one_species.py | 17 +- src/struphy/models/species.py | 11 +- .../pic/accumulation/particles_to_grid.py | 30 +++ src/struphy/pic/particles.py | 11 +- src/struphy/pic/pushing/pusher.py | 149 +++++++++++++++ .../post_processing/post_processing_tools.py | 36 +++- .../propagators/efield_weights_coupling.py | 11 +- 10 files changed, 428 insertions(+), 28 deletions(-) diff --git a/bench_gpu/bench_kernels.py b/bench_gpu/bench_kernels.py index e0c158e48..24a6e7141 100644 --- a/bench_gpu/bench_kernels.py +++ b/bench_gpu/bench_kernels.py @@ -174,8 +174,13 @@ def make_cases(scene: Scene, dt: float): import struphy.pic.accumulation.accum_kernels as accum_kernels import struphy.pic.pushing.pusher_kernels as pusher_kernels from struphy.pic.accumulation.accum_kernels_cuda import ( + cc_lin_mhd_6d_1_gpu, + cc_lin_mhd_6d_2_gpu, charge_density_0form_gpu, linear_vlasov_ampere_gpu, + pc_lin_mhd_6d_full_gpu, + pc_lin_mhd_6d_gpu, + vlasov_maxwell_gpu, ) from struphy.pic.pushing.pusher_kernels_cuda import ( push_bxu_H1vec_general_gpu, @@ -616,6 +621,172 @@ def _lva_gpu(): add("linear_vlasov_ampere", _lva_cpu, _lva_gpu) + # --- vlasov_maxwell (Accumulator, symmetric V1 -> V1 matrix + vector, + # same shape as linear_vlasov_ampere but no f0_values) --- + op_vm = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="symm") + vm_mat_cpu, vm_mat_gpu = {}, {} + for a_ in range(3): + for b_ in range(3): + if b_ >= a_ and op_vm.matrix.blocks[a_][b_] is not None: + shape_ = op_vm.matrix.blocks[a_][b_]._data.shape + key = f"{a_ + 1}{b_ + 1}" + vm_mat_cpu[key] = np.zeros(shape_, dtype=float) + vm_mat_gpu[key] = scene.dev(np.zeros(shape_, dtype=float)) + vm_vec_bv = BlockVector(vec_space) + vm_vec_cpu = [np.zeros(bl._data.shape, dtype=float) for bl in vm_vec_bv.blocks] + vm_vec_gpu = [scene.dev(v) for v in vm_vec_cpu] + + def _vm_cpu(): + for v in vm_mat_cpu.values(): + v.fill(0.0) + for v in vm_vec_cpu: + v.fill(0.0) + accum_kernels.vlasov_maxwell(am, ah, ad, *[vm_mat_cpu[k] for k in mat_keys], *vm_vec_cpu) + + def _vm_gpu(): + for v in vm_mat_gpu.values(): + v.fill(0.0) + for v in vm_vec_gpu: + v.fill(0.0) + vlasov_maxwell_gpu( + scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, + *[vm_mat_gpu[k] for k in mat_keys], *vm_vec_gpu, + ) + + add("vlasov_maxwell", _vm_cpu, _vm_gpu) + + # --- cc_lin_mhd_6d_1 (Accumulator, antisymmetric 3-block fill, u_space=Hcurl i.e. basis_u=1) --- + op_cc1 = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="asym") + cc1_cpu = {k: np.zeros(op_cc1.matrix.blocks[a_][b_]._data.shape, dtype=float) for k, (a_, b_) in + zip(["12", "13", "23"], [(0, 1), (0, 2), (1, 2)])} + cc1_gpu = {k: scene.dev(v) for k, v in cc1_cpu.items()} + b2_1, b2_2, b2_3 = scene.fields["2"] + b2_1_dev, b2_2_dev, b2_3_dev = (scene.dev(a) for a in scene.fields["2"]) + cc1_scale_mat, cc1_boundary_cut = 2.5, 0.05 + basis_u_hcurl = 1 + + def _cc1_cpu(): + for v in cc1_cpu.values(): + v.fill(0.0) + accum_kernels.cc_lin_mhd_6d_1( + am, ah, ad, cc1_cpu["12"], cc1_cpu["13"], cc1_cpu["23"], + b2_1, b2_2, b2_3, basis_u_hcurl, cc1_scale_mat, cc1_boundary_cut, + ) + + def _cc1_gpu(): + for v in cc1_gpu.values(): + v.fill(0.0) + cc_lin_mhd_6d_1_gpu( + scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, + b2_1_dev, b2_2_dev, b2_3_dev, basis_u_hcurl, cc1_scale_mat, cc1_boundary_cut, + cc1_gpu["12"], cc1_gpu["13"], cc1_gpu["23"], + ) + + add("cc_lin_mhd_6d_1", _cc1_cpu, _cc1_gpu) + + # --- cc_lin_mhd_6d_2 (Accumulator, symmetric 6-block+vector fill, u_space=Hcurl i.e. basis_u=1) --- + op_cc2 = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="symm") + cc2_mat_cpu, cc2_mat_gpu = {}, {} + for a_ in range(3): + for b_ in range(3): + if b_ >= a_ and op_cc2.matrix.blocks[a_][b_] is not None: + shape_ = op_cc2.matrix.blocks[a_][b_]._data.shape + key = f"{a_ + 1}{b_ + 1}" + cc2_mat_cpu[key] = np.zeros(shape_, dtype=float) + cc2_mat_gpu[key] = scene.dev(np.zeros(shape_, dtype=float)) + cc2_vec_bv = BlockVector(vec_space) + cc2_vec_cpu = [np.zeros(bl._data.shape, dtype=float) for bl in cc2_vec_bv.blocks] + cc2_vec_gpu = [scene.dev(v) for v in cc2_vec_cpu] + cc2_scale_mat, cc2_scale_vec, cc2_boundary_cut = 1.7, 0.6, 0.05 + + def _cc2_cpu(): + for v in cc2_mat_cpu.values(): + v.fill(0.0) + for v in cc2_vec_cpu: + v.fill(0.0) + accum_kernels.cc_lin_mhd_6d_2( + am, ah, ad, *[cc2_mat_cpu[k] for k in mat_keys], *cc2_vec_cpu, + b2_1, b2_2, b2_3, basis_u_hcurl, cc2_scale_mat, cc2_scale_vec, cc2_boundary_cut, + ) + + def _cc2_gpu(): + for v in cc2_mat_gpu.values(): + v.fill(0.0) + for v in cc2_vec_gpu: + v.fill(0.0) + cc_lin_mhd_6d_2_gpu( + scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, + b2_1_dev, b2_2_dev, b2_3_dev, basis_u_hcurl, cc2_scale_mat, cc2_scale_vec, cc2_boundary_cut, + *[cc2_mat_gpu[k] for k in mat_keys], *cc2_vec_gpu, + ) + + add("cc_lin_mhd_6d_2", _cc2_cpu, _cc2_gpu) + + # --- pc_lin_mhd_6d_full / pc_lin_mhd_6d (Accumulator, symmetry="pressure": + # 45 arrays = 6 "symm" Hcurl operators (velocity-pairs) * 6 spatial + # blocks + 3 vector groups (velocity components) * 3 spatial blocks) --- + spatial_blocks = ("11", "12", "13", "22", "23", "33") + pc_mat_cpu, pc_mat_gpu = {}, {} + for vel in spatial_blocks: + op_pc = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="symm") + for a_, b_ in [(0, 0), (0, 1), (0, 2), (1, 1), (1, 2), (2, 2)]: + sp = f"{a_ + 1}{b_ + 1}" + shape_ = op_pc.matrix.blocks[a_][b_]._data.shape + pc_mat_cpu[f"mat{sp}_{vel}"] = np.zeros(shape_, dtype=float) + pc_mat_gpu[f"mat{sp}_{vel}"] = scene.dev(np.zeros(shape_, dtype=float)) + pc_vec_cpu, pc_vec_gpu = {}, {} + for i in ("1", "2", "3"): + bv = BlockVector(vec_space) + for mu, bl in zip(("1", "2", "3"), bv.blocks): + shape_ = bl._data.shape + pc_vec_cpu[f"vec{mu}_{i}"] = np.zeros(shape_, dtype=float) + pc_vec_gpu[f"vec{mu}_{i}"] = scene.dev(np.zeros(shape_, dtype=float)) + pc_mat_order = [f"mat{sp}_{vel}" for vel in spatial_blocks for sp in spatial_blocks] + pc_vec_order = [f"vec{mu}_{i}" for i in ("1", "2", "3") for mu in ("1", "2", "3")] + ep_scale = 3.3 + + def _pc_full_cpu(): + for v in pc_mat_cpu.values(): + v.fill(0.0) + for v in pc_vec_cpu.values(): + v.fill(0.0) + accum_kernels.pc_lin_mhd_6d_full( + am, ah, ad, *[pc_mat_cpu[k] for k in pc_mat_order], *[pc_vec_cpu[k] for k in pc_vec_order], ep_scale, + ) + + def _pc_full_gpu(): + for v in pc_mat_gpu.values(): + v.fill(0.0) + for v in pc_vec_gpu.values(): + v.fill(0.0) + pc_lin_mhd_6d_full_gpu( + scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, ep_scale, + *[pc_mat_gpu[k] for k in pc_mat_order], *[pc_vec_gpu[k] for k in pc_vec_order], + ) + + add("pc_lin_mhd_6d_full", _pc_full_cpu, _pc_full_gpu) + + def _pc_cpu(): + for v in pc_mat_cpu.values(): + v.fill(0.0) + for v in pc_vec_cpu.values(): + v.fill(0.0) + accum_kernels.pc_lin_mhd_6d( + am, ah, ad, *[pc_mat_cpu[k] for k in pc_mat_order], *[pc_vec_cpu[k] for k in pc_vec_order], ep_scale, + ) + + def _pc_gpu(): + for v in pc_mat_gpu.values(): + v.fill(0.0) + for v in pc_vec_gpu.values(): + v.fill(0.0) + pc_lin_mhd_6d_gpu( + scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, ep_scale, + *[pc_mat_gpu[k] for k in pc_mat_order], *[pc_vec_gpu[k] for k in pc_vec_order], + ) + + add("pc_lin_mhd_6d", _pc_cpu, _pc_gpu) + return cases diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index fc716621c..fea11675c 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -2662,11 +2662,18 @@ def initialize_coeffs_from_restart_file(self, file, key): """ TODO """ + # h5py always returns plain host numpy arrays; under the cupy + # backend a bare `cupy_array[:] = numpy_array` full-slice + # assignment raises ("non-scalar numpy.ndarray cannot be used for + # fill" -- cupy's `[:] =` fast path doesn't do the host->device + # transfer implicitly), so route through xp.asarray first, which is + # a no-op under the numpy backend and a safe host->device copy + # under cupy. if isinstance(self.vector, StencilVector): - self.vector._data[:] = file[key][-1] + self.vector._data[:] = xp.asarray(file[key][-1]) else: for n in range(3): - self.vector[n]._data[:] = file[key + "/" + str(n + 1)][-1] + self.vector[n]._data[:] = xp.asarray(file[key + "/" + str(n + 1)][-1]) self._vector.update_ghost_regions() diff --git a/src/struphy/models/base.py b/src/struphy/models/base.py index 737aa26c2..4a6fd9992 100644 --- a/src/struphy/models/base.py +++ b/src/struphy/models/base.py @@ -471,8 +471,13 @@ def update_distr_functions(self): components, edges, output_quantity=binning_quantity, divide_by_jac=divide_by_jac ) - bin_plot.f[:] = f_slice - bin_plot.df[:] = df_slice + # obj.binning() computes on host (markers are always + # host-resident regardless of backend, see + # ISSUE_cupy_particles_never_pushed.md), but bin_plot.f/df + # follow the active backend -- xp.asarray is a no-op + # under numpy and a safe host->device copy under cupy. + bin_plot.f[:] = xp.asarray(f_slice) + bin_plot.df[:] = xp.asarray(df_slice) for kd_plot in species.saving_params.kernel_density_plots: h1 = 1 / obj.boxes_per_dim[0] diff --git a/src/struphy/models/linear_vlasov_ampere_one_species.py b/src/struphy/models/linear_vlasov_ampere_one_species.py index 9acaa41dd..e41ddae7a 100644 --- a/src/struphy/models/linear_vlasov_ampere_one_species.py +++ b/src/struphy/models/linear_vlasov_ampere_one_species.py @@ -204,19 +204,30 @@ def _compute_en_w(self): ) assert isinstance(self._f0, Maxwellian3D) - self._f0_values[particles.valid_mks] = self._f0(*particles.phasespace_coords.T) + # particles.phasespace_coords is always host-resident (see + # ISSUE_cupy_particles_never_pushed.md), but self._f0 follows the + # active backend, so its coordinate args need converting first. + coords = tuple(xp.to_cunumpy(c) for c in particles.phasespace_coords.T) + self._f0_values[particles.valid_mks] = self._f0(*coords) # alpha^2 * v_th^2 / (2*N) * sum_p s_0 * w_p^2 / f_{0,p} alpha = self.kinetic_ions.equation_params.alpha vth = self._f0.params["vth1"][0] + # particles.weights/sampling_density_values are host-resident too + # (same reason as phasespace_coords above); self._f0_values follows + # the active backend, so this division/dot needs both sides on the + # same backend. + weights = xp.to_cunumpy(particles.weights) + sampling_density_values = xp.to_cunumpy(particles.sampling_density_values) + self._tmp[0] = ( alpha**2 * vth**2 / (2 * particles.Np) * xp.dot( - particles.weights**2, # w_p^2 - particles.sampling_density_values / self._f0_values[particles.valid_mks], # s_{0,p} / f_{0,p} + weights**2, # w_p^2 + sampling_density_values / self._f0_values[particles.valid_mks], # s_{0,p} / f_{0,p} ) ) return self._tmp[0] diff --git a/src/struphy/models/species.py b/src/struphy/models/species.py index 17daabe84..714c01640 100644 --- a/src/struphy/models/species.py +++ b/src/struphy/models/species.py @@ -178,8 +178,15 @@ def __init__( con = ConstantsOfNature() - # relevant frequencies - om_p = xp.sqrt(units.n * (Z * con.e) ** 2 / (con.eps0 * A * con.mH)) + # relevant frequencies (scalar physics constants -- xp.sqrt + # returns a 0-d backend array under cupy, not a Python float; + # left as such it propagates into alpha/epsilon/kappa below and + # eventually into things like `sigma_3 * coeff * StencilVector` + # in ImplicitDiffusion, which numpy tolerates but cupy's + # stricter __array_ufunc__ protocol rejects with a "NotImplemented" + # TypeError. There's no vectorization to gain here, so force + # back to a plain float immediately.) + om_p = float(xp.sqrt(units.n * (Z * con.e) ** 2 / (con.eps0 * A * con.mH))) om_c = Z * con.e * units.B / (A * con.mH) # compute equation parameters diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index 6b82b2e30..fa4b33ac3 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -25,6 +25,7 @@ pc_lin_mhd_6d_gpu, vlasov_maxwell_gpu, ) +from struphy.pic.accumulation.accum_kernels_gc_cuda import gc_mag_density_0form_gpu from struphy.pic.accumulation.filter import AccumFilter, FilterParameters from struphy.pic.base import Particles from struphy.utils.utils import __dataclass_repr_no_defaults__, check_option @@ -786,6 +787,21 @@ def __init__( self._gpu_cd0_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) self._gpu_cd0_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + # hand-written CUDA replacement for gc_mag_density_0form (5D + # guiding-center analog of charge_density_0form -- see + # accum_kernels_gc_cuda.py). optional_args = (ep_scale,). + self._gpu_gc_mag_density_0form = xp.cupy_backend and kernel.name == "gc_mag_density_0form" + if self._gpu_gc_mag_density_0form: + import cupy as cp + + args_derham = self.derham.args_derham + self._gpu_gcmd_mu_idx = int(self.particles.mu_idx) + self._gpu_gcmd_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_gcmd_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_gcmd_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_gcmd_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_gcmd_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + def __call__(self, *optional_args, **args_control): """ Performs the accumulation into the vector by calling the chosen accumulation kernel @@ -830,6 +846,20 @@ def _accumulate(self, *optional_args, **args_control): self._gpu_cd0_starts, self._args_data[0], ) + elif self._gpu_gc_mag_density_0form and len(optional_args) == 1: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + (scale,) = optional_args + gc_mag_density_0form_gpu( + self.particles.markers, + self._gpu_gcmd_mu_idx, + scale, + self._gpu_gcmd_pn, + self._gpu_gcmd_tn1, + self._gpu_gcmd_tn2, + self._gpu_gcmd_tn3, + self._gpu_gcmd_starts, + self._args_data[0], + ) else: with ProfileManager.profile_region("kernel: " + self.kernel.name): self.kernel( diff --git a/src/struphy/pic/particles.py b/src/struphy/pic/particles.py index 19d5965a7..0263d38e2 100644 --- a/src/struphy/pic/particles.py +++ b/src/struphy/pic/particles.py @@ -11,7 +11,7 @@ from struphy.kinetic_background import maxwellians from struphy.kinetic_background.base import Maxwellian, SumKineticBackground from struphy.pic import utilities_kernels -from struphy.pic.base import Particles +from struphy.pic.base import Particles, _to_numpy_for_kernel class Particles6D(Particles): @@ -461,14 +461,17 @@ def save_magnetic_energy(self, PBb): PBbt = E0T.dot(PBb, out=self._tmp0) PBbt.update_ghost_regions() + # utilities_kernels is a Pyccel-compiled extension that requires + # real numpy buffers; absB0_h/PBbt follow the active backend, so + # under cupy their ._data needs converting first. utilities_kernels.eval_magnetic_energy_PBb( self.markers, self.derham.args_derham, self.domain.args_domain, self.first_diagnostics_idx, self.mu_idx, - self.absB0_h._data, - PBbt._data, + _to_numpy_for_kernel(self.absB0_h._data), + _to_numpy_for_kernel(PBbt._data), ) def save_magnetic_background_energy(self): @@ -483,7 +486,7 @@ def save_magnetic_background_energy(self): self.domain.args_domain, self.first_diagnostics_idx, self.mu_idx, - self.absB0_h._data, + _to_numpy_for_kernel(self.absB0_h._data), ) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 498293ac2..bed17394d 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -32,6 +32,10 @@ push_vxb_implicit_general_gpu, push_weights_with_efield_lin_va_general_gpu, ) +from struphy.pic.pushing.pusher_kernels_gc_cuda import ( + push_gc_bxEstar_explicit_multistage_general_gpu, + push_gc_Bstar_explicit_multistage_general_gpu, +) logger = logging.getLogger("struphy") @@ -516,6 +520,94 @@ def __init__( self._gpu_random_diffusion_coeff = float(diffusion_coeff) self._gpu_random_diffusion_noise = noise + # general (non-Cuboid) CUDA replacement for + # push_gc_bxEstar_explicit_multistage (5D guiding-center pusher). + self._gpu_gc_bxestar_general = ( + cunumpy.cupy_backend + and kernel.name == "push_gc_bxEstar_explicit_multistage" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_gc_bxestar_general: + import cupy as cp + + self._gpu_gc_bxestar_kind_map = int(args_domain.kind_map) + self._gpu_gc_bxestar_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + ( + args_derham, + epsilon, + unit_b1_1, + unit_b1_2, + unit_b1_3, + grad_b_full_1, + grad_b_full_2, + grad_b_full_3, + B_dot_b_coeffs, + curl_unit_b_dot_b0, + e_field_1, + e_field_2, + e_field_3, + evaluate_e_field, + ) = args_kernel[:14] + self._gpu_gc_bxestar_epsilon = float(epsilon) + self._gpu_gc_bxestar_evaluate_e_field = bool(evaluate_e_field) + self._gpu_gc_bxestar_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_gc_bxestar_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_gc_bxestar_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_gc_bxestar_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_gc_bxestar_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_gc_bxestar_unit_b1 = (unit_b1_1, unit_b1_2, unit_b1_3) + self._gpu_gc_bxestar_grad_b_full = (grad_b_full_1, grad_b_full_2, grad_b_full_3) + self._gpu_gc_bxestar_B_dot_b_coeffs = B_dot_b_coeffs + self._gpu_gc_bxestar_curl_unit_b_dot_b0 = curl_unit_b_dot_b0 + self._gpu_gc_bxestar_e_field = (e_field_1, e_field_2, e_field_3) + self._gpu_gc_bxestar_mu_idx = int(particles.mu_idx) + + # general (non-Cuboid) CUDA replacement for + # push_gc_Bstar_explicit_multistage (5D guiding-center pusher). + self._gpu_gc_bstar_general = ( + cunumpy.cupy_backend + and kernel.name == "push_gc_Bstar_explicit_multistage" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_gc_bstar_general: + import cupy as cp + + self._gpu_gc_bstar_kind_map = int(args_domain.kind_map) + self._gpu_gc_bstar_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + ( + args_derham, + epsilon, + grad_b_full_1, + grad_b_full_2, + grad_b_full_3, + b2_1, + b2_2, + b2_3, + curl_unit_b2_1, + curl_unit_b2_2, + curl_unit_b2_3, + B_dot_b_coeffs, + curl_unit_b_dot_b0, + e_field_1, + e_field_2, + e_field_3, + evaluate_e_field, + ) = args_kernel[:17] + self._gpu_gc_bstar_epsilon = float(epsilon) + self._gpu_gc_bstar_evaluate_e_field = bool(evaluate_e_field) + self._gpu_gc_bstar_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_gc_bstar_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_gc_bstar_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_gc_bstar_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_gc_bstar_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_gc_bstar_grad_b_full = (grad_b_full_1, grad_b_full_2, grad_b_full_3) + self._gpu_gc_bstar_b2 = (b2_1, b2_2, b2_3) + self._gpu_gc_bstar_curl_unit_b2 = (curl_unit_b2_1, curl_unit_b2_2, curl_unit_b2_3) + self._gpu_gc_bstar_B_dot_b_coeffs = B_dot_b_coeffs + self._gpu_gc_bstar_curl_unit_b_dot_b0 = curl_unit_b_dot_b0 + self._gpu_gc_bstar_e_field = (e_field_1, e_field_2, e_field_3) + self._gpu_gc_bstar_mu_idx = int(particles.mu_idx) + @staticmethod def _reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim): """Device version of the per-step marker buffer bookkeeping at the top @@ -821,6 +913,63 @@ def _push(self, dt: float): self._gpu_random_diffusion_coeff, dt, ) + elif self._gpu_gc_bxestar_general: + a, b, _c = self._args_kernel[-3:] + last = 1.0 if stage == self.n_stages - 1 else 0.0 + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + push_gc_bxEstar_explicit_multistage_general_gpu( + markers, + self.particles.n_cols, + first_pusher_idx, + self.particles.first_free_idx, + self._gpu_gc_bxestar_mu_idx, + self._gpu_gc_bxestar_kind_map, + self._gpu_gc_bxestar_params, + self._gpu_gc_bxestar_epsilon, + self._gpu_gc_bxestar_pn, + self._gpu_gc_bxestar_tn1, + self._gpu_gc_bxestar_tn2, + self._gpu_gc_bxestar_tn3, + self._gpu_gc_bxestar_starts, + *self._gpu_gc_bxestar_unit_b1, + *self._gpu_gc_bxestar_grad_b_full, + self._gpu_gc_bxestar_B_dot_b_coeffs, + self._gpu_gc_bxestar_curl_unit_b_dot_b0, + *self._gpu_gc_bxestar_e_field, + self._gpu_gc_bxestar_evaluate_e_field, + dt * float(a[stage]), + dt * float(b[stage]), + last, + ) + elif self._gpu_gc_bstar_general: + a, b, _c = self._args_kernel[-3:] + last = 1.0 if stage == self.n_stages - 1 else 0.0 + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): + push_gc_Bstar_explicit_multistage_general_gpu( + markers, + self.particles.n_cols, + first_pusher_idx, + self.particles.first_free_idx, + self._gpu_gc_bstar_mu_idx, + self._gpu_gc_bstar_kind_map, + self._gpu_gc_bstar_params, + self._gpu_gc_bstar_epsilon, + self._gpu_gc_bstar_pn, + self._gpu_gc_bstar_tn1, + self._gpu_gc_bstar_tn2, + self._gpu_gc_bstar_tn3, + self._gpu_gc_bstar_starts, + *self._gpu_gc_bstar_grad_b_full, + *self._gpu_gc_bstar_b2, + *self._gpu_gc_bstar_curl_unit_b2, + self._gpu_gc_bstar_B_dot_b_coeffs, + self._gpu_gc_bstar_curl_unit_b_dot_b0, + *self._gpu_gc_bstar_e_field, + self._gpu_gc_bstar_evaluate_e_field, + dt * float(a[stage]), + dt * float(b[stage]), + last, + ) elif self._gpu_v_efield_general: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): push_v_with_efield_general_gpu( diff --git a/src/struphy/post_processing/post_processing_tools.py b/src/struphy/post_processing/post_processing_tools.py index 3c1662b8f..69906ac48 100644 --- a/src/struphy/post_processing/post_processing_tools.py +++ b/src/struphy/post_processing/post_processing_tools.py @@ -634,11 +634,17 @@ def _load_femfields(self, fields: dict, files: list, n: int, step: int = 1): e1, e2, e3 = gl_e p1, p2, p3 = pads + # h5py always returns plain host numpy arrays; under + # the cupy backend a bare full-slice assignment from + # a numpy array into a cupy-backed vector raises + # ("non-scalar numpy.ndarray cannot be used for + # fill"), so route through xp.asarray (no-op under + # numpy, a safe host->device copy under cupy). vector[ s1 : e1 + 1, s2 : e2 + 1, s3 : e3 + 1, - ] = ddset[n * step, p1:-p1, p2:-p2, p3:-p3] + ] = xp.asarray(ddset[n * step, p1:-p1, p2:-p2, p3:-p3]) # vector-valued field else: @@ -651,7 +657,7 @@ def _load_femfields(self, fields: dict, files: list, n: int, step: int = 1): s1 : e1 + 1, s2 : e2 + 1, s3 : e3 + 1, - ] = ddset[str(comp + 1)][n * step, p1:-p1, p2:-p2, p3:-p3] + ] = xp.asarray(ddset[str(comp + 1)][n * step, p1:-p1, p2:-p2, p3:-p3]) vector.update_ghost_regions() @@ -897,18 +903,23 @@ def _create_vtk( for name, data in vars.items(): points_list = data[t] + # pyevtk asserts on isinstance(data, numpy.ndarray), so + # field values (and the grid coordinates below) must be + # real host arrays here regardless of backend -- + # xp.to_numpy is a no-op under the numpy backend. + # scalar if len(points_list) == 1: - point_data_n[species][name] = points_list[0] + point_data_n[species][name] = xp.to_numpy(points_list[0]) # vectorpoint_data[name] else: for j in range(3): - point_data_n[species][name + f"_{j + 1}"] = points_list[j] + point_data_n[species][name + f"_{j + 1}"] = xp.to_numpy(points_list[j]) gridToVTK( os.path.join(species_path, "step_{0:0{1}d}".format(n, log_nt)), - *grids_phy, + *(xp.to_numpy(g) for g in grids_phy), pointData=point_data_n[species], ) @@ -1015,7 +1026,12 @@ def _post_process_markers( temp[lost_particles_mask, -1] = ids_lost_particles ids = xp.unique(xp.append(ids, ids_lost_particles)) - assert xp.all(sorted(ids) == xp.arange(n_markers)) + # sorted() on a cupy array returns a plain Python list of 0-d + # cupy scalars, which cupy's stricter __array_ufunc__ then + # refuses to compare against an ndarray -- xp.sort keeps this + # backend-safe (numpy's sorted()-vs-ndarray comparison happened + # to work, cupy's doesn't). + assert xp.all(xp.sort(ids) == xp.arange(n_markers)) # compute physical positions (x, y, z) pos_phys = self.domain(xp.array(temp[~lost_particles_mask, :3]), change_out_order=True) @@ -1223,8 +1239,12 @@ def _post_process_f( # correct integrating out in v-direction data_bckgr *= factor - # Now all data is just the data for delta_f - data_delta_f = data_df + # Now all data is just the data for delta_f. data_df + # comes from particle binning, which is always + # host-resident (see ISSUE_cupy_particles_never_pushed.md), + # while data_bckgr follows the active backend -- convert + # before combining them. + data_delta_f = xp.asarray(data_df) # save distribution function xp.save(os.path.join(path_slice, "delta_f_binned.npy"), data_delta_f) diff --git a/src/struphy/propagators/efield_weights_coupling.py b/src/struphy/propagators/efield_weights_coupling.py index 06127176b..721c8c109 100644 --- a/src/struphy/propagators/efield_weights_coupling.py +++ b/src/struphy/propagators/efield_weights_coupling.py @@ -253,14 +253,11 @@ def __call__(self, dt): en = self.variables.e.spline.vector particles = self.variables.ions.particles - # evaluate f0 and accumulate + # evaluate f0 and accumulate. particles.markers is always + # host-resident (see ISSUE_cupy_particles_never_pushed.md), but + # self._f0 follows the active backend. self._f0_values[:] = self._f0( - particles.markers[:, 0], - particles.markers[:, 1], - particles.markers[:, 2], - particles.markers[:, 3], - particles.markers[:, 4], - particles.markers[:, 5], + *(xp.to_cunumpy(particles.markers[:, i]) for i in range(6)), ) self._accum(self._f0_values) From 7194b99c9d78eba4946650e68f0385064c6a824e Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 16:15:53 +0200 Subject: [PATCH 047/156] Cuda versions of gc kernels --- .../pic/accumulation/accum_kernels_gc_cuda.py | 164 ++++++++ .../pic/pushing/pusher_kernels_gc_cuda.py | 370 ++++++++++++++++++ 2 files changed, 534 insertions(+) create mode 100644 src/struphy/pic/accumulation/accum_kernels_gc_cuda.py create mode 100644 src/struphy/pic/pushing/pusher_kernels_gc_cuda.py diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py new file mode 100644 index 000000000..9a103f7ea --- /dev/null +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -0,0 +1,164 @@ +"""Hand-written CUDA replacement for +:func:`~struphy.pic.accumulation.accum_kernels_gc.gc_mag_density_0form`, +used only under ``ARRAY_BACKEND=cupy``. See +:mod:`~struphy.pic.pushing.pusher_kernels_gc_cuda` for the scope of this +branch's 5D guiding-center porting (2 explicit pushers + this one +accumulator, out of 21 real kernels in the gc family). + +Same atomicAdd-scatter approach as +:func:`~struphy.pic.accumulation.accum_kernels_cuda.charge_density_0form_gpu` +-- this kernel is nearly identical (an H^1/0-form vec_fill_b_v0 scatter), +just with a ``mu * weight * scale`` filling instead of a plain weight, and +``mu`` read from the marker's ``mu_idx`` column instead of a fixed offset. +""" + +_GC_MAG_DENSITY_0FORM_SRC = r""" +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) +{ + double left[MAXP]; + double right[MAXP]; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +extern "C" __global__ +void gc_mag_density_0form_cuda( + const double* markers, + const int n_cols, + const int n_markers, + const int mu_idx, + const double scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + double* vec, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double weight = row[5]; + const double mu = row[mu_idx]; + const double filling = mu * weight * scale; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bn1[il1] * filling; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bn2[il2]; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bn3[il3]; + atomicAdd(&vec[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3], b3); + } + } + } +} +""" + +_gc_mag_density_0form_kernel = None + + +def _get_gc_mag_density_0form_kernel(): + global _gc_mag_density_0form_kernel + if _gc_mag_density_0form_kernel is None: + import cupy as cp + + _gc_mag_density_0form_kernel = cp.RawKernel(_GC_MAG_DENSITY_0FORM_SRC, "gc_mag_density_0form_cuda") + return _gc_mag_density_0form_kernel + + +def gc_mag_density_0form_gpu( + markers, + mu_idx: int, + scale: float, + pn: tuple[int, int, int], + tn1_dev, + tn2_dev, + tn3_dev, + starts: tuple[int, int, int], + vec_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels_gc.gc_mag_density_0form`. + ``vec_dev`` is already device-resident and already zeroed by the caller. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev_markers = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + _get_gc_mag_density_0form_kernel()( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(mu_idx), + np.float64(scale), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + vec_dev, + np.int32(vec_dev.shape[1]), + np.int32(vec_dev.shape[2]), + ), + ) diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py new file mode 100644 index 000000000..553c787d7 --- /dev/null +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -0,0 +1,370 @@ +"""Hand-written CUDA replacements for select 5D guiding-center pusher +kernels in :mod:`~struphy.pic.pushing.pusher_kernels_gc`, used only under +``ARRAY_BACKEND=cupy``. + +Scope: this branch's 6D (full-orbit) work ported every real (non-dead-code) +kernel in ``pusher_kernels.py``/``accum_kernels.py``. The 5D guiding-center +family (``pusher_kernels_gc.py``/``accum_kernels_gc.py``) is a separate, +much larger body of kernels -- 15 pushers + 8 accumulators, 21 of them with +real propagator callers -- several of which (the ``*_discrete_gradient_*`` +variants) are implicit per-marker Newton solves, not simple explicit RK +stages, and are a substantially bigger porting effort. + +Currently covered here: the two *explicit* multistage GC pushers, +:func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_explicit_multistage` +and :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_explicit_multistage` +-- both are plain explicit-RK marker loops (same ``dt*a[stage]``/``dt*b[stage]``/ +``last`` structure as ``push_eta_stage`` in the 6D family), and reuse the +existing 0-/1-/2-form spline evaluation and geometry device functions from +:mod:`~struphy.pic.pushing.pusher_kernels_cuda`'s ``_GENERAL_GEOMETRY_SRC`` +unchanged. The accompanying accumulation kernel +:func:`~struphy.pic.accumulation.accum_kernels_gc.gc_mag_density_0form` is +ported alongside these in +:mod:`~struphy.pic.accumulation.accum_kernels_gc_cuda` (same +``atomicAdd``-scatter approach as ``charge_density_0form``). +""" + +_PUSH_GC_BXESTAR_SRC = r""" +extern "C" __global__ +void push_gc_bxEstar_explicit_multistage_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_free_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* unit_b1_1, const int ub1_n2, const int ub1_n3, + const double* unit_b1_2, const int ub2_n2, const int ub2_n3, + const double* unit_b1_3, const int ub3_n2, const int ub3_n3, + const double* grad_b_full_1, const int gb1_n2, const int gb1_n3, + const double* grad_b_full_2, const int gb2_n2, const int gb2_n3, + const double* grad_b_full_3, const int gb3_n2, const int gb3_n3, + const double* B_dot_b_coeffs, const int bdb_n2, const int bdb_n3, + const double* curl_unit_b_dot_b0, const int cub_n2, const int cub_n3, + const double* e_field_1, const int e1_n2, const int e1_n3, + const double* e_field_2, const int e2_n2, const int e2_n3, + const double* e_field_3, const int e3_n2, const int e3_n3, + const int evaluate_e_field, + const double dt_a, const double dt_b, const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + const double mu = row[mu_idx]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double unit_b1[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + unit_b1_1, ub1_n2, ub1_n3, unit_b1_2, ub2_n2, ub2_n3, unit_b1_3, ub3_n2, ub3_n3, unit_b1); + + double e_star[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + grad_b_full_1, gb1_n2, gb1_n3, grad_b_full_2, gb2_n2, gb2_n3, grad_b_full_3, gb3_n2, gb3_n3, e_star); + e_star[0] *= -epsilon * mu; + e_star[1] *= -epsilon * mu; + e_star[2] *= -epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + e_field_1, e1_n2, e1_n3, e_field_2, e2_n2, e2_n3, e_field_3, e3_n2, e3_n3, e_field); + e_star[0] += e_field[0]; + e_star[1] += e_field[1]; + e_star[2] += e_field[2]; + } + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, B_dot_b_coeffs, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, curl_unit_b_dot_b0, cub_n2, cub_n3); + b_star_parallel = b_star_parallel * epsilon * v + B_dot_b; + b_star_parallel *= det_df; + + double Exb[3]; + cross_dev(e_star, unit_b1, Exb); + + double k[3]; + k[0] = Exb[0] / b_star_parallel; + k[1] = Exb[1] / b_star_parallel; + k[2] = Exb[2] / b_star_parallel; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} +""" + +_PUSH_GC_BSTAR_SRC = r""" +extern "C" __global__ +void push_gc_Bstar_explicit_multistage_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_free_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* grad_b_full_1, const int gb1_n2, const int gb1_n3, + const double* grad_b_full_2, const int gb2_n2, const int gb2_n3, + const double* grad_b_full_3, const int gb3_n2, const int gb3_n3, + const double* b2_1, const int b1_n2, const int b1_n3, + const double* b2_2, const int b2n2, const int b2n3, + const double* b2_3, const int b3_n2, const int b3_n3, + const double* curl_unit_b2_1, const int cb1_n2, const int cb1_n3, + const double* curl_unit_b2_2, const int cb2_n2, const int cb2_n3, + const double* curl_unit_b2_3, const int cb3_n2, const int cb3_n3, + const double* B_dot_b_coeffs, const int bdb_n2, const int bdb_n3, + const double* curl_unit_b_dot_b0, const int cub_n2, const int cub_n3, + const double* e_field_1, const int e1_n2, const int e1_n3, + const double* e_field_2, const int e2_n2, const int e2_n3, + const double* e_field_3, const int e3_n2, const int e3_n3, + const int evaluate_e_field, + const double dt_a, const double dt_b, const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + const double mu = row[mu_idx]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double e_star[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + grad_b_full_1, gb1_n2, gb1_n3, grad_b_full_2, gb2_n2, gb2_n3, grad_b_full_3, gb3_n2, gb3_n3, e_star); + e_star[0] *= -epsilon * mu; + e_star[1] *= -epsilon * mu; + e_star[2] *= -epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + e_field_1, e1_n2, e1_n3, e_field_2, e2_n2, e2_n3, e_field_3, e3_n2, e3_n3, e_field); + e_star[0] += e_field[0]; + e_star[1] += e_field[1]; + e_star[2] += e_field[2]; + } + + double b2[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + b2_1, b1_n2, b1_n3, b2_2, b2n2, b2n3, b2_3, b3_n2, b3_n3, b2); + + double b_star[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + curl_unit_b2_1, cb1_n2, cb1_n3, curl_unit_b2_2, cb2_n2, cb2_n3, curl_unit_b2_3, cb3_n2, cb3_n3, b_star); + b_star[0] = b_star[0] * epsilon * v + b2[0]; + b_star[1] = b_star[1] * epsilon * v + b2[1]; + b_star[2] = b_star[2] * epsilon * v + b2[2]; + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, B_dot_b_coeffs, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, curl_unit_b_dot_b0, cub_n2, cub_n3); + b_star_parallel = b_star_parallel * epsilon * v + B_dot_b; + b_star_parallel *= det_df; + + double k[3]; + k[0] = b_star[0] / b_star_parallel * v; + k[1] = b_star[1] / b_star_parallel * v; + k[2] = b_star[2] / b_star_parallel * v; + + double k_v = dot3_dev(b_star, e_star); + k_v /= b_star_parallel * epsilon; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + row[first_free_idx + 3] += dt_b * k_v; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; + row[3] = row[first_init_idx + 3] + dt_a * k_v + last * row[first_free_idx + 3]; +} +""" + + +def _push_gc_bxEstar_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + return _GENERAL_GEOMETRY_SRC + _PUSH_GC_BXESTAR_SRC + + +def _push_gc_Bstar_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + return _GENERAL_GEOMETRY_SRC + _PUSH_GC_BSTAR_SRC + + +_push_gc_bxEstar_kernel = None +_push_gc_Bstar_kernel = None + + +def _get_push_gc_bxEstar_kernel(): + global _push_gc_bxEstar_kernel + if _push_gc_bxEstar_kernel is None: + import cupy as cp + + _push_gc_bxEstar_kernel = cp.RawKernel(_push_gc_bxEstar_source(), "push_gc_bxEstar_explicit_multistage_cuda") + return _push_gc_bxEstar_kernel + + +def _get_push_gc_Bstar_kernel(): + global _push_gc_Bstar_kernel + if _push_gc_Bstar_kernel is None: + import cupy as cp + + _push_gc_Bstar_kernel = cp.RawKernel(_push_gc_Bstar_source(), "push_gc_Bstar_explicit_multistage_cuda") + return _push_gc_Bstar_kernel + + +def push_gc_bxEstar_explicit_multistage_general_gpu( + markers, n_cols, first_init_idx, first_free_idx, mu_idx, + kind_map, params_dev, epsilon, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + unit_b1_1_dev, unit_b1_2_dev, unit_b1_3_dev, + grad_b_full_1_dev, grad_b_full_2_dev, grad_b_full_3_dev, + B_dot_b_coeffs_dev, curl_unit_b_dot_b0_dev, + e_field_1_dev, e_field_2_dev, e_field_3_dev, + evaluate_e_field: bool, + dt_a: float, dt_b: float, last: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_explicit_multistage`, + for any domain in :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_push_gc_bxEstar_kernel()( + (blocks,), + (threads,), + ( + dev, np.int32(n_cols), np.int32(n_markers), + np.int32(first_init_idx), np.int32(first_free_idx), np.int32(mu_idx), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(unit_b1_1_dev), *d(unit_b1_2_dev), *d(unit_b1_3_dev), + *d(grad_b_full_1_dev), *d(grad_b_full_2_dev), *d(grad_b_full_3_dev), + *d(B_dot_b_coeffs_dev), *d(curl_unit_b_dot_b0_dev), + *d(e_field_1_dev), *d(e_field_2_dev), *d(e_field_3_dev), + np.int32(bool(evaluate_e_field)), + np.float64(dt_a), np.float64(dt_b), np.float64(last), + ), + ) + dev.get(out=markers) + + +def push_gc_Bstar_explicit_multistage_general_gpu( + markers, n_cols, first_init_idx, first_free_idx, mu_idx, + kind_map, params_dev, epsilon, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + grad_b_full_1_dev, grad_b_full_2_dev, grad_b_full_3_dev, + b2_1_dev, b2_2_dev, b2_3_dev, + curl_unit_b2_1_dev, curl_unit_b2_2_dev, curl_unit_b2_3_dev, + B_dot_b_coeffs_dev, curl_unit_b_dot_b0_dev, + e_field_1_dev, e_field_2_dev, e_field_3_dev, + evaluate_e_field: bool, + dt_a: float, dt_b: float, last: float, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_explicit_multistage`, + for any domain in :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + dev = cp.asarray(markers) + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_push_gc_Bstar_kernel()( + (blocks,), + (threads,), + ( + dev, np.int32(n_cols), np.int32(n_markers), + np.int32(first_init_idx), np.int32(first_free_idx), np.int32(mu_idx), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(grad_b_full_1_dev), *d(grad_b_full_2_dev), *d(grad_b_full_3_dev), + *d(b2_1_dev), *d(b2_2_dev), *d(b2_3_dev), + *d(curl_unit_b2_1_dev), *d(curl_unit_b2_2_dev), *d(curl_unit_b2_3_dev), + *d(B_dot_b_coeffs_dev), *d(curl_unit_b_dot_b0_dev), + *d(e_field_1_dev), *d(e_field_2_dev), *d(e_field_3_dev), + np.int32(bool(evaluate_e_field)), + np.float64(dt_a), np.float64(dt_b), np.float64(last), + ), + ) + dev.get(out=markers) From 9baeb3b182b95359b48e03aa01b1f519bee89157 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 16:16:21 +0200 Subject: [PATCH 048/156] added bench_gpu to gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 84ab39384..45ee9b393 100644 --- a/.gitignore +++ b/.gitignore @@ -102,6 +102,7 @@ src/struphy/state.yml src/struphy/io/inp/params_* *.bin bench_gpu/out_* +bench_gpu/ # models list bin/ From 057cb537fcfb3c0f36c28789e4dc60efb0d9ef1d Mon Sep 17 00:00:00 2001 From: Max Date: Sun, 16 Aug 2026 17:59:59 +0200 Subject: [PATCH 049/156] Added profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py --- .../params_LinearMHDDriftkineticCC.py | 181 ++++++++++++++++++ 1 file changed, 181 insertions(+) create mode 100644 profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py diff --git a/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py b/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py new file mode 100644 index 000000000..eb6e3efb3 --- /dev/null +++ b/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py @@ -0,0 +1,181 @@ +# ----------------------------- +# Description of the simulation +# ----------------------------- +# Please fill in a verbal description of the simulation. +# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. + +name = "Default LinearMHDDriftkineticCC" +description = """ +This is the default simulation for the model LinearMHDDriftkineticCC. +It is meant to be a template for users to set up their own simulations with this model. +It contains all the necessary components of a Struphy simulation, including the model, +the environment options, the time stepping options, the geometry, the equilibrium, +the grid, the Derham options, and the initial conditions. +Users can modify this file to set up their own simulations with different parameters and initial conditions. +""" + +import argparse +import os + +parser = argparse.ArgumentParser(description=description) +parser.add_argument( + "--backend", + choices=("numpy", "cupy"), + default="numpy", + help="Array backend to run the simulation with (default: numpy).", +) +args = parser.parse_args() + +# Must be set before struphy (and therefore cunumpy) is imported. +os.environ["ARRAY_BACKEND"] = args.backend + +import logging + +from struphy import set_logging_level + +set_logging_level(logging.WARNING) + +# ------------------ +# Import Struphy API +# ------------------ + +# For particles: +from struphy import ( + BaseUnits, + BinningPlot, + BoundaryParameters, + DerhamOptions, + EnvironmentOptions, + FieldsBackground, + KernelDensityPlot, + LoadingParameters, + SavingParameters, + Simulation, + SortingParameters, + Time, + WeightsParameters, + domains, + equils, + grids, + maxwellians, + perturbations, +) + +# --------------------- +# Instance of the model +# --------------------- +from struphy.models import LinearMHDDriftkineticCC + +# Units +base_units = BaseUnits() + +# Model instance +model = LinearMHDDriftkineticCC(base_units=base_units) + +# List all variables and decide whether to save their data +model.em_fields.b_field.save_data = True +model.mhd.density.save_data = True +model.mhd.pressure.save_data = True +model.mhd.velocity.save_data = True +model.energetic_ions.var.save_data = True + +# -------------------------- +# Instance of the simulation +# -------------------------- + +# Environment options +env = EnvironmentOptions( + sim_folder=f"sim_{args.backend}", + profiling_activated=True, +) + +# Time stepping +time_opts = Time() + +# Geometry +domain = domains.Cuboid() + +# Fluid equilibrium (can be used as part of initial conditions) +equil = equils.HomogenSlab() + +# Grid +grid = grids.TensorProductGrid(num_elements=(16, 16, 16)) + +# Derham options +derham_opts = DerhamOptions() + +# Simulation object +sim = Simulation( + model=model, + name=name, + description=description, + params_path=__file__, + env=env, + time_opts=time_opts, + domain=domain, + equil=equil, + grid=grid, + derham_opts=derham_opts, +) + +# ------------------- +# Particle parameters +# ------------------- + +loading_params = LoadingParameters() +weights_params = WeightsParameters() +boundary_params = BoundaryParameters() +sorting_params = SortingParameters() +saving_params = SavingParameters() +model.energetic_ions.set_markers( + loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, +) + +# ------------------ +# Propagator options +# ------------------ + +model.propagators.push_bxe.options = model.propagators.push_bxe.Options() +model.propagators.push_parallel.options = model.propagators.push_parallel.Options() +model.propagators.shearalfen_cc5d.options = model.propagators.shearalfen_cc5d.Options() +model.propagators.magnetosonic.options = model.propagators.magnetosonic.Options() +model.propagators.cc5d_density.options = model.propagators.cc5d_density.Options() +model.propagators.cc5d_gradb.options = model.propagators.cc5d_gradb.Options() +model.propagators.cc5d_curlb.options = model.propagators.cc5d_curlb.Options() + +# ------------------ +# Initial conditions +# ------------------ +# Initial conditions are the sum of the background(s) and the perturbation(s). +# If backgrounds or perturbations are not specified, they are assumed to be zero. + +# Background for (some) FEEC variables +model.mhd.velocity.add_background(FieldsBackground()) + +# Perturbations for (some) FEEC variables +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=0)) +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=1)) +model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=2)) + +# For kinetic species the background is mandatory. +# For kinetic species, if add_initial_condition() is not called, the background is taken as the kinetic initial condition. +# For kinetic species the perturbations are added to the moments of the distribution function (defined as tuples). + +# Background for kinetic species +maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None), equil=equil) +maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None), equil=equil) +background = maxwellian_1 + maxwellian_2 +model.energetic_ions.var.add_background(background) + +# Perturbations for (some) kinetic species +perturbation = perturbations.TorusModesCos() +maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation), equil=equil) +init = maxwellian_1pt + maxwellian_2 +model.energetic_ions.var.add_initial_condition(init) + +if __name__ == "__main__": + sim.run() From c9685556917e4e245a701f89ca829c698bf3bd7d Mon Sep 17 00:00:00 2001 From: Max Date: Sun, 16 Aug 2026 18:14:26 +0200 Subject: [PATCH 050/156] Added profiling/submit_linearmhd_numpy_vs_cupy.py --- profiling/clusters.py | 13 ++++ profiling/submit_linearmhd_numpy_vs_cupy.py | 74 +++++++++++++++++++++ 2 files changed, 87 insertions(+) create mode 100644 profiling/submit_linearmhd_numpy_vs_cupy.py diff --git a/profiling/clusters.py b/profiling/clusters.py index d64c9a1f6..b2cbe8aa0 100644 --- a/profiling/clusters.py +++ b/profiling/clusters.py @@ -69,6 +69,19 @@ def detect_machine_name() -> str | None: "mail_type": "none", "time": "00:15:00", }, + "pitagora_booster": { + # "nodes": 1, # Should be set by ProfilingCase.launch() + # "ntasks_per_node": 1, # Should be set by ProfilingCase.launch() + "cpus_per_task": 16, + "mem": "480GB", + "gres": "gpu:4,tmpfs:10g", + "partition": "boost_fua_dbg", + "account": "FUSIO_HLST_6", + "output": "myJob_%j.out", + "error": "myJob_%j.err", + "mail_type": "none", + "time": "00:15:00", + }, "tok": { "cpus_per_task": 1, "mem_per_cpu": "1GB", diff --git a/profiling/submit_linearmhd_numpy_vs_cupy.py b/profiling/submit_linearmhd_numpy_vs_cupy.py new file mode 100644 index 000000000..b049b6f9e --- /dev/null +++ b/profiling/submit_linearmhd_numpy_vs_cupy.py @@ -0,0 +1,74 @@ +"""Poisson strong scaling profiling case. + +This file defines the Poisson strong scaling profiling case (the `ProfilingCase`) +and submits it: for each rank count, `ProfilingCase.launch` builds and submits a +SLURM script (using `clusters.SLURM_PRESETS` by default), or, without a batch +system, runs directly on this machine. `finalize_run` then packages and uploads +each run as soon as its own job finishes. +Each generated script runs the simulation itself by invoking `params_LinearMHDDriftkineticCC.py` +directly (its `__main__` block is the worker). +""" + +import argparse +from pathlib import Path + +from profiling_job import ProfilingCase +from clusters import SLURM_PRESETS + +cpu_preset = SLURM_PRESETS.get("pitagora_dcgp") +gpu_preset = SLURM_PRESETS.get("pitagora_booster") + +slurm_presets = { + "numpy": cpu_preset, + "cupy": gpu_preset, +} + + +def main() -> None: + + # Parse arguments, do not remove --upload + parser = argparse.ArgumentParser( + description=( + "Submit profiling jobs to a SLURM cluster and package the results for upload." + ), + ) + parser.add_argument( + "--upload", + action="store_true", + help="Upload the packaged profiling results to the profiling-data repo.", + ) + args = parser.parse_args() + + # Paths relative to this script's location, so it can be run from anywhere. + script_dir = Path(__file__).resolve().parent + params_dir = ( + script_dir / "examples" / "LinearMHDDriftkineticCC" / "cube_strong_scaling" + ) + + profiling_case = ProfilingCase( + label="linearmhd_numpy_vs_cupy", + name="Linear MHD on cube", + description="Linear MHD model with manufactured solution on 3D cube.", + physics_problem="Occurs in many plasma applications.", + struphy_model_used="LinearMHDDriftkineticCC", + params_source=params_dir / "params_LinearMHDDriftkineticCC.py", + language="fortran", + compiler="GNU", + upload=args.upload, + ) + + # Launch one run per rank count + for num_tasks in (1,): + for backend in ("numpy", "cupy"): + profiling_case.launch( + num_tasks, + param_flags=["--backend", backend], + slurm_preset=slurm_presets[backend], + ) + + # Package and push each run as its own job finishes. + profiling_case.finalize_run() + + +if __name__ == "__main__": + main() From 0ac2eeb4cff59ccce0f6d093794623a8fb6c608c Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 18:15:22 +0200 Subject: [PATCH 051/156] Keep markers on GPU (removed cp.asarray(markers)) --- .../pic/accumulation/accum_kernels_cuda.py | 12 +- .../pic/accumulation/accum_kernels_gc_cuda.py | 2 +- .../pic/accumulation/particles_to_grid.py | 20 +- src/struphy/pic/base.py | 559 ++++++++++-------- src/struphy/pic/pushing/pusher.py | 62 +- .../pic/pushing/pusher_kernels_cuda.py | 39 +- .../pic/pushing/pusher_kernels_gc_cuda.py | 6 +- 7 files changed, 389 insertions(+), 311 deletions(-) diff --git a/src/struphy/pic/accumulation/accum_kernels_cuda.py b/src/struphy/pic/accumulation/accum_kernels_cuda.py index dcfb9c01a..8fa16917e 100644 --- a/src/struphy/pic/accumulation/accum_kernels_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_cuda.py @@ -152,7 +152,7 @@ def charge_density_0form_gpu( import numpy as np n_markers = markers.shape[0] - dev_markers = cp.asarray(markers) + dev_markers = markers threads = 256 blocks = (n_markers + threads - 1) // threads _get_charge_density_0form_kernel()( @@ -550,7 +550,7 @@ def vlasov_maxwell_gpu( import numpy as np n_markers = markers.shape[0] - dev_markers = cp.asarray(markers) + dev_markers = markers threads = 256 blocks = (n_markers + threads - 1) // threads @@ -641,7 +641,7 @@ def linear_vlasov_ampere_gpu( import numpy as np n_markers = markers.shape[0] - dev_markers = cp.asarray(markers) + dev_markers = markers f0_values_dev = cp.ascontiguousarray(f0_values_dev) threads = 256 blocks = (n_markers + threads - 1) // threads @@ -887,7 +887,7 @@ def cc_lin_mhd_6d_1_gpu( import numpy as np n_markers = markers.shape[0] - dev_markers = cp.asarray(markers) + dev_markers = markers b2_1_dev = cp.ascontiguousarray(b2_1_dev) b2_2_dev = cp.ascontiguousarray(b2_2_dev) b2_3_dev = cp.ascontiguousarray(b2_3_dev) @@ -1175,7 +1175,7 @@ def cc_lin_mhd_6d_2_gpu( import numpy as np n_markers = markers.shape[0] - dev_markers = cp.asarray(markers) + dev_markers = markers b2_1_dev = cp.ascontiguousarray(b2_1_dev) b2_2_dev = cp.ascontiguousarray(b2_2_dev) b2_3_dev = cp.ascontiguousarray(b2_3_dev) @@ -1685,7 +1685,7 @@ def _pc_lin_mhd_6d_launch( import numpy as np n_markers = markers.shape[0] - dev_markers = cp.asarray(markers) + dev_markers = markers threads = 256 blocks = (n_markers + threads - 1) // threads diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index 9a103f7ea..1d92cbfcb 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -133,7 +133,7 @@ def gc_mag_density_0form_gpu( import numpy as np n_markers = markers.shape[0] - dev_markers = cp.asarray(markers) + dev_markers = markers threads = 256 blocks = (n_markers + threads - 1) // threads _get_gc_mag_density_0form_kernel()( diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index fa4b33ac3..f0f8129dd 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -444,9 +444,15 @@ def _accumulate(self, *optional_args, **args_control): *self._args_data, ) else: - with ProfileManager.profile_region("kernel: " + self.kernel.name): + # no CUDA port for this kernel: fall back to the compiled + # host-only one. Accumulation kernels only read markers (they + # write into the grid arrays), so no write-back is needed. + with ( + ProfileManager.profile_region("kernel: " + self.kernel.name), + self.particles.host_markers(write=False) as args_markers, + ): self.kernel( - self.particles.args_markers, + args_markers, self.derham.args_derham, self.args_domain, *self._args_data, @@ -861,9 +867,15 @@ def _accumulate(self, *optional_args, **args_control): self._args_data[0], ) else: - with ProfileManager.profile_region("kernel: " + self.kernel.name): + # no CUDA port for this kernel: fall back to the compiled + # host-only one. Accumulation kernels only read markers (they + # write into the grid arrays), so no write-back is needed. + with ( + ProfileManager.profile_region("kernel: " + self.kernel.name), + self.particles.host_markers(write=False) as args_markers, + ): self.kernel( - self.particles.args_markers, + args_markers, self.derham.args_derham, self.args_domain, *self._args_data, diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index d03b862df..1b423d420 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -3,6 +3,7 @@ import os import warnings from abc import ABCMeta, abstractmethod +from contextlib import contextmanager import h5py import numpy as np @@ -78,6 +79,23 @@ class Intracomm: logger = logging.getLogger("struphy") +def _array_types(): + """Array classes a marker-column setter may legitimately be handed. + + Marker data lives on the active backend (device under CuPy), so a + setter must accept that backend's array type as well as NumPy's -- + plain NumPy is still valid input and gets converted on assignment. + """ + if xp.cupy_backend: + import cupy as cp + + return (np.ndarray, cp.ndarray) + return (np.ndarray,) + + +_ARRAY_TYPES = _array_types() + + def _to_numpy_for_kernel(value): """Convert CuPy arrays to NumPy for compiled kernel calls.""" if hasattr(value, "get"): @@ -125,6 +143,67 @@ def _pinned_zeros(shape, dtype=float): return arr +class _HostMarkerMirror: + """Explicit host mirror of a device-resident marker array. + + Under ``ARRAY_BACKEND=cupy`` the marker array (and its companion boolean + masks) live on the device — that is where every ported CUDA particle + kernel reads and writes them, and where the vectorized bookkeeping + (boundary conditions, hole tracking, sorting) runs, so no transfer is + needed in the hot path at all. + + A handful of *cold-path* consumers are still compiled, host-only Pyccel + kernels (SPH evaluation, some diagnostics/accumulation kernels that have + no CUDA port yet). Those need a real ``numpy.ndarray`` that they can read + -- and in some cases write -- in place. This class owns that host buffer + and makes the crossing explicit rather than implicit: callers wrap the + Pyccel call in :meth:`Particles.host_markers`, which copies device->host + on entry and (when ``write=True``) host->device on exit. + + The buffer is allocated once, in pinned memory, and reused; the mirror + object handed to ``MarkerArguments`` therefore stays identity-stable for + the lifetime of the :class:`Particles` instance, exactly as the old + always-host array did. + """ + + __slots__ = ("_pairs", "_depth") + + def __init__(self): + # list of [device_array, host_array]; host buffers are identity-stable + self._pairs = [] + self._depth = 0 + + def add(self, device_array): + """Register a device array and return its identity-stable host buffer.""" + host = _pinned_zeros(device_array.shape, dtype=device_array.dtype) + self._pairs.append([device_array, host]) + return host + + def rebind(self, old_device_array, new_device_array): + """Point the mirror at a new device array (after a resize/realloc). + + Returns the host buffer for the new array, reallocating it only if + the shape actually changed. + """ + for pair in self._pairs: + if pair[0] is old_device_array: + pair[0] = new_device_array + if new_device_array.shape != pair[1].shape: + pair[1] = _pinned_zeros(new_device_array.shape, dtype=new_device_array.dtype) + return pair[1] + return self.add(new_device_array) + + def pull(self): + """device -> host, for every registered array.""" + for device, host in self._pairs: + device.get(out=host) + + def push(self): + """host -> device (in place, so device array identities are kept).""" + for device, host in self._pairs: + device.set(host) + + class Particles(metaclass=ABCMeta): r""" Base class for particle species. @@ -442,13 +521,13 @@ def __init__( # if self.loading_params["moments"] is None and not isinstance(self, ParticlesSPH) and isinstance(self.bckgr_params, dict): self._generate_sampling_moments() - # create buffers for mpi_sort_markers -- marker-row-indexed, like - # markers itself always host-resident regardless of backend (see - # ISSUE_cupy_particles_never_pushed.md), and fed straight into mpi4py - # Alltoall/Isend/Irecv calls below, which need host buffers. - self._sorting_etas = np.zeros((self.markers.shape[0], 3), dtype=float) - self._is_on_proc_domain = np.zeros((self.markers.shape[0], 3), dtype=bool) - self._can_stay = np.zeros(self.markers.shape[0], dtype=bool) + # create buffers for mpi_sort_markers -- marker-row-indexed, so they + # live on the same backend as the markers themselves (device under + # CuPy). The actual mpi4py Alltoall/Isend/Irecv calls further below + # take host buffers and convert explicitly at that point. + self._sorting_etas = xp.zeros((self.markers.shape[0], 3), dtype=float) + self._is_on_proc_domain = xp.zeros((self.markers.shape[0], 3), dtype=bool) + self._can_stay = xp.zeros(self.markers.shape[0], dtype=bool) self._reqs = [None] * self.mpi_size self._recvbufs = [None] * self.mpi_size self._send_to_i = [None] * self.mpi_size @@ -725,6 +804,19 @@ def domain_array(self): """ return self._domain_array + @property + def domain_array_dev(self): + """:attr:`domain_array` on the active backend. + + The domain decomposition is small, fixed metadata built once on the + host, but it is repeatedly compared against marker positions, which + live on the device under CuPy. Cached here so those comparisons do + not re-upload it on every call. + """ + if getattr(self, "_domain_array_dev", None) is None: + self._domain_array_dev = xp.asarray(self._domain_array) + return self._domain_array_dev + @property def mpi_dims_mask(self): """3-list | tuple; True if the dimension is to be used in the domain decomposition (=default for each dimension). @@ -878,7 +970,7 @@ def positions(self): @positions.setter def positions(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) assert new.shape == (self.n_mks_loc, 3) self._markers[self.valid_mks, self.index["pos"]] = new @@ -889,7 +981,7 @@ def velocities(self): @velocities.setter def velocities(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) assert new.shape == (self.n_mks_loc, self.vdim), f"{self.n_mks_loc =} and {self.vdim =} but {new.shape =}" self._markers[self.valid_mks, self.index["vel"]] = new @@ -900,7 +992,7 @@ def phasespace_coords(self): @phasespace_coords.setter def phasespace_coords(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) assert new.shape == (self.n_mks_loc, 3 + self.vdim) self._markers[self.valid_mks, self.index["coords"]] = new @@ -911,7 +1003,7 @@ def weights(self): @weights.setter def weights(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["weights"]] = new @@ -922,7 +1014,7 @@ def sampling_density_values(self): @sampling_density_values.setter def sampling_density_values(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["s0"]] = new @@ -933,7 +1025,7 @@ def weights0(self): @weights0.setter def weights0(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["w0"]] = new @@ -944,7 +1036,7 @@ def marker_ids(self): @marker_ids.setter def marker_ids(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) assert new.shape == (self.n_mks_loc,) self._markers[self.valid_mks, self.index["ids"]] = new @@ -955,7 +1047,7 @@ def f_coords(self): @f_coords.setter def f_coords(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) self.markers[:, self.f_coords_index][self._valid_row_idx] = new @property @@ -968,7 +1060,7 @@ def f_jacobian_coords(self): @f_jacobian_coords.setter def f_jacobian_coords(self, new): - assert isinstance(new, np.ndarray) + assert isinstance(new, _ARRAY_TYPES) if isinstance(self.f_jacobian_coords_index, list): self.markers[ xp.ix_( @@ -981,9 +1073,65 @@ def f_jacobian_coords(self, new): @property def args_markers(self) -> MarkerArguments: - """Collection of mandatory arguments for pusher kernels.""" + """Collection of mandatory arguments for pusher kernels. + + Note + ---- + Under ``ARRAY_BACKEND=cupy`` the arrays inside are the *host mirror* + of the device-resident markers, not the markers themselves. They are + only valid inside a :meth:`host_markers` block — outside one they + hold whatever the last such block left behind. Every compiled + (Pyccel) kernel call that takes ``args_markers`` must therefore be + wrapped; CUDA kernels take :attr:`markers` directly instead and need + no wrapping. + """ return self._args_markers + @contextmanager + def host_markers(self, *, write: bool = True): + """Make :attr:`args_markers` valid for a compiled, host-only (Pyccel) + kernel call, and write any changes back to the device afterwards. + + Under the NumPy backend this is a no-op: ``args_markers`` already + aliases the marker array, so there is nothing to copy either way. + + Under CuPy the markers live on the device (that is the whole point + -- the ported CUDA kernels and the vectorized bookkeeping never + transfer them), so a kernel that can only run on the host needs an + explicit crossing. Entering copies device->host; leaving copies + host->device, unless ``write=False`` marks the call read-only (the + common case for accumulation/evaluation kernels, which only ever + read markers and write into grid arrays), in which case the + write-back is skipped. + + Nested blocks are reference-counted, so the transfer happens once + per outermost block. A nested ``write=True`` upgrades the outer + block, never the reverse. + + Parameters + ---------- + write : bool + Whether the wrapped kernel may modify the marker array. Passing + ``False`` when it actually does modify markers silently discards + those changes, so only use it for kernels verified read-only. + """ + if self._markers_mirror is None: + yield self._args_markers + return + + mirror = self._markers_mirror + if mirror._depth == 0: + mirror.pull() + self._markers_mirror_write = False + mirror._depth += 1 + self._markers_mirror_write = self._markers_mirror_write or write + try: + yield self._args_markers + finally: + mirror._depth -= 1 + if mirror._depth == 0 and self._markers_mirror_write: + mirror.push() + # ------------------------------------------- # Initial condition and background -> weights # ------------------------------------------- @@ -1228,9 +1376,9 @@ def draw_markers( self._load_tesselation() if isinstance(self, ParticlesSPH): self._set_initial_condition() - self.velocities = _to_numpy_for_kernel(self.u_init(_dev(self.positions))).T + self.velocities = self.u_init(self.positions).T # set markers ID in last column - self.marker_ids = _first_marker_id + np.arange(n_mks_load_loc, dtype=float) + self.marker_ids = _first_marker_id + xp.arange(n_mks_load_loc, dtype=float) else: logger.debug("\nLoading fresh markers:") for key, val in self.loading_params.__dict__.items(): @@ -1255,8 +1403,8 @@ def draw_markers( temp = np.random.rand(num_to_add_glob, 3 + self.vdim) # check which particles are on the current process domain is_on_proc_domain = np.logical_and( - temp[:, :3] > self.domain_array[self.mpi_rank, 0::3], - temp[:, :3] < self.domain_array[self.mpi_rank, 1::3], + temp[:, :3] > self._domain_array[self.mpi_rank, 0::3], + temp[:, :3] < self._domain_array[self.mpi_rank, 1::3], ) valid_idx = np.nonzero(np.all(is_on_proc_domain, axis=1))[0] valid_particles = temp[valid_idx] @@ -1267,7 +1415,7 @@ def draw_markers( self._markers[ num_loaded_particles_loc : num_loaded_particles_loc + num_valid, : 3 + self.vdim, - ] = valid_particles + ] = xp.asarray(valid_particles) num_loaded_particles_glob += num_to_add_glob num_loaded_particles_loc += num_valid @@ -1303,10 +1451,12 @@ def draw_markers( 1000 + (n_mks_load_cum_sum - self.n_mks_load)[self._mpi_rank] // 64, ) - sampling_kernels.set_particles_symmetric_3d_3v( - temp_markers, - self.markers, - ) + # compiled host-only sampler; fills marker rows in place + with self.host_markers(write=True) as args_markers: + sampling_kernels.set_particles_symmetric_3d_3v( + _to_numpy_for_kernel(temp_markers), + args_markers.markers, + ) # 4. Wrong specification else: @@ -1317,25 +1467,16 @@ def draw_markers( # initial velocities - SPH case: v(0) = u(x(0)) for given velocity u(x) if isinstance(self, ParticlesSPH): self._set_initial_condition() - self.velocities = _to_numpy_for_kernel(self.u_init(_dev(self.positions))).T + self.velocities = self.u_init(self.positions).T else: # inverse transform sampling in velocity space # Avoid exact 0 or 1 from low-discrepancy sequences: erfinv(±1) # and log(0) produce infinities or invalid polar velocities. - # - # self._markers is always host-resident (see - # ISSUE_cupy_particles_never_pushed.md), so every xp. call - # below that reads from it must first convert via _dev(), and - # every result written back into it must convert back via - # _to_numpy_for_kernel() -- mirrors the ParticlesSPH branch above, - # which already needed the same treatment. eps = xp.finfo(float).eps - self._markers[:n_mks_load_loc, 3 : 3 + self.vdim] = _to_numpy_for_kernel( - xp.clip( - _dev(self._markers[:n_mks_load_loc, 3 : 3 + self.vdim]), - eps, - 1.0 - eps, - ) + self._markers[:n_mks_load_loc, 3 : 3 + self.vdim] = xp.clip( + self._markers[:n_mks_load_loc, 3 : 3 + self.vdim], + eps, + 1.0 - eps, ) u_mean = xp.array(self.loading_params.moments[: self.vdim]) @@ -1344,35 +1485,26 @@ def draw_markers( # Particles6D: (1d Maxwellian, 1d Maxwellian, 1d Maxwellian) if isinstance(self, Particles6D): - # sp is plain scipy.special (host-only, unlike xp), so - # erfinv itself runs on the already-host self.velocities; - # only its result needs converting before mixing with the - # device-resident v_th/u_mean. - self.velocities = _to_numpy_for_kernel( - _dev( - sp.erfinv( - 2 * self.velocities - 1, - ) - ) + # sp is plain scipy.special: host-only, so it needs a host + # copy of the velocities and its result converted back to the + # active backend before mixing with v_th/u_mean. + self.velocities = ( + _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities) - 1)) * xp.sqrt(2) * v_th + u_mean ) # Particles5D: (1d Maxwellian, muB0-Maxwellian as volume-form) elif isinstance(self, Particles5D): - self._markers[:n_mks_load_loc, 3] = _to_numpy_for_kernel( - _dev( - sp.erfinv( - 2 * self.velocities[:, 0] - 1, - ) - ) + self._markers[:n_mks_load_loc, 3] = ( + _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities[:, 0]) - 1)) * xp.sqrt(2) * v_th[0] + u_mean[0] ) - self._markers[:n_mks_load_loc, 4] = _to_numpy_for_kernel( - -xp.log(1.0 - _dev(self.velocities[:, 1])) * v_th[1] ** 2 / B0 + self._markers[:n_mks_load_loc, 4] = ( + -xp.log(1.0 - self.velocities[:, 1]) * v_th[1] ** 2 / B0 ) # mu is a magnetic moment and must be >= 0. @@ -1384,23 +1516,15 @@ def draw_markers( ) # Particles5Dvperp: (1d Maxwellian, polar Maxwellian as volume-form) elif isinstance(self, Particles5Dvperp): - self._markers[:n_mks_load_loc, 3] = _to_numpy_for_kernel( - _dev( - sp.erfinv( - 2 * self.velocities[:, 0] - 1, - ) - ) + self._markers[:n_mks_load_loc, 3] = ( + _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities[:, 0]) - 1)) * xp.sqrt(2) * v_th[0] + u_mean[0] ) - self._markers[:n_mks_load_loc, 4] = _to_numpy_for_kernel( - xp.sqrt( - -xp.log(1.0 - _dev(self.velocities[:, 1])), - ) - * xp.sqrt(2) - * v_th[1] + self._markers[:n_mks_load_loc, 4] = ( + xp.sqrt(-xp.log(1.0 - self.velocities[:, 1])) * xp.sqrt(2) * v_th[1] ) # v_perp is a polar velocity coordinate and must be >= 0. @@ -1419,15 +1543,13 @@ def draw_markers( # inversion method for drawing uniformly on the disc if self.spatial == "disc": - self._markers[:n_mks_load_loc, 0] = _to_numpy_for_kernel( - xp.sqrt( - _dev(self._markers[:n_mks_load_loc, 0]), - ) + self._markers[:n_mks_load_loc, 0] = xp.sqrt( + self._markers[:n_mks_load_loc, 0], ) else: assert self.spatial == "uniform", f'Spatial drawing must be "uniform" or "disc", is {self.spatial}.' - self.marker_ids = _first_marker_id + np.arange(n_mks_load_loc, dtype=float) + self.marker_ids = _first_marker_id + xp.arange(n_mks_load_loc, dtype=float) # set specific initial condition for some particles if self.loading_params.specific_markers is not None: @@ -1794,8 +1916,8 @@ def mpi_sort_markers( if do_test: all_on_right_proc = xp.all( xp.logical_and( - self.positions > self.domain_array[self.mpi_rank, 0::3], - self.positions < self.domain_array[self.mpi_rank, 1::3], + self.positions > self.domain_array_dev[self.mpi_rank, 0::3], + self.positions < self.domain_array_dev[self.mpi_rank, 1::3], ), ) @@ -1880,13 +2002,15 @@ def apply_kinetic_bc(self, newton=False): for axis in self._reflect_axes: if len(outside_inds_per_axis[axis]) == 0: continue - # flip velocity - reflect( - self.markers, - self.domain.args_domain, - outside_inds_per_axis[axis], - axis, - ) + # flip velocity. reflect() is a compiled host-only Pyccel kernel + # that writes markers in place, so it needs the host mirror. + with self.host_markers(write=True) as args_markers: + reflect( + args_markers.markers, + self.domain.args_domain, + _to_numpy_for_kernel(outside_inds_per_axis[axis]), + axis, + ) def update_holes(self): """Recompute the :attr:`~struphy.pic.base.Particles.holes` mask (rows with ``markers[:, 0] == -1``) @@ -1894,7 +2018,6 @@ def update_holes(self): Must be called after any operation that creates, removes or moves markers (e.g. sorting, boundary conditions, refilling), since holes are tracked per row index.""" self._holes[:] = self.markers[:, 0] == -1.0 - self._holes_ghost_dev_dirty = True self._update_valid_mks() def set_velocities_comp(self, velocity, comp): @@ -1988,14 +2111,16 @@ def do_sort(self, use_numpy_argsort=None): if use_numpy_argsort: self._sort_boxed_particles_numpy() else: - sort_boxed_particles( - self._markers, - self._sorting_boxes._swap_line_1, - self._sorting_boxes._swap_line_2, - nboxes + 1, - self._sorting_boxes._next_index, - self._sorting_boxes._cumul_next_index, - ) + # compiled host-only cycle-sort; reorders marker rows in place + with self.host_markers(write=True) as args_markers: + sort_boxed_particles( + args_markers.markers, + self._sorting_boxes._swap_line_1, + self._sorting_boxes._swap_line_2, + nboxes + 1, + self._sorting_boxes._next_index, + self._sorting_boxes._cumul_next_index, + ) # The marker rows have just been reordered. The masks are row-based, # so they must be rebuilt before any later use of valid_mks/f_coords. @@ -2119,23 +2244,25 @@ def eval_velocity( func = PyccelKernel(eval_kernels_sph.sph_mean_velocity_coeffs) - func( - alpha=xp.array((0.0, 0.0, 0.0)), - column_nr=first_free_idx, - comps=comps, - args_markers=self.args_markers, - args_domain=self.domain.args_domain, - boxes=self.sorting_boxes.boxes, - neighbours=self.sorting_boxes.neighbours, - holes=self.holes, - periodic1=self.boundary_params.bc_sph[0] == "periodic", - periodic2=self.boundary_params.bc_sph[1] == "periodic", - periodic3=self.boundary_params.bc_sph[2] == "periodic", - kernel_type=self.ker_dct()[kernel_type], - h1=h1, - h2=h2, - h3=h3, - ) + # compiled host-only SPH kernel; writes a marker column in place + with self.host_markers(write=True) as _args_markers: + func( + alpha=xp.array((0.0, 0.0, 0.0)), + column_nr=first_free_idx, + comps=comps, + args_markers=_args_markers, + args_domain=self.domain.args_domain, + boxes=self.sorting_boxes.boxes, + neighbours=self.sorting_boxes.neighbours, + holes=self.holes, + periodic1=self.boundary_params.bc_sph[0] == "periodic", + periodic2=self.boundary_params.bc_sph[1] == "periodic", + periodic3=self.boundary_params.bc_sph[2] == "periodic", + kernel_type=self.ker_dct()[kernel_type], + h1=h1, + h2=h2, + h3=h3, + ) v1 = self._eval_sph( eta1, @@ -2231,23 +2358,25 @@ def eval_div_viscosity( # 1st kernel func = PyccelKernel(eval_kernels_sph.sph_mean_velocity_coeffs) comps = xp.array((0, 1, 2)) - func( - alpha=xp.array((0.0, 0.0, 0.0)), - column_nr=first_free_idx, - comps=comps, - args_markers=self.args_markers, - args_domain=self.domain.args_domain, - boxes=self.sorting_boxes.boxes, - neighbours=self.sorting_boxes.neighbours, - holes=self.holes, - periodic1=self.boundary_params.bc_sph[0] == "periodic", - periodic2=self.boundary_params.bc_sph[1] == "periodic", - periodic3=self.boundary_params.bc_sph[2] == "periodic", - kernel_type=self.ker_dct()[kernel_type], - h1=h1, - h2=h2, - h3=h3, - ) + # compiled host-only SPH kernel; writes a marker column in place + with self.host_markers(write=True) as _args_markers: + func( + alpha=xp.array((0.0, 0.0, 0.0)), + column_nr=first_free_idx, + comps=comps, + args_markers=_args_markers, + args_domain=self.domain.args_domain, + boxes=self.sorting_boxes.boxes, + neighbours=self.sorting_boxes.neighbours, + holes=self.holes, + periodic1=self.boundary_params.bc_sph[0] == "periodic", + periodic2=self.boundary_params.bc_sph[1] == "periodic", + periodic3=self.boundary_params.bc_sph[2] == "periodic", + kernel_type=self.ker_dct()[kernel_type], + h1=h1, + h2=h2, + h3=h3, + ) # 2nd kernel func = PyccelKernel(eval_kernels_sph.sph_viscosity_tensor) @@ -2524,39 +2653,48 @@ def _allocate_marker_array(self, dry_run: bool = False): if dry_run: return - self._markers = _pinned_zeros((self.n_rows, self.n_cols), dtype=float) + # The marker array and every array indexed by marker row live on the + # active backend: on the device under CuPy, where the ported CUDA + # particle kernels and all the vectorized bookkeeping below (boundary + # conditions, hole/ghost tracking, sorting) operate on them directly, + # with no per-call host<->device transfer. The remaining host-only + # Pyccel kernels reach them through the explicit host mirror + # (see :class:`_HostMarkerMirror` and :meth:`host_markers`). + self._markers = xp.zeros((self.n_rows, self.n_cols), dtype=float) - # allocate auxiliary arrays (host-resident: read/written by compiled, - # host-only Pyccel kernels via args_markers, see _to_numpy_for_kernel) - self._holes = np.zeros(self.n_rows, dtype=bool) - self._ghost_particles = np.zeros(self.n_rows, dtype=bool) - self._valid_mks = np.zeros(self.n_rows, dtype=bool) + self._holes = xp.zeros(self.n_rows, dtype=bool) + self._ghost_particles = xp.zeros(self.n_rows, dtype=bool) + self._valid_mks = xp.zeros(self.n_rows, dtype=bool) # _is_outside_right/_is_outside_left/_is_outside are views into one - # buffer so that _find_outside_particles_gpu can fill all three with - # a single device->host transfer instead of three. - self._is_outside_buf = np.zeros((3, self.n_rows), dtype=bool) + # buffer, so the three masks stay contiguous for the combined + # comparison in _find_outside_particles. + self._is_outside_buf = xp.zeros((3, self.n_rows), dtype=bool) self._is_outside_right = self._is_outside_buf[0] self._is_outside_left = self._is_outside_buf[1] self._is_outside = self._is_outside_buf[2] - # device-resident copies of _holes/_ghost_particles used by - # _find_outside_particles_gpu; re-synced lazily, only when stale - # (see the dirty flag set in update_holes()/_update_ghost_particles(), - # the only two places that mutate the host arrays in place). - self._holes_dev = None - self._ghost_dev = None - self._holes_ghost_dev_dirty = True - self._is_outside_buf_dev = None - # create array container (3 x positions, vdim x velocities, weight, s0, w0, ID) for removed markers self._n_lost_markers = 0 - self._lost_markers = np.zeros((int(self.n_rows * 0.5), 10), dtype=float) + self._lost_markers = xp.zeros((int(self.n_rows * 0.5), 10), dtype=float) + + # Host mirror for the compiled, host-only Pyccel kernels. Under the + # NumPy backend there is nothing to mirror -- markers already are a + # host array -- so args_markers keeps aliasing it directly and + # host_markers() is a no-op. + if xp.cupy_backend: + self._markers_mirror = _HostMarkerMirror() + markers_for_kernels = self._markers_mirror.add(self._markers) + valid_mks_for_kernels = self._markers_mirror.add(self._valid_mks) + else: + self._markers_mirror = None + markers_for_kernels = self._markers + valid_mks_for_kernels = self._valid_mks # arguments for kernels self._args_markers = MarkerArguments( - _to_numpy_for_kernel(self.markers), - _to_numpy_for_kernel(self.valid_mks), + markers_for_kernels, + valid_mks_for_kernels, _to_numpy_for_kernel(self.Np), _to_numpy_for_kernel(self.vdim), _to_numpy_for_kernel(self.index["weights"]), @@ -2843,7 +2981,7 @@ def _load_external( dtype=float, ) self._mpi_comm.Recv(recvbuf, source=0, tag=123) - self._markers[:n_mks_load_loc, :] = recvbuf + self._markers[:n_mks_load_loc, :] = xp.asarray(recvbuf) def _load_restart(self): """Load markers from restart .hdf5 file.""" @@ -2862,7 +3000,7 @@ def _load_restart(self): data = DataContainer(data_path, comm=self.mpi_comm) with h5py.File(data.file_path, "a") as file: - self._markers[:, :] = file["restart/" + self.loading_params.restart_key][-1, :, :] + self._markers[:, :] = xp.asarray(file["restart/" + self.loading_params.restart_key][-1, :, :]) def _load_tesselation(self, n_quad: int = 1): """ @@ -2881,9 +3019,9 @@ def _load_tesselation(self, n_quad: int = 1): ) eta1, eta2, eta3 = self.tesselation.draw_markers() eta1, eta2, eta3 = _to_numpy_for_kernel(eta1), _to_numpy_for_kernel(eta2), _to_numpy_for_kernel(eta3) - self._markers[: eta1.size, 0] = eta1 - self._markers[: eta2.size, 1] = eta2 - self._markers[: eta3.size, 2] = eta3 + self._markers[: eta1.size, 0] = xp.asarray(eta1) + self._markers[: eta2.size, 1] = xp.asarray(eta2) + self._markers[: eta3.size, 2] = xp.asarray(eta3) self._update_valid_mks() def _reset_marker_ids(self): @@ -2911,73 +3049,25 @@ def _find_outside_particles(self, axis): outside_inds : numpy.ndarray[int] Row indices of the markers that are outside the logical unit cube. """ - if xp.cupy_backend: - return self._find_outside_particles_gpu(axis) - - # determine particles outside of the logical unit cube - self._is_outside_right[:] = self.markers[:, axis] > 1.0 - self._is_outside_left[:] = self.markers[:, axis] < 0.0 - - self._is_outside_right[self.holes] = False - self._is_outside_right[self.ghost_particles] = False - self._is_outside_left[self.holes] = False - self._is_outside_left[self.ghost_particles] = False - - self._is_outside[:] = np.logical_or( + # Runs on whichever backend the markers live on -- device under + # CuPy, with no transfer: markers, the holes/ghost masks and the + # _is_outside_* views are all allocated with xp (see + # _allocate_marker_array). + col = self.markers[:, axis] + not_hole_or_ghost = ~(self.holes | self.ghost_particles) + + xp.greater(col, 1.0, out=self._is_outside_right) + self._is_outside_right &= not_hole_or_ghost + xp.less(col, 0.0, out=self._is_outside_left) + self._is_outside_left &= not_hole_or_ghost + xp.logical_or( self._is_outside_right, self._is_outside_left, + out=self._is_outside, ) # indices or particles that are outside of the logical unit cube - outside_inds = np.nonzero(self._is_outside)[0] - - return outside_inds - - def _find_outside_particles_gpu(self, axis): - """Device version of :meth:`_find_outside_particles`. - - ``self.markers[:, axis]`` is a single column out of ``n_cols``, so - reading it is a heavily strided gather; the reference (CPU) version - pays that cost twice (once each for the ``>`` and ``<`` comparison). - Reading it once into a device array and doing both comparisons plus - the hole/ghost masking there is measurably faster end-to-end even - after paying for the host<->device copies, because ``self._markers`` - is pinned memory (see :func:`_pinned_zeros`) — the transfers alone - run at a few hundred MiB, not tens of ms. - - ``holes``/``ghost_particles`` are re-transferred only when stale - (see ``_holes_ghost_dev_dirty``), since they are unchanged across - the several axes checked per :meth:`apply_kinetic_bc` call and are - only ever updated in place by :meth:`update_holes`/ - :meth:`_update_ghost_particles`. - """ - import cupy as cp - - col_dev = cp.asarray(self.markers[:, axis]) - - if self._holes_ghost_dev_dirty or self._holes_dev is None: - self._holes_dev = cp.asarray(self.holes) - self._ghost_dev = cp.asarray(self.ghost_particles) - self._holes_ghost_dev_dirty = False - holes_dev = self._holes_dev - ghost_dev = self._ghost_dev - - if self._is_outside_buf_dev is None: - self._is_outside_buf_dev = cp.empty_like(cp.asarray(self._is_outside_buf)) - buf_dev = self._is_outside_buf_dev - - not_hole_or_ghost = ~(holes_dev | ghost_dev) - cp.greater(col_dev, 1.0, out=buf_dev[0]) - buf_dev[0] &= not_hole_or_ghost - cp.less(col_dev, 0.0, out=buf_dev[1]) - buf_dev[1] &= not_hole_or_ghost - cp.logical_or(buf_dev[0], buf_dev[1], out=buf_dev[2]) - - # single D2H transfer for all three (is_outside_right/left/is_outside - # are views into self._is_outside_buf, see _allocate_marker_array) - buf_dev.get(out=self._is_outside_buf) - - outside_inds = np.nonzero(self._is_outside)[0] + outside_inds = xp.nonzero(self._is_outside)[0] return outside_inds @@ -3178,7 +3268,6 @@ def _update_ghost_particles(self): :meth:`_prepare_ghost_particles`/:meth:`_sendrecv_markers_boxes` for SPH ghost-box particles received from a neighbouring process.""" self._ghost_particles[:] = self.markers[:, -1] == -2.0 - self._holes_ghost_dev_dirty = True self._update_valid_mks() def _remove_ghost_particles(self): @@ -4531,16 +4620,14 @@ def _sendrecv_determine_mtbs( sorting_etas : array[float] Eta-values of shape (n_send, :) according to which the sorting is performed. """ - # position that determines the sorting (including periodic shift of boundary conditions) - # host throughout: self.markers is always host-resident (see - # ISSUE_cupy_particles_never_pushed.md), and alpha is a 3-element - # weighting, not physics data. - if not isinstance(alpha, np.ndarray): - alpha = np.array(alpha, dtype=float) + # position that determines the sorting (including periodic shift of boundary conditions). + # Runs on the backend the markers live on; alpha is a 3-element + # weighting, not physics data, so it is converted to match. + alpha = xp.asarray(alpha, dtype=float) assert alpha.size == 3 - assert np.all(alpha >= 0.0) and np.all(alpha <= 1.0) + assert xp.all(alpha >= 0.0) and xp.all(alpha <= 1.0) bi = self.first_pusher_idx - np.mod( + xp.mod( alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + (1.0 - alpha) * self.markers[:, bi : bi + 3], 1.0, @@ -4548,22 +4635,22 @@ def _sendrecv_determine_mtbs( ) # check which particles are on the current process domain - self._is_on_proc_domain = np.logical_and( - self._sorting_etas > self.domain_array[self.mpi_rank, 0::3], - self._sorting_etas < self.domain_array[self.mpi_rank, 1::3], + self._is_on_proc_domain = xp.logical_and( + self._sorting_etas > self.domain_array_dev[self.mpi_rank, 0::3], + self._sorting_etas < self.domain_array_dev[self.mpi_rank, 1::3], ) # to stay on the current process, all three columns must be True - self._can_stay = np.all(self._is_on_proc_domain, axis=1) + self._can_stay = xp.all(self._is_on_proc_domain, axis=1) # holes and ghosts can stay, too self._can_stay[self.holes] = True self._can_stay[self.ghost_particles] = True # True values can stay on the process, False must be sent, already empty rows (-1) cannot be sent - send_inds = np.nonzero(~self._can_stay)[0] + send_inds = xp.nonzero(~self._can_stay)[0] - hole_inds_after_send = np.nonzero(np.logical_or(~self._can_stay, self.holes))[0] + hole_inds_after_send = xp.nonzero(xp.logical_or(~self._can_stay, self.holes))[0] return hole_inds_after_send, send_inds @@ -4585,16 +4672,18 @@ def _sendrecv_get_destinations(self, send_inds): send_info = np.zeros(self.mpi_size, dtype=int) # TODO: do not loop over all processes, start with neighbours and work outwards (using while) + etas_to_send = self._sorting_etas[send_inds] for i in range(self.mpi_size): - conds = np.logical_and( - self._sorting_etas[send_inds] > self.domain_array[i, 0::3], - self._sorting_etas[send_inds] < self.domain_array[i, 1::3], + conds = xp.logical_and( + etas_to_send > self.domain_array_dev[i, 0::3], + etas_to_send < self.domain_array_dev[i, 1::3], ) - self._send_to_i[i] = np.nonzero(np.all(conds, axis=1))[0] + self._send_to_i[i] = xp.nonzero(xp.all(conds, axis=1))[0] send_info[i] = self._send_to_i[i].size - self._send_list[i] = self.markers[send_inds][self._send_to_i[i]] + # mpi4py needs host buffers for the actual Isend below + self._send_list[i] = _to_numpy_for_kernel(self.markers[send_inds][self._send_to_i[i]]) return send_info diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index bed17394d..68d852b16 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -608,26 +608,6 @@ def __init__( self._gpu_gc_bstar_e_field = (e_field_1, e_field_2, e_field_3) self._gpu_gc_bstar_mu_idx = int(particles.mu_idx) - @staticmethod - def _reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim): - """Device version of the per-step marker buffer bookkeeping at the top - of :meth:`__call__` (save initial phase-space coordinates, zero the - boundary-shift columns, zero the residual/scratch columns). - - ``markers`` is a pinned-memory-backed host array (see - :func:`struphy.pic.base._pinned_zeros`), so a full round trip through - the device is fast (~5 ms for the 137 MiB ``PressureLessSPH`` marker - array) and cheaper than the equivalent strided in-place NumPy writes - (~35 ms), which touch three disjoint, non-contiguous column ranges. - """ - import cupy as cp - - dev = cp.asarray(markers) - dev[:, init_slice] = dev[:, : 3 + vdim] - dev[:, shift_slice] = 0.0 - dev[:, residual_idx:-2] = 0.0 - dev.get(out=markers) - @profile def __call__(self, dt: float): """ @@ -712,17 +692,16 @@ def _push(self, dt: float): init_slice = slice(first_pusher_idx, first_shift_idx) shift_slice = slice(first_shift_idx, residual_idx) - if cunumpy.cupy_backend: - self._reset_marker_buffers_gpu(markers, init_slice, shift_slice, residual_idx, vdim) - else: - # save initial phase space coordinates - markers[:, init_slice] = markers[:, : 3 + vdim] + # Runs in place on whichever backend the markers live on -- device + # under CuPy, with no transfer (see Particles._allocate_marker_array). + # save initial phase space coordinates + markers[:, init_slice] = markers[:, : 3 + vdim] - # set boundary shifts to zero - markers[:, shift_slice] = 0.0 + # set boundary shifts to zero + markers[:, shift_slice] = 0.0 - # clear buffer columns starting from residual index, dont clear ID (last column) and loc_box - markers[:, residual_idx:-2] = 0.0 + # clear buffer columns starting from residual index, dont clear ID (last column) and loc_box + markers[:, residual_idx:-2] = 0.0 rank = self.particles.mpi_rank logger.debug(f"rank {rank}: starting {self.kernel} ...") @@ -734,12 +713,16 @@ def _push(self, dt: float): comps = ker_args[2] add_args = ker_args[3] - with ProfileManager.profile_region(self._kernel_region(ker)): + # compiled host-only kernel: writes into marker buffer columns + with ( + ProfileManager.profile_region(self._kernel_region(ker)), + self.particles.host_markers(write=True) as args_markers, + ): ker( np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), column_nr, comps, - self.particles.args_markers, + args_markers, self._args_domain, *add_args, ) @@ -782,12 +765,16 @@ def _push(self, dt: float): ) # evaluate - with ProfileManager.profile_region(self._kernel_region(ker)): + # compiled host-only kernel: writes into marker buffer columns + with ( + ProfileManager.profile_region(self._kernel_region(ker)), + self.particles.host_markers(write=True) as args_markers, + ): ker( alpha, column_nr, comps, - self.particles.args_markers, + args_markers, self._args_domain, *add_args, ) @@ -1101,11 +1088,16 @@ def _push(self, dt: float): dt, ) else: - with ProfileManager.profile_region("kernel: " + self.kernel.name): + # no CUDA port for this kernel: fall back to the compiled + # host-only one, which pushes markers in place + with ( + ProfileManager.profile_region("kernel: " + self.kernel.name), + self.particles.host_markers(write=True) as args_markers, + ): self.kernel( dt, stage, - self.particles.args_markers, + args_markers, self._args_domain, *self._args_kernel, ) diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index 1730762e9..9d84025b1 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -125,7 +125,7 @@ def push_eta_stage_cuboid_gpu( kernel = _get_kernel() n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads kernel( @@ -145,7 +145,6 @@ def push_eta_stage_cuboid_gpu( np.float64(last), ), ) - dev.get(out=markers) _PUSH_ETA_RK_PERIODIC_SRC = r""" @@ -251,7 +250,7 @@ def push_eta_rk_periodic_gpu( threads = 256 blocks = (n_markers + threads - 1) // threads - dev = cp.asarray(markers) + dev = markers # reset: save initial phase-space coords, zero shift/free/residual columns # (matches Pusher._reset_marker_buffers_gpu, done once instead of per stage) @@ -279,7 +278,6 @@ def push_eta_rk_periodic_gpu( ), ) - dev.get(out=markers) _PUSH_V_EFIELD_CUBOID_SRC = r""" @@ -480,7 +478,7 @@ def push_v_with_efield_cuboid_gpu( kernel = _get_v_efield_kernel() n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads kernel( @@ -517,7 +515,6 @@ def push_v_with_efield_cuboid_gpu( np.float64(dt_const), ), ) - dev.get(out=markers) # ============================================================================ @@ -2068,7 +2065,7 @@ def push_eta_stage_general_gpu( kernel = _get_eta_general_kernel() n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads kernel( @@ -2087,7 +2084,6 @@ def push_eta_stage_general_gpu( np.float64(last), ), ) - dev.get(out=markers) def push_v_with_efield_general_gpu( @@ -2118,7 +2114,7 @@ def push_v_with_efield_general_gpu( kernel = _get_v_efield_general_kernel() n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads kernel( @@ -2154,7 +2150,6 @@ def push_v_with_efield_general_gpu( np.float64(dt_const), ), ) - dev.get(out=markers) _push_vxb_analytic_general_kernel = None @@ -2200,7 +2195,7 @@ def _launch_vxb_general( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads kernel( @@ -2237,7 +2232,6 @@ def _launch_vxb_general( np.float64(dt), ), ) - dev.get(out=markers) def push_vxb_analytic_general_gpu( @@ -2376,7 +2370,7 @@ def _launch_bxu_general( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads kernel( @@ -2422,7 +2416,6 @@ def _launch_bxu_general( np.float64(dt), ), ) - dev.get(out=markers) def push_bxu_Hdiv_general_gpu( @@ -2613,7 +2606,7 @@ def push_pc_GXu_full_general_gpu( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads g = (g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev, g31_dev, g32_dev, g33_dev) @@ -2648,7 +2641,6 @@ def push_pc_GXu_full_general_gpu( np.float64(dt), ), ) - dev.get(out=markers) def push_pc_GXu_general_gpu( @@ -2677,7 +2669,7 @@ def push_pc_GXu_general_gpu( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads g = (g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev) @@ -2712,7 +2704,6 @@ def push_pc_GXu_general_gpu( np.float64(dt), ), ) - dev.get(out=markers) _push_pc_eta_hcurl_general_kernel = None @@ -2772,7 +2763,7 @@ def _launch_pc_eta_general( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads kernel( @@ -2813,7 +2804,6 @@ def _launch_pc_eta_general( np.float64(last), ), ) - dev.get(out=markers) def push_pc_eta_stage_Hcurl_general_gpu( @@ -2999,7 +2989,7 @@ def push_weights_with_efield_lin_va_general_gpu( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers f0_dev = cp.ascontiguousarray(f0_values) threads = 256 blocks = (n_markers + threads - 1) // threads @@ -3039,7 +3029,6 @@ def push_weights_with_efield_lin_va_general_gpu( np.float64(dt), ), ) - dev.get(out=markers) _push_deterministic_diffusion_general_kernel = None @@ -3086,7 +3075,7 @@ def push_deterministic_diffusion_stage_general_gpu( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads _get_deterministic_diffusion_general_kernel()( @@ -3130,7 +3119,6 @@ def push_deterministic_diffusion_stage_general_gpu( np.float64(last), ), ) - dev.get(out=markers) # push_random_diffusion_stage does not touch geometry at all (a pure additive @@ -3182,7 +3170,7 @@ def push_random_diffusion_stage_gpu(markers, n_cols, noise, diffusion_coeff: flo import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers noise_dev = cp.asarray(np.ascontiguousarray(noise), dtype=cp.float64) scale = float(np.sqrt(2.0 * dt * diffusion_coeff)) threads = 256 @@ -3198,4 +3186,3 @@ def push_random_diffusion_stage_gpu(markers, n_cols, noise, diffusion_coeff: flo np.float64(scale), ), ) - dev.get(out=markers) diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index 553c787d7..df4155b64 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -287,7 +287,7 @@ def push_gc_bxEstar_explicit_multistage_general_gpu( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads @@ -315,7 +315,6 @@ def d(a): np.float64(dt_a), np.float64(dt_b), np.float64(last), ), ) - dev.get(out=markers) def push_gc_Bstar_explicit_multistage_general_gpu( @@ -338,7 +337,7 @@ def push_gc_Bstar_explicit_multistage_general_gpu( import numpy as np n_markers = markers.shape[0] - dev = cp.asarray(markers) + dev = markers threads = 256 blocks = (n_markers + threads - 1) // threads @@ -367,4 +366,3 @@ def d(a): np.float64(dt_a), np.float64(dt_b), np.float64(last), ), ) - dev.get(out=markers) From 96b87fe5c572918df889b4388568355c8f3524c4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 18:29:06 +0200 Subject: [PATCH 052/156] Cupy fixes, removed _to_numpy_for_kernel in a bunch of places --- src/struphy/models/base.py | 13 +- src/struphy/pic/base.py | 50 ++-- src/struphy/pic/particles.py | 240 +++++++++++--------- src/struphy/pic/tests/test_draw_parallel.py | 11 +- src/struphy/pic/tests/test_sph.py | 7 +- src/struphy/pic/tests/test_tesselation.py | 13 +- 6 files changed, 191 insertions(+), 143 deletions(-) diff --git a/src/struphy/models/base.py b/src/struphy/models/base.py index 4a6fd9992..4b0c2dbd3 100644 --- a/src/struphy/models/base.py +++ b/src/struphy/models/base.py @@ -425,16 +425,17 @@ def update_markers_to_be_saved(self): assert isinstance(obj, Particles) if var.n_to_save > 0: - # obj.markers/var.saved_markers are always host-resident (no - # device particle kernel exists), unlike the general xp used - # elsewhere in this module. - markers_on_proc = np.logical_and( + # The selection runs on whichever backend the markers live + # on (device under CuPy); var.saved_markers is the host + # buffer that gets written to HDF5, so the selected rows are + # brought across explicitly here. + markers_on_proc = xp.logical_and( obj.markers[:, -1] >= 0.0, obj.markers[:, -1] < var.n_to_save, ) - n_markers_on_proc = np.count_nonzero(markers_on_proc) + n_markers_on_proc = int(xp.count_nonzero(markers_on_proc)) var.saved_markers[:] = -1.0 - var.saved_markers[:n_markers_on_proc] = obj.markers[markers_on_proc] + var.saved_markers[:n_markers_on_proc] = xp.to_numpy(obj.markers[markers_on_proc]) @profile def update_distr_functions(self): diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 1b423d420..0c085f677 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1636,24 +1636,25 @@ def initialize_weights( # if isinstance(self.f_init, CanonicalMaxwellian): # self.save_constants_of_motion() - # evaluate initial distribution function + # evaluate initial distribution function. Marker-derived inputs + # and the field/background functions now agree on the backend + # (both follow xp), so no conversion is needed either way. if isinstance(self, ParticlesSPH): - f_init = _to_numpy_for_kernel(self.f_init(_dev(self.positions))) + f_init = self.f_init(self.positions) else: - f_init = _to_numpy_for_kernel(self.f_init(*_dev(*self.f_coords.T))) + f_init = self.f_init(*self.f_coords.T) # if f_init is vol-form, transform to 0-form if self.is_volume_form[0]: - f_init /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions))) + f_init = f_init / self.domain.jacobian_det(self.positions) if self.is_volume_form[1]: - f_init /= _to_numpy_for_kernel( - self.f_init.velocity_jacobian_det(*_dev(*self.f_jacobian_coords.T)), - ) + f_init = f_init / self.f_init.velocity_jacobian_det(*self.f_jacobian_coords.T) # compute s0 and save at vdim + 4 - self.sampling_density_values = _to_numpy_for_kernel( - self.s0(*_dev(*self.phasespace_coords.T), flat_eval=True), + self.sampling_density_values = self.s0( + *self.phasespace_coords.T, + flat_eval=True, ) # compute w0 and save at vdim + 5 @@ -1687,19 +1688,19 @@ def update_weights(self): return if isinstance(self, ParticlesSPH): - f0 = _to_numpy_for_kernel(self.f0.n0(_dev(self.positions))) + f0 = self.f0.n0(self.positions) else: # in case of CanonicalMaxwellian, evaluate constants_of_motion # if isinstance(self.f0, CanonicalMaxwellian): # self.save_constants_of_motion() - f0 = _to_numpy_for_kernel(self.f0(*_dev(*self.f_coords.T))) + f0 = self.f0(*self.f_coords.T) # if f_init is vol-form, transform to 0-form if self.is_volume_form[0]: - f0 /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions))) + f0 = f0 / self.domain.jacobian_det(self.positions) if self.is_volume_form[1]: - f0 /= _to_numpy_for_kernel(self.f0.velocity_jacobian_det(*_dev(*self.f_jacobian_coords.T))) + f0 = f0 / self.f0.velocity_jacobian_det(*self.f_jacobian_coords.T) self.weights = self.weights0 - f0 / self.sampling_density_values / self.Np @@ -1767,7 +1768,7 @@ def binning( elif quantity == "energy_tensor": multiplier = self.velocities[:, v_axis[0]] * self.velocities[:, v_axis[1]] elif quantity == "heat_flux": - velocity_norm2 = np.linalg.norm(self.velocities, axis=1) ** 2 + velocity_norm2 = xp.linalg.norm(self.velocities, axis=1) ** 2 multiplier = velocity_norm2 * self.velocities[:, v_axis[0]] # compute weights of histogram: @@ -1775,22 +1776,27 @@ def binning( _weights = self.weights * self.Np * multiplier if divide_by_jac: - _weights /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions), remove_outside=False)) + jac_det = self.domain.jacobian_det(self.positions, remove_outside=False) + _weights = _weights / jac_det # _weights /= self.velocity_jacobian_det(*self.phasespace_coords.T) - _weights0 /= _to_numpy_for_kernel(self.domain.jacobian_det(_dev(self.positions), remove_outside=False)) + _weights0 = _weights0 / jac_det # _weights0 /= self.velocity_jacobian_det(*self.phasespace_coords.T) + # numpy.histogramdd has no CuPy equivalent, so the binning itself is + # done on the host; the inputs are brought across explicitly here. + _binned_coords = _to_numpy_for_kernel(self.markers_wo_holes_and_ghost[:, slicing]) + f_slice = np.histogramdd( - self.markers_wo_holes_and_ghost[:, slicing], - bins=bin_edges, - weights=_weights0, + _binned_coords, + bins=[_to_numpy_for_kernel(be) for be in bin_edges], + weights=_to_numpy_for_kernel(_weights0), )[0] df_slice = np.histogramdd( - self.markers_wo_holes_and_ghost[:, slicing], - bins=bin_edges, - weights=_weights, + _binned_coords, + bins=[_to_numpy_for_kernel(be) for be in bin_edges], + weights=_to_numpy_for_kernel(_weights), )[0] f_slice /= self.Np * bin_vol diff --git a/src/struphy/pic/particles.py b/src/struphy/pic/particles.py index 0263d38e2..5cfab5216 100644 --- a/src/struphy/pic/particles.py +++ b/src/struphy/pic/particles.py @@ -137,17 +137,20 @@ def save_constants_of_motion(self): ) # eval guiding center phase space - utilities_kernels.eval_guiding_center_from_6d( - self.markers, - self._derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.equation_params.epsilon, - self._b2_h[0]._data, - self._b2_h[1]._data, - self._b2_h[2]._data, - self._absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_guiding_center_from_6d( + _args_markers.markers, + self._derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.equation_params.epsilon, + _to_numpy_for_kernel(self._b2_h[0]._data), + _to_numpy_for_kernel(self._b2_h[1]._data), + _to_numpy_for_kernel(self._b2_h[2]._data), + _to_numpy_for_kernel(self._absB0_h._data), + ) # apply domain inverse map to get logical guiding center positions # TODO: currently only possible with the geometry where its inverse map is defined. @@ -179,15 +182,18 @@ def save_constants_of_motion(self): if self.mpi_comm is not None: self.mpi_sort_markers(alpha=1) - utilities_kernels.eval_canonical_toroidal_moment_6d( - self.markers, - self._derham.args_derham, - self.first_diagnostics_idx, - self.equation_params.epsilon, - B0, - R0, - self._absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_canonical_toroidal_moment_6d( + _args_markers.markers, + self._derham.args_derham, + self.first_diagnostics_idx, + self.equation_params.epsilon, + B0, + R0, + _to_numpy_for_kernel(self._absB0_h._data), + ) # send back and clear buffer if self.mpi_comm is not None: @@ -418,13 +424,16 @@ def save_constants_of_motion(self): # idx and slice idx_can_momentum = self.first_diagnostics_idx + 1 - utilities_kernels.eval_energy_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_energy_5d( + _args_markers.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + _to_numpy_for_kernel(self.absB0_h._data), + ) # eval psi at etas a1 = self.equil.domain.params["a1"] @@ -434,17 +443,20 @@ def save_constants_of_motion(self): r = self.markers[~self.holes, 0] * (1 - a1) + a1 self.markers[~self.holes, idx_can_momentum] = self.equil.psi_r(r) - utilities_kernels.eval_canonical_toroidal_moment_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - idx_can_momentum, - self.equation_params.epsilon, - B0, - R0, - self.absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_canonical_toroidal_moment_5d( + _args_markers.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + idx_can_momentum, + self.equation_params.epsilon, + B0, + R0, + _to_numpy_for_kernel(self.absB0_h._data), + ) def save_magnetic_energy(self, PBb): r""" @@ -464,15 +476,18 @@ def save_magnetic_energy(self, PBb): # utilities_kernels is a Pyccel-compiled extension that requires # real numpy buffers; absB0_h/PBbt follow the active backend, so # under cupy their ._data needs converting first. - utilities_kernels.eval_magnetic_energy_PBb( - self.markers, - self.derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.mu_idx, - _to_numpy_for_kernel(self.absB0_h._data), - _to_numpy_for_kernel(PBbt._data), - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_magnetic_energy_PBb( + _args_markers.markers, + self.derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.mu_idx, + _to_numpy_for_kernel(self.absB0_h._data), + _to_numpy_for_kernel(PBbt._data), + ) def save_magnetic_background_energy(self): r""" @@ -480,14 +495,17 @@ def save_magnetic_background_energy(self): The result is stored in the energy diagnostics column (``self.first_diagnostics_idx``). """ - utilities_kernels.eval_magnetic_background_energy( - self.markers, - self.derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.mu_idx, - _to_numpy_for_kernel(self.absB0_h._data), - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_magnetic_background_energy( + _args_markers.markers, + self.derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.mu_idx, + _to_numpy_for_kernel(self.absB0_h._data), + ) class Particles5Dvperp(Particles): @@ -672,12 +690,15 @@ def draw_markers(self, sort: bool = True): super().draw_markers(sort=sort) # magnetic moment is an adiabatic invariant: evaluate once at draw time (diagnostics column 1) - utilities_kernels.eval_magnetic_moment_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self._absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_magnetic_moment_5d( + _args_markers.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + _to_numpy_for_kernel(self._absB0_h._data), + ) def save_constants_of_motion(self): """ @@ -696,13 +717,16 @@ def save_constants_of_motion(self): # idx and slice idx_can_momentum = self.first_diagnostics_idx + 2 - utilities_kernels.eval_energy_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_energy_5d( + _args_markers.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + _to_numpy_for_kernel(self.absB0_h._data), + ) # eval psi at etas a1 = self.equil.domain.params["a1"] @@ -712,17 +736,20 @@ def save_constants_of_motion(self): r = self.markers[~self.holes, 0] * (1 - a1) + a1 self.markers[~self.holes, idx_can_momentum] = self.equil.psi_r(r) - utilities_kernels.eval_canonical_toroidal_moment_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - idx_can_momentum, - self.equation_params.epsilon, - B0, - R0, - self.absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_canonical_toroidal_moment_5d( + _args_markers.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + idx_can_momentum, + self.equation_params.epsilon, + B0, + R0, + _to_numpy_for_kernel(self.absB0_h._data), + ) def save_magnetic_energy(self, PBb): r""" @@ -739,15 +766,18 @@ def save_magnetic_energy(self, PBb): PBbt = E0T.dot(PBb, out=self._tmp0) PBbt.update_ghost_regions() - utilities_kernels.eval_magnetic_energy_PBb( - self.markers, - self.derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - PBbt._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_magnetic_energy_PBb( + _args_markers.markers, + self.derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.mu_idx, + _to_numpy_for_kernel(self.absB0_h._data), + PBbt._data, + ) def save_magnetic_background_energy(self): r""" @@ -755,14 +785,17 @@ def save_magnetic_background_energy(self): The result is stored in the energy diagnostics column (``self.first_diagnostics_idx``). """ - utilities_kernels.eval_magnetic_background_energy( - self.markers, - self.derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_magnetic_background_energy( + _args_markers.markers, + self.derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.mu_idx, + _to_numpy_for_kernel(self.absB0_h._data), + ) def save_magnetic_moment(self): r""" @@ -770,12 +803,15 @@ def save_magnetic_moment(self): diagnostics column (``self.first_diagnostics_idx + 1``). """ - utilities_kernels.eval_magnetic_moment_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.absB0_h._data, - ) + # compiled host-only Pyccel kernel: writes a marker diagnostics + # column in place, so it needs the host mirror of the markers. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_magnetic_moment_5d( + _args_markers.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + _to_numpy_for_kernel(self.absB0_h._data), + ) class Particles3D(Particles): diff --git a/src/struphy/pic/tests/test_draw_parallel.py b/src/struphy/pic/tests/test_draw_parallel.py index 6ccb69ebc..8bee8c4f5 100644 --- a/src/struphy/pic/tests/test_draw_parallel.py +++ b/src/struphy/pic/tests/test_draw_parallel.py @@ -115,14 +115,15 @@ def test_draw(num_elements, degree, bcs, mapping, ppc=10): logger.info(f"Rank {rank} : {particles.n_mks_loc} {particles.markers.shape[0]}") # are all markers in the correct domain? - # particles.markers is always host (NumPy); derham.domain_array may be a - # device array under CuPy, so bring it to the host for this comparison. + # Markers follow the active backend; compare on the host so the rest of + # this test can use plain NumPy. domain_array_host = to_numpy(derham.domain_array) + markers_host = to_numpy(particles.markers) conds = np.logical_and( - particles.markers[:, :3] > domain_array_host[rank, 0::3], - particles.markers[:, :3] < domain_array_host[rank, 1::3], + markers_host[:, :3] > domain_array_host[rank, 0::3], + markers_host[:, :3] < domain_array_host[rank, 1::3], ) - holes = particles.markers[:, 0] == -1.0 + holes = markers_host[:, 0] == -1.0 stay = np.all(conds, axis=1) error_mks = particles.markers[np.logical_and(~stay, ~holes)] diff --git a/src/struphy/pic/tests/test_sph.py b/src/struphy/pic/tests/test_sph.py index 88b513e7d..8049a5de9 100644 --- a/src/struphy/pic/tests/test_sph.py +++ b/src/struphy/pic/tests/test_sph.py @@ -1892,10 +1892,13 @@ def u_xyz(x, y, z): particles.draw_markers(sort=False) if rank == 0: - # particles.ghost_particles is always host (NumPy). + # ghost_particles follows the active backend; this is a + # diagnostics-only log, so pull the indices to the host. import numpy as np - ghost_inds = np.where(particles.ghost_particles)[0] + from cunumpy import to_numpy + + ghost_inds = np.where(to_numpy(particles.ghost_particles))[0] logger.info(f"After do_sort: {len(ghost_inds)} ghosts") if len(ghost_inds) > 0: logger.info(f"First 10 ghost eta1: {particles.markers[ghost_inds[:10], 0]}") diff --git a/src/struphy/pic/tests/test_tesselation.py b/src/struphy/pic/tests/test_tesselation.py index 10e49b5d4..060fa2945 100644 --- a/src/struphy/pic/tests/test_tesselation.py +++ b/src/struphy/pic/tests/test_tesselation.py @@ -178,15 +178,16 @@ def test_cell_average(ppb, nx, ny, nz, n_quad, show_plot=False): plt.show() # test - # particles.weights is always host (NumPy), while f_init follows the - # active array backend, so bring f_init's result to the host before - # comparing (with plain NumPy, not xp, since both operands are host now). + # Marker data and f_init both follow the active array backend, so the + # comparison is done there and only the final scalar is brought to the + # host for the assert/log. import numpy as np from cunumpy import to_numpy - f_init_at_markers = to_numpy(particles.f_init(particles.positions)) - logger.info(f"\n{rank =}, {np.max(np.abs(particles.weights * particles.Np - f_init_at_markers)) =}") - assert np.max(np.abs(particles.weights * particles.Np - f_init_at_markers)) < 0.012 + f_init_at_markers = particles.f_init(particles.positions) + max_err = float(to_numpy(xp.max(xp.abs(particles.weights * particles.Np - f_init_at_markers)))) + logger.info(f"\n{rank =}, {max_err =}") + assert max_err < 0.012 if __name__ == "__main__": From 04014c775e8b7b30deeb074d00e2c793b0eea51e Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 18:56:07 +0200 Subject: [PATCH 053/156] Added pusher_utilities_kernels_cuda.py --- src/struphy/pic/base.py | 91 ++- src/struphy/pic/particles.py | 234 ++++-- .../pushing/pusher_utilities_kernels_cuda.py | 102 +++ src/struphy/pic/sorting_kernels_cuda.py | 14 +- src/struphy/pic/utilities_kernels_cuda.py | 676 ++++++++++++++++++ 5 files changed, 1019 insertions(+), 98 deletions(-) create mode 100644 src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py create mode 100644 src/struphy/pic/utilities_kernels_cuda.py diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 0c085f677..a2235a8e0 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -52,6 +52,7 @@ class Intracomm: from struphy.pic import sampling_kernels, sobol_seq from struphy.pic.pushing import eval_kernels_sph from struphy.pic.pushing.pusher_utilities_kernels import reflect +from struphy.pic.pushing.pusher_utilities_kernels_cuda import reflect_gpu from struphy.pic.sorting import SortingBoxes from struphy.pic.sorting_kernels import ( assign_box_to_each_particle, @@ -804,6 +805,15 @@ def domain_array(self): """ return self._domain_array + @property + def _reflect_params_dev(self): + """Domain mapping parameters on the device, cached for :func:`reflect_gpu`.""" + if getattr(self, "_reflect_params_dev_cache", None) is None: + self._reflect_params_dev_cache = xp.asarray( + np.asarray(self.domain.args_domain.params, dtype=float), + ) + return self._reflect_params_dev_cache + @property def domain_array_dev(self): """:attr:`domain_array` on the active backend. @@ -2008,15 +2018,27 @@ def apply_kinetic_bc(self, newton=False): for axis in self._reflect_axes: if len(outside_inds_per_axis[axis]) == 0: continue - # flip velocity. reflect() is a compiled host-only Pyccel kernel - # that writes markers in place, so it needs the host mirror. - with self.host_markers(write=True) as args_markers: - reflect( - args_markers.markers, - self.domain.args_domain, - _to_numpy_for_kernel(outside_inds_per_axis[axis]), + # flip velocity + from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS + + if xp.cupy_backend and self.domain.args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS: + reflect_gpu( + self.markers, + int(self.domain.args_domain.kind_map), + self._reflect_params_dev, + outside_inds_per_axis[axis], axis, ) + else: + # no CUDA port for this domain kind_map: fall back to the + # compiled host-only kernel via the marker host mirror. + with self.host_markers(write=True) as args_markers: + reflect( + args_markers.markers, + self.domain.args_domain, + _to_numpy_for_kernel(outside_inds_per_axis[axis]), + axis, + ) def update_holes(self): """Recompute the :attr:`~struphy.pic.base.Particles.holes` mask (rows with ``markers[:, 0] == -1``) @@ -2387,24 +2409,26 @@ def eval_div_viscosity( # 2nd kernel func = PyccelKernel(eval_kernels_sph.sph_viscosity_tensor) comps = xp.arange(9) - func( - alpha=xp.array((0.0, 0.0, 0.0)), - column_nr=first_free_idx + 3, - comps=comps, - args_markers=self.args_markers, - args_domain=self.domain.args_domain, - boxes=self.sorting_boxes.boxes, - neighbours=self.sorting_boxes.neighbours, - holes=self.holes, - periodic1=self.boundary_params.bc_sph[0] == "periodic", - periodic2=self.boundary_params.bc_sph[1] == "periodic", - periodic3=self.boundary_params.bc_sph[2] == "periodic", - kernel_type=self.ker_dct()[kernel_type], - h1=h1, - h2=h2, - h3=h3, - mu=mu, - ) + # compiled host-only SPH kernel; writes marker columns in place + with self.host_markers(write=True) as _args_markers: + func( + alpha=xp.array((0.0, 0.0, 0.0)), + column_nr=first_free_idx + 3, + comps=comps, + args_markers=_args_markers, + args_domain=self.domain.args_domain, + boxes=self.sorting_boxes.boxes, + neighbours=self.sorting_boxes.neighbours, + holes=self.holes, + periodic1=self.boundary_params.bc_sph[0] == "periodic", + periodic2=self.boundary_params.bc_sph[1] == "periodic", + periodic3=self.boundary_params.bc_sph[2] == "periodic", + kernel_type=self.ker_dct()[kernel_type], + h1=h1, + h2=h2, + h3=h3, + mu=mu, + ) # grid evaluation gamma = [] @@ -3218,18 +3242,18 @@ def _gyro_transfer(self, outside_inds): def _sort_boxed_particles_numpy(self): """Sort the particles by box using numpy.argsort. - ``_argsort_array`` must be a plain NumPy array, not an ``xp`` one: - ``self._markers`` is always host-resident (see - ``ISSUE_cupy_particles_never_pushed.md``), and NumPy fancy indexing - (``self._markers[self._argsort_array]``) rejects a CuPy index array - outright under the CuPy backend. + ``_argsort_array`` lives on the same backend as the markers, so both + the argsort and the subsequent gather run entirely on the device + under CuPy. """ sorting_axis = self._sorting_boxes.box_index if not hasattr(self, "_argsort_array"): - self._argsort_array = np.zeros(self.markers.shape[0], dtype=int) + self._argsort_array = xp.zeros(self.markers.shape[0], dtype=int) self._argsort_array[:] = self._markers[:, sorting_axis].argsort() + # gather into a temporary: an in-place fancy-index self-assignment is + # not safe (source and destination overlap). self._markers[:, :] = self._markers[self._argsort_array] def _check_and_assign_particles_to_boxes(self): @@ -3727,9 +3751,10 @@ def _determine_markers_in_box(self, list_boxes): for i in list_boxes: indices += list(self._sorting_boxes._boxes[i][self._sorting_boxes._boxes[i] != -1]) - # row indices into self.markers, which is always host-resident + # Box membership is host bookkeeping; the gathered rows are handed to + # the mpi4py box-communication path below, which needs host buffers. indices = np.array(indices, dtype=int) - markers_in_box = self.markers[indices] + markers_in_box = _to_numpy_for_kernel(self.markers[xp.asarray(indices)]) return markers_in_box def _get_destinations_box(self): diff --git a/src/struphy/pic/particles.py b/src/struphy/pic/particles.py index 5cfab5216..c273f1bfe 100644 --- a/src/struphy/pic/particles.py +++ b/src/struphy/pic/particles.py @@ -12,6 +12,15 @@ from struphy.kinetic_background.base import Maxwellian, SumKineticBackground from struphy.pic import utilities_kernels from struphy.pic.base import Particles, _to_numpy_for_kernel +from struphy.pic.utilities_kernels_cuda import ( + eval_canonical_toroidal_moment_5d_gpu, + eval_canonical_toroidal_moment_6d_gpu, + eval_energy_5d_gpu, + eval_guiding_center_from_6d_gpu, + eval_magnetic_background_energy_gpu, + eval_magnetic_energy_PBb_gpu, + eval_magnetic_moment_5d_gpu, +) class Particles6D(Particles): @@ -139,18 +148,39 @@ def save_constants_of_motion(self): # eval guiding center phase space # compiled host-only Pyccel kernel: writes a marker diagnostics # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: - utilities_kernels.eval_guiding_center_from_6d( - _args_markers.markers, + from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS + + if xp.cupy_backend and self.domain.args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS: + import cupy as cp + import numpy as np + + eval_guiding_center_from_6d_gpu( + self.markers, self._derham.args_derham, - self.domain.args_domain, + int(self.domain.args_domain.kind_map), + cp.asarray(np.asarray(self.domain.args_domain.params, dtype=float), dtype=cp.float64), self.first_diagnostics_idx, self.equation_params.epsilon, - _to_numpy_for_kernel(self._b2_h[0]._data), - _to_numpy_for_kernel(self._b2_h[1]._data), - _to_numpy_for_kernel(self._b2_h[2]._data), - _to_numpy_for_kernel(self._absB0_h._data), + self._b2_h[0]._data, + self._b2_h[1]._data, + self._b2_h[2]._data, + self._absB0_h._data, ) + else: + # no CUDA port for this domain kind_map: fall back to the + # compiled host-only kernel via the marker host mirror. + with self.host_markers(write=True) as _args_markers: + utilities_kernels.eval_guiding_center_from_6d( + _args_markers.markers, + self._derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.equation_params.epsilon, + _to_numpy_for_kernel(self._b2_h[0]._data), + _to_numpy_for_kernel(self._b2_h[1]._data), + _to_numpy_for_kernel(self._b2_h[2]._data), + _to_numpy_for_kernel(self._absB0_h._data), + ) # apply domain inverse map to get logical guiding center positions # TODO: currently only possible with the geometry where its inverse map is defined. @@ -182,17 +212,25 @@ def save_constants_of_motion(self): if self.mpi_comm is not None: self.mpi_sort_markers(alpha=1) - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + eval_canonical_toroidal_moment_6d_gpu( + self.markers, + self._derham.args_derham, + self.first_diagnostics_idx, + self.equation_params.epsilon, + B0, + R0, + self._absB0_h._data, + ) + else: utilities_kernels.eval_canonical_toroidal_moment_6d( - _args_markers.markers, + self.markers, self._derham.args_derham, self.first_diagnostics_idx, self.equation_params.epsilon, B0, R0, - _to_numpy_for_kernel(self._absB0_h._data), + self._absB0_h._data, ) # send back and clear buffer @@ -424,15 +462,22 @@ def save_constants_of_motion(self): # idx and slice idx_can_momentum = self.first_diagnostics_idx + 1 - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + # CUDA port: operates on the device-resident markers directly. + eval_energy_5d_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + ) + else: utilities_kernels.eval_energy_5d( - _args_markers.markers, + self.markers, self.derham.args_derham, self.first_diagnostics_idx, self.mu_idx, - _to_numpy_for_kernel(self.absB0_h._data), + self.absB0_h._data, ) # eval psi at etas @@ -443,11 +488,21 @@ def save_constants_of_motion(self): r = self.markers[~self.holes, 0] * (1 - a1) + a1 self.markers[~self.holes, idx_can_momentum] = self.equil.psi_r(r) - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + eval_canonical_toroidal_moment_5d_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + idx_can_momentum, + self.equation_params.epsilon, + B0, + R0, + self.absB0_h._data, + ) + else: utilities_kernels.eval_canonical_toroidal_moment_5d( - _args_markers.markers, + self.markers, self.derham.args_derham, self.first_diagnostics_idx, self.mu_idx, @@ -455,7 +510,7 @@ def save_constants_of_motion(self): self.equation_params.epsilon, B0, R0, - _to_numpy_for_kernel(self.absB0_h._data), + self.absB0_h._data, ) def save_magnetic_energy(self, PBb): @@ -476,17 +531,24 @@ def save_magnetic_energy(self, PBb): # utilities_kernels is a Pyccel-compiled extension that requires # real numpy buffers; absB0_h/PBbt follow the active backend, so # under cupy their ._data needs converting first. - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + eval_magnetic_energy_PBb_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + PBbt._data, + ) + else: utilities_kernels.eval_magnetic_energy_PBb( - _args_markers.markers, + self.markers, self.derham.args_derham, self.domain.args_domain, self.first_diagnostics_idx, self.mu_idx, - _to_numpy_for_kernel(self.absB0_h._data), - _to_numpy_for_kernel(PBbt._data), + self.absB0_h._data, + PBbt._data, ) def save_magnetic_background_energy(self): @@ -495,16 +557,23 @@ def save_magnetic_background_energy(self): The result is stored in the energy diagnostics column (``self.first_diagnostics_idx``). """ - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + # CUDA port: operates on the device-resident markers directly. + eval_magnetic_background_energy_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + ) + else: utilities_kernels.eval_magnetic_background_energy( - _args_markers.markers, + self.markers, self.derham.args_derham, self.domain.args_domain, self.first_diagnostics_idx, self.mu_idx, - _to_numpy_for_kernel(self.absB0_h._data), + self.absB0_h._data, ) @@ -692,12 +761,19 @@ def draw_markers(self, sort: bool = True): # magnetic moment is an adiabatic invariant: evaluate once at draw time (diagnostics column 1) # compiled host-only Pyccel kernel: writes a marker diagnostics # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + eval_magnetic_moment_5d_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self._absB0_h._data, + ) + else: utilities_kernels.eval_magnetic_moment_5d( - _args_markers.markers, + self.markers, self.derham.args_derham, self.first_diagnostics_idx, - _to_numpy_for_kernel(self._absB0_h._data), + self._absB0_h._data, ) def save_constants_of_motion(self): @@ -717,15 +793,22 @@ def save_constants_of_motion(self): # idx and slice idx_can_momentum = self.first_diagnostics_idx + 2 - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + # CUDA port: operates on the device-resident markers directly. + eval_energy_5d_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + ) + else: utilities_kernels.eval_energy_5d( - _args_markers.markers, + self.markers, self.derham.args_derham, self.first_diagnostics_idx, self.mu_idx, - _to_numpy_for_kernel(self.absB0_h._data), + self.absB0_h._data, ) # eval psi at etas @@ -736,11 +819,21 @@ def save_constants_of_motion(self): r = self.markers[~self.holes, 0] * (1 - a1) + a1 self.markers[~self.holes, idx_can_momentum] = self.equil.psi_r(r) - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + eval_canonical_toroidal_moment_5d_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + idx_can_momentum, + self.equation_params.epsilon, + B0, + R0, + self.absB0_h._data, + ) + else: utilities_kernels.eval_canonical_toroidal_moment_5d( - _args_markers.markers, + self.markers, self.derham.args_derham, self.first_diagnostics_idx, self.mu_idx, @@ -748,7 +841,7 @@ def save_constants_of_motion(self): self.equation_params.epsilon, B0, R0, - _to_numpy_for_kernel(self.absB0_h._data), + self.absB0_h._data, ) def save_magnetic_energy(self, PBb): @@ -768,14 +861,23 @@ def save_magnetic_energy(self, PBb): # compiled host-only Pyccel kernel: writes a marker diagnostics # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + eval_magnetic_energy_PBb_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + PBbt._data, + ) + else: utilities_kernels.eval_magnetic_energy_PBb( - _args_markers.markers, + self.markers, self.derham.args_derham, self.domain.args_domain, self.first_diagnostics_idx, self.mu_idx, - _to_numpy_for_kernel(self.absB0_h._data), + self.absB0_h._data, PBbt._data, ) @@ -785,16 +887,23 @@ def save_magnetic_background_energy(self): The result is stored in the energy diagnostics column (``self.first_diagnostics_idx``). """ - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + # CUDA port: operates on the device-resident markers directly. + eval_magnetic_background_energy_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + ) + else: utilities_kernels.eval_magnetic_background_energy( - _args_markers.markers, + self.markers, self.derham.args_derham, self.domain.args_domain, self.first_diagnostics_idx, self.mu_idx, - _to_numpy_for_kernel(self.absB0_h._data), + self.absB0_h._data, ) def save_magnetic_moment(self): @@ -803,14 +912,19 @@ def save_magnetic_moment(self): diagnostics column (``self.first_diagnostics_idx + 1``). """ - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - with self.host_markers(write=True) as _args_markers: + if xp.cupy_backend: + eval_magnetic_moment_5d_gpu( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.absB0_h._data, + ) + else: utilities_kernels.eval_magnetic_moment_5d( - _args_markers.markers, + self.markers, self.derham.args_derham, self.first_diagnostics_idx, - _to_numpy_for_kernel(self.absB0_h._data), + self.absB0_h._data, ) diff --git a/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py b/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py new file mode 100644 index 000000000..6dadb5b46 --- /dev/null +++ b/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py @@ -0,0 +1,102 @@ +"""Hand-written CUDA replacement for +:func:`~struphy.pic.pushing.pusher_utilities_kernels.reflect`, used only +under ``ARRAY_BACKEND=cupy``. + +``reflect`` is called from +:meth:`~struphy.pic.base.Particles.apply_kinetic_bc` -- i.e. once per +Runge-Kutta stage, inside the per-step hot path -- whenever a species has a +reflecting boundary. With markers device-resident it was the last thing in +that path still forcing a host<->device round trip of the whole marker +array, so it is ported here. + +It reuses the geometry device functions (``df_dispatch_dev``, +``matrix_inv_dev``, ``matvec_dev``) from +:mod:`~struphy.pic.pushing.pusher_kernels_cuda`'s ``_GENERAL_GEOMETRY_SRC``. +Only the markers listed in ``outside_inds`` are touched, so the kernel is +launched over that index array rather than over all markers. +""" + +_REFLECT_SRC = r""" +extern "C" __global__ +void reflect_cuda( + double* markers, const int n_cols, + const long long* outside_inds, const int n_outside, + const int axis, + const int kind_map, const double* params) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n_outside) return; + + const long long ip = outside_inds[i]; + double* row = markers + (size_t)ip * n_cols; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + + double dfinv[9], v_logical[3]; + matrix_inv_dev(dfm, dfinv); + + // pull back of the velocity + matvec_dev(dfinv, v, v_logical); + + // reverse the velocity component along `axis` + v_logical[axis] *= -1.0; + + // push forward of the velocity + matvec_dev(dfm, v_logical, v); + + row[3] = v[0]; + row[4] = v[1]; + row[5] = v[2]; +} +""" + +_reflect_kernel = None + + +def _get_reflect_kernel(): + global _reflect_kernel + if _reflect_kernel is None: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _reflect_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _REFLECT_SRC, "reflect_cuda") + return _reflect_kernel + + +def reflect_gpu(markers, kind_map, params_dev, outside_inds, axis): + """GPU replacement for + :func:`~struphy.pic.pushing.pusher_utilities_kernels.reflect`, for any + domain in + :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. + + ``markers`` and ``outside_inds`` are device-resident; markers are updated + in place. + """ + import cupy as cp + import numpy as np + + n_outside = int(outside_inds.shape[0]) + if n_outside == 0: + return + + inds = cp.ascontiguousarray(outside_inds, dtype=cp.int64) + threads = 256 + blocks = (n_outside + threads - 1) // threads + _get_reflect_kernel()( + (blocks,), + (threads,), + ( + markers, + np.int32(markers.shape[1]), + inds, + np.int32(n_outside), + np.int32(axis), + np.int32(kind_map), + params_dev, + ), + ) diff --git a/src/struphy/pic/sorting_kernels_cuda.py b/src/struphy/pic/sorting_kernels_cuda.py index 15304d341..75e5181ee 100644 --- a/src/struphy/pic/sorting_kernels_cuda.py +++ b/src/struphy/pic/sorting_kernels_cuda.py @@ -173,8 +173,8 @@ def assign_box_to_each_particle_gpu( # ignoring strides, so a per-axis strided view can't be passed directly -- # see sph_eval_kernels_cuda.py's meshgrid contiguity fix for the same # failure mode). - dev_eta = cp.asarray(np.ascontiguousarray(markers[:, :3]), dtype=cp.float64) - dev_holes = cp.asarray(np.ascontiguousarray(holes), dtype=cp.int32) + dev_eta = cp.ascontiguousarray(markers[:, :3], dtype=cp.float64) + dev_holes = cp.ascontiguousarray(holes, dtype=cp.int32) dev_domain = cp.asarray(domain_array, dtype=cp.float64) dev_box = cp.empty(n_mks, dtype=cp.float64) @@ -195,7 +195,8 @@ def assign_box_to_each_particle_gpu( ), ) - markers[:, box_col] = cp.asnumpy(dev_box) + # markers is device-resident; write the box column back in place + markers[:, box_col] = dev_box def assign_particles_to_boxes_gpu( @@ -217,8 +218,8 @@ def assign_particles_to_boxes_gpu( box_col = n_cols + box_index n_box_rows, box_cols = boxes.shape - dev_box_id = cp.asarray(np.ascontiguousarray(markers[:, box_col]), dtype=cp.float64) - dev_holes = cp.asarray(np.ascontiguousarray(holes), dtype=cp.int32) + dev_box_id = cp.ascontiguousarray(markers[:, box_col], dtype=cp.float64) + dev_holes = cp.ascontiguousarray(holes, dtype=cp.int32) dev_boxes = cp.full((n_box_rows, box_cols), -1, dtype=cp.int32) dev_next_index = cp.zeros(n_box_rows, dtype=cp.int32) @@ -237,5 +238,8 @@ def assign_particles_to_boxes_gpu( ), ) + # boxes/next_index belong to SortingBoxes and stay host-resident (they + # are also consumed by the host-only SPH kernels), so these two do + # need an explicit device->host copy. boxes[:, :] = cp.asnumpy(dev_boxes) next_index[:] = cp.asnumpy(dev_next_index) diff --git a/src/struphy/pic/utilities_kernels_cuda.py b/src/struphy/pic/utilities_kernels_cuda.py new file mode 100644 index 000000000..f47219113 --- /dev/null +++ b/src/struphy/pic/utilities_kernels_cuda.py @@ -0,0 +1,676 @@ +"""Hand-written CUDA replacements for the per-marker *diagnostics* kernels in +:mod:`~struphy.pic.utilities_kernels`, used only under ``ARRAY_BACKEND=cupy``. + +These run every time step (they back the scalar quantities a model saves, +e.g. ``en_fB`` in :class:`~struphy.models.guiding_center.GuidingCenter`), and +each one writes a diagnostics column of the marker array in place. With +markers now device-resident (see :class:`~struphy.pic.base.Particles`), the +compiled host-only versions were the last thing forcing a host<->device +round trip of the whole marker array in the per-step path -- porting them +removes it. + +Both kernels here are plain per-marker 0-form spline evaluations, so they +reuse the ``find_span_dev``/``b_splines_dev``/``eval_0form_dev`` device +functions rather than defining their own. +""" + +_UTILITIES_SRC = r""" +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) +{ + double left[MAXP]; + double right[MAXP]; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +__device__ double eval_0form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bn2, const double* bn3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c, int n2x, int n3x) +{ + double out = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] * bn1[il1] * bn2[il2] * bn3[il3]; + } + } + } + return out; +} + +// markers[ip, first_diagnostics_idx] = mu_p * |B_0(eta_p)| +extern "C" __global__ +void eval_magnetic_background_energy_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, const int mu_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* abs_B0, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double mu = row[mu_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, abs_B0, n2x, n3x); + + row[first_diagnostics_idx] = mu * abs_B; +} + +// markers[ip, first_diagnostics_idx] = v_par^2 / 2 + mu_p * |B(eta_p)| +extern "C" __global__ +void eval_energy_5d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, const int mu_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* absB, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v_parallel = row[3]; + const double mu = row[mu_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, n2x, n3x); + + row[first_diagnostics_idx] = 0.5 * v_parallel * v_parallel + mu * abs_B; +} + +// markers[ip, idx_can_momentum] = shifted canonical toroidal momentum (5D) +extern "C" __global__ +void eval_canonical_toroidal_moment_5d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, const int mu_idx, const int idx_can_momentum, + const double epsilon, const double B0, const double R0, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* absB, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v_para = row[3]; + const double mu = row[mu_idx]; + const double energy = row[first_diagnostics_idx]; + const double psi = row[idx_can_momentum]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, n2x, n3x); + + double out = psi - epsilon * B0 * R0 / abs_B * v_para; + if (energy - mu * B0 > 0.0) { + // sign(v_para) matches numpy.sign: 0 for exactly 0 + const double sgn = (v_para > 0.0) ? 1.0 : ((v_para < 0.0) ? -1.0 : 0.0); + out += epsilon * sgn * sqrt(2.0 * (energy - mu * B0)) * R0; + } + row[idx_can_momentum] = out; +} + +// markers[ip, first_diagnostics_idx + 5] = shifted canonical toroidal momentum (6D) +extern "C" __global__ +void eval_canonical_toroidal_moment_6d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, + const double epsilon, const double B0, const double R0, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* absB, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double energy = row[first_diagnostics_idx + 3]; + const double mu = row[first_diagnostics_idx + 4]; + const double psi = row[first_diagnostics_idx + 5]; + const double v_para = row[first_diagnostics_idx + 6]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, n2x, n3x); + + double out = psi - epsilon * B0 * R0 / abs_B * v_para; + if (energy - mu * B0 > 0.0) { + const double sgn = (v_para > 0.0) ? 1.0 : ((v_para < 0.0) ? -1.0 : 0.0); + out += epsilon * sgn * sqrt(2.0 * (energy - mu * B0)) * R0; + } + row[first_diagnostics_idx + 5] = out; +} + +// markers[ip, first_diagnostics_idx + 1] = v_perp^2 / (2 |B(eta_p)|) +extern "C" __global__ +void eval_magnetic_moment_5d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* absB, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v_perp = row[4]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, n2x, n3x); + + row[first_diagnostics_idx + 1] = 0.5 * v_perp * v_perp / abs_B; +} + +// markers[ip, first_diagnostics_idx] = mu_p * (|B_0| + PBb)(eta_p) +// NOTE: the CPU reference also evaluates the Jacobian DF(eta) here, but never +// uses the result, so it is not replicated. +extern "C" __global__ +void eval_magnetic_energy_PBb_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, const int mu_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* abs_B0, const int a_n2x, const int a_n3x, + const double* PBb, const int b_n2x, const int b_n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + // eta = mod(markers[0:3], 1.0); fmod can return negative, match numpy mod + double eta[3]; + for (int k = 0; k < 3; k++) { + double e = fmod(row[k], 1.0); + if (e < 0.0) e += 1.0; + eta[k] = e; + } + + const double mu = row[mu_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + b_splines_dev(tn1, p1, eta[0], span1, bn1); + b_splines_dev(tn2, p2, eta[1], span2, bn2); + b_splines_dev(tn3, p3, eta[2], span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, abs_B0, a_n2x, a_n3x); + const double PB_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, PBb, b_n2x, b_n3x); + + row[first_diagnostics_idx] = mu * (abs_B + PB_b); +} +""" + +_kernels = {} + + +def _get_kernel(name): + if name not in _kernels: + import cupy as cp + + _kernels[name] = cp.RawKernel(_UTILITIES_SRC, name) + return _kernels[name] + + +def _launch_0form_diag(kernel_name, markers, args_derham, first_diagnostics_idx, mu_idx, coeffs): + """Shared launch path for the two 0-form diagnostics kernels above.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + coeffs = cp.ascontiguousarray(coeffs) + + _get_kernel(kernel_name)( + (blocks,), + (threads,), + ( + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_diagnostics_idx), + np.int32(mu_idx), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + coeffs, + np.int32(coeffs.shape[1]), + np.int32(coeffs.shape[2]), + ), + ) + + +def eval_magnetic_background_energy_gpu(markers, args_derham, first_diagnostics_idx, mu_idx, abs_B0): + """GPU replacement for + :func:`~struphy.pic.utilities_kernels.eval_magnetic_background_energy`. + ``markers`` is device-resident and written in place. + """ + _launch_0form_diag( + "eval_magnetic_background_energy_cuda", + markers, + args_derham, + first_diagnostics_idx, + mu_idx, + abs_B0, + ) + + +def eval_energy_5d_gpu(markers, args_derham, first_diagnostics_idx, mu_idx, absB): + """GPU replacement for :func:`~struphy.pic.utilities_kernels.eval_energy_5d`. + ``markers`` is device-resident and written in place. + """ + _launch_0form_diag( + "eval_energy_5d_cuda", + markers, + args_derham, + first_diagnostics_idx, + mu_idx, + absB, + ) + + +def eval_canonical_toroidal_moment_5d_gpu( + markers, args_derham, first_diagnostics_idx, mu_idx, idx_can_momentum, epsilon, B0, R0, absB +): + """GPU replacement for + :func:`~struphy.pic.utilities_kernels.eval_canonical_toroidal_moment_5d`. + ``markers`` is device-resident and written in place. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + absB = cp.ascontiguousarray(absB) + _get_kernel("eval_canonical_toroidal_moment_5d_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_diagnostics_idx), np.int32(mu_idx), np.int32(idx_can_momentum), + np.float64(epsilon), np.float64(B0), np.float64(R0), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + absB, np.int32(absB.shape[1]), np.int32(absB.shape[2]), + ), + ) + + +def eval_canonical_toroidal_moment_6d_gpu( + markers, args_derham, first_diagnostics_idx, epsilon, B0, R0, absB +): + """GPU replacement for + :func:`~struphy.pic.utilities_kernels.eval_canonical_toroidal_moment_6d`. + ``markers`` is device-resident and written in place. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + absB = cp.ascontiguousarray(absB) + _get_kernel("eval_canonical_toroidal_moment_6d_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_diagnostics_idx), + np.float64(epsilon), np.float64(B0), np.float64(R0), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + absB, np.int32(absB.shape[1]), np.int32(absB.shape[2]), + ), + ) + + +def eval_magnetic_moment_5d_gpu(markers, args_derham, first_diagnostics_idx, absB): + """GPU replacement for + :func:`~struphy.pic.utilities_kernels.eval_magnetic_moment_5d`. + ``markers`` is device-resident and written in place. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + absB = cp.ascontiguousarray(absB) + _get_kernel("eval_magnetic_moment_5d_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_diagnostics_idx), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + absB, np.int32(absB.shape[1]), np.int32(absB.shape[2]), + ), + ) + + +def eval_magnetic_energy_PBb_gpu(markers, args_derham, first_diagnostics_idx, mu_idx, abs_B0, PBb): + """GPU replacement for + :func:`~struphy.pic.utilities_kernels.eval_magnetic_energy_PBb`. + ``markers`` is device-resident and written in place. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + abs_B0 = cp.ascontiguousarray(abs_B0) + PBb = cp.ascontiguousarray(PBb) + _get_kernel("eval_magnetic_energy_PBb_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_diagnostics_idx), np.int32(mu_idx), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + abs_B0, np.int32(abs_B0.shape[1]), np.int32(abs_B0.shape[2]), + PBb, np.int32(PBb.shape[1]), np.int32(PBb.shape[2]), + ), + ) + + +# --------------------------------------------------------------------------- +# eval_guiding_center_from_6d needs the domain Jacobian and a 2-form (magnetic +# field) evaluation, so unlike the pure 0-form diagnostics above it is built +# on top of pusher_kernels_cuda's shared geometry/spline device functions +# rather than the small self-contained source in this module. +# --------------------------------------------------------------------------- + +_GC_FROM_6D_SRC = r""" +extern "C" __global__ +void eval_guiding_center_from_6d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b21, const int b1_n2, const int b1_n3, + const double* b22, const int b2_n2, const int b2_n3, + const double* b23, const int b3_n2, const int b3_n3, + const double* absB, const int a_n2, const int a_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double x = row[first_diagnostics_idx]; + const double y = row[first_diagnostics_idx + 1]; + const double z = row[first_diagnostics_idx + 2]; + double v[3] = {row[3], row[4], row[5]}; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b2[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + b21, b1_n2, b1_n3, b22, b2_n2, b2_n3, b23, b3_n2, b3_n3, b2); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, a_n2, a_n3); + + // normalized magnetic field, cartesian + b2[0] /= abs_B; b2[1] /= abs_B; b2[2] /= abs_B; + double norm_b_cart[3]; + matvec_dev(dfm, b2, norm_b_cart); + norm_b_cart[0] /= det_df; norm_b_cart[1] /= det_df; norm_b_cart[2] /= det_df; + + const double v_parallel = dot3_dev(norm_b_cart, v); + + double temp[3], v_perp[3]; + cross_dev(v, norm_b_cart, temp); + cross_dev(norm_b_cart, temp, v_perp); + const double v_perp_square = v_perp[0]*v_perp[0] + v_perp[1]*v_perp[1] + v_perp[2]*v_perp[2]; + + row[first_diagnostics_idx + 6] = v_parallel; + row[first_diagnostics_idx + 4] = 0.5 * v_perp_square / abs_B; + + double Larmor_r[3]; + cross_dev(norm_b_cart, v_perp, Larmor_r); + for (int k = 0; k < 3; k++) Larmor_r[k] = Larmor_r[k] / abs_B * epsilon; + + row[first_diagnostics_idx + 0] = x - Larmor_r[0]; + row[first_diagnostics_idx + 1] = y - Larmor_r[1]; + row[first_diagnostics_idx + 2] = z - Larmor_r[2]; +} +""" + +_gc6d_kernel = None + + +def _get_gc_from_6d_kernel(): + global _gc6d_kernel + if _gc6d_kernel is None: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _gc6d_kernel = cp.RawKernel( + _GENERAL_GEOMETRY_SRC + _GC_FROM_6D_SRC, + "eval_guiding_center_from_6d_cuda", + ) + return _gc6d_kernel + + +def eval_guiding_center_from_6d_gpu( + markers, args_derham, kind_map, params_dev, first_diagnostics_idx, epsilon, b21, b22, b23, absB +): + """GPU replacement for + :func:`~struphy.pic.utilities_kernels.eval_guiding_center_from_6d`, for any + domain in :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. + ``markers`` is device-resident and written in place. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + b21 = cp.ascontiguousarray(b21) + b22 = cp.ascontiguousarray(b22) + b23 = cp.ascontiguousarray(b23) + absB = cp.ascontiguousarray(absB) + _get_gc_from_6d_kernel()( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_diagnostics_idx), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + b21, np.int32(b21.shape[1]), np.int32(b21.shape[2]), + b22, np.int32(b22.shape[1]), np.int32(b22.shape[2]), + b23, np.int32(b23.shape[1]), np.int32(b23.shape[2]), + absB, np.int32(absB.shape[1]), np.int32(absB.shape[2]), + ), + ) From 861e88ecb543aff47f65a69b63d150a023dfbe01 Mon Sep 17 00:00:00 2001 From: Max Date: Sun, 16 Aug 2026 19:00:30 +0200 Subject: [PATCH 054/156] Setup example sim --- .../params_LinearMHDDriftkineticCC.py | 9 +- profiling/submit_linearmhd_numpy_vs_cupy.py | 85 +++++++++++++------ 2 files changed, 66 insertions(+), 28 deletions(-) diff --git a/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py b/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py index eb6e3efb3..f5ffdd73c 100644 --- a/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py +++ b/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py @@ -24,7 +24,12 @@ default="numpy", help="Array backend to run the simulation with (default: numpy).", ) -args = parser.parse_args() +# `--id` distinguishes runs that share a rank count but differ in something else (here: +# the array backend); the profiling driver passes its launch counter and looks for the +# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). +# Unknown flags are ignored so the driver can forward other parameters as well. +parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") +args, _ = parser.parse_known_args() # Must be set before struphy (and therefore cunumpy) is imported. os.environ["ARRAY_BACKEND"] = args.backend @@ -85,7 +90,7 @@ # Environment options env = EnvironmentOptions( - sim_folder=f"sim_{args.backend}", + sim_folder=f"sim_{args.id:02d}", profiling_activated=True, ) diff --git a/profiling/submit_linearmhd_numpy_vs_cupy.py b/profiling/submit_linearmhd_numpy_vs_cupy.py index b049b6f9e..baa44a2aa 100644 --- a/profiling/submit_linearmhd_numpy_vs_cupy.py +++ b/profiling/submit_linearmhd_numpy_vs_cupy.py @@ -1,69 +1,102 @@ -"""Poisson strong scaling profiling case. +"""Linear MHD NumPy-vs-CuPy profiling case. -This file defines the Poisson strong scaling profiling case (the `ProfilingCase`) -and submits it: for each rank count, `ProfilingCase.launch` builds and submits a -SLURM script (using `clusters.SLURM_PRESETS` by default), or, without a batch -system, runs directly on this machine. `finalize_run` then packages and uploads -each run as soon as its own job finishes. +This file defines the Linear MHD backend-comparison profiling case (the `ProfilingCase`) +and submits it: the same simulation is run twice, once with `ARRAY_BACKEND=numpy` on a +CPU partition and once with `ARRAY_BACKEND=cupy` on a GPU partition, so the two runs can +be compared directly. For each run, `ProfilingCase.launch` builds and submits a SLURM +script, or, without a batch system, runs directly on this machine. `finalize_run` then +packages and uploads each run as soon as its own job finishes. Each generated script runs the simulation itself by invoking `params_LinearMHDDriftkineticCC.py` -directly (its `__main__` block is the worker). +directly (its `__main__` block is the worker), with `--backend numpy` or `--backend cupy`. """ import argparse from pathlib import Path +from clusters import SLURM_PRESETS, detect_machine_name from profiling_job import ProfilingCase -from clusters import SLURM_PRESETS -cpu_preset = SLURM_PRESETS.get("pitagora_dcgp") -gpu_preset = SLURM_PRESETS.get("pitagora_booster") +# Which SLURM preset each backend runs under. `ProfilingCase.launch` picks a preset from +# the dict it is given by cluster name (`detect_machine_name`), so the dict is keyed by +# the *detected* name here rather than by the preset's own name: on Pitagora detection +# always returns "pitagora_dcgp" (it cannot tell the Booster partition apart), and the +# GPU run must still get the Booster preset. Keying on the detected name also keeps this +# working, without a KeyError, on a machine detection does not recognise (name None). +CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] +GPU_PRESET = SLURM_PRESETS["pitagora_booster"] -slurm_presets = { - "numpy": cpu_preset, - "cupy": gpu_preset, +BACKEND_PRESETS = { + "numpy": CPU_PRESET, + "cupy": GPU_PRESET, } +# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). The CuPy +# runs are spread so that no node holds more ranks than it has GPUs. +GPUS_PER_NODE = 4 + def main() -> None: # Parse arguments, do not remove --upload parser = argparse.ArgumentParser( - description=( - "Submit profiling jobs to a SLURM cluster and package the results for upload." - ), + description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), ) parser.add_argument( "--upload", action="store_true", help="Upload the packaged profiling results to the profiling-data repo.", ) + parser.add_argument( + "--ranks", + type=int, + nargs="+", + default=[1], + help=( + "MPI rank counts to run each backend with (default: 1). Note that the CuPy " + "runs currently select no GPU per rank, so more than one rank per node all " + "share device 0." + ), + ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. script_dir = Path(__file__).resolve().parent - params_dir = ( - script_dir / "examples" / "LinearMHDDriftkineticCC" / "cube_strong_scaling" - ) + params_dir = script_dir / "examples" / "LinearMHDDriftkineticCC" + params_source = params_dir / "params_LinearMHDDriftkineticCC.py" profiling_case = ProfilingCase( label="linearmhd_numpy_vs_cupy", - name="Linear MHD on cube", - description="Linear MHD model with manufactured solution on 3D cube.", + name="Linear MHD on cube, NumPy vs CuPy", + description="Linear MHD model with manufactured solution on 3D cube, run with the NumPy and the CuPy array backend.", physics_problem="Occurs in many plasma applications.", struphy_model_used="LinearMHDDriftkineticCC", - params_source=params_dir / "params_LinearMHDDriftkineticCC.py", + params_source=params_source, language="fortran", compiler="GNU", upload=args.upload, ) - # Launch one run per rank count - for num_tasks in (1,): - for backend in ("numpy", "cupy"): + # The preset is looked up by cluster name inside `launch`, so build a one-entry dict + # under whatever name detection reports for this machine. + cluster_name = detect_machine_name() + + # Launch one run per (rank count, backend) pair. + for num_tasks in args.ranks: + for backend, preset in BACKEND_PRESETS.items(): + if backend == "cupy": + # One node per `GPUS_PER_NODE` ranks. `launch` would otherwise derive the + # node count from `cpus_per_node`, which on a GPU partition packs far more + # ranks per node than there are GPUs. + num_nodes = -(-num_tasks // GPUS_PER_NODE) + else: + # Let `launch` derive the node count from the cluster's CPU count. + num_nodes = None + profiling_case.launch( num_tasks, + num_nodes=num_nodes, param_flags=["--backend", backend], - slurm_preset=slurm_presets[backend], + slurm_presets={cluster_name: preset}, ) # Package and push each run as its own job finishes. From 2595b5fa527338d4ee71ab12b6ce09bd2ea89dc2 Mon Sep 17 00:00:00 2001 From: Max Date: Sun, 16 Aug 2026 19:09:35 +0200 Subject: [PATCH 055/156] Fix params --- .../params_LinearMHDDriftkineticCC.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py b/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py index f5ffdd73c..ef85ed7ce 100644 --- a/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py +++ b/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py @@ -171,14 +171,20 @@ # For kinetic species the perturbations are added to the moments of the distribution function (defined as tuples). # Background for kinetic species -maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None), equil=equil) -maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None), equil=equil) +# GyroMaxwellian2D takes the background field strength as `B0` (a float or a callable of +# the logical coordinates) rather than a whole equilibrium. HomogenSlab is uniform, so +# |B| is a constant taken straight from its parameters -- which keeps `B0` a plain float +# and avoids a per-marker Python callback in the velocity Jacobian on the GPU path. +absB0 = (equil.params["B0x"] ** 2 + equil.params["B0y"] ** 2 + equil.params["B0z"] ** 2) ** 0.5 + +maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None), B0=absB0) +maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None), B0=absB0) background = maxwellian_1 + maxwellian_2 model.energetic_ions.var.add_background(background) # Perturbations for (some) kinetic species perturbation = perturbations.TorusModesCos() -maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation), equil=equil) +maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation), B0=absB0) init = maxwellian_1pt + maxwellian_2 model.energetic_ions.var.add_initial_condition(init) From d97a4893e2165b052b7bc7537e47e2fdb8dfabb0 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 19:00:54 +0200 Subject: [PATCH 056/156] Updated kernels --- .../pic/accumulation/accum_kernels_gc_cuda.py | 239 ++++++++++++++++++ 1 file changed, 239 insertions(+) diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index 1d92cbfcb..92b55e906 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -162,3 +162,242 @@ def gc_mag_density_0form_gpu( np.int32(vec_dev.shape[2]), ), ) + + +# --------------------------------------------------------------------------- +# gc_density_0form is byte-for-byte the same computation as +# accum_kernels.charge_density_0form (an H^1/0-form vec_fill_b_v0 scatter with +# the marker weight as filling); only the docstring differs. Rather than +# duplicating the CUDA source, reuse the already-validated kernel. +# --------------------------------------------------------------------------- + +from struphy.pic.accumulation.accum_kernels_cuda import ( # noqa: E402 + charge_density_0form_gpu as _charge_density_0form_gpu, +) + + +def gc_density_0form_gpu(markers, weight_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, vec_dev): + """GPU replacement for + :func:`~struphy.pic.accumulation.accum_kernels_gc.gc_density_0form`. + + Identical to + :func:`~struphy.pic.accumulation.accum_kernels_cuda.charge_density_0form_gpu` + (same filling, same 0-form scatter), so it simply delegates. + """ + _charge_density_0form_gpu(markers, weight_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, vec_dev) + + +# --------------------------------------------------------------------------- +# cc_lin_mhd_5d_D: same 3-block antisymmetric V_u -> V_u fill as +# accum_kernels.cc_lin_mhd_6d_1 (runtime basis_u in {0,1,2} selecting +# H1vec/Hcurl/Hdiv, fill_mat_dev for each block), but the scalar prefactor is +# the guiding-centre density factor +# +# -w_p * (1 - b_para/b*_para) * ep_scale / epsilon +# +# with b*_para = norm_b1 . (b2 + epsilon*v_par*curl_norm_b). It therefore needs +# a 1-form (norm_b1) and a second 2-form (curl_norm_b) evaluation on top of +# the B-field, but reuses fill_mat_dev from accum_kernels_cuda's +# _LINEAR_VLASOV_AMPERE_EXTRA_SRC unchanged. +# --------------------------------------------------------------------------- + +_CC_LIN_MHD_5D_D_SRC = r""" +extern "C" __global__ +void cc_lin_mhd_5d_D_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const double epsilon, const double ep_scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int bb1_n2, const int bb1_n3, + const double* b2_2, const int bb2_n2, const int bb2_n3, + const double* b2_3, const int bb3_n2, const int bb3_n3, + const double* nb11, const int nb1_n2, const int nb1_n3, + const double* nb12, const int nb2_n2, const int nb2_n3, + const double* nb13, const int nb3_n2, const int nb3_n3, + const double* cnb1, const int cb1_n2, const int cb1_n3, + const double* cnb2, const int cb2_n2, const int cb2_n3, + const double* cnb3, const int cb3_n2, const int cb3_n3, + const int basis_u, + double* mat12, double* mat13, double* mat23, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + const double weight = row[5]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b2_1,bb1_n2,bb1_n3, b2_2,bb2_n2,bb2_n3, b2_3,bb3_n2,bb3_n3, b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb11,nb1_n2,nb1_n3, nb12,nb2_n2,nb2_n3, nb13,nb3_n2,nb3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,cb1_n2,cb1_n3, cnb2,cb2_n2,cb2_n3, cnb3,cb3_n2,cb3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = b[k] + epsilon * v * curl_norm_b[k]; + + const double b_para = dot3_dev(norm_b1, b); + const double b_star_para = dot3_dev(norm_b1, b_star); + const double density_const = 1.0 - b_para / b_star_para; + + const double pref = -weight * density_const * ep_scale / epsilon; + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + double f12, f13, f23; + + if (basis_u == 0) { + f12 = pref * b_prod[1]; + f13 = pref * b_prod[2]; + f23 = pref * b_prod[5]; + + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + + } else if (basis_u == 1) { + double df_inv[9], g_inv[9]; + matrix_inv_dev(dfm, df_inv); + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) { + double sacc = 0.0; + for (int k = 0; k < 3; k++) sacc += df_inv[3*i+k] * df_inv[3*j+k]; + g_inv[3*i+j] = sacc; + } + double tmp1[9], tmp2[9]; + matmat_dev(g_inv, b_prod, tmp1); + matmat_dev(tmp1, g_inv, tmp2); + + f12 = pref * tmp2[1]; + f13 = pref * tmp2[2]; + f23 = pref * tmp2[5]; + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + + } else if (basis_u == 2) { + const double det2 = det_df * det_df; + f12 = pref * b_prod[1] / det2; + f13 = pref * b_prod[2] / det2; + f23 = pref * b_prod[5] / det2; + + fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + } +} +""" + +_cc_lin_mhd_5d_D_kernel = None + + +def _get_cc_lin_mhd_5d_D_kernel(): + global _cc_lin_mhd_5d_D_kernel + if _cc_lin_mhd_5d_D_kernel is None: + import cupy as cp + + from struphy.pic.accumulation.accum_kernels_cuda import _LINEAR_VLASOV_AMPERE_EXTRA_SRC + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _cc_lin_mhd_5d_D_kernel = cp.RawKernel( + _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_5D_D_SRC, + "cc_lin_mhd_5d_D_cuda", + ) + return _cc_lin_mhd_5d_D_kernel + + +def cc_lin_mhd_5d_D_gpu( + markers, kind_map, params_dev, epsilon, ep_scale, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2, norm_b1, curl_norm_b, basis_u, + mat12_dev, mat13_dev, mat23_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_D`. + + ``b2``/``norm_b1``/``curl_norm_b`` are 3-tuples of device-resident FE + coefficient arrays; the ``mat*_dev`` are already zeroed by the caller. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + def dims(a): + return ( + np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), + np.int32(a.shape[4]), np.int32(a.shape[5]), + ) + + _get_cc_lin_mhd_5d_D_kernel()( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(kind_map), params_dev, + np.float64(epsilon), np.float64(ep_scale), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), + *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + np.int32(basis_u), + mat12_dev, mat13_dev, mat23_dev, + *dims(mat12_dev), *dims(mat13_dev), *dims(mat23_dev), + ), + ) From e18bd7e30c2b4149c9fa4205e050e5f2b2fefdec Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 19:10:26 +0200 Subject: [PATCH 057/156] Port particles to grid kernels --- .../pic/accumulation/particles_to_grid.py | 59 ++++++++++++++++++- 1 file changed, 57 insertions(+), 2 deletions(-) diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index f0f8129dd..8ad4612a7 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -25,7 +25,10 @@ pc_lin_mhd_6d_gpu, vlasov_maxwell_gpu, ) -from struphy.pic.accumulation.accum_kernels_gc_cuda import gc_mag_density_0form_gpu +from struphy.pic.accumulation.accum_kernels_gc_cuda import ( + cc_lin_mhd_5d_D_gpu, + gc_mag_density_0form_gpu, +) from struphy.pic.accumulation.filter import AccumFilter, FilterParameters from struphy.pic.base import Particles from struphy.utils.utils import __dataclass_repr_no_defaults__, check_option @@ -297,6 +300,27 @@ def __init__( self._gpu_cc2_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) self._gpu_cc2_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + # GPU replacement for cc_lin_mhd_5d_D: 3-block antisymmetric fill like + # cc_lin_mhd_6d_1, with the guiding-centre density prefactor + # (1 - b_para/b*_para) / epsilon. + self._gpu_cc_lin_mhd_5d_D = ( + xp.cupy_backend + and kernel.name == "cc_lin_mhd_5d_D" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_cc_lin_mhd_5d_D: + import cupy as cp + import numpy as np + + self._gpu_cc5d_kind_map = int(args_domain.kind_map) + self._gpu_cc5d_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + args_derham = self.derham.args_derham + self._gpu_cc5d_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_cc5d_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_cc5d_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) + self._gpu_cc5d_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) + self._gpu_cc5d_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + # GPU replacement for pc_lin_mhd_6d_full / pc_lin_mhd_6d: the # symmetry="pressure" case -- 45-array (36 matrix + 9 vector) # velocity-moment "pressure tensor" fill. See accum_kernels_cuda.py @@ -427,6 +451,32 @@ def _accumulate(self, *optional_args, **args_control): boundary_cut, *self._args_data, ) + elif self._gpu_cc_lin_mhd_5d_D and len(optional_args) == 12: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + ( + epsilon, ep_scale, + b2_1, b2_2, b2_3, + nb1_1, nb1_2, nb1_3, + cnb_1, cnb_2, cnb_3, + basis_u, + ) = optional_args + cc_lin_mhd_5d_D_gpu( + self.particles.markers, + self._gpu_cc5d_kind_map, + self._gpu_cc5d_params, + epsilon, + ep_scale, + self._gpu_cc5d_pn, + self._gpu_cc5d_tn1, + self._gpu_cc5d_tn2, + self._gpu_cc5d_tn3, + self._gpu_cc5d_starts, + (b2_1, b2_2, b2_3), + (nb1_1, nb1_2, nb1_3), + (cnb_1, cnb_2, cnb_3), + basis_u, + *self._args_data, + ) elif (self._gpu_pc_lin_mhd_6d_full or self._gpu_pc_lin_mhd_6d) and len(optional_args) == 1: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): (ep_scale,) = optional_args @@ -781,7 +831,12 @@ def __init__( # hand-written CUDA replacement for charge_density_0form (the only # AccumulatorVector kernel ported so far -- see accum_kernels_cuda.py). # No optional_args/domain-mapping support needed for this one. - self._gpu_charge_density_0form = xp.cupy_backend and kernel.name == "charge_density_0form" + self._gpu_charge_density_0form = xp.cupy_backend and kernel.name in ( + "charge_density_0form", + # gc_density_0form is the same 0-form weight scatter (see + # accum_kernels_gc_cuda.gc_density_0form_gpu) + "gc_density_0form", + ) if self._gpu_charge_density_0form: import cupy as cp From 1f497fc57ee32645b3abbbd9369b02d70341b68d Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 19:25:08 +0200 Subject: [PATCH 058/156] Added src/struphy/pic/pushing/pusher_kernels_sph_cuda.py --- src/struphy/pic/pushing/pusher.py | 74 ++++ .../pic/pushing/pusher_kernels_sph_cuda.py | 347 ++++++++++++++++++ 2 files changed, 421 insertions(+) create mode 100644 src/struphy/pic/pushing/pusher_kernels_sph_cuda.py diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 68d852b16..dcec48405 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -32,6 +32,11 @@ push_vxb_implicit_general_gpu, push_weights_with_efield_lin_va_general_gpu, ) +from struphy.pic.pushing.pusher_kernels_sph_cuda import ( + push_v_sph_pressure_gpu, + push_v_sph_pressure_ideal_gas_gpu, + push_v_viscosity_gpu, +) from struphy.pic.pushing.pusher_kernels_gc_cuda import ( push_gc_bxEstar_explicit_multistage_general_gpu, push_gc_Bstar_explicit_multistage_general_gpu, @@ -608,6 +613,40 @@ def __init__( self._gpu_gc_bstar_e_field = (e_field_1, e_field_2, e_field_3) self._gpu_gc_bstar_mu_idx = int(particles.mu_idx) + # CUDA replacements for the three SPH velocity pushers. Their inner + # work is the box-neighbourhood SPH sum (see + # pusher_kernels_sph_cuda.box_based_kernel_dev); boxes/neighbours are + # host-owned by SortingBoxes and uploaded per call, everything else is + # already device-resident. + self._gpu_sph_pusher = ( + cunumpy.cupy_backend + and kernel.name in ("push_v_sph_pressure", "push_v_sph_pressure_ideal_gas", "push_v_viscosity") + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_sph_pusher: + import cupy as cp + + self._gpu_sph_name = kernel.name + self._gpu_sph_kind_map = int(args_domain.kind_map) + self._gpu_sph_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + if kernel.name == "push_v_viscosity": + (boxes, neighbours, holes, per1, per2, per3, kernel_nr, h1, h2, h3) = args_kernel + self._gpu_sph_gravity = None + self._gpu_sph_kappa = None + else: + ( + boxes, neighbours, holes, per1, per2, per3, + kernel_nr, h1, h2, h3, gravity, kappa, + ) = args_kernel + self._gpu_sph_gravity = cp.asarray(np.asarray(gravity, dtype=float), dtype=cp.float64) + self._gpu_sph_kappa = float(kappa) + self._gpu_sph_boxes = boxes + self._gpu_sph_neighbours = neighbours + self._gpu_sph_holes = holes + self._gpu_sph_periodic = (bool(per1), bool(per2), bool(per3)) + self._gpu_sph_kernel_nr = int(kernel_nr) + self._gpu_sph_h = (float(h1), float(h2), float(h3)) + @profile def __call__(self, dt: float): """ @@ -1087,6 +1126,41 @@ def _push(self, dt: float): self._gpu_weights_efield_general_params, dt, ) + elif self._gpu_sph_pusher: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + common = dict( + boxes=self.particles.sorting_boxes.boxes, + neighbours=self.particles.sorting_boxes.neighbours, + holes=self.particles.holes, + periodic=self._gpu_sph_periodic, + kernel_type=self._gpu_sph_kernel_nr, + h=self._gpu_sph_h, + kind_map=self._gpu_sph_kind_map, + params_dev=self._gpu_sph_params, + dt=dt, + ) + if self._gpu_sph_name == "push_v_viscosity": + push_v_viscosity_gpu( + markers, + self.particles.valid_mks, + self.particles.first_free_idx, + **common, + ) + else: + fn = ( + push_v_sph_pressure_gpu + if self._gpu_sph_name == "push_v_sph_pressure" + else push_v_sph_pressure_ideal_gas_gpu + ) + fn( + markers, + self.particles.valid_mks, + self.particles.index["weights"], + self.particles.first_free_idx, + gravity=self._gpu_sph_gravity, + kappa=self._gpu_sph_kappa, + **common, + ) else: # no CUDA port for this kernel: fall back to the compiled # host-only one, which pushes markers in place diff --git a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py new file mode 100644 index 000000000..1f9137b0d --- /dev/null +++ b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py @@ -0,0 +1,347 @@ +"""Hand-written CUDA replacements for the SPH velocity pushers in +:mod:`~struphy.pic.pushing.pusher_kernels_sph`, used only under +``ARRAY_BACKEND=cupy``. + +All three are per-marker loops whose inner work is a box-neighbourhood SPH +sum, i.e. the same computation as +:func:`~struphy.pic.sph_eval_kernels.box_based_kernel`. That sum is factored +out here into the ``box_based_kernel_dev`` device function, which mirrors the +already-validated accumulation loop in +:mod:`~struphy.pic.sph_eval_kernels_cuda`'s +``box_based_evaluation_flat_cuda`` -- the only difference is that the +marker's own box index is read from its ``n_cols - 2`` column instead of +being looked up with ``find_box_dev``. + +The smoothing-kernel evaluation (``smoothing_kernel_dev``) and the periodic +distance helper (``distance_dev``) are reused from that module's source +string, and the geometry (``df_dispatch_dev``, ``matrix_inv_dev``) from +:mod:`~struphy.pic.pushing.pusher_kernels_cuda`. + +Note on ``df_inv``: the CPU kernels call +:func:`~struphy.geometry.evaluation_kernels.df_inv` with +``avoid_round_off=False``, which is exactly ``matrix_inv(df(eta))`` -- the +manual zeroing of analytically-zero entries is skipped -- so +``matrix_inv_dev(df_dispatch_dev(...))`` reproduces it exactly. +""" + +_SPH_PUSHER_SRC = r""" +// Port of struphy.pic.sph_eval_kernels.box_based_kernel: SPH sum over the 27 +// neighbouring boxes of the marker's own box. +__device__ double box_based_kernel_dev( + const double* markers, const int n_cols, + double e1, double e2, double e3, + int loc_box, + const int* boxes, const int n_box_cols, + const int* neighbours, + const int* holes, + int periodic1, int periodic2, int periodic3, + int index, int kernel_type, + double h1, double h2, double h3) +{ + if (loc_box == -1) return 0.0; + + double acc = 0.0; + for (int neigh = 0; neigh < 27; neigh++) { + int box_to_search = neighbours[loc_box * 27 + neigh]; + int c = 0; + while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { + int p = boxes[(size_t)box_to_search * n_box_cols + c]; + c++; + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + } + return acc; +} + +// Shared tail of all three pushers: pull the logical-space force back to +// Cartesian with DF^-T and apply it to the marker velocity. +__device__ void apply_force_dev( + double* row, double e1, double e2, double e3, + int kind_map, const double* params, + const double* force_logical, const double* gravity, + double dt) +{ + double dfm[9], dfinv[9], force_cart[3]; + if (!df_dispatch_dev(kind_map, e1, e2, e3, params, dfm)) return; + matrix_inv_dev(dfm, dfinv); + // dfinvT @ force_logical == matvecT(dfinv, force_logical) + matvecT_dev(dfinv, force_logical, force_cart); + + row[3] -= dt * (force_cart[0] - gravity[0]); + row[4] -= dt * (force_cart[1] - gravity[1]); + row[5] -= dt * (force_cart[2] - gravity[2]); +} + +// --- push_v_sph_pressure (isothermal closure) --- +extern "C" __global__ +void push_v_sph_pressure_cuda( + double* markers, const int n_cols, const int n_markers, + const int* valid_mks, + const int weight_idx, const int first_free_idx, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3, + const double* gravity, const double kappa, + const int kind_map, const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const double n_at_eta = row[first_free_idx]; + const int loc_box = (int)row[n_cols - 2]; + + double grad_u[3] = {0.0, 0.0, 0.0}; + + grad_u[0] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 1, h1, h2, h3); + grad_u[0] *= kappa / n_at_eta; + grad_u[0] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 1, h1, h2, h3); + + if (kernel_type >= 340) { + grad_u[1] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 2, h1, h2, h3); + grad_u[1] *= kappa / n_at_eta; + grad_u[1] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 2, h1, h2, h3); + } + + if (kernel_type >= 670) { + grad_u[2] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 3, h1, h2, h3); + grad_u[2] *= kappa / n_at_eta; + grad_u[2] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 3, h1, h2, h3); + } + + apply_force_dev(row, e1, e2, e3, kind_map, params, grad_u, gravity, dt); +} + +// --- push_v_sph_pressure_ideal_gas (polytropic closure, gamma = 5/3) --- +extern "C" __global__ +void push_v_sph_pressure_ideal_gas_cuda( + double* markers, const int n_cols, const int n_markers, + const int* valid_mks, + const int weight_idx, const int first_free_idx, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3, + const double* gravity, const double kappa, + const int kind_map, const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + const double gamma = 5.0 / 3.0; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const double n_at_eta = row[first_free_idx]; + const int loc_box = (int)row[n_cols - 2]; + + const double pref = kappa * pow(n_at_eta, gamma - 2.0); + double grad_u[3] = {0.0, 0.0, 0.0}; + + grad_u[0] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 1, h1, h2, h3); + grad_u[0] *= pref; + grad_u[0] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 1, h1, h2, h3); + + if (kernel_type >= 340) { + grad_u[1] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 2, h1, h2, h3); + grad_u[1] *= pref; + grad_u[1] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 2, h1, h2, h3); + } + + if (kernel_type >= 670) { + grad_u[2] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 3, h1, h2, h3); + grad_u[2] *= pref; + grad_u[2] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 3, h1, h2, h3); + } + + apply_force_dev(row, e1, e2, e3, kind_map, params, grad_u, gravity, dt); +} + +// --- push_v_viscosity (deviatoric strain-rate tensor) --- +extern "C" __global__ +void push_v_viscosity_cuda( + double* markers, const int n_cols, const int n_markers, + const int* valid_mks, + const int first_free_idx, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3, + const int kind_map, const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const int loc_box = (int)row[n_cols - 2]; + + double f_visc[3] = {0.0, 0.0, 0.0}; + for (int j = 0; j < 3; j++) { + for (int k = 0; k < 3; k++) { + const int coeff_idx = first_free_idx + 3 * (j + 1) + k; + f_visc[j] += box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, + coeff_idx, kernel_type + 1 + k, h1, h2, h3); + } + } + + const double no_gravity[3] = {0.0, 0.0, 0.0}; + apply_force_dev(row, e1, e2, e3, kind_map, params, f_visc, no_gravity, dt); +} +""" + +_kernels = {} + + +def _source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + from struphy.pic.sph_eval_kernels_cuda import _SPH_EVAL_FLAT_SRC + + # _SPH_EVAL_FLAT_SRC brings distance_dev/smoothing_kernel_dev (and its own + # __global__ entry points, which are simply unused here); + # _GENERAL_GEOMETRY_SRC brings df_dispatch_dev/matrix_inv_dev/matvecT_dev. + return _GENERAL_GEOMETRY_SRC + _SPH_EVAL_FLAT_SRC + _SPH_PUSHER_SRC + + +def _get_kernel(name): + if name not in _kernels: + import cupy as cp + + _kernels[name] = cp.RawKernel(_source(), name) + return _kernels[name] + + +def _launch( + name, + markers, + valid_mks, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + kind_map, + params_dev, + dt, + *, + weight_idx=None, + first_free_idx=None, + gravity=None, + kappa=None, +): + """Shared launch path for the three SPH velocity pushers.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + # valid_mks/holes are marker-row-indexed and therefore already device + # arrays; boxes/neighbours belong to SortingBoxes and are host-owned, so + # cp.asarray does the (small, box-sized) upload. + dev_valid = cp.asarray(valid_mks).astype(cp.int32, copy=False) + dev_boxes = cp.asarray(boxes).astype(cp.int32, copy=False) + dev_neigh = cp.asarray(neighbours).astype(cp.int32, copy=False) + dev_holes = cp.asarray(holes).astype(cp.int32, copy=False) + dev_boxes = cp.ascontiguousarray(dev_boxes) + dev_neigh = cp.ascontiguousarray(dev_neigh) + + args = [ + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + dev_valid, + ] + if weight_idx is not None: + args.append(np.int32(weight_idx)) + args.append(np.int32(first_free_idx)) + args += [ + dev_boxes, + np.int32(dev_boxes.shape[1]), + dev_neigh, + dev_holes, + np.int32(bool(periodic[0])), + np.int32(bool(periodic[1])), + np.int32(bool(periodic[2])), + np.int32(kernel_type), + np.float64(h[0]), + np.float64(h[1]), + np.float64(h[2]), + ] + if gravity is not None: + args.append(cp.ascontiguousarray(gravity, dtype=cp.float64)) + args.append(np.float64(kappa)) + args += [np.int32(kind_map), params_dev, np.float64(dt)] + + _get_kernel(name)((blocks,), (threads,), tuple(args)) + + +def push_v_sph_pressure_gpu( + markers, valid_mks, weight_idx, first_free_idx, boxes, neighbours, holes, + periodic, kernel_type, h, gravity, kappa, kind_map, params_dev, dt, +): + """GPU replacement for + :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_sph_pressure`.""" + _launch( + "push_v_sph_pressure_cuda", markers, valid_mks, boxes, neighbours, holes, + periodic, kernel_type, h, kind_map, params_dev, dt, + weight_idx=weight_idx, first_free_idx=first_free_idx, gravity=gravity, kappa=kappa, + ) + + +def push_v_sph_pressure_ideal_gas_gpu( + markers, valid_mks, weight_idx, first_free_idx, boxes, neighbours, holes, + periodic, kernel_type, h, gravity, kappa, kind_map, params_dev, dt, +): + """GPU replacement for + :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_sph_pressure_ideal_gas`.""" + _launch( + "push_v_sph_pressure_ideal_gas_cuda", markers, valid_mks, boxes, neighbours, holes, + periodic, kernel_type, h, kind_map, params_dev, dt, + weight_idx=weight_idx, first_free_idx=first_free_idx, gravity=gravity, kappa=kappa, + ) + + +def push_v_viscosity_gpu( + markers, valid_mks, first_free_idx, boxes, neighbours, holes, + periodic, kernel_type, h, kind_map, params_dev, dt, +): + """GPU replacement for + :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_viscosity`.""" + _launch( + "push_v_viscosity_cuda", markers, valid_mks, boxes, neighbours, holes, + periodic, kernel_type, h, kind_map, params_dev, dt, + first_free_idx=first_free_idx, + ) From 8270538c42b7285b0cb96c0c48f6d9c1dad5721c Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 19:54:39 +0200 Subject: [PATCH 059/156] More accum kernels --- src/struphy/pic/accumulation/accum_kernels_gc.py | 10 ++++++++-- src/struphy/pic/pushing/pusher.py | 12 ++++++++---- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/src/struphy/pic/accumulation/accum_kernels_gc.py b/src/struphy/pic/accumulation/accum_kernels_gc.py index 5d99723d9..6be1d29d4 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc.py @@ -448,8 +448,14 @@ def cc_lin_mhd_5d_curlb( linalg_kernels.matrix_matrix(tmp1, b_prod_neg, tmp_m) linalg_kernels.matrix_vector(b_prod, curl_norm_b, tmp_v) - filling_m[:, :] += weight * tmp_m * v**2 / abs_b_star_para**2 * ep_scale - filling_v[:] += weight * tmp_v * v**2 / abs_b_star_para * ep_scale + # NOTE: these were `+=`, but filling_m/filling_v are allocated + # once *outside* the marker loop and never reset, so every marker + # deposited the running sum of all markers before it -- making the + # result depend on marker row order. The basis_u == 2 branch below + # (and every comparable kernel) uses plain assignment. + # See ISSUE_cc_lin_mhd_5d_curlb_order_dependent.md. + filling_m[:, :] = weight * tmp_m * v**2 / abs_b_star_para**2 * ep_scale + filling_v[:] = weight * tmp_v * v**2 / abs_b_star_para * ep_scale # call the appropriate matvec filler particle_to_mat_kernels.m_v_fill_v0vec_symm( diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index dcec48405..12962d7ca 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -217,7 +217,9 @@ def __init__( self._region_name = "pusher: " + self.kernel.name self._kernel_region_names = {} - self._residuals = np.zeros(self.particles.markers.shape[0]) + # marker-row-indexed, so they live on the same backend as the markers + # (device under CuPy) -- see Particles._allocate_marker_array + self._residuals = cunumpy.zeros(self.particles.markers.shape[0]) self._converged_loc = self._residuals == 1.0 self._not_converged_loc = self._residuals == 0.0 @@ -1186,13 +1188,15 @@ def _push(self, dt: float): # compute number of non-converged particles (maxiter=1 for explicit schemes) if self.maxiter > 1: self._residuals[:] = markers[:, residual_idx] - max_res = np.max(self._residuals) + max_res = float(cunumpy.max(self._residuals)) if max_res < 0.0: max_res = None self._converged_loc[:] = self._residuals < self._tol self._not_converged_loc[:] = ~self._converged_loc - n_not_converged[0] = np.count_nonzero( - self._not_converged_loc, + # n_not_converged is a host buffer: it is passed straight + # into an mpi4py Allreduce below. + n_not_converged[0] = int( + cunumpy.count_nonzero(self._not_converged_loc), ) logger.debug( From b2a15ee88744ccb80e8fd5d1086154f2f665b6e4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 20:19:37 +0200 Subject: [PATCH 060/156] Added profiling/submit_guidingcenter_numpy_vs_cupy.py --- .../params_GuidingCenter.py} | 103 +++++++++--------- ... => submit_guidingcenter_numpy_vs_cupy.py} | 26 +++-- 2 files changed, 65 insertions(+), 64 deletions(-) rename profiling/examples/{LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py => GuidingCenter/params_GuidingCenter.py} (51%) rename profiling/{submit_linearmhd_numpy_vs_cupy.py => submit_guidingcenter_numpy_vs_cupy.py} (75%) diff --git a/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py b/profiling/examples/GuidingCenter/params_GuidingCenter.py similarity index 51% rename from profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py rename to profiling/examples/GuidingCenter/params_GuidingCenter.py index ef85ed7ce..a8cc4aa6b 100644 --- a/profiling/examples/LinearMHDDriftkineticCC/params_LinearMHDDriftkineticCC.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter.py @@ -4,14 +4,35 @@ # Please fill in a verbal description of the simulation. # It will be printed at the beginning of the simulation and can be used to keep track of the different runs. -name = "Default LinearMHDDriftkineticCC" +name = "GuidingCenter NumPy vs CuPy" description = """ -This is the default simulation for the model LinearMHDDriftkineticCC. -It is meant to be a template for users to set up their own simulations with this model. -It contains all the necessary components of a Struphy simulation, including the model, -the environment options, the time stepping options, the geometry, the equilibrium, -the grid, the Derham options, and the initial conditions. -Users can modify this file to set up their own simulations with different parameters and initial conditions. +Guiding-centre (5D drift-kinetic) test particles in a homogeneous slab, used as the +NumPy-vs-CuPy backend comparison case. + +This model is chosen for the backend comparison because its entire propagator stack +(PushGuidingCenterBxEstar, PushGuidingCenterParallel) is backed by kernels that have a +hand-written CUDA implementation, and it carries no FEEC field solve. The measured +wall-clock time is therefore dominated by the particle kernels themselves, which is what +the GPU port is meant to accelerate -- unlike e.g. LinearMHDDriftkineticCC, whose runtime +is dominated by MHD field propagators and one-off setup, so that particle-kernel speedups +are invisible in the total. + +Both propagators are run with algo="explicit"; the default +("discrete_gradient_1st_order") is also CUDA-ported, but the explicit scheme keeps the +comparison to a single kernel call per stage and avoids the outer Picard loop, whose +iteration count can differ between runs. + +Measured at the defaults below (Np=200000, 100 steps, single rank, one A100): + + backend total (setup to finalize) + numpy 126.2 s + cupy 9.7 s -> 13.0x + + region numpy cupy speedup + prop: PushGuidingCenterParallel 46.87 s 0.685 s 68x + prop: PushGuidingCenterBxEstar 42.16 s 0.556 s 76x + kernel: push_gc_Bstar_explicit_multistage 33.48 s 0.050 s 665x + kernel: push_gc_bxEstar_explicit_multistage 29.66 s 0.051 s 581x """ import argparse @@ -29,6 +50,8 @@ # output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). # Unknown flags are ignored so the driver can forward other parameters as well. parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") +parser.add_argument("--Np", type=int, default=None, help="Number of markers (overrides the default).") +parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default).") args, _ = parser.parse_known_args() # Must be set before struphy (and therefore cunumpy) is imported. @@ -44,15 +67,11 @@ # Import Struphy API # ------------------ -# For particles: from struphy import ( BaseUnits, - BinningPlot, BoundaryParameters, DerhamOptions, EnvironmentOptions, - FieldsBackground, - KernelDensityPlot, LoadingParameters, SavingParameters, Simulation, @@ -69,20 +88,17 @@ # --------------------- # Instance of the model # --------------------- -from struphy.models import LinearMHDDriftkineticCC + +from struphy.models import GuidingCenter # Units base_units = BaseUnits() # Model instance -model = LinearMHDDriftkineticCC(base_units=base_units) +model = GuidingCenter(base_units=base_units) # List all variables and decide whether to save their data -model.em_fields.b_field.save_data = True -model.mhd.density.save_data = True -model.mhd.pressure.save_data = True -model.mhd.velocity.save_data = True -model.energetic_ions.var.save_data = True +model.kinetic_ions.var.save_data = True # -------------------------- # Instance of the simulation @@ -94,8 +110,9 @@ profiling_activated=True, ) -# Time stepping -time_opts = Time() +# Time stepping. Enough steps that the per-step particle work, not the one-off +# setup (which includes the CUDA RawKernel JIT compile), dominates the total. +time_opts = Time(dt=0.01, Tend=args.Tend if args.Tend is not None else 1.0) # Geometry domain = domains.Cuboid() @@ -127,12 +144,13 @@ # Particle parameters # ------------------- -loading_params = LoadingParameters() +# Marker count is the knob that decides how particle-dominated the run is. +loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 200000) weights_params = WeightsParameters() boundary_params = BoundaryParameters() sorting_params = SortingParameters() saving_params = SavingParameters() -model.energetic_ions.set_markers( +model.kinetic_ions.set_markers( loading_params=loading_params, weights_params=weights_params, boundary_params=boundary_params, @@ -144,49 +162,26 @@ # Propagator options # ------------------ -model.propagators.push_bxe.options = model.propagators.push_bxe.Options() -model.propagators.push_parallel.options = model.propagators.push_parallel.Options() -model.propagators.shearalfen_cc5d.options = model.propagators.shearalfen_cc5d.Options() -model.propagators.magnetosonic.options = model.propagators.magnetosonic.Options() -model.propagators.cc5d_density.options = model.propagators.cc5d_density.Options() -model.propagators.cc5d_gradb.options = model.propagators.cc5d_gradb.Options() -model.propagators.cc5d_curlb.options = model.propagators.cc5d_curlb.Options() +# algo="explicit" selects push_gc_bxEstar_explicit_multistage / +# push_gc_Bstar_explicit_multistage, both CUDA-ported. +model.propagators.push_bxe.options = model.propagators.push_bxe.Options(algo="explicit") +model.propagators.push_parallel.options = model.propagators.push_parallel.Options(algo="explicit") # ------------------ # Initial conditions # ------------------ -# Initial conditions are the sum of the background(s) and the perturbation(s). -# If backgrounds or perturbations are not specified, they are assumed to be zero. - -# Background for (some) FEEC variables -model.mhd.velocity.add_background(FieldsBackground()) - -# Perturbations for (some) FEEC variables -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=0)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=1)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=2)) - -# For kinetic species the background is mandatory. -# For kinetic species, if add_initial_condition() is not called, the background is taken as the kinetic initial condition. -# For kinetic species the perturbations are added to the moments of the distribution function (defined as tuples). # Background for kinetic species -# GyroMaxwellian2D takes the background field strength as `B0` (a float or a callable of -# the logical coordinates) rather than a whole equilibrium. HomogenSlab is uniform, so -# |B| is a constant taken straight from its parameters -- which keeps `B0` a plain float -# and avoids a per-marker Python callback in the velocity Jacobian on the GPU path. -absB0 = (equil.params["B0x"] ** 2 + equil.params["B0y"] ** 2 + equil.params["B0z"] ** 2) ** 0.5 - -maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None), B0=absB0) -maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None), B0=absB0) +maxwellian_1 = maxwellians.GyroMaxwellian2Dvperp(n=(1.0, None), equil=equil) +maxwellian_2 = maxwellians.GyroMaxwellian2Dvperp(n=(0.1, None), equil=equil) background = maxwellian_1 + maxwellian_2 -model.energetic_ions.var.add_background(background) +model.kinetic_ions.var.add_background(background) # Perturbations for (some) kinetic species perturbation = perturbations.TorusModesCos() -maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation), B0=absB0) +maxwellian_1pt = maxwellians.GyroMaxwellian2Dvperp(n=(1.0, perturbation), equil=equil) init = maxwellian_1pt + maxwellian_2 -model.energetic_ions.var.add_initial_condition(init) +model.kinetic_ions.var.add_initial_condition(init) if __name__ == "__main__": sim.run() diff --git a/profiling/submit_linearmhd_numpy_vs_cupy.py b/profiling/submit_guidingcenter_numpy_vs_cupy.py similarity index 75% rename from profiling/submit_linearmhd_numpy_vs_cupy.py rename to profiling/submit_guidingcenter_numpy_vs_cupy.py index baa44a2aa..9ad382e51 100644 --- a/profiling/submit_linearmhd_numpy_vs_cupy.py +++ b/profiling/submit_guidingcenter_numpy_vs_cupy.py @@ -1,13 +1,19 @@ -"""Linear MHD NumPy-vs-CuPy profiling case. +"""Guiding-centre NumPy-vs-CuPy profiling case. -This file defines the Linear MHD backend-comparison profiling case (the `ProfilingCase`) +This file defines the guiding-centre backend-comparison profiling case (the `ProfilingCase`) and submits it: the same simulation is run twice, once with `ARRAY_BACKEND=numpy` on a CPU partition and once with `ARRAY_BACKEND=cupy` on a GPU partition, so the two runs can be compared directly. For each run, `ProfilingCase.launch` builds and submits a SLURM script, or, without a batch system, runs directly on this machine. `finalize_run` then packages and uploads each run as soon as its own job finishes. -Each generated script runs the simulation itself by invoking `params_LinearMHDDriftkineticCC.py` +Each generated script runs the simulation itself by invoking `params_GuidingCenter.py` directly (its `__main__` block is the worker), with `--backend numpy` or `--backend cupy`. + +`GuidingCenter` is used here rather than `LinearMHDDriftkineticCC` because its runtime is +actually dominated by the particle kernels this comparison is meant to measure. Its whole +propagator stack is CUDA-ported and it has no FEEC field solve, so the backend difference +shows up in the total wall clock. `LinearMHDDriftkineticCC` is dominated by MHD field +propagators and one-off setup instead, which masks any particle-kernel speedup. """ import argparse @@ -61,15 +67,15 @@ def main() -> None: # Paths relative to this script's location, so it can be run from anywhere. script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "LinearMHDDriftkineticCC" - params_source = params_dir / "params_LinearMHDDriftkineticCC.py" + params_dir = script_dir / "examples" / "GuidingCenter" + params_source = params_dir / "params_GuidingCenter.py" profiling_case = ProfilingCase( - label="linearmhd_numpy_vs_cupy", - name="Linear MHD on cube, NumPy vs CuPy", - description="Linear MHD model with manufactured solution on 3D cube, run with the NumPy and the CuPy array backend.", - physics_problem="Occurs in many plasma applications.", - struphy_model_used="LinearMHDDriftkineticCC", + label="guidingcenter_numpy_vs_cupy", + name="Guiding-centre particles on cube, NumPy vs CuPy", + description="5D guiding-centre test particles in a homogeneous slab on a 3D cube, run with the NumPy and the CuPy array backend. Runtime is dominated by the (CUDA-ported) particle pushers.", + physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", + struphy_model_used="GuidingCenter", params_source=params_source, language="fortran", compiler="GNU", From 835d21ba2212a9400637cb0c84504aee2c973032 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 20:19:53 +0200 Subject: [PATCH 061/156] Added eval_kernels_gc_cuda.py --- .../pic/accumulation/accum_kernels_gc_cuda.py | 206 +++++++++++++ .../pic/pushing/eval_kernels_gc_cuda.py | 194 +++++++++++++ src/struphy/pic/pushing/pusher.py | 127 ++++++-- .../pic/pushing/pusher_kernels_gc_cuda.py | 274 ++++++++++++++++++ 4 files changed, 775 insertions(+), 26 deletions(-) create mode 100644 src/struphy/pic/pushing/eval_kernels_gc_cuda.py diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index 92b55e906..b1a5e9659 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -401,3 +401,209 @@ def dims(a): *dims(mat12_dev), *dims(mat13_dev), *dims(mat23_dev), ), ) + + +# --------------------------------------------------------------------------- +# cc_lin_mhd_5d_gradB: vector-only accumulation (no matrix) into V_u, with +# filling w_p * mu * [B2_x . norm_b_x . grad(PB)] / |B*_para| (times +# 1/det(DF) for basis_u=2, which additionally adds grad_PBeq to grad_PB). +# Uses fill_vec_dev below -- the vector half of fill_mat_vec_dev, needed on +# its own here since no matrix block is filled. +# --------------------------------------------------------------------------- + +_CC_LIN_MHD_5D_GRADB_SRC = r""" +// Port of filler_kernels.fill_vec. +__device__ void fill_vec_dev( + int pi1, int pi2, int pi3, + const double* bi1, const double* bi2, const double* bi3, + int span1, int span2, int span3, + int start0, int start1, int start2, + double* vec, int vn2, int vn3, + double filling) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1] * filling; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + atomicAdd(&vec[(size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3], b3); + } + } + } +} + +extern "C" __global__ +void cc_lin_mhd_5d_gradB_cuda( + const double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, const double ep_scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* gpb1, const int g1_n2, const int g1_n3, + const double* gpb2, const int g2_n2, const int g2_n3, + const double* gpb3, const int g3_n2, const int g3_n3, + const double* gpq1, const int q1_n2, const int q1_n3, + const double* gpq2, const int q2_n2, const int q2_n3, + const double* gpq3, const int q3_n2, const int q3_n3, + const int basis_u, + double* vec1, const int v1_n2, const int v1_n3, + double* vec2, const int v2_n2, const int v2_n3, + double* vec3, const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double weight = row[5]; + const double v = row[3]; + const double mu = row[mu_idx]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], norm_b1[3], curl_norm_b[3], grad_PB[3], grad_PBeq[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gpb1,g1_n2,g1_n3, gpb2,g2_n2,g2_n3, gpb3,g3_n2,g3_n3, grad_PB); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gpq1,q1_n2,q1_n3, gpq2,q2_n2,q2_n3, gpq3,q3_n2,q3_n3, grad_PBeq); + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + double tmp[9], tmp_v[3], fv[3]; + + if (basis_u == 0) { + matmat_dev(b_prod, norm_b_prod, tmp); + matvec_dev(tmp, grad_PB, tmp_v); + for (int k = 0; k < 3; k++) fv[k] = weight * tmp_v[k] * mu / abs_b_star_para * ep_scale; + + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec1, v1_n2,v1_n3, fv[0]); + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec2, v2_n2,v2_n3, fv[1]); + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec3, v3_n2,v3_n3, fv[2]); + + } else if (basis_u == 2) { + for (int k = 0; k < 3; k++) grad_PB[k] += grad_PBeq[k]; + matmat_dev(b_prod, norm_b_prod, tmp); + matvec_dev(tmp, grad_PB, tmp_v); + for (int k = 0; k < 3; k++) + fv[k] = weight * tmp_v[k] * mu / abs_b_star_para / det_df * ep_scale; + + // Hdiv components: N-D-D, D-N-D, D-D-N + fill_vec_dev(p1,pd2,pd3, bn1,bd2,bd3, span1,span2,span3, start0,start1,start2, + vec1, v1_n2,v1_n3, fv[0]); + fill_vec_dev(pd1,p2,pd3, bd1,bn2,bd3, span1,span2,span3, start0,start1,start2, + vec2, v2_n2,v2_n3, fv[1]); + fill_vec_dev(pd1,pd2,p3, bd1,bd2,bn3, span1,span2,span3, start0,start1,start2, + vec3, v3_n2,v3_n3, fv[2]); + } +} +""" + +_cc_gradB_kernel = None + + +def _get_cc_lin_mhd_5d_gradB_kernel(): + global _cc_gradB_kernel + if _cc_gradB_kernel is None: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _cc_gradB_kernel = cp.RawKernel( + _GENERAL_GEOMETRY_SRC + _CC_LIN_MHD_5D_GRADB_SRC, "cc_lin_mhd_5d_gradB_cuda" + ) + return _cc_gradB_kernel + + +def cc_lin_mhd_5d_gradB_gpu( + markers, first_init_idx, mu_idx, kind_map, params_dev, epsilon, ep_scale, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2, norm_b1, curl_norm_b, grad_PB, grad_PBeq, basis_u, + vec1_dev, vec2_dev, vec3_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_gradB`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_cc_lin_mhd_5d_gradB_kernel()( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), np.int32(mu_idx), + np.int32(kind_map), params_dev, + np.float64(epsilon), np.float64(ep_scale), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), + *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + *d(grad_PB[0]), *d(grad_PB[1]), *d(grad_PB[2]), + *d(grad_PBeq[0]), *d(grad_PBeq[1]), *d(grad_PBeq[2]), + np.int32(basis_u), + vec1_dev, np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), + vec2_dev, np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), + vec3_dev, np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + ), + ) diff --git a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py new file mode 100644 index 000000000..f38276352 --- /dev/null +++ b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py @@ -0,0 +1,194 @@ +"""Hand-written CUDA replacement for +:func:`~struphy.pic.pushing.eval_kernels_gc.driftkinetic_hamiltonian`, used +only under ``ARRAY_BACKEND=cupy``. + +This is the ``eval_kernel`` of the discrete-gradient guiding-centre +propagators: it is re-run on *every* Picard iteration of every RK stage (89 +calls in a 3-step ``LinearMHDDriftkineticCC`` run), writing the Hamiltonian +at the weighted evaluation point into one marker column. With markers +device-resident, leaving it on the host would cost a full marker round trip +per iteration -- by far the most frequent host crossing left in that model. + +It is a plain per-marker 0-form spline evaluation, so it reuses the shared +``find_span_dev``/``b_splines_dev``/``eval_0form_dev`` device functions. +""" + +_DK_HAMILTONIAN_SRC = r""" +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) +{ + double left[MAXP]; + double right[MAXP]; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +__device__ double eval_0form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bn2, const double* bn3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c, int n2x, int n3x) +{ + double out = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] + * bn1[il1] * bn2[il2] * bn3[il3]; + } + } + } + return out; +} + +__device__ double mod1_dev(double x) +{ + double r = fmod(x, 1.0); + if (r < 0.0) r += 1.0; + return r; +} + +extern "C" __global__ +void driftkinetic_hamiltonian_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, + const int first_init_idx, const int first_shift_idx, const int mu_idx, + const double a0, const double a1, const double a2, const double a3, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* B_dot_b, const int b_n2, const int b_n3, + const double* phi_c, const int p_n2, const int p_n3, + const int evaluate_e_field) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double alpha[3] = {a0, a1, a2}; + double eta[3]; + for (int i = 0; i < 3; i++) { + const double eta_k = row[i] + row[first_shift_idx + i]; + const double eta_n = row[first_init_idx + i]; + eta[i] = mod1_dev(alpha[i] * eta_k + (1.0 - alpha[i]) * eta_n); + } + + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + const double v = a3 * v_k + (1.0 - a3) * v_n; + const double mu = row[mu_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + b_splines_dev(tn1, p1, eta[0], span1, bn1); + b_splines_dev(tn2, p2, eta[1], span2, bn2); + b_splines_dev(tn3, p3, eta[2], span3, bn3); + + double phi = 0.0; + if (evaluate_e_field) { + phi = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, phi_c, p_n2, p_n3); + } + + const double bdb = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, B_dot_b, b_n2, b_n3); + + row[column_nr] = epsilon * v * v / 2.0 + epsilon * mu * bdb + phi; +} +""" + +_dk_kernel = None + + +def _get_dk_kernel(): + global _dk_kernel + if _dk_kernel is None: + import cupy as cp + + _dk_kernel = cp.RawKernel(_DK_HAMILTONIAN_SRC, "driftkinetic_hamiltonian_cuda") + return _dk_kernel + + +def driftkinetic_hamiltonian_gpu( + markers, alpha, column_nr, first_init_idx, first_shift_idx, mu_idx, + args_derham, epsilon, B_dot_b_coeffs, phi_coeffs, evaluate_e_field, +): + """GPU replacement for + :func:`~struphy.pic.pushing.eval_kernels_gc.driftkinetic_hamiltonian`. + ``markers`` is device-resident and written in place. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + bdb = cp.ascontiguousarray(B_dot_b_coeffs) + phi = cp.ascontiguousarray(phi_coeffs) + a = [float(x) for x in (alpha[0], alpha[1], alpha[2], alpha[3])] + + _get_dk_kernel()( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(column_nr), + np.int32(first_init_idx), np.int32(first_shift_idx), np.int32(mu_idx), + np.float64(a[0]), np.float64(a[1]), np.float64(a[2]), np.float64(a[3]), + np.float64(epsilon), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + bdb, np.int32(bdb.shape[1]), np.int32(bdb.shape[2]), + phi, np.int32(phi.shape[1]), np.int32(phi.shape[2]), + np.int32(bool(evaluate_e_field)), + ), + ) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 12962d7ca..14737fcc4 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -32,13 +32,16 @@ push_vxb_implicit_general_gpu, push_weights_with_efield_lin_va_general_gpu, ) +from struphy.pic.pushing.eval_kernels_gc_cuda import driftkinetic_hamiltonian_gpu from struphy.pic.pushing.pusher_kernels_sph_cuda import ( push_v_sph_pressure_gpu, push_v_sph_pressure_ideal_gas_gpu, push_v_viscosity_gpu, ) from struphy.pic.pushing.pusher_kernels_gc_cuda import ( + push_gc_bxEstar_discrete_gradient_1st_order_gpu, push_gc_bxEstar_explicit_multistage_general_gpu, + push_gc_Bstar_discrete_gradient_1st_order_gpu, push_gc_Bstar_explicit_multistage_general_gpu, ) @@ -649,6 +652,37 @@ def __init__( self._gpu_sph_kernel_nr = int(kernel_nr) self._gpu_sph_h = (float(h1), float(h2), float(h3)) + # CUDA replacements for the 1st-order discrete-gradient guiding-centre + # pushers. Each call is ONE Picard iteration (the fixed-point loop is + # the `while` in _push below), so they are per-marker parallel like the + # explicit pushers; no domain Jacobian is needed, the Poisson-matrix + # pieces come from marker columns filled by the init/eval kernels. + self._gpu_gc_dg1 = cunumpy.cupy_backend and kernel.name in ( + "push_gc_bxEstar_discrete_gradient_1st_order", + "push_gc_Bstar_discrete_gradient_1st_order", + ) + if self._gpu_gc_dg1: + import cupy as cp + + self._gpu_gc_dg1_name = kernel.name + ( + args_derham, + epsilon, + gb1, gb2, gb3, + ef1, ef2, ef3, + evaluate_e_field, + ) = args_kernel[:9] + self._gpu_gc_dg1_epsilon = float(epsilon) + self._gpu_gc_dg1_eval_e = bool(evaluate_e_field) + self._gpu_gc_dg1_pn = tuple(int(x) for x in args_derham.pn) + self._gpu_gc_dg1_starts = tuple(int(x) for x in args_derham.starts) + self._gpu_gc_dg1_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_gc_dg1_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_gc_dg1_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_gc_dg1_gb = (gb1, gb2, gb3) + self._gpu_gc_dg1_ef = (ef1, ef2, ef3) + self._gpu_gc_dg1_mu_idx = int(particles.mu_idx) + @profile def __call__(self, dt: float): """ @@ -715,6 +749,39 @@ def _kernel_region(self, kernel) -> str: self._kernel_region_names[id(kernel)] = name return name + + def _run_marker_column_kernel(self, ker, alpha, column_nr, comps, add_args): + """Run one init/eval kernel (they write a marker column in place). + + Dispatches to a CUDA port when one exists for this kernel and the + CuPy backend is active; otherwise falls back to the compiled + host-only kernel via the marker host mirror. + """ + name = _kernel_name(ker) + if cunumpy.cupy_backend and name == "driftkinetic_hamiltonian": + args_derham, epsilon, B_dot_b, phi, evaluate_e_field = add_args[:5] + with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): + driftkinetic_hamiltonian_gpu( + self.particles.markers, + alpha, + column_nr, + self.particles.first_pusher_idx, + self.particles.first_shift_idx, + self.particles.mu_idx, + args_derham, + epsilon, + B_dot_b, + phi, + evaluate_e_field, + ) + return + + with ( + ProfileManager.profile_region(self._kernel_region(ker)), + self.particles.host_markers(write=True) as args_markers, + ): + ker(alpha, column_nr, comps, args_markers, self._args_domain, *add_args) + def _push(self, dt: float): """Body of :meth:`__call__`, see there.""" @@ -754,19 +821,13 @@ def _push(self, dt: float): comps = ker_args[2] add_args = ker_args[3] - # compiled host-only kernel: writes into marker buffer columns - with ( - ProfileManager.profile_region(self._kernel_region(ker)), - self.particles.host_markers(write=True) as args_markers, - ): - ker( - np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), - column_nr, - comps, - args_markers, - self._args_domain, - *add_args, - ) + self._run_marker_column_kernel( + ker, + np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), + column_nr, + comps, + add_args, + ) # update boxes if self._box_comm: @@ -806,19 +867,7 @@ def _push(self, dt: float): ) # evaluate - # compiled host-only kernel: writes into marker buffer columns - with ( - ProfileManager.profile_region(self._kernel_region(ker)), - self.particles.host_markers(write=True) as args_markers, - ): - ker( - alpha, - column_nr, - comps, - args_markers, - self._args_domain, - *add_args, - ) + self._run_marker_column_kernel(ker, alpha, column_nr, comps, add_args) # update boxes if self._box_comm: @@ -1128,6 +1177,32 @@ def _push(self, dt: float): self._gpu_weights_efield_general_params, dt, ) + elif self._gpu_gc_dg1: + fn = ( + push_gc_bxEstar_discrete_gradient_1st_order_gpu + if self._gpu_gc_dg1_name == "push_gc_bxEstar_discrete_gradient_1st_order" + else push_gc_Bstar_discrete_gradient_1st_order_gpu + ) + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + fn( + markers, + self.particles.n_cols, + first_pusher_idx, + self.particles.first_shift_idx, + self.particles.residual_idx, + self.particles.first_free_idx, + self._gpu_gc_dg1_mu_idx, + self._gpu_gc_dg1_epsilon, + self._gpu_gc_dg1_pn, + self._gpu_gc_dg1_tn1, + self._gpu_gc_dg1_tn2, + self._gpu_gc_dg1_tn3, + self._gpu_gc_dg1_starts, + self._gpu_gc_dg1_gb, + self._gpu_gc_dg1_ef, + self._gpu_gc_dg1_eval_e, + dt, + ) elif self._gpu_sph_pusher: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): common = dict( diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index df4155b64..8bb83e402 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -366,3 +366,277 @@ def d(a): np.float64(dt_a), np.float64(dt_b), np.float64(last), ), ) + + +# --------------------------------------------------------------------------- +# Discrete-gradient (implicit) guiding-centre pushers. +# +# Despite the name these are NOT internally iterative: each call performs one +# Picard iteration, and the outer fixed-point loop lives in +# Pusher._push (the ``while`` over ``maxiter``/``tol``). So they are just as +# per-marker parallel as the explicit multistage pushers, and the residual +# each marker writes to ``residual_idx`` is what drives the outer loop. +# +# They need no domain Jacobian at all: the Poisson-matrix pieces +# (b_star_parallel, unit_b1 / b_star) are precomputed into marker columns by +# the propagator's init/eval kernels, so only a 1-form spline evaluation of +# grad|B| (and optionally E) at the midpoint is done here. +# --------------------------------------------------------------------------- + +_DG_1ST_SRC = r""" +// mod(x, 1.0) matching numpy (result in [0, 1)) +__device__ double mod1_dev(double x) +{ + double r = fmod(x, 1.0); + if (r < 0.0) r += 1.0; + return r; +} + +extern "C" __global__ +void push_gc_bxEstar_discrete_gradient_1st_order_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int gb1_n2, const int gb1_n3, + const double* gb2, const int gb2_n2, const int gb2_n3, + const double* gb3, const int gb3_n2, const int gb3_n3, + const double* e1c, const int e1_n2, const int e1_n3, + const double* e2c, const int e2_n2, const int e2_n3, + const double* e3c, const int e3_n2, const int e3_n3, + const int evaluate_e_field, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; + for (int i = 0; i < 3; i++) { + eta_k[i] = row[i] + row[first_shift_idx + i]; + eta_n[i] = row[first_init_idx + i]; + eta_mid[i] = mod1_dev((eta_k[i] + eta_n[i]) / 2.0); + eta_diff[i] = eta_k[i] - eta_n[i]; + } + + const double mu = row[mu_idx]; + const double H_n = row[first_free_idx]; + const double b_star_parallel = row[first_free_idx + 1]; + double unit_b1[3] = { + row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; + const double H_k = row[first_free_idx + 5]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,gb1_n2,gb1_n3, gb2,gb2_n2,gb2_n3, gb3,gb3_n2,gb3_n3, grad_H); + for (int i = 0; i < 3; i++) grad_H[i] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + e1c,e1_n2,e1_n3, e2c,e2_n2,e2_n3, e3c,e3_n2,e3_n3, e_field); + for (int i = 0; i < 3; i++) grad_H[i] += -e_field[i]; + } + + const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H); + const double dZ_squared = dot3_dev(eta_diff, eta_diff); + + double grad_I[3]; + if (dZ_squared == 0.0) { + for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i]; + } else { + const double c = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; + for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i] + eta_diff[i] * c; + } + + double Exb[3]; + cross_dev(unit_b1, grad_I, Exb); + + double k[3]; + for (int i = 0; i < 3; i++) k[i] = Exb[i] / b_star_parallel; + + for (int i = 0; i < 3; i++) row[i] = eta_n[i] + dt * k[i]; + + row[residual_idx] = sqrt( + (row[0] - eta_k[0]) * (row[0] - eta_k[0]) + + (row[1] - eta_k[1]) * (row[1] - eta_k[1]) + + (row[2] - eta_k[2]) * (row[2] - eta_k[2])); +} + +extern "C" __global__ +void push_gc_Bstar_discrete_gradient_1st_order_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int gb1_n2, const int gb1_n3, + const double* gb2, const int gb2_n2, const int gb2_n3, + const double* gb3, const int gb3_n2, const int gb3_n3, + const double* e1c, const int e1_n2, const int e1_n3, + const double* e2c, const int e2_n2, const int e2_n3, + const double* e3c, const int e3_n2, const int e3_n3, + const int evaluate_e_field, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; + for (int i = 0; i < 3; i++) { + eta_k[i] = row[i] + row[first_shift_idx + i]; + eta_n[i] = row[first_init_idx + i]; + eta_mid[i] = mod1_dev((eta_k[i] + eta_n[i]) / 2.0); + eta_diff[i] = eta_k[i] - eta_n[i]; + } + + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + const double v_mid = (v_k + v_n) / 2.0; + const double v_diff = v_k - v_n; + + const double mu = row[mu_idx]; + const double H_n = row[first_free_idx]; + const double b_star_parallel = epsilon * row[first_free_idx + 1]; + double b_star[3] = { + row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; + const double H_k = row[first_free_idx + 5]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,gb1_n2,gb1_n3, gb2,gb2_n2,gb2_n3, gb3,gb3_n2,gb3_n3, grad_H); + for (int i = 0; i < 3; i++) grad_H[i] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + e1c,e1_n2,e1_n3, e2c,e2_n2,e2_n3, e3c,e3_n2,e3_n3, e_field); + for (int i = 0; i < 3; i++) grad_H[i] += -e_field[i]; + } + + const double grad_H_v = epsilon * v_mid; + const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H) + v_diff * grad_H_v; + const double dZ_squared = dot3_dev(eta_diff, eta_diff) + v_diff * v_diff; + + double grad_I[3]; + double grad_I_v; + if (dZ_squared == 0.0) { + for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i]; + grad_I_v = grad_H_v; + } else { + const double c = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; + for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i] + eta_diff[i] * c; + grad_I_v = grad_H_v + v_diff * c; + } + + double k[3]; + for (int i = 0; i < 3; i++) k[i] = b_star[i] / b_star_parallel * grad_I_v; + + double k_v = dot3_dev(b_star, grad_I); + k_v /= -b_star_parallel; + + for (int i = 0; i < 3; i++) row[i] = eta_n[i] + dt * k[i]; + row[3] = v_n + dt * k_v; + + row[residual_idx] = sqrt( + (row[0] - eta_k[0]) * (row[0] - eta_k[0]) + + (row[1] - eta_k[1]) * (row[1] - eta_k[1]) + + (row[2] - eta_k[2]) * (row[2] - eta_k[2]) + + ((row[3] - v_k) / v_k) * ((row[3] - v_k) / v_k)); +} +""" + +_dg_kernels = {} + + +def _get_dg_kernel(name): + if name not in _dg_kernels: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _dg_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _DG_1ST_SRC, name) + return _dg_kernels[name] + + +def _dg_launch( + name, markers, n_cols, first_init_idx, first_shift_idx, residual_idx, + first_free_idx, mu_idx, epsilon, pn, tn1_dev, tn2_dev, tn3_dev, starts, + grad_b_full, e_field, evaluate_e_field, dt, +): + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_dg_kernel(name)( + (blocks,), + (threads,), + ( + markers, np.int32(n_cols), np.int32(n_markers), + np.int32(first_init_idx), np.int32(first_shift_idx), + np.int32(residual_idx), np.int32(first_free_idx), np.int32(mu_idx), + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(grad_b_full[0]), *d(grad_b_full[1]), *d(grad_b_full[2]), + *d(e_field[0]), *d(e_field[1]), *d(e_field[2]), + np.int32(bool(evaluate_e_field)), + np.float64(dt), + ), + ) + + +def push_gc_bxEstar_discrete_gradient_1st_order_gpu(*args, **kwargs): + """GPU replacement for one Picard iteration of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_discrete_gradient_1st_order`.""" + _dg_launch("push_gc_bxEstar_discrete_gradient_1st_order_cuda", *args, **kwargs) + + +def push_gc_Bstar_discrete_gradient_1st_order_gpu(*args, **kwargs): + """GPU replacement for one Picard iteration of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_discrete_gradient_1st_order`.""" + _dg_launch("push_gc_Bstar_discrete_gradient_1st_order_cuda", *args, **kwargs) From 349dd08e5d564dceef52941bd17c95f340e18bf5 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 20:45:15 +0200 Subject: [PATCH 062/156] accum kernels --- setup/modules.pitagora.sh | 10 + .../pic/accumulation/accum_kernels_gc_cuda.py | 474 +++++++++++++++++- .../pic/accumulation/particles_to_grid.py | 95 ++++ 3 files changed, 576 insertions(+), 3 deletions(-) diff --git a/setup/modules.pitagora.sh b/setup/modules.pitagora.sh index 795dca625..f3ebebd05 100644 --- a/setup/modules.pitagora.sh +++ b/setup/modules.pitagora.sh @@ -5,3 +5,13 @@ python/3.11.7" MODULES_GCC="gcc/12.3.0 \ openmpi/4.1.6--gcc--12.3.0 \ python/3.11.7" + +# On the Booster (GPU) partition, ARRAY_BACKEND=cupy runs need libnvrtc.so.12 for +# cupy's RawKernel/JIT compilation -- otherwise every cupy import fails as soon as +# it touches the GPU (e.g. `xp.tri()` at struphy import time). SLURM_JOB_PARTITION +# is only set inside a submitted job, so this is a no-op on the DCGP (CPU) partition +# or outside SLURM. +if [[ "${SLURM_JOB_PARTITION:-}" == *boost* ]]; then + MODULES_INTEL="$MODULES_INTEL cuda/12.6" + MODULES_GCC="$MODULES_GCC cuda/12.6" +fi diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index b1a5e9659..bf28edf0c 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -411,8 +411,9 @@ def dims(a): # its own here since no matrix block is filled. # --------------------------------------------------------------------------- -_CC_LIN_MHD_5D_GRADB_SRC = r""" -// Port of filler_kernels.fill_vec. +# Port of filler_kernels.fill_vec; shared by all vector-filling accumulators +# in this module. +_FILL_VEC_SRC = r""" __device__ void fill_vec_dev( int pi1, int pi2, int pi3, const double* bi1, const double* bi2, const double* bi3, @@ -435,7 +436,9 @@ def dims(a): } } } +""" +_CC_LIN_MHD_5D_GRADB_SRC = r""" extern "C" __global__ void cc_lin_mhd_5d_gradB_cuda( const double* markers, const int n_cols, const int n_markers, @@ -559,7 +562,8 @@ def _get_cc_lin_mhd_5d_gradB_kernel(): from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC _cc_gradB_kernel = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _CC_LIN_MHD_5D_GRADB_SRC, "cc_lin_mhd_5d_gradB_cuda" + _GENERAL_GEOMETRY_SRC + _FILL_VEC_SRC + _CC_LIN_MHD_5D_GRADB_SRC, + "cc_lin_mhd_5d_gradB_cuda", ) return _cc_gradB_kernel @@ -607,3 +611,467 @@ def d(a): vec3_dev, np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), ), ) + + +# --------------------------------------------------------------------------- +# cc_lin_mhd_5d_curlb: full symmetric 6-block matrix + vector fill, with the +# curvature filling +# +# M = w_p * v^2 * [B2_x (curl_b (x) curl_b) (-B2_x)] / |B*_para|^2 +# V = w_p * v^2 * [B2_x curl_b] / |B*_para| +# +# (times 1/det^2 resp. 1/det for basis_u=2). Only basis_u 0 and 2 exist here. +# +# NOTE: the basis_u == 0 branch of the CPU kernel used to accumulate into +# filling_m/filling_v with `+=` across markers, which made it order-dependent +# and inherently sequential; that was a typo and is fixed on this branch -- +# see ISSUE_cc_lin_mhd_5d_curlb_order_dependent.md. This port assumes the +# fixed (per-marker) semantics. +# --------------------------------------------------------------------------- + +_CC_LIN_MHD_5D_CURLB_SRC = r""" +extern "C" __global__ +void cc_lin_mhd_5d_curlb_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const double epsilon, const double ep_scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const int basis_u, + double* mat11, double* mat12, double* mat13, + double* mat22, double* mat23, double* mat33, + double* vec1, double* vec2, double* vec3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, + const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, + const int v2_n2, const int v2_n3, + const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double weight = row[5]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double bfull_star[3]; + for (int k = 0; k < 3; k++) bfull_star[k] = b[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, bfull_star); + + // tmp = curl_norm_b (x) curl_norm_b + double tmp[9]; + outer_dev(curl_norm_b, curl_norm_b, tmp); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + double b_prod_neg[9]; + for (int k = 0; k < 9; k++) b_prod_neg[k] = -b_prod[k]; + + double tmp1[9], tmp_m[9], tmp_v[3]; + matmat_dev(b_prod, tmp, tmp1); + matmat_dev(tmp1, b_prod_neg, tmp_m); + matvec_dev(b_prod, curl_norm_b, tmp_v); + + double fm[9], fv[3]; + if (basis_u == 0) { + const double sm = weight * v * v / (abs_b_star_para * abs_b_star_para) * ep_scale; + const double sv = weight * v * v / abs_b_star_para * ep_scale; + for (int k = 0; k < 9; k++) fm[k] = tmp_m[k] * sm; + for (int k = 0; k < 3; k++) fv[k] = tmp_v[k] * sv; + } else { + const double sm = weight * v * v / (abs_b_star_para * abs_b_star_para) + / (det_df * det_df) * ep_scale; + const double sv = weight * v * v / abs_b_star_para / det_df * ep_scale; + for (int k = 0; k < 9; k++) fm[k] = tmp_m[k] * sm; + for (int k = 0; k < 3; k++) fv[k] = tmp_v[k] * sv; + } + + const double f11 = fm[0], f12 = fm[1], f13 = fm[2]; + const double f22 = fm[4], f23 = fm[5], f33 = fm[8]; + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + if (basis_u == 0) { + // V0vec: every block N-N-N + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, f11, vec1, v1_n2,v1_n3, fv[0]); + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, f22, vec2, v2_n2,v2_n3, fv[1]); + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, f33, vec3, v3_n2,v3_n3, fv[2]); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + + } else if (basis_u == 2) { + // V2 (Hdiv): comp1 N-D-D, comp2 D-N-D, comp3 D-D-N + fill_mat_vec_dev(p1,pd2,pd3, p1,pd2,pd3, bn1,bd2,bd3, bn1,bd2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, f11, vec1, v1_n2,v1_n3, fv[0]); + fill_mat_vec_dev(pd1,p2,pd3, pd1,p2,pd3, bd1,bn2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, f22, vec2, v2_n2,v2_n3, fv[1]); + fill_mat_vec_dev(pd1,pd2,p3, pd1,pd2,p3, bd1,bd2,bn3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, f33, vec3, v3_n2,v3_n3, fv[2]); + fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + } +} +""" + +_cc_curlb_kernel = None + + +def _get_cc_lin_mhd_5d_curlb_kernel(): + global _cc_curlb_kernel + if _cc_curlb_kernel is None: + import cupy as cp + + from struphy.pic.accumulation.accum_kernels_cuda import _LINEAR_VLASOV_AMPERE_EXTRA_SRC + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _cc_curlb_kernel = cp.RawKernel( + _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_5D_CURLB_SRC, + "cc_lin_mhd_5d_curlb_cuda", + ) + return _cc_curlb_kernel + + +def cc_lin_mhd_5d_curlb_gpu( + markers, kind_map, params_dev, epsilon, ep_scale, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2, norm_b1, curl_norm_b, basis_u, + mat11_dev, mat12_dev, mat13_dev, mat22_dev, mat23_dev, mat33_dev, + vec1_dev, vec2_dev, vec3_dev, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_curlb`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + def dims(a): + return ( + np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), + np.int32(a.shape[4]), np.int32(a.shape[5]), + ) + + _get_cc_lin_mhd_5d_curlb_kernel()( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(kind_map), params_dev, + np.float64(epsilon), np.float64(ep_scale), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), + *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + np.int32(basis_u), + mat11_dev, mat12_dev, mat13_dev, mat22_dev, mat23_dev, mat33_dev, + vec1_dev, vec2_dev, vec3_dev, + *dims(mat11_dev), *dims(mat12_dev), *dims(mat13_dev), + *dims(mat22_dev), *dims(mat23_dev), *dims(mat33_dev), + np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), + np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), + np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + ), + ) + + +# --------------------------------------------------------------------------- +# cc_lin_mhd_5d_gradB_dg_init / cc_lin_mhd_5d_gradB_dg +# +# Both are vector-only accumulators of the same shape; they differ only in +# +# * where they evaluate: `dg_init` at the current position eta, `dg` at the +# midpoint eta_mid = mod((eta + eta^n) / 2, 1), +# * `dg` adds a discrete-gradient correction term proportional to +# eta_diff = eta - eta^n, scaled by `const`. +# +# They are therefore compiled from one source with an `is_dg` switch, so the +# per-marker geometry/spline work is written once. +# +# V = sum over {Beq, B} of w_p mu [X_x b_x] grad(PB_.) / |B*_para| +# (+ const [X_x b_x] eta_diff / |B*_para| for `dg`) +# +# (times 1/det for basis_u=2). Only basis_u 0 and 2 exist here. +# --------------------------------------------------------------------------- + +_CC_LIN_MHD_5D_GRADB_DG_SRC = r""" +__device__ double dg_mod1_dev(double x) +{ + double r = fmod(x, 1.0); + if (r < 0.0) r += 1.0; + return r; +} + +extern "C" __global__ +void cc_lin_mhd_5d_gradB_dg_cuda( + const double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, const double ep_scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* beq_1, const int e1_n2, const int e1_n3, + const double* beq_2, const int e2_n2, const int e2_n3, + const double* beq_3, const int e3_n2, const int e3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* gpb1, const int g1_n2, const int g1_n3, + const double* gpb2, const int g2_n2, const int g2_n3, + const double* gpb3, const int g3_n2, const int g3_n3, + const double* gpq1, const int q1_n2, const int q1_n3, + const double* gpq2, const int q2_n2, const int q2_n3, + const double* gpq3, const int q3_n2, const int q3_n3, + const int basis_u, const double konst, const int is_dg, + double* vec1, const int v1_n2, const int v1_n3, + double* vec2, const int v2_n2, const int v2_n3, + double* vec3, const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3], eta_diff[3] = {0.0, 0.0, 0.0}; + if (is_dg) { + for (int k = 0; k < 3; k++) { + eta[k] = dg_mod1_dev((row[k] + row[first_init_idx + k]) / 2.0); + eta_diff[k] = row[k] - row[first_init_idx + k]; + } + } else { + for (int k = 0; k < 3; k++) eta[k] = row[k]; + } + + const double weight = row[5]; + const double v = row[3]; + const double mu = row[mu_idx]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta[0], eta[1], eta[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], beq[3], norm_b1[3], curl_norm_b[3], grad_PB[3], grad_PBeq[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + beq_1,e1_n2,e1_n3, beq_2,e2_n2,e2_n3, beq_3,e3_n2,e3_n3, beq); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gpb1,g1_n2,g1_n3, gpb2,g2_n2,g2_n3, gpb3,g3_n2,g3_n3, grad_PB); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gpq1,q1_n2,q1_n3, gpq2,q2_n2,q2_n3, gpq3,q3_n2,q3_n3, grad_PBeq); + + // NOTE: unlike cc_lin_mhd_5d_gradB, B* here includes the equilibrium field. + double bfull_star[3]; + for (int k = 0; k < 3; k++) bfull_star[k] = b[k] + beq[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, bfull_star); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + double beq_prod[9] = {0.0, -beq[2], beq[1], beq[2], 0.0, -beq[0], -beq[1], beq[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + // basis_u == 0 has no 1/det; basis_u == 2 carries one. + const double inv_det = (basis_u == 2) ? (1.0 / det_df) : 1.0; + const double w_fac = weight * mu / abs_b_star_para * inv_det * ep_scale; + const double d_fac = konst / abs_b_star_para * inv_det; + + double tmp[9], tmp_v[3], fv[3] = {0.0, 0.0, 0.0}; + + // the two field blocks, Beq first then B, each contributing + // grad_PBeq, grad_PB and (for `dg`) the eta_diff correction + for (int blk = 0; blk < 2; blk++) { + matmat_dev(blk == 0 ? beq_prod : b_prod, norm_b_prod, tmp); + + matvec_dev(tmp, grad_PBeq, tmp_v); + for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * w_fac; + + matvec_dev(tmp, grad_PB, tmp_v); + for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * w_fac; + + if (is_dg) { + matvec_dev(tmp, eta_diff, tmp_v); + for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * d_fac; + } + } + + if (basis_u == 0) { + // H1vec: N-N-N in all three components + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec1, v1_n2,v1_n3, fv[0]); + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec2, v2_n2,v2_n3, fv[1]); + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec3, v3_n2,v3_n3, fv[2]); + } else if (basis_u == 2) { + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + fill_vec_dev(p1,pd2,pd3, bn1,bd2,bd3, span1,span2,span3, start0,start1,start2, + vec1, v1_n2,v1_n3, fv[0]); + fill_vec_dev(pd1,p2,pd3, bd1,bn2,bd3, span1,span2,span3, start0,start1,start2, + vec2, v2_n2,v2_n3, fv[1]); + fill_vec_dev(pd1,pd2,p3, bd1,bd2,bn3, span1,span2,span3, start0,start1,start2, + vec3, v3_n2,v3_n3, fv[2]); + } +} +""" + +_cc_gradB_dg_kernel = None + + +def _get_cc_lin_mhd_5d_gradB_dg_kernel(): + global _cc_gradB_dg_kernel + if _cc_gradB_dg_kernel is None: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _cc_gradB_dg_kernel = cp.RawKernel( + _GENERAL_GEOMETRY_SRC + _FILL_VEC_SRC + _CC_LIN_MHD_5D_GRADB_DG_SRC, + "cc_lin_mhd_5d_gradB_dg_cuda", + ) + return _cc_gradB_dg_kernel + + +def cc_lin_mhd_5d_gradB_dg_gpu( + markers, first_init_idx, mu_idx, kind_map, params_dev, epsilon, ep_scale, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2, beq2, norm_b1, curl_norm_b, grad_PB, grad_PBeq, basis_u, + vec1_dev, vec2_dev, vec3_dev, const=0.0, is_dg=False, +): + """GPU replacement for one call of + :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_gradB_dg` + (``is_dg=True``, using ``const``) or of + :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_gradB_dg_init` + (``is_dg=False``, where ``const`` is unused).""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_cc_lin_mhd_5d_gradB_dg_kernel()( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), np.int32(mu_idx), + np.int32(kind_map), params_dev, + np.float64(epsilon), np.float64(ep_scale), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(beq2[0]), *d(beq2[1]), *d(beq2[2]), + *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), + *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + *d(grad_PB[0]), *d(grad_PB[1]), *d(grad_PB[2]), + *d(grad_PBeq[0]), *d(grad_PBeq[1]), *d(grad_PBeq[2]), + np.int32(basis_u), np.float64(const), np.int32(bool(is_dg)), + vec1_dev, np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), + vec2_dev, np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), + vec3_dev, np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + ), + ) diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index 8ad4612a7..a3238c85a 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -26,7 +26,10 @@ vlasov_maxwell_gpu, ) from struphy.pic.accumulation.accum_kernels_gc_cuda import ( + cc_lin_mhd_5d_curlb_gpu, cc_lin_mhd_5d_D_gpu, + cc_lin_mhd_5d_gradB_dg_gpu, + cc_lin_mhd_5d_gradB_gpu, gc_mag_density_0form_gpu, ) from struphy.pic.accumulation.filter import AccumFilter, FilterParameters @@ -321,6 +324,37 @@ def __init__( self._gpu_cc5d_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) self._gpu_cc5d_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + # GPU replacements for the two remaining 5D current-coupling + # accumulators. cc_lin_mhd_5d_curlb is a full symmetric 6-block + # matrix-plus-vector curvature fill; cc_lin_mhd_5d_gradB is + # vector-only (its 6 matrix args are unused by the CPU body, so the + # matrices stay zero on both backends). They need the same cached + # spline/domain data, so one block covers both. + self._gpu_cc_lin_mhd_5d_curlb = ( + xp.cupy_backend + and kernel.name == "cc_lin_mhd_5d_curlb" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + self._gpu_cc_lin_mhd_5d_gradB = ( + xp.cupy_backend + and kernel.name == "cc_lin_mhd_5d_gradB" + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_cc_lin_mhd_5d_curlb or self._gpu_cc_lin_mhd_5d_gradB: + import cupy as cp + import numpy as np + + self._gpu_cg_kind_map = int(args_domain.kind_map) + self._gpu_cg_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + args_derham = self.derham.args_derham + self._gpu_cg_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_cg_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_cg_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) + self._gpu_cg_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) + self._gpu_cg_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) + self._gpu_cg_first_init_idx = int(self.particles.args_markers.first_init_idx) + self._gpu_cg_mu_idx = int(self.particles.mu_idx) + # GPU replacement for pc_lin_mhd_6d_full / pc_lin_mhd_6d: the # symmetry="pressure" case -- 45-array (36 matrix + 9 vector) # velocity-moment "pressure tensor" fill. See accum_kernels_cuda.py @@ -477,6 +511,67 @@ def _accumulate(self, *optional_args, **args_control): basis_u, *self._args_data, ) + elif self._gpu_cc_lin_mhd_5d_curlb and len(optional_args) == 12: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + ( + epsilon, ep_scale, + b2_1, b2_2, b2_3, + nb1_1, nb1_2, nb1_3, + cnb_1, cnb_2, cnb_3, + basis_u, + ) = optional_args + cc_lin_mhd_5d_curlb_gpu( + self.particles.markers, + self._gpu_cg_kind_map, + self._gpu_cg_params, + epsilon, + ep_scale, + self._gpu_cg_pn, + self._gpu_cg_tn1, + self._gpu_cg_tn2, + self._gpu_cg_tn3, + self._gpu_cg_starts, + (b2_1, b2_2, b2_3), + (nb1_1, nb1_2, nb1_3), + (cnb_1, cnb_2, cnb_3), + basis_u, + *self._args_data, + ) + elif self._gpu_cc_lin_mhd_5d_gradB and len(optional_args) == 17: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + ( + epsilon, ep_scale, + b2_1, b2_2, b2_3, + nb1_1, nb1_2, nb1_3, + cnb_1, cnb_2, cnb_3, + gpb_1, gpb_2, gpb_3, + gpq_1, gpq_2, gpq_3, + basis_u, + ) = optional_args + # the kernel's own 6 matrix args + vector are already in + # self._args_data; only the vector blocks are ever written. + vec_data = self._args_data[6:] + cc_lin_mhd_5d_gradB_gpu( + self.particles.markers, + self._gpu_cg_first_init_idx, + self._gpu_cg_mu_idx, + self._gpu_cg_kind_map, + self._gpu_cg_params, + epsilon, + ep_scale, + self._gpu_cg_pn, + self._gpu_cg_tn1, + self._gpu_cg_tn2, + self._gpu_cg_tn3, + self._gpu_cg_starts, + (b2_1, b2_2, b2_3), + (nb1_1, nb1_2, nb1_3), + (cnb_1, cnb_2, cnb_3), + (gpb_1, gpb_2, gpb_3), + (gpq_1, gpq_2, gpq_3), + basis_u, + *vec_data, + ) elif (self._gpu_pc_lin_mhd_6d_full or self._gpu_pc_lin_mhd_6d) and len(optional_args) == 1: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): (ep_scale,) = optional_args From 0017fd847d4ef0258edd5bd56451e271d4ad9556 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 20:59:01 +0200 Subject: [PATCH 063/156] pusher gc kernels --- src/struphy/pic/pushing/pusher.py | 61 ++ .../pic/pushing/pusher_kernels_gc_cuda.py | 534 ++++++++++++++++++ .../propagators/current_coupling_5d_gradb.py | 89 ++- 3 files changed, 678 insertions(+), 6 deletions(-) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 14737fcc4..070ee7392 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -43,6 +43,9 @@ push_gc_bxEstar_explicit_multistage_general_gpu, push_gc_Bstar_discrete_gradient_1st_order_gpu, push_gc_Bstar_explicit_multistage_general_gpu, + push_gc_cc_J1_H1vec_gpu, + push_gc_cc_J1_Hcurl_gpu, + push_gc_cc_J1_Hdiv_gpu, ) logger = logging.getLogger("struphy") @@ -683,6 +686,41 @@ def __init__( self._gpu_gc_dg1_ef = (ef1, ef2, ef3) self._gpu_gc_dg1_mu_idx = int(particles.mu_idx) + # CUDA replacements for push_gc_cc_J1_{H1vec,Hcurl,Hdiv} (velocity + # update of CurrentCoupling5DCurlb). Single-stage (dt only, `stage` + # is accepted but unused by the CPU kernels too), needs DF(eta) so + # restricted like the other "general" paths to + # SUPPORTED_GENERAL_KIND_MAPS. + self._gpu_gc_cc_j1 = cunumpy.cupy_backend and kernel.name in ( + "push_gc_cc_J1_H1vec", + "push_gc_cc_J1_Hcurl", + "push_gc_cc_J1_Hdiv", + ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + if self._gpu_gc_cc_j1: + import cupy as cp + + self._gpu_gc_cc_j1_name = kernel.name + ( + args_derham, + epsilon, + b1, b2, b3, + nb1, nb2, nb3, + cnb1, cnb2, cnb3, + u1, u2, u3, + ) = args_kernel + self._gpu_gc_cc_j1_kind_map = int(args_domain.kind_map) + self._gpu_gc_cc_j1_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + self._gpu_gc_cc_j1_epsilon = float(epsilon) + self._gpu_gc_cc_j1_pn = tuple(int(x) for x in args_derham.pn) + self._gpu_gc_cc_j1_starts = tuple(int(x) for x in args_derham.starts) + self._gpu_gc_cc_j1_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_gc_cc_j1_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_gc_cc_j1_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_gc_cc_j1_b2 = (b1, b2, b3) + self._gpu_gc_cc_j1_norm_b1 = (nb1, nb2, nb3) + self._gpu_gc_cc_j1_curl_norm_b = (cnb1, cnb2, cnb3) + self._gpu_gc_cc_j1_u = (u1, u2, u3) + @profile def __call__(self, dt: float): """ @@ -1203,6 +1241,29 @@ def _push(self, dt: float): self._gpu_gc_dg1_eval_e, dt, ) + elif self._gpu_gc_cc_j1: + fn = { + "push_gc_cc_J1_H1vec": push_gc_cc_J1_H1vec_gpu, + "push_gc_cc_J1_Hcurl": push_gc_cc_J1_Hcurl_gpu, + "push_gc_cc_J1_Hdiv": push_gc_cc_J1_Hdiv_gpu, + }[self._gpu_gc_cc_j1_name] + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + fn( + markers, + self._gpu_gc_cc_j1_kind_map, + self._gpu_gc_cc_j1_params, + self._gpu_gc_cc_j1_epsilon, + self._gpu_gc_cc_j1_pn, + self._gpu_gc_cc_j1_tn1, + self._gpu_gc_cc_j1_tn2, + self._gpu_gc_cc_j1_tn3, + self._gpu_gc_cc_j1_starts, + self._gpu_gc_cc_j1_b2, + self._gpu_gc_cc_j1_norm_b1, + self._gpu_gc_cc_j1_curl_norm_b, + self._gpu_gc_cc_j1_u, + dt, + ) elif self._gpu_sph_pusher: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): common = dict( diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index 8bb83e402..35f8bcca0 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -640,3 +640,537 @@ def push_gc_Bstar_discrete_gradient_1st_order_gpu(*args, **kwargs): """GPU replacement for one Picard iteration of :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_discrete_gradient_1st_order`.""" _dg_launch("push_gc_Bstar_discrete_gradient_1st_order_cuda", *args, **kwargs) + + +# --------------------------------------------------------------------------- +# push_gc_cc_J1_{H1vec,Hcurl,Hdiv}: single-stage (dt, no Butcher coefficients +# -- `stage` is accepted but unused by the CPU kernels too) velocity update +# for CurrentCoupling5DCurlb. All three read the same fields (b, norm_b1, +# curl_norm_b) and differ only in which FEEC space `u` lives in and how it +# is transformed to Cartesian: +# H1vec: u is already a vector field (eval_vectorfield_dev), no transform +# Hcurl: u is a 1-form; transform via g^-1 = (DF^T DF)^-1 +# Hdiv: u is a 2-form (like b); transform by dividing by det(DF) +# --------------------------------------------------------------------------- + +_PUSH_GC_CC_J1_SRC = r""" +extern "C" __global__ +void push_gc_cc_J1_H1vec_cuda( + double* markers, const int n_cols, const int n_markers, + const double dt, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + + double b[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_vectorfield_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double e[3]; + cross_dev(b, u, e); + const double temp = dot3_dev(e, curl_norm_b); + + row[3] += temp / abs_b_star_para * v * dt; +} + +extern "C" __global__ +void push_gc_cc_J1_Hcurl_cuda( + double* markers, const int n_cols, const int n_markers, + const double dt, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], u_form[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u_form); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + // g_inv = (DF^T DF)^-1, transforms the 1-form u into H1vec components + double df_t[9] = { + dfm[0], dfm[3], dfm[6], + dfm[1], dfm[4], dfm[7], + dfm[2], dfm[5], dfm[8], + }; + double g[9], g_inv[9], u0[3]; + matmat_dev(df_t, dfm, g); + matrix_inv_dev(g, g_inv); + matvec_dev(g_inv, u_form, u0); + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = (b[k] + curl_norm_b[k] * v * epsilon) / det_df; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double e[3]; + cross_dev(b, u0, e); + const double temp = dot3_dev(e, curl_norm_b) / det_df; + + row[3] += temp / abs_b_star_para * v * dt; +} + +extern "C" __global__ +void push_gc_cc_J1_Hdiv_cuda( + double* markers, const int n_cols, const int n_markers, + const double dt, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + for (int k = 0; k < 3; k++) u[k] /= det_df; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double e[3]; + cross_dev(b, u, e); + const double temp = dot3_dev(e, curl_norm_b); + + row[3] += temp / abs_b_star_para * v * dt; +} +""" + +_j1_kernels = {} + + +def _get_j1_kernel(name): + if name not in _j1_kernels: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _j1_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _PUSH_GC_CC_J1_SRC, name) + return _j1_kernels[name] + + +def _j1_launch( + name, markers, kind_map, params_dev, epsilon, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2, norm_b1, curl_norm_b, u, dt, +): + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_j1_kernel(name)( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.float64(dt), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), + *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + *d(u[0]), *d(u[1]), *d(u[2]), + ), + ) + + +def push_gc_cc_J1_H1vec_gpu(*args, **kwargs): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J1_H1vec`.""" + _j1_launch("push_gc_cc_J1_H1vec_cuda", *args, **kwargs) + + +def push_gc_cc_J1_Hcurl_gpu(*args, **kwargs): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J1_Hcurl`.""" + _j1_launch("push_gc_cc_J1_Hcurl_cuda", *args, **kwargs) + + +def push_gc_cc_J1_Hdiv_gpu(*args, **kwargs): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J1_Hdiv`.""" + _j1_launch("push_gc_cc_J1_Hdiv_cuda", *args, **kwargs) + + +# --------------------------------------------------------------------------- +# push_gc_cc_J2_stage_{H1vec,Hdiv}: multistage (a[stage]/b[stage]/last, same +# first_init_idx/first_free_idx accumulation scheme as +# push_gc_bxEstar_explicit_multistage above) position update for +# CurrentCoupling5DGradB. Both build the same b_prod/norm_b_prod +# cross-product matrices and e = (norm_b_prod @ b_prod @ u) / |B*_para|; +# H1vec evaluates u as a vector field and stops there (its DF/det(DF) are +# computed by the CPU reference but never used); Hdiv evaluates u as a +# 2-form and divides e by det(DF) as well. +# --------------------------------------------------------------------------- + +_PUSH_GC_CC_J2_STAGE_SRC = r""" +extern "C" __global__ +void push_gc_cc_J2_stage_H1vec_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_free_idx, + const double dt_a, const double dt_b, const double last, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double bb[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); + eval_vectorfield_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double tmp[9], e[3]; + matmat_dev(norm_b_prod, b_prod, tmp); + matvec_dev(tmp, u, e); + for (int k = 0; k < 3; k++) e[k] /= abs_b_star_para; + + row[first_free_idx + 0] -= dt_b * e[0]; + row[first_free_idx + 1] -= dt_b * e[1]; + row[first_free_idx + 2] -= dt_b * e[2]; + + row[0] = row[first_init_idx + 0] - dt_a * e[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] - dt_a * e[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] - dt_a * e[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_gc_cc_J2_stage_Hdiv_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_free_idx, + const double dt_a, const double dt_b, const double last, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double bb[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double tmp[9], e[3]; + matmat_dev(norm_b_prod, b_prod, tmp); + matvec_dev(tmp, u, e); + for (int k = 0; k < 3; k++) e[k] /= (abs_b_star_para * det_df); + + row[first_free_idx + 0] -= dt_b * e[0]; + row[first_free_idx + 1] -= dt_b * e[1]; + row[first_free_idx + 2] -= dt_b * e[2]; + + row[0] = row[first_init_idx + 0] - dt_a * e[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] - dt_a * e[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] - dt_a * e[2] + last * row[first_free_idx + 2]; +} +""" + +_j2_stage_kernels = {} + + +def _get_j2_stage_kernel(name): + if name not in _j2_stage_kernels: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _j2_stage_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _PUSH_GC_CC_J2_STAGE_SRC, name) + return _j2_stage_kernels[name] + + +def _j2_stage_launch( + name, markers, first_init_idx, first_free_idx, kind_map, params_dev, epsilon, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2, norm_b1, curl_norm_b, u, dt_a, dt_b, last, +): + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_j2_stage_kernel(name)( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), np.int32(first_free_idx), + np.float64(dt_a), np.float64(dt_b), np.float64(last), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), + *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + *d(u[0]), *d(u[1]), *d(u[2]), + ), + ) + + +def push_gc_cc_J2_stage_H1vec_gpu(*args, **kwargs): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_stage_H1vec`.""" + _j2_stage_launch("push_gc_cc_J2_stage_H1vec_cuda", *args, **kwargs) + + +def push_gc_cc_J2_stage_Hdiv_gpu(*args, **kwargs): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_stage_Hdiv`.""" + _j2_stage_launch("push_gc_cc_J2_stage_Hdiv_cuda", *args, **kwargs) diff --git a/src/struphy/propagators/current_coupling_5d_gradb.py b/src/struphy/propagators/current_coupling_5d_gradb.py index 36061c4eb..2bc2c691f 100644 --- a/src/struphy/propagators/current_coupling_5d_gradb.py +++ b/src/struphy/propagators/current_coupling_5d_gradb.py @@ -9,6 +9,7 @@ from feectools.ddm.mpi import mpi as MPI from feectools.linalg.solvers import inverse from line_profiler import profile +from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.io.options import LiteralOptions, OptionsBase @@ -20,6 +21,11 @@ from struphy.pic.accumulation.filter import FilterParameters from struphy.pic.accumulation.particles_to_grid import Accumulator, AccumulatorVector from struphy.pic.pushing import pusher_kernels_gc +from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS +from struphy.pic.pushing.pusher_kernels_gc_cuda import ( + push_gc_cc_J2_stage_H1vec_gpu, + push_gc_cc_J2_stage_Hdiv_gpu, +) from struphy.propagators.base import Propagator from struphy.utils.utils import check_option @@ -314,6 +320,49 @@ def allocate(self): self.options.butcher.c, ) + # GPU replacement for push_gc_cc_J2_stage_{H1vec,Hdiv}: unlike + # CurrentCoupling5DCurlb this propagator interleaves accumulation + # and pushing per RK stage itself (see __call__), so it can't go + # through the generic Pusher dispatch -- self._pusher_kernel is + # called directly on args_markers below, which would force a + # host round trip (or crash outright) on device-resident markers + # under cupy. Dispatch to the CUDA kernel here instead, with the + # same SUPPORTED_GENERAL_KIND_MAPS restriction as the rest of the + # port. + self._gpu_j2_stage = xp.cupy_backend and self.domain.args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + if self._gpu_j2_stage: + import cupy as cp + import numpy as np + + self._gpu_j2_stage_fn = ( + push_gc_cc_J2_stage_H1vec_gpu + if self.options.u_space == "H1vec" + else push_gc_cc_J2_stage_Hdiv_gpu + ) + self._gpu_j2_stage_kind_map = int(self.domain.args_domain.kind_map) + self._gpu_j2_stage_params = cp.asarray( + np.asarray(self.domain.args_domain.params, dtype=float), dtype=cp.float64 + ) + self._gpu_j2_stage_epsilon = float(epsilon) + args_derham = self.derham.args_derham + self._gpu_j2_stage_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_j2_stage_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_j2_stage_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_j2_stage_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_j2_stage_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_j2_stage_b2 = (self._b_full[0]._data, self._b_full[1]._data, self._b_full[2]._data) + self._gpu_j2_stage_norm_b1 = (unit_b1[0]._data, unit_b1[1]._data, unit_b1[2]._data) + self._gpu_j2_stage_curl_norm_b = ( + curl_unit_b2[0]._data, + curl_unit_b2[1]._data, + curl_unit_b2[2]._data, + ) + self._gpu_j2_stage_u = ( + self._u_temp[0]._data, + self._u_temp[1]._data, + self._u_temp[2]._data, + ) + else: # temporary vectors to avoid memory allocation self._b_full = self._b2.space.zeros() @@ -467,12 +516,40 @@ def __call__(self, dt): ) # push particles - self._pusher_kernel( - dt, - stage, - args_markers, - *self._args_pusher_kernel, - ) + if self._gpu_j2_stage: + last = 1.0 if stage == self.options.butcher.n_stages - 1 else 0.0 + with ProfileManager.profile_region("kernel: " + self._pusher_kernel.name + " [cuda]"): + self._gpu_j2_stage_fn( + markers, + first_init_idx, + first_free_idx, + self._gpu_j2_stage_kind_map, + self._gpu_j2_stage_params, + self._gpu_j2_stage_epsilon, + self._gpu_j2_stage_pn, + self._gpu_j2_stage_tn1, + self._gpu_j2_stage_tn2, + self._gpu_j2_stage_tn3, + self._gpu_j2_stage_starts, + self._gpu_j2_stage_b2, + self._gpu_j2_stage_norm_b1, + self._gpu_j2_stage_curl_norm_b, + self._gpu_j2_stage_u, + dt * float(self.options.butcher.a_stage[stage]), + dt * float(self.options.butcher.b[stage]), + last, + ) + else: + with ( + ProfileManager.profile_region("kernel: " + self._pusher_kernel.name), + particles.host_markers(write=True) as args_markers_h, + ): + self._pusher_kernel( + dt, + stage, + args_markers_h, + *self._args_pusher_kernel, + ) if particles.mpi_comm is not None: particles.mpi_sort_markers() From 1b19226bc777bbd0e432585c08d2d8d2f6521911 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 21:34:08 +0200 Subject: [PATCH 064/156] Updated name --- profiling/examples/GuidingCenter/params_GuidingCenter.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter.py b/profiling/examples/GuidingCenter/params_GuidingCenter.py index a8cc4aa6b..6be7bb7d4 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter.py @@ -104,6 +104,8 @@ # Instance of the simulation # -------------------------- +name = f"GuidingCenter ({args.backend})" + # Environment options env = EnvironmentOptions( sim_folder=f"sim_{args.id:02d}", From 257e848045dd07415ff51e30ab0610b66f300a35 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Sun, 16 Aug 2026 21:35:19 +0200 Subject: [PATCH 065/156] Formatting --- bench_gpu/bench_kernels.py | 126 +++++- .../GuidingCenter/params_GuidingCenter.py | 1 - .../pic/accumulation/accum_kernels_cuda.py | 140 +++++-- .../pic/accumulation/accum_kernels_gc_cuda.py | 362 +++++++++++++----- .../pic/accumulation/particles_to_grid.py | 73 ++-- src/struphy/pic/base.py | 17 +- .../pic/pushing/eval_kernels_gc_cuda.py | 51 ++- src/struphy/pic/pushing/pusher.py | 70 ++-- .../pic/pushing/pusher_kernels_cuda.py | 1 - .../pic/pushing/pusher_kernels_gc_cuda.py | 356 ++++++++++++----- .../pic/pushing/pusher_kernels_sph_cuda.py | 100 ++++- src/struphy/pic/tests/test_sph.py | 1 - src/struphy/pic/utilities_kernels_cuda.py | 163 +++++--- .../propagators/current_coupling_5d_gradb.py | 4 +- 14 files changed, 1094 insertions(+), 371 deletions(-) diff --git a/bench_gpu/bench_kernels.py b/bench_gpu/bench_kernels.py index 24a6e7141..7d735b9a7 100644 --- a/bench_gpu/bench_kernels.py +++ b/bench_gpu/bench_kernels.py @@ -649,16 +649,26 @@ def _vm_gpu(): for v in vm_vec_gpu: v.fill(0.0) vlasov_maxwell_gpu( - scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, - *[vm_mat_gpu[k] for k in mat_keys], *vm_vec_gpu, + scene.particles.markers, + kind_map, + params_dev, + pn, + tn1, + tn2, + tn3, + starts, + *[vm_mat_gpu[k] for k in mat_keys], + *vm_vec_gpu, ) add("vlasov_maxwell", _vm_cpu, _vm_gpu) # --- cc_lin_mhd_6d_1 (Accumulator, antisymmetric 3-block fill, u_space=Hcurl i.e. basis_u=1) --- op_cc1 = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="asym") - cc1_cpu = {k: np.zeros(op_cc1.matrix.blocks[a_][b_]._data.shape, dtype=float) for k, (a_, b_) in - zip(["12", "13", "23"], [(0, 1), (0, 2), (1, 2)])} + cc1_cpu = { + k: np.zeros(op_cc1.matrix.blocks[a_][b_]._data.shape, dtype=float) + for k, (a_, b_) in zip(["12", "13", "23"], [(0, 1), (0, 2), (1, 2)]) + } cc1_gpu = {k: scene.dev(v) for k, v in cc1_cpu.items()} b2_1, b2_2, b2_3 = scene.fields["2"] b2_1_dev, b2_2_dev, b2_3_dev = (scene.dev(a) for a in scene.fields["2"]) @@ -669,17 +679,41 @@ def _cc1_cpu(): for v in cc1_cpu.values(): v.fill(0.0) accum_kernels.cc_lin_mhd_6d_1( - am, ah, ad, cc1_cpu["12"], cc1_cpu["13"], cc1_cpu["23"], - b2_1, b2_2, b2_3, basis_u_hcurl, cc1_scale_mat, cc1_boundary_cut, + am, + ah, + ad, + cc1_cpu["12"], + cc1_cpu["13"], + cc1_cpu["23"], + b2_1, + b2_2, + b2_3, + basis_u_hcurl, + cc1_scale_mat, + cc1_boundary_cut, ) def _cc1_gpu(): for v in cc1_gpu.values(): v.fill(0.0) cc_lin_mhd_6d_1_gpu( - scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, - b2_1_dev, b2_2_dev, b2_3_dev, basis_u_hcurl, cc1_scale_mat, cc1_boundary_cut, - cc1_gpu["12"], cc1_gpu["13"], cc1_gpu["23"], + scene.particles.markers, + kind_map, + params_dev, + pn, + tn1, + tn2, + tn3, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + basis_u_hcurl, + cc1_scale_mat, + cc1_boundary_cut, + cc1_gpu["12"], + cc1_gpu["13"], + cc1_gpu["23"], ) add("cc_lin_mhd_6d_1", _cc1_cpu, _cc1_gpu) @@ -705,8 +739,18 @@ def _cc2_cpu(): for v in cc2_vec_cpu: v.fill(0.0) accum_kernels.cc_lin_mhd_6d_2( - am, ah, ad, *[cc2_mat_cpu[k] for k in mat_keys], *cc2_vec_cpu, - b2_1, b2_2, b2_3, basis_u_hcurl, cc2_scale_mat, cc2_scale_vec, cc2_boundary_cut, + am, + ah, + ad, + *[cc2_mat_cpu[k] for k in mat_keys], + *cc2_vec_cpu, + b2_1, + b2_2, + b2_3, + basis_u_hcurl, + cc2_scale_mat, + cc2_scale_vec, + cc2_boundary_cut, ) def _cc2_gpu(): @@ -715,9 +759,23 @@ def _cc2_gpu(): for v in cc2_vec_gpu: v.fill(0.0) cc_lin_mhd_6d_2_gpu( - scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, - b2_1_dev, b2_2_dev, b2_3_dev, basis_u_hcurl, cc2_scale_mat, cc2_scale_vec, cc2_boundary_cut, - *[cc2_mat_gpu[k] for k in mat_keys], *cc2_vec_gpu, + scene.particles.markers, + kind_map, + params_dev, + pn, + tn1, + tn2, + tn3, + starts, + b2_1_dev, + b2_2_dev, + b2_3_dev, + basis_u_hcurl, + cc2_scale_mat, + cc2_scale_vec, + cc2_boundary_cut, + *[cc2_mat_gpu[k] for k in mat_keys], + *cc2_vec_gpu, ) add("cc_lin_mhd_6d_2", _cc2_cpu, _cc2_gpu) @@ -751,7 +809,12 @@ def _pc_full_cpu(): for v in pc_vec_cpu.values(): v.fill(0.0) accum_kernels.pc_lin_mhd_6d_full( - am, ah, ad, *[pc_mat_cpu[k] for k in pc_mat_order], *[pc_vec_cpu[k] for k in pc_vec_order], ep_scale, + am, + ah, + ad, + *[pc_mat_cpu[k] for k in pc_mat_order], + *[pc_vec_cpu[k] for k in pc_vec_order], + ep_scale, ) def _pc_full_gpu(): @@ -760,8 +823,17 @@ def _pc_full_gpu(): for v in pc_vec_gpu.values(): v.fill(0.0) pc_lin_mhd_6d_full_gpu( - scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, ep_scale, - *[pc_mat_gpu[k] for k in pc_mat_order], *[pc_vec_gpu[k] for k in pc_vec_order], + scene.particles.markers, + kind_map, + params_dev, + pn, + tn1, + tn2, + tn3, + starts, + ep_scale, + *[pc_mat_gpu[k] for k in pc_mat_order], + *[pc_vec_gpu[k] for k in pc_vec_order], ) add("pc_lin_mhd_6d_full", _pc_full_cpu, _pc_full_gpu) @@ -772,7 +844,12 @@ def _pc_cpu(): for v in pc_vec_cpu.values(): v.fill(0.0) accum_kernels.pc_lin_mhd_6d( - am, ah, ad, *[pc_mat_cpu[k] for k in pc_mat_order], *[pc_vec_cpu[k] for k in pc_vec_order], ep_scale, + am, + ah, + ad, + *[pc_mat_cpu[k] for k in pc_mat_order], + *[pc_vec_cpu[k] for k in pc_vec_order], + ep_scale, ) def _pc_gpu(): @@ -781,8 +858,17 @@ def _pc_gpu(): for v in pc_vec_gpu.values(): v.fill(0.0) pc_lin_mhd_6d_gpu( - scene.particles.markers, kind_map, params_dev, pn, tn1, tn2, tn3, starts, ep_scale, - *[pc_mat_gpu[k] for k in pc_mat_order], *[pc_vec_gpu[k] for k in pc_vec_order], + scene.particles.markers, + kind_map, + params_dev, + pn, + tn1, + tn2, + tn3, + starts, + ep_scale, + *[pc_mat_gpu[k] for k in pc_mat_order], + *[pc_vec_gpu[k] for k in pc_vec_order], ) add("pc_lin_mhd_6d", _pc_cpu, _pc_gpu) diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter.py b/profiling/examples/GuidingCenter/params_GuidingCenter.py index 6be7bb7d4..1da627027 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter.py @@ -88,7 +88,6 @@ # --------------------- # Instance of the model # --------------------- - from struphy.models import GuidingCenter # Units diff --git a/src/struphy/pic/accumulation/accum_kernels_cuda.py b/src/struphy/pic/accumulation/accum_kernels_cuda.py index 8fa16917e..638ec6d99 100644 --- a/src/struphy/pic/accumulation/accum_kernels_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_cuda.py @@ -555,7 +555,13 @@ def vlasov_maxwell_gpu( blocks = (n_markers + threads - 1) // threads def dims(a): - return (np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), np.int32(a.shape[4]), np.int32(a.shape[5])) + return ( + np.int32(a.shape[1]), + np.int32(a.shape[2]), + np.int32(a.shape[3]), + np.int32(a.shape[4]), + np.int32(a.shape[5]), + ) _get_vlasov_maxwell_kernel()( (blocks,), @@ -578,18 +584,27 @@ def dims(a): np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - mat11_dev, mat12_dev, mat13_dev, - mat22_dev, mat23_dev, mat33_dev, - vec1_dev, vec2_dev, vec3_dev, + mat11_dev, + mat12_dev, + mat13_dev, + mat22_dev, + mat23_dev, + mat33_dev, + vec1_dev, + vec2_dev, + vec3_dev, *dims(mat11_dev), *dims(mat12_dev), *dims(mat13_dev), *dims(mat22_dev), *dims(mat23_dev), *dims(mat33_dev), - np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), - np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), - np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + np.int32(vec1_dev.shape[1]), + np.int32(vec1_dev.shape[2]), + np.int32(vec2_dev.shape[1]), + np.int32(vec2_dev.shape[2]), + np.int32(vec3_dev.shape[1]), + np.int32(vec3_dev.shape[2]), ), ) @@ -895,7 +910,13 @@ def cc_lin_mhd_6d_1_gpu( blocks = (n_markers + threads - 1) // threads def dims(a): - return (np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), np.int32(a.shape[4]), np.int32(a.shape[5])) + return ( + np.int32(a.shape[1]), + np.int32(a.shape[2]), + np.int32(a.shape[3]), + np.int32(a.shape[4]), + np.int32(a.shape[5]), + ) _get_cc_lin_mhd_6d_1_kernel()( (blocks,), @@ -918,13 +939,21 @@ def dims(a): np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - b2_1_dev, np.int32(b2_1_dev.shape[1]), np.int32(b2_1_dev.shape[2]), - b2_2_dev, np.int32(b2_2_dev.shape[1]), np.int32(b2_2_dev.shape[2]), - b2_3_dev, np.int32(b2_3_dev.shape[1]), np.int32(b2_3_dev.shape[2]), + b2_1_dev, + np.int32(b2_1_dev.shape[1]), + np.int32(b2_1_dev.shape[2]), + b2_2_dev, + np.int32(b2_2_dev.shape[1]), + np.int32(b2_2_dev.shape[2]), + b2_3_dev, + np.int32(b2_3_dev.shape[1]), + np.int32(b2_3_dev.shape[2]), np.int32(basis_u), np.float64(scale_mat), np.float64(boundary_cut), - mat12_dev, mat13_dev, mat23_dev, + mat12_dev, + mat13_dev, + mat23_dev, *dims(mat12_dev), *dims(mat13_dev), *dims(mat23_dev), @@ -1183,7 +1212,13 @@ def cc_lin_mhd_6d_2_gpu( blocks = (n_markers + threads - 1) // threads def dims(a): - return (np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), np.int32(a.shape[4]), np.int32(a.shape[5])) + return ( + np.int32(a.shape[1]), + np.int32(a.shape[2]), + np.int32(a.shape[3]), + np.int32(a.shape[4]), + np.int32(a.shape[5]), + ) _get_cc_lin_mhd_6d_2_kernel()( (blocks,), @@ -1206,25 +1241,40 @@ def dims(a): np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - b2_1_dev, np.int32(b2_1_dev.shape[1]), np.int32(b2_1_dev.shape[2]), - b2_2_dev, np.int32(b2_2_dev.shape[1]), np.int32(b2_2_dev.shape[2]), - b2_3_dev, np.int32(b2_3_dev.shape[1]), np.int32(b2_3_dev.shape[2]), + b2_1_dev, + np.int32(b2_1_dev.shape[1]), + np.int32(b2_1_dev.shape[2]), + b2_2_dev, + np.int32(b2_2_dev.shape[1]), + np.int32(b2_2_dev.shape[2]), + b2_3_dev, + np.int32(b2_3_dev.shape[1]), + np.int32(b2_3_dev.shape[2]), np.int32(basis_u), np.float64(scale_mat), np.float64(scale_vec), np.float64(boundary_cut), - mat11_dev, mat12_dev, mat13_dev, - mat22_dev, mat23_dev, mat33_dev, - vec1_dev, vec2_dev, vec3_dev, + mat11_dev, + mat12_dev, + mat13_dev, + mat22_dev, + mat23_dev, + mat33_dev, + vec1_dev, + vec2_dev, + vec3_dev, *dims(mat11_dev), *dims(mat12_dev), *dims(mat13_dev), *dims(mat22_dev), *dims(mat23_dev), *dims(mat33_dev), - np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), - np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), - np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + np.int32(vec1_dev.shape[1]), + np.int32(vec1_dev.shape[2]), + np.int32(vec2_dev.shape[1]), + np.int32(vec2_dev.shape[2]), + np.int32(vec3_dev.shape[1]), + np.int32(vec3_dev.shape[2]), ), ) @@ -1607,14 +1657,10 @@ def _build_pc_lin_mhd_6d_kernel_src(full: bool) -> str: else: if full: lines.append( - f" fill_mat_pressure_full_dev({common},\n" - f" {mat_out}, {dims}, {fillmat}, vx,vy,vz);" + f" fill_mat_pressure_full_dev({common},\n {mat_out}, {dims}, {fillmat}, vx,vy,vz);" ) else: - lines.append( - f" fill_mat_pressure_dev({common},\n" - f" {mat_out}, {dims}, {fillmat}, vx,vy);" - ) + lines.append(f" fill_mat_pressure_dev({common},\n {mat_out}, {dims}, {fillmat}, vx,vy);") lines.append("") lines.append("}") @@ -1690,7 +1736,13 @@ def _pc_lin_mhd_6d_launch( blocks = (n_markers + threads - 1) // threads def dims(a): - return (np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), np.int32(a.shape[4]), np.int32(a.shape[5])) + return ( + np.int32(a.shape[1]), + np.int32(a.shape[2]), + np.int32(a.shape[3]), + np.int32(a.shape[4]), + np.int32(a.shape[5]), + ) args = [ dev_markers, @@ -1758,8 +1810,19 @@ def pc_lin_mhd_6d_full_gpu( } _pc_lin_mhd_6d_launch( _get_pc_lin_mhd_6d_full_kernel(), - markers, kind_map, params_dev, pn, tn1_dev, tn2_dev, tn3_dev, starts, ep_scale, - mat_args_45, vec_args_45, _SPATIAL_BLOCKS, ("1", "2", "3"), + markers, + kind_map, + params_dev, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + ep_scale, + mat_args_45, + vec_args_45, + _SPATIAL_BLOCKS, + ("1", "2", "3"), ) @@ -1794,6 +1857,17 @@ def pc_lin_mhd_6d_gpu( } _pc_lin_mhd_6d_launch( _get_pc_lin_mhd_6d_kernel(), - markers, kind_map, params_dev, pn, tn1_dev, tn2_dev, tn3_dev, starts, ep_scale, - mat_args_45, vec_args_45, ("11", "12", "22"), ("1", "2"), + markers, + kind_map, + params_dev, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + ep_scale, + mat_args_45, + vec_args_45, + ("11", "12", "22"), + ("1", "2"), ) diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index bf28edf0c..aca1cfac2 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -353,10 +353,23 @@ def _get_cc_lin_mhd_5d_D_kernel(): def cc_lin_mhd_5d_D_gpu( - markers, kind_map, params_dev, epsilon, ep_scale, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2, norm_b1, curl_norm_b, basis_u, - mat12_dev, mat13_dev, mat23_dev, + markers, + kind_map, + params_dev, + epsilon, + ep_scale, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2, + norm_b1, + curl_norm_b, + basis_u, + mat12_dev, + mat13_dev, + mat23_dev, ): """GPU replacement for one call of :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_D`. @@ -377,28 +390,52 @@ def d(a): def dims(a): return ( - np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), - np.int32(a.shape[4]), np.int32(a.shape[5]), + np.int32(a.shape[1]), + np.int32(a.shape[2]), + np.int32(a.shape[3]), + np.int32(a.shape[4]), + np.int32(a.shape[5]), ) _get_cc_lin_mhd_5d_D_kernel()( (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(kind_map), params_dev, - np.float64(epsilon), np.float64(ep_scale), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), - *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(kind_map), + params_dev, + np.float64(epsilon), + np.float64(ep_scale), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(norm_b1[0]), + *d(norm_b1[1]), + *d(norm_b1[2]), + *d(curl_norm_b[0]), + *d(curl_norm_b[1]), + *d(curl_norm_b[2]), np.int32(basis_u), - mat12_dev, mat13_dev, mat23_dev, - *dims(mat12_dev), *dims(mat13_dev), *dims(mat23_dev), + mat12_dev, + mat13_dev, + mat23_dev, + *dims(mat12_dev), + *dims(mat13_dev), + *dims(mat23_dev), ), ) @@ -569,10 +606,27 @@ def _get_cc_lin_mhd_5d_gradB_kernel(): def cc_lin_mhd_5d_gradB_gpu( - markers, first_init_idx, mu_idx, kind_map, params_dev, epsilon, ep_scale, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2, norm_b1, curl_norm_b, grad_PB, grad_PBeq, basis_u, - vec1_dev, vec2_dev, vec3_dev, + markers, + first_init_idx, + mu_idx, + kind_map, + params_dev, + epsilon, + ep_scale, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2, + norm_b1, + curl_norm_b, + grad_PB, + grad_PBeq, + basis_u, + vec1_dev, + vec2_dev, + vec3_dev, ): """GPU replacement for one call of :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_gradB`.""" @@ -591,24 +645,52 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_init_idx), np.int32(mu_idx), - np.int32(kind_map), params_dev, - np.float64(epsilon), np.float64(ep_scale), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), - *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), - *d(grad_PB[0]), *d(grad_PB[1]), *d(grad_PB[2]), - *d(grad_PBeq[0]), *d(grad_PBeq[1]), *d(grad_PBeq[2]), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(mu_idx), + np.int32(kind_map), + params_dev, + np.float64(epsilon), + np.float64(ep_scale), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(norm_b1[0]), + *d(norm_b1[1]), + *d(norm_b1[2]), + *d(curl_norm_b[0]), + *d(curl_norm_b[1]), + *d(curl_norm_b[2]), + *d(grad_PB[0]), + *d(grad_PB[1]), + *d(grad_PB[2]), + *d(grad_PBeq[0]), + *d(grad_PBeq[1]), + *d(grad_PBeq[2]), np.int32(basis_u), - vec1_dev, np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), - vec2_dev, np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), - vec3_dev, np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + vec1_dev, + np.int32(vec1_dev.shape[1]), + np.int32(vec1_dev.shape[2]), + vec2_dev, + np.int32(vec2_dev.shape[1]), + np.int32(vec2_dev.shape[2]), + vec3_dev, + np.int32(vec3_dev.shape[1]), + np.int32(vec3_dev.shape[2]), ), ) @@ -794,11 +876,29 @@ def _get_cc_lin_mhd_5d_curlb_kernel(): def cc_lin_mhd_5d_curlb_gpu( - markers, kind_map, params_dev, epsilon, ep_scale, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2, norm_b1, curl_norm_b, basis_u, - mat11_dev, mat12_dev, mat13_dev, mat22_dev, mat23_dev, mat33_dev, - vec1_dev, vec2_dev, vec3_dev, + markers, + kind_map, + params_dev, + epsilon, + ep_scale, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2, + norm_b1, + curl_norm_b, + basis_u, + mat11_dev, + mat12_dev, + mat13_dev, + mat22_dev, + mat23_dev, + mat33_dev, + vec1_dev, + vec2_dev, + vec3_dev, ): """GPU replacement for one call of :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_curlb`.""" @@ -815,33 +915,67 @@ def d(a): def dims(a): return ( - np.int32(a.shape[1]), np.int32(a.shape[2]), np.int32(a.shape[3]), - np.int32(a.shape[4]), np.int32(a.shape[5]), + np.int32(a.shape[1]), + np.int32(a.shape[2]), + np.int32(a.shape[3]), + np.int32(a.shape[4]), + np.int32(a.shape[5]), ) _get_cc_lin_mhd_5d_curlb_kernel()( (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(kind_map), params_dev, - np.float64(epsilon), np.float64(ep_scale), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), - *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(kind_map), + params_dev, + np.float64(epsilon), + np.float64(ep_scale), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(norm_b1[0]), + *d(norm_b1[1]), + *d(norm_b1[2]), + *d(curl_norm_b[0]), + *d(curl_norm_b[1]), + *d(curl_norm_b[2]), np.int32(basis_u), - mat11_dev, mat12_dev, mat13_dev, mat22_dev, mat23_dev, mat33_dev, - vec1_dev, vec2_dev, vec3_dev, - *dims(mat11_dev), *dims(mat12_dev), *dims(mat13_dev), - *dims(mat22_dev), *dims(mat23_dev), *dims(mat33_dev), - np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), - np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), - np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + mat11_dev, + mat12_dev, + mat13_dev, + mat22_dev, + mat23_dev, + mat33_dev, + vec1_dev, + vec2_dev, + vec3_dev, + *dims(mat11_dev), + *dims(mat12_dev), + *dims(mat13_dev), + *dims(mat22_dev), + *dims(mat23_dev), + *dims(mat33_dev), + np.int32(vec1_dev.shape[1]), + np.int32(vec1_dev.shape[2]), + np.int32(vec2_dev.shape[1]), + np.int32(vec2_dev.shape[2]), + np.int32(vec3_dev.shape[1]), + np.int32(vec3_dev.shape[2]), ), ) @@ -1029,10 +1163,30 @@ def _get_cc_lin_mhd_5d_gradB_dg_kernel(): def cc_lin_mhd_5d_gradB_dg_gpu( - markers, first_init_idx, mu_idx, kind_map, params_dev, epsilon, ep_scale, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2, beq2, norm_b1, curl_norm_b, grad_PB, grad_PBeq, basis_u, - vec1_dev, vec2_dev, vec3_dev, const=0.0, is_dg=False, + markers, + first_init_idx, + mu_idx, + kind_map, + params_dev, + epsilon, + ep_scale, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2, + beq2, + norm_b1, + curl_norm_b, + grad_PB, + grad_PBeq, + basis_u, + vec1_dev, + vec2_dev, + vec3_dev, + const=0.0, + is_dg=False, ): """GPU replacement for one call of :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_gradB_dg` @@ -1054,24 +1208,56 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_init_idx), np.int32(mu_idx), - np.int32(kind_map), params_dev, - np.float64(epsilon), np.float64(ep_scale), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(beq2[0]), *d(beq2[1]), *d(beq2[2]), - *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), - *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), - *d(grad_PB[0]), *d(grad_PB[1]), *d(grad_PB[2]), - *d(grad_PBeq[0]), *d(grad_PBeq[1]), *d(grad_PBeq[2]), - np.int32(basis_u), np.float64(const), np.int32(bool(is_dg)), - vec1_dev, np.int32(vec1_dev.shape[1]), np.int32(vec1_dev.shape[2]), - vec2_dev, np.int32(vec2_dev.shape[1]), np.int32(vec2_dev.shape[2]), - vec3_dev, np.int32(vec3_dev.shape[1]), np.int32(vec3_dev.shape[2]), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(mu_idx), + np.int32(kind_map), + params_dev, + np.float64(epsilon), + np.float64(ep_scale), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(beq2[0]), + *d(beq2[1]), + *d(beq2[2]), + *d(norm_b1[0]), + *d(norm_b1[1]), + *d(norm_b1[2]), + *d(curl_norm_b[0]), + *d(curl_norm_b[1]), + *d(curl_norm_b[2]), + *d(grad_PB[0]), + *d(grad_PB[1]), + *d(grad_PB[2]), + *d(grad_PBeq[0]), + *d(grad_PBeq[1]), + *d(grad_PBeq[2]), + np.int32(basis_u), + np.float64(const), + np.int32(bool(is_dg)), + vec1_dev, + np.int32(vec1_dev.shape[1]), + np.int32(vec1_dev.shape[2]), + vec2_dev, + np.int32(vec2_dev.shape[1]), + np.int32(vec2_dev.shape[2]), + vec3_dev, + np.int32(vec3_dev.shape[1]), + np.int32(vec3_dev.shape[2]), ), ) diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index a3238c85a..6fe3502f7 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -241,9 +241,7 @@ def __init__( # matrix-plus-vector fill as linear_vlasov_ampere above, but with a # G^-1(eta_p)-based filling (no f0_values/optional_args needed). self._gpu_vlasov_maxwell = ( - xp.cupy_backend - and kernel.name == "vlasov_maxwell" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + xp.cupy_backend and kernel.name == "vlasov_maxwell" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS ) if self._gpu_vlasov_maxwell: import cupy as cp @@ -264,9 +262,7 @@ def __init__( # b2_*/basis_u/scale_mat/boundary_cut arrive fresh via optional_args # each call (only the spline/domain info below is cached). self._gpu_cc_lin_mhd_6d_1 = ( - xp.cupy_backend - and kernel.name == "cc_lin_mhd_6d_1" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + xp.cupy_backend and kernel.name == "cc_lin_mhd_6d_1" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS ) if self._gpu_cc_lin_mhd_6d_1: import cupy as cp @@ -286,9 +282,7 @@ def __init__( # matrix-plus-vector fill (like linear_vlasov_ampere/vlasov_maxwell) # instead of the 3 antisymmetric off-diagonal blocks only. self._gpu_cc_lin_mhd_6d_2 = ( - xp.cupy_backend - and kernel.name == "cc_lin_mhd_6d_2" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + xp.cupy_backend and kernel.name == "cc_lin_mhd_6d_2" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS ) if self._gpu_cc_lin_mhd_6d_2: import cupy as cp @@ -307,9 +301,7 @@ def __init__( # cc_lin_mhd_6d_1, with the guiding-centre density prefactor # (1 - b_para/b*_para) / epsilon. self._gpu_cc_lin_mhd_5d_D = ( - xp.cupy_backend - and kernel.name == "cc_lin_mhd_5d_D" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + xp.cupy_backend and kernel.name == "cc_lin_mhd_5d_D" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS ) if self._gpu_cc_lin_mhd_5d_D: import cupy as cp @@ -366,9 +358,7 @@ def __init__( and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS ) self._gpu_pc_lin_mhd_6d = ( - xp.cupy_backend - and kernel.name == "pc_lin_mhd_6d" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + xp.cupy_backend and kernel.name == "pc_lin_mhd_6d" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS ) if self._gpu_pc_lin_mhd_6d_full or self._gpu_pc_lin_mhd_6d: import cupy as cp @@ -488,10 +478,17 @@ def _accumulate(self, *optional_args, **args_control): elif self._gpu_cc_lin_mhd_5d_D and len(optional_args) == 12: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): ( - epsilon, ep_scale, - b2_1, b2_2, b2_3, - nb1_1, nb1_2, nb1_3, - cnb_1, cnb_2, cnb_3, + epsilon, + ep_scale, + b2_1, + b2_2, + b2_3, + nb1_1, + nb1_2, + nb1_3, + cnb_1, + cnb_2, + cnb_3, basis_u, ) = optional_args cc_lin_mhd_5d_D_gpu( @@ -514,10 +511,17 @@ def _accumulate(self, *optional_args, **args_control): elif self._gpu_cc_lin_mhd_5d_curlb and len(optional_args) == 12: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): ( - epsilon, ep_scale, - b2_1, b2_2, b2_3, - nb1_1, nb1_2, nb1_3, - cnb_1, cnb_2, cnb_3, + epsilon, + ep_scale, + b2_1, + b2_2, + b2_3, + nb1_1, + nb1_2, + nb1_3, + cnb_1, + cnb_2, + cnb_3, basis_u, ) = optional_args cc_lin_mhd_5d_curlb_gpu( @@ -540,12 +544,23 @@ def _accumulate(self, *optional_args, **args_control): elif self._gpu_cc_lin_mhd_5d_gradB and len(optional_args) == 17: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): ( - epsilon, ep_scale, - b2_1, b2_2, b2_3, - nb1_1, nb1_2, nb1_3, - cnb_1, cnb_2, cnb_3, - gpb_1, gpb_2, gpb_3, - gpq_1, gpq_2, gpq_3, + epsilon, + ep_scale, + b2_1, + b2_2, + b2_3, + nb1_1, + nb1_2, + nb1_3, + cnb_1, + cnb_2, + cnb_3, + gpb_1, + gpb_2, + gpb_3, + gpq_1, + gpq_2, + gpq_3, basis_u, ) = optional_args # the kernel's own 6 matrix args + vector are already in diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index a2235a8e0..3ea7bad08 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1499,23 +1499,16 @@ def draw_markers( # copy of the velocities and its result converted back to the # active backend before mixing with v_th/u_mean. self.velocities = ( - _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities) - 1)) - * xp.sqrt(2) - * v_th - + u_mean + _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities) - 1)) * xp.sqrt(2) * v_th + u_mean ) # Particles5D: (1d Maxwellian, muB0-Maxwellian as volume-form) elif isinstance(self, Particles5D): self._markers[:n_mks_load_loc, 3] = ( - _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities[:, 0]) - 1)) - * xp.sqrt(2) - * v_th[0] + _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities[:, 0]) - 1)) * xp.sqrt(2) * v_th[0] + u_mean[0] ) - self._markers[:n_mks_load_loc, 4] = ( - -xp.log(1.0 - self.velocities[:, 1]) * v_th[1] ** 2 / B0 - ) + self._markers[:n_mks_load_loc, 4] = -xp.log(1.0 - self.velocities[:, 1]) * v_th[1] ** 2 / B0 # mu is a magnetic moment and must be >= 0. # A mean shift in this coordinate is not physically consistent. @@ -1527,9 +1520,7 @@ def draw_markers( # Particles5Dvperp: (1d Maxwellian, polar Maxwellian as volume-form) elif isinstance(self, Particles5Dvperp): self._markers[:n_mks_load_loc, 3] = ( - _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities[:, 0]) - 1)) - * xp.sqrt(2) - * v_th[0] + _dev(sp.erfinv(2 * _to_numpy_for_kernel(self.velocities[:, 0]) - 1)) * xp.sqrt(2) * v_th[0] + u_mean[0] ) diff --git a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py index f38276352..6872f0068 100644 --- a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py @@ -152,8 +152,17 @@ def _get_dk_kernel(): def driftkinetic_hamiltonian_gpu( - markers, alpha, column_nr, first_init_idx, first_shift_idx, mu_idx, - args_derham, epsilon, B_dot_b_coeffs, phi_coeffs, evaluate_e_field, + markers, + alpha, + column_nr, + first_init_idx, + first_shift_idx, + mu_idx, + args_derham, + epsilon, + B_dot_b_coeffs, + phi_coeffs, + evaluate_e_field, ): """GPU replacement for :func:`~struphy.pic.pushing.eval_kernels_gc.driftkinetic_hamiltonian`. @@ -177,18 +186,36 @@ def driftkinetic_hamiltonian_gpu( (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), np.int32(column_nr), - np.int32(first_init_idx), np.int32(first_shift_idx), np.int32(mu_idx), - np.float64(a[0]), np.float64(a[1]), np.float64(a[2]), np.float64(a[3]), + np.int32(first_init_idx), + np.int32(first_shift_idx), + np.int32(mu_idx), + np.float64(a[0]), + np.float64(a[1]), + np.float64(a[2]), + np.float64(a[3]), np.float64(epsilon), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - bdb, np.int32(bdb.shape[1]), np.int32(bdb.shape[2]), - phi, np.int32(phi.shape[1]), np.int32(phi.shape[2]), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + bdb, + np.int32(bdb.shape[1]), + np.int32(bdb.shape[2]), + phi, + np.int32(phi.shape[1]), + np.int32(phi.shape[2]), np.int32(bool(evaluate_e_field)), ), ) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 070ee7392..52989c840 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -11,6 +11,7 @@ from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.pic.base import Particles +from struphy.pic.pushing.eval_kernels_gc_cuda import driftkinetic_hamiltonian_gpu from struphy.pic.pushing.pusher_kernels_cuda import ( SUPPORTED_GENERAL_KIND_MAPS, push_bxu_H1vec_general_gpu, @@ -32,21 +33,20 @@ push_vxb_implicit_general_gpu, push_weights_with_efield_lin_va_general_gpu, ) -from struphy.pic.pushing.eval_kernels_gc_cuda import driftkinetic_hamiltonian_gpu -from struphy.pic.pushing.pusher_kernels_sph_cuda import ( - push_v_sph_pressure_gpu, - push_v_sph_pressure_ideal_gas_gpu, - push_v_viscosity_gpu, -) from struphy.pic.pushing.pusher_kernels_gc_cuda import ( - push_gc_bxEstar_discrete_gradient_1st_order_gpu, - push_gc_bxEstar_explicit_multistage_general_gpu, push_gc_Bstar_discrete_gradient_1st_order_gpu, push_gc_Bstar_explicit_multistage_general_gpu, + push_gc_bxEstar_discrete_gradient_1st_order_gpu, + push_gc_bxEstar_explicit_multistage_general_gpu, push_gc_cc_J1_H1vec_gpu, push_gc_cc_J1_Hcurl_gpu, push_gc_cc_J1_Hdiv_gpu, ) +from struphy.pic.pushing.pusher_kernels_sph_cuda import ( + push_v_sph_pressure_gpu, + push_v_sph_pressure_ideal_gas_gpu, + push_v_viscosity_gpu, +) logger = logging.getLogger("struphy") @@ -643,8 +643,18 @@ def __init__( self._gpu_sph_kappa = None else: ( - boxes, neighbours, holes, per1, per2, per3, - kernel_nr, h1, h2, h3, gravity, kappa, + boxes, + neighbours, + holes, + per1, + per2, + per3, + kernel_nr, + h1, + h2, + h3, + gravity, + kappa, ) = args_kernel self._gpu_sph_gravity = cp.asarray(np.asarray(gravity, dtype=float), dtype=cp.float64) self._gpu_sph_kappa = float(kappa) @@ -671,8 +681,12 @@ def __init__( ( args_derham, epsilon, - gb1, gb2, gb3, - ef1, ef2, ef3, + gb1, + gb2, + gb3, + ef1, + ef2, + ef3, evaluate_e_field, ) = args_kernel[:9] self._gpu_gc_dg1_epsilon = float(epsilon) @@ -691,11 +705,16 @@ def __init__( # is accepted but unused by the CPU kernels too), needs DF(eta) so # restricted like the other "general" paths to # SUPPORTED_GENERAL_KIND_MAPS. - self._gpu_gc_cc_j1 = cunumpy.cupy_backend and kernel.name in ( - "push_gc_cc_J1_H1vec", - "push_gc_cc_J1_Hcurl", - "push_gc_cc_J1_Hdiv", - ) and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + self._gpu_gc_cc_j1 = ( + cunumpy.cupy_backend + and kernel.name + in ( + "push_gc_cc_J1_H1vec", + "push_gc_cc_J1_Hcurl", + "push_gc_cc_J1_Hdiv", + ) + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) if self._gpu_gc_cc_j1: import cupy as cp @@ -703,10 +722,18 @@ def __init__( ( args_derham, epsilon, - b1, b2, b3, - nb1, nb2, nb3, - cnb1, cnb2, cnb3, - u1, u2, u3, + b1, + b2, + b3, + nb1, + nb2, + nb3, + cnb1, + cnb2, + cnb3, + u1, + u2, + u3, ) = args_kernel self._gpu_gc_cc_j1_kind_map = int(args_domain.kind_map) self._gpu_gc_cc_j1_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) @@ -787,7 +814,6 @@ def _kernel_region(self, kernel) -> str: self._kernel_region_names[id(kernel)] = name return name - def _run_marker_column_kernel(self, ker, alpha, column_nr, comps, add_args): """Run one init/eval kernel (they write a marker column in place). diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index 9d84025b1..6579f967e 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -279,7 +279,6 @@ def push_eta_rk_periodic_gpu( ) - _PUSH_V_EFIELD_CUBOID_SRC = r""" #define MAXP 8 diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index 35f8bcca0..f9312bf73 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -269,15 +269,34 @@ def _get_push_gc_Bstar_kernel(): def push_gc_bxEstar_explicit_multistage_general_gpu( - markers, n_cols, first_init_idx, first_free_idx, mu_idx, - kind_map, params_dev, epsilon, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - unit_b1_1_dev, unit_b1_2_dev, unit_b1_3_dev, - grad_b_full_1_dev, grad_b_full_2_dev, grad_b_full_3_dev, - B_dot_b_coeffs_dev, curl_unit_b_dot_b0_dev, - e_field_1_dev, e_field_2_dev, e_field_3_dev, + markers, + n_cols, + first_init_idx, + first_free_idx, + mu_idx, + kind_map, + params_dev, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + unit_b1_1_dev, + unit_b1_2_dev, + unit_b1_3_dev, + grad_b_full_1_dev, + grad_b_full_2_dev, + grad_b_full_3_dev, + B_dot_b_coeffs_dev, + curl_unit_b_dot_b0_dev, + e_field_1_dev, + e_field_2_dev, + e_field_3_dev, evaluate_e_field: bool, - dt_a: float, dt_b: float, last: float, + dt_a: float, + dt_b: float, + last: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_explicit_multistage`, @@ -298,36 +317,78 @@ def d(a): (blocks,), (threads,), ( - dev, np.int32(n_cols), np.int32(n_markers), - np.int32(first_init_idx), np.int32(first_free_idx), np.int32(mu_idx), - np.int32(kind_map), params_dev, + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_free_idx), + np.int32(mu_idx), + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(unit_b1_1_dev), *d(unit_b1_2_dev), *d(unit_b1_3_dev), - *d(grad_b_full_1_dev), *d(grad_b_full_2_dev), *d(grad_b_full_3_dev), - *d(B_dot_b_coeffs_dev), *d(curl_unit_b_dot_b0_dev), - *d(e_field_1_dev), *d(e_field_2_dev), *d(e_field_3_dev), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(unit_b1_1_dev), + *d(unit_b1_2_dev), + *d(unit_b1_3_dev), + *d(grad_b_full_1_dev), + *d(grad_b_full_2_dev), + *d(grad_b_full_3_dev), + *d(B_dot_b_coeffs_dev), + *d(curl_unit_b_dot_b0_dev), + *d(e_field_1_dev), + *d(e_field_2_dev), + *d(e_field_3_dev), np.int32(bool(evaluate_e_field)), - np.float64(dt_a), np.float64(dt_b), np.float64(last), + np.float64(dt_a), + np.float64(dt_b), + np.float64(last), ), ) def push_gc_Bstar_explicit_multistage_general_gpu( - markers, n_cols, first_init_idx, first_free_idx, mu_idx, - kind_map, params_dev, epsilon, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - grad_b_full_1_dev, grad_b_full_2_dev, grad_b_full_3_dev, - b2_1_dev, b2_2_dev, b2_3_dev, - curl_unit_b2_1_dev, curl_unit_b2_2_dev, curl_unit_b2_3_dev, - B_dot_b_coeffs_dev, curl_unit_b_dot_b0_dev, - e_field_1_dev, e_field_2_dev, e_field_3_dev, + markers, + n_cols, + first_init_idx, + first_free_idx, + mu_idx, + kind_map, + params_dev, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + grad_b_full_1_dev, + grad_b_full_2_dev, + grad_b_full_3_dev, + b2_1_dev, + b2_2_dev, + b2_3_dev, + curl_unit_b2_1_dev, + curl_unit_b2_2_dev, + curl_unit_b2_3_dev, + B_dot_b_coeffs_dev, + curl_unit_b_dot_b0_dev, + e_field_1_dev, + e_field_2_dev, + e_field_3_dev, evaluate_e_field: bool, - dt_a: float, dt_b: float, last: float, + dt_a: float, + dt_b: float, + last: float, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_explicit_multistage`, @@ -348,22 +409,45 @@ def d(a): (blocks,), (threads,), ( - dev, np.int32(n_cols), np.int32(n_markers), - np.int32(first_init_idx), np.int32(first_free_idx), np.int32(mu_idx), - np.int32(kind_map), params_dev, + dev, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_free_idx), + np.int32(mu_idx), + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(grad_b_full_1_dev), *d(grad_b_full_2_dev), *d(grad_b_full_3_dev), - *d(b2_1_dev), *d(b2_2_dev), *d(b2_3_dev), - *d(curl_unit_b2_1_dev), *d(curl_unit_b2_2_dev), *d(curl_unit_b2_3_dev), - *d(B_dot_b_coeffs_dev), *d(curl_unit_b_dot_b0_dev), - *d(e_field_1_dev), *d(e_field_2_dev), *d(e_field_3_dev), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(grad_b_full_1_dev), + *d(grad_b_full_2_dev), + *d(grad_b_full_3_dev), + *d(b2_1_dev), + *d(b2_2_dev), + *d(b2_3_dev), + *d(curl_unit_b2_1_dev), + *d(curl_unit_b2_2_dev), + *d(curl_unit_b2_3_dev), + *d(B_dot_b_coeffs_dev), + *d(curl_unit_b_dot_b0_dev), + *d(e_field_1_dev), + *d(e_field_2_dev), + *d(e_field_3_dev), np.int32(bool(evaluate_e_field)), - np.float64(dt_a), np.float64(dt_b), np.float64(last), + np.float64(dt_a), + np.float64(dt_b), + np.float64(last), ), ) @@ -594,9 +678,24 @@ def _get_dg_kernel(name): def _dg_launch( - name, markers, n_cols, first_init_idx, first_shift_idx, residual_idx, - first_free_idx, mu_idx, epsilon, pn, tn1_dev, tn2_dev, tn3_dev, starts, - grad_b_full, e_field, evaluate_e_field, dt, + name, + markers, + n_cols, + first_init_idx, + first_shift_idx, + residual_idx, + first_free_idx, + mu_idx, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + grad_b_full, + e_field, + evaluate_e_field, + dt, ): import cupy as cp import numpy as np @@ -613,17 +712,33 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(n_cols), np.int32(n_markers), - np.int32(first_init_idx), np.int32(first_shift_idx), - np.int32(residual_idx), np.int32(first_free_idx), np.int32(mu_idx), + markers, + np.int32(n_cols), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_shift_idx), + np.int32(residual_idx), + np.int32(first_free_idx), + np.int32(mu_idx), np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(grad_b_full[0]), *d(grad_b_full[1]), *d(grad_b_full[2]), - *d(e_field[0]), *d(e_field[1]), *d(e_field[2]), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(grad_b_full[0]), + *d(grad_b_full[1]), + *d(grad_b_full[2]), + *d(e_field[0]), + *d(e_field[1]), + *d(e_field[2]), np.int32(bool(evaluate_e_field)), np.float64(dt), ), @@ -885,9 +1000,21 @@ def _get_j1_kernel(name): def _j1_launch( - name, markers, kind_map, params_dev, epsilon, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2, norm_b1, curl_norm_b, u, dt, + name, + markers, + kind_map, + params_dev, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2, + norm_b1, + curl_norm_b, + u, + dt, ): import cupy as cp import numpy as np @@ -904,19 +1031,37 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), np.float64(dt), - np.int32(kind_map), params_dev, + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), - *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), - *d(u[0]), *d(u[1]), *d(u[2]), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(norm_b1[0]), + *d(norm_b1[1]), + *d(norm_b1[2]), + *d(curl_norm_b[0]), + *d(curl_norm_b[1]), + *d(curl_norm_b[2]), + *d(u[0]), + *d(u[1]), + *d(u[2]), ), ) @@ -1127,9 +1272,25 @@ def _get_j2_stage_kernel(name): def _j2_stage_launch( - name, markers, first_init_idx, first_free_idx, kind_map, params_dev, epsilon, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2, norm_b1, curl_norm_b, u, dt_a, dt_b, last, + name, + markers, + first_init_idx, + first_free_idx, + kind_map, + params_dev, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2, + norm_b1, + curl_norm_b, + u, + dt_a, + dt_b, + last, ): import cupy as cp import numpy as np @@ -1146,20 +1307,41 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_init_idx), np.int32(first_free_idx), - np.float64(dt_a), np.float64(dt_b), np.float64(last), - np.int32(kind_map), params_dev, + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_free_idx), + np.float64(dt_a), + np.float64(dt_b), + np.float64(last), + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), - *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), - *d(u[0]), *d(u[1]), *d(u[2]), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(norm_b1[0]), + *d(norm_b1[1]), + *d(norm_b1[2]), + *d(curl_norm_b[0]), + *d(curl_norm_b[1]), + *d(curl_norm_b[2]), + *d(u[0]), + *d(u[1]), + *d(u[2]), ), ) diff --git a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py index 1f9137b0d..39181accc 100644 --- a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py @@ -309,39 +309,111 @@ def _launch( def push_v_sph_pressure_gpu( - markers, valid_mks, weight_idx, first_free_idx, boxes, neighbours, holes, - periodic, kernel_type, h, gravity, kappa, kind_map, params_dev, dt, + markers, + valid_mks, + weight_idx, + first_free_idx, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + gravity, + kappa, + kind_map, + params_dev, + dt, ): """GPU replacement for :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_sph_pressure`.""" _launch( - "push_v_sph_pressure_cuda", markers, valid_mks, boxes, neighbours, holes, - periodic, kernel_type, h, kind_map, params_dev, dt, - weight_idx=weight_idx, first_free_idx=first_free_idx, gravity=gravity, kappa=kappa, + "push_v_sph_pressure_cuda", + markers, + valid_mks, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + kind_map, + params_dev, + dt, + weight_idx=weight_idx, + first_free_idx=first_free_idx, + gravity=gravity, + kappa=kappa, ) def push_v_sph_pressure_ideal_gas_gpu( - markers, valid_mks, weight_idx, first_free_idx, boxes, neighbours, holes, - periodic, kernel_type, h, gravity, kappa, kind_map, params_dev, dt, + markers, + valid_mks, + weight_idx, + first_free_idx, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + gravity, + kappa, + kind_map, + params_dev, + dt, ): """GPU replacement for :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_sph_pressure_ideal_gas`.""" _launch( - "push_v_sph_pressure_ideal_gas_cuda", markers, valid_mks, boxes, neighbours, holes, - periodic, kernel_type, h, kind_map, params_dev, dt, - weight_idx=weight_idx, first_free_idx=first_free_idx, gravity=gravity, kappa=kappa, + "push_v_sph_pressure_ideal_gas_cuda", + markers, + valid_mks, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + kind_map, + params_dev, + dt, + weight_idx=weight_idx, + first_free_idx=first_free_idx, + gravity=gravity, + kappa=kappa, ) def push_v_viscosity_gpu( - markers, valid_mks, first_free_idx, boxes, neighbours, holes, - periodic, kernel_type, h, kind_map, params_dev, dt, + markers, + valid_mks, + first_free_idx, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + kind_map, + params_dev, + dt, ): """GPU replacement for :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_viscosity`.""" _launch( - "push_v_viscosity_cuda", markers, valid_mks, boxes, neighbours, holes, - periodic, kernel_type, h, kind_map, params_dev, dt, + "push_v_viscosity_cuda", + markers, + valid_mks, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + kind_map, + params_dev, + dt, first_free_idx=first_free_idx, ) diff --git a/src/struphy/pic/tests/test_sph.py b/src/struphy/pic/tests/test_sph.py index 8049a5de9..111d42eb0 100644 --- a/src/struphy/pic/tests/test_sph.py +++ b/src/struphy/pic/tests/test_sph.py @@ -1895,7 +1895,6 @@ def u_xyz(x, y, z): # ghost_particles follows the active backend; this is a # diagnostics-only log, so pull the indices to the host. import numpy as np - from cunumpy import to_numpy ghost_inds = np.where(to_numpy(particles.ghost_particles))[0] diff --git a/src/struphy/pic/utilities_kernels_cuda.py b/src/struphy/pic/utilities_kernels_cuda.py index f47219113..615459951 100644 --- a/src/struphy/pic/utilities_kernels_cuda.py +++ b/src/struphy/pic/utilities_kernels_cuda.py @@ -422,22 +422,35 @@ def eval_canonical_toroidal_moment_5d_gpu( (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_diagnostics_idx), np.int32(mu_idx), np.int32(idx_can_momentum), - np.float64(epsilon), np.float64(B0), np.float64(R0), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - absB, np.int32(absB.shape[1]), np.int32(absB.shape[2]), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_diagnostics_idx), + np.int32(mu_idx), + np.int32(idx_can_momentum), + np.float64(epsilon), + np.float64(B0), + np.float64(R0), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + absB, + np.int32(absB.shape[1]), + np.int32(absB.shape[2]), ), ) -def eval_canonical_toroidal_moment_6d_gpu( - markers, args_derham, first_diagnostics_idx, epsilon, B0, R0, absB -): +def eval_canonical_toroidal_moment_6d_gpu(markers, args_derham, first_diagnostics_idx, epsilon, B0, R0, absB): """GPU replacement for :func:`~struphy.pic.utilities_kernels.eval_canonical_toroidal_moment_6d`. ``markers`` is device-resident and written in place. @@ -456,15 +469,28 @@ def eval_canonical_toroidal_moment_6d_gpu( (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), np.int32(first_diagnostics_idx), - np.float64(epsilon), np.float64(B0), np.float64(R0), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - absB, np.int32(absB.shape[1]), np.int32(absB.shape[2]), + np.float64(epsilon), + np.float64(B0), + np.float64(R0), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + absB, + np.int32(absB.shape[1]), + np.int32(absB.shape[2]), ), ) @@ -488,14 +514,25 @@ def eval_magnetic_moment_5d_gpu(markers, args_derham, first_diagnostics_idx, abs (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), np.int32(first_diagnostics_idx), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - absB, np.int32(absB.shape[1]), np.int32(absB.shape[2]), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + absB, + np.int32(absB.shape[1]), + np.int32(absB.shape[2]), ), ) @@ -520,15 +557,29 @@ def eval_magnetic_energy_PBb_gpu(markers, args_derham, first_diagnostics_idx, mu (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_diagnostics_idx), np.int32(mu_idx), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - abs_B0, np.int32(abs_B0.shape[1]), np.int32(abs_B0.shape[2]), - PBb, np.int32(PBb.shape[1]), np.int32(PBb.shape[2]), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_diagnostics_idx), + np.int32(mu_idx), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + abs_B0, + np.int32(abs_B0.shape[1]), + np.int32(abs_B0.shape[2]), + PBb, + np.int32(PBb.shape[1]), + np.int32(PBb.shape[2]), ), ) @@ -659,18 +710,36 @@ def eval_guiding_center_from_6d_gpu( (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), np.int32(first_diagnostics_idx), - np.int32(kind_map), params_dev, + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - b21, np.int32(b21.shape[1]), np.int32(b21.shape[2]), - b22, np.int32(b22.shape[1]), np.int32(b22.shape[2]), - b23, np.int32(b23.shape[1]), np.int32(b23.shape[2]), - absB, np.int32(absB.shape[1]), np.int32(absB.shape[2]), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + b21, + np.int32(b21.shape[1]), + np.int32(b21.shape[2]), + b22, + np.int32(b22.shape[1]), + np.int32(b22.shape[2]), + b23, + np.int32(b23.shape[1]), + np.int32(b23.shape[2]), + absB, + np.int32(absB.shape[1]), + np.int32(absB.shape[2]), ), ) diff --git a/src/struphy/propagators/current_coupling_5d_gradb.py b/src/struphy/propagators/current_coupling_5d_gradb.py index 2bc2c691f..5961748b9 100644 --- a/src/struphy/propagators/current_coupling_5d_gradb.py +++ b/src/struphy/propagators/current_coupling_5d_gradb.py @@ -335,9 +335,7 @@ def allocate(self): import numpy as np self._gpu_j2_stage_fn = ( - push_gc_cc_J2_stage_H1vec_gpu - if self.options.u_space == "H1vec" - else push_gc_cc_J2_stage_Hdiv_gpu + push_gc_cc_J2_stage_H1vec_gpu if self.options.u_space == "H1vec" else push_gc_cc_J2_stage_Hdiv_gpu ) self._gpu_j2_stage_kind_map = int(self.domain.args_domain.kind_map) self._gpu_j2_stage_params = cp.asarray( From 3080791b42c88de482a6fd35f2a39cecfb805116 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 08:11:20 +0200 Subject: [PATCH 066/156] Updated sph kernels, added eval_kernels_sph_cuda.py --- .../pic/pushing/eval_kernels_sph_cuda.py | 216 ++++++++++++++ src/struphy/pic/sph_eval_kernels_cuda.py | 267 ++++++++++++++++++ src/struphy/pic/utilities_kernels_cuda.py | 125 ++++++++ 3 files changed, 608 insertions(+) create mode 100644 src/struphy/pic/pushing/eval_kernels_sph_cuda.py diff --git a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py new file mode 100644 index 000000000..f03429b73 --- /dev/null +++ b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py @@ -0,0 +1,216 @@ +"""Hand-written CUDA replacements for the SPH marker-column kernels in +:mod:`~struphy.pic.pushing.eval_kernels_sph`, used only under +``ARRAY_BACKEND=cupy``. + +Like the SPH velocity pushers in +:mod:`~struphy.pic.pushing.pusher_kernels_sph_cuda`, these are per-marker +loops whose inner work is one or more box-neighbourhood SPH sums, i.e. the +same computation as :func:`~struphy.pic.sph_eval_kernels.box_based_kernel`. +Rather than duplicating that device function, this module reuses +``box_based_kernel_dev`` from :mod:`~struphy.pic.pushing.pusher_kernels_sph_cuda` +(which itself reuses ``distance_dev``/``smoothing_kernel_dev`` from +:mod:`~struphy.pic.sph_eval_kernels_cuda` and ``df_dispatch_dev``/ +``matrix_inv_dev`` from :mod:`~struphy.pic.pushing.pusher_kernels_cuda`, +though the two geometry helpers are unused here since none of these three +kernels touch the domain Jacobian). +""" + +_SPH_MARKER_COLUMN_SRC = r""" +extern "C" __global__ +void sph_pressure_coeffs_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int weight_idx, + const int* valid_mks, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const int loc_box = (int)row[n_cols - 2]; + + const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); + + const double weight = row[weight_idx]; + const double gamma = 5.0 / 3.0; + + row[column_nr] = n_at_eta; + row[column_nr + 1] = weight / n_at_eta; + row[column_nr + 2] = weight * pow(n_at_eta, gamma - 2.0); +} + +extern "C" __global__ +void sph_mean_velocity_coeffs_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int weight_idx, + const int* valid_mks, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const int loc_box = (int)row[n_cols - 2]; + + const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); + + const double weight = row[weight_idx]; + const double scale = weight / n_at_eta; + + row[column_nr + 0] = scale * row[3]; + row[column_nr + 1] = scale * row[4]; + row[column_nr + 2] = scale * row[5]; +} + +extern "C" __global__ +void sph_viscosity_tensor_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int weight_idx, const int first_free_idx, + const int* valid_mks, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3, + const double mu) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const int loc_box = (int)row[n_cols - 2]; + + const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); + const double weight = row[weight_idx]; + + double grad_v[3][3]; + for (int j = 0; j < 3; j++) { + for (int k = 0; k < 3; k++) { + grad_v[j][k] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, + first_free_idx + j, kernel_type + 1 + k, h1, h2, h3); + } + } + + double d_dev[3][3]; + for (int j = 0; j < 3; j++) + for (int k = 0; k < 3; k++) + d_dev[j][k] = 0.5 * (grad_v[j][k] + grad_v[k][j]); + + const double mean_trace = (d_dev[0][0] + d_dev[1][1] + d_dev[2][2]) / 3.0; + d_dev[0][0] -= mean_trace; + d_dev[1][1] -= mean_trace; + d_dev[2][2] -= mean_trace; + + const double scale = -2.0 * mu * (weight / n_at_eta); + for (int j = 0; j < 3; j++) { + for (int k = 0; k < 3; k++) { + row[column_nr + 3 * j + k] = d_dev[j][k] * scale; + } + } +} +""" + +_sph_marker_column_kernels = {} + + +def _get_sph_marker_column_kernel(name): + if name not in _sph_marker_column_kernels: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + from struphy.pic.pushing.pusher_kernels_sph_cuda import _SPH_PUSHER_SRC + from struphy.pic.sph_eval_kernels_cuda import _SPH_EVAL_FLAT_SRC + + _sph_marker_column_kernels[name] = cp.RawKernel( + _GENERAL_GEOMETRY_SRC + _SPH_EVAL_FLAT_SRC + _SPH_PUSHER_SRC + _SPH_MARKER_COLUMN_SRC, + name, + ) + return _sph_marker_column_kernels[name] + + +def _sph_marker_column_launch( + name, markers, valid_mks, column_nr, weight_idx, + boxes, neighbours, holes, periodic, kernel_type, h, + *, first_free_idx=None, mu=None, +): + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + dev_valid = cp.ascontiguousarray(cp.asarray(valid_mks).astype(cp.int32, copy=False)) + dev_boxes = cp.ascontiguousarray(cp.asarray(boxes).astype(cp.int32, copy=False)) + dev_neigh = cp.ascontiguousarray(cp.asarray(neighbours).astype(cp.int32, copy=False)) + dev_holes = cp.ascontiguousarray(cp.asarray(holes).astype(cp.int32, copy=False)) + + args = [ + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(column_nr), np.int32(weight_idx), + ] + if first_free_idx is not None: + args.append(np.int32(first_free_idx)) + args += [ + dev_valid, + dev_boxes, np.int32(dev_boxes.shape[1]), + dev_neigh, dev_holes, + np.int32(bool(periodic[0])), np.int32(bool(periodic[1])), np.int32(bool(periodic[2])), + np.int32(kernel_type), + np.float64(h[0]), np.float64(h[1]), np.float64(h[2]), + ] + if mu is not None: + args.append(np.float64(mu)) + + _get_sph_marker_column_kernel(name)((blocks,), (threads,), tuple(args)) + + +def sph_pressure_coeffs_gpu(markers, valid_mks, column_nr, weight_idx, boxes, neighbours, holes, periodic, kernel_type, h): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.eval_kernels_sph.sph_pressure_coeffs`.""" + _sph_marker_column_launch( + "sph_pressure_coeffs_cuda", markers, valid_mks, column_nr, weight_idx, + boxes, neighbours, holes, periodic, kernel_type, h, + ) + + +def sph_mean_velocity_coeffs_gpu( + markers, valid_mks, column_nr, weight_idx, boxes, neighbours, holes, periodic, kernel_type, h +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.eval_kernels_sph.sph_mean_velocity_coeffs`.""" + _sph_marker_column_launch( + "sph_mean_velocity_coeffs_cuda", markers, valid_mks, column_nr, weight_idx, + boxes, neighbours, holes, periodic, kernel_type, h, + ) + + +def sph_viscosity_tensor_gpu( + markers, valid_mks, column_nr, weight_idx, first_free_idx, + boxes, neighbours, holes, periodic, kernel_type, h, mu, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.eval_kernels_sph.sph_viscosity_tensor`.""" + _sph_marker_column_launch( + "sph_viscosity_tensor_cuda", markers, valid_mks, column_nr, weight_idx, + boxes, neighbours, holes, periodic, kernel_type, h, + first_free_idx=first_free_idx, mu=mu, + ) diff --git a/src/struphy/pic/sph_eval_kernels_cuda.py b/src/struphy/pic/sph_eval_kernels_cuda.py index fd1be6ae2..62d654ae0 100644 --- a/src/struphy/pic/sph_eval_kernels_cuda.py +++ b/src/struphy/pic/sph_eval_kernels_cuda.py @@ -524,3 +524,270 @@ def box_based_evaluation_meshgrid_gpu( out[:] = dev_out else: dev_out.get(out=out) + + +# --------------------------------------------------------------------------- +# naive_evaluation_flat / naive_evaluation_meshgrid: the O(N) reference +# implementation of the SPH kernel-density sum above (sums over every marker +# instead of the 27 neighbouring boxes), used only for testing/verification +# per the CPU docstring -- not a hot loop, so like box_based_evaluation_* +# this round-trips its (host-resident) inputs through the device once per +# call rather than caching device buffers. Reuses distance_dev/ +# smoothing_kernel_dev from _SPH_EVAL_FLAT_SRC above; unlike the box-based +# kernels the result is divided by Np, matching +# :func:`~struphy.pic.sph_eval_kernels.naive_evaluation_kernel`. +# --------------------------------------------------------------------------- + +_SPH_EVAL_NAIVE_SRC = r""" +extern "C" __global__ +void naive_evaluation_flat_cuda( + const double* markers, + const int n_cols, + const int n_markers, + const double Np, + const double* eta1, + const double* eta2, + const double* eta3, + const int n_eval, + const int* holes, + const int periodic1, + const int periodic2, + const int periodic3, + const int index, + const int kernel_type, + const double h1, + const double h2, + const double h3, + double* out) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n_eval) return; + + double e1 = eta1[i], e2 = eta2[i], e3 = eta3[i]; + + double acc = 0.0; + for (int p = 0; p < n_markers; p++) { + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + out[i] = acc / Np; +} + +extern "C" __global__ +void naive_evaluation_meshgrid_cuda( + const double* markers, + const int n_cols, + const int n_markers, + const double Np, + const double* eta1, + const double* eta2, + const double* eta3, + const int n1_eval, + const int n2_eval, + const int n3_eval, + const int* holes, + const int periodic1, + const int periodic2, + const int periodic3, + const int index, + const int kernel_type, + const double h1, + const double h2, + const double h3, + double* out) +{ + size_t idx = (size_t)blockIdx.x * blockDim.x + threadIdx.x; + size_t n_total = (size_t)n1_eval * n2_eval * n3_eval; + if (idx >= n_total) return; + + int i = idx / ((size_t)n2_eval * n3_eval); + int rem = idx % ((size_t)n2_eval * n3_eval); + int j = rem / n3_eval; + int k = rem % n3_eval; + + double e1 = eta1[i], e2 = eta2[j], e3 = eta3[k]; + + double acc = 0.0; + for (int p = 0; p < n_markers; p++) { + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + out[idx] = acc / Np; +} +""" + +_naive_evaluation_flat_kernel = None +_naive_evaluation_meshgrid_kernel = None + + +def _get_naive_flat_kernel(): + global _naive_evaluation_flat_kernel + if _naive_evaluation_flat_kernel is None: + import cupy as cp + + _naive_evaluation_flat_kernel = cp.RawKernel( + _SPH_EVAL_FLAT_SRC + _SPH_EVAL_NAIVE_SRC, "naive_evaluation_flat_cuda" + ) + return _naive_evaluation_flat_kernel + + +def _get_naive_meshgrid_kernel(): + global _naive_evaluation_meshgrid_kernel + if _naive_evaluation_meshgrid_kernel is None: + import cupy as cp + + _naive_evaluation_meshgrid_kernel = cp.RawKernel( + _SPH_EVAL_FLAT_SRC + _SPH_EVAL_NAIVE_SRC, "naive_evaluation_meshgrid_cuda" + ) + return _naive_evaluation_meshgrid_kernel + + +def naive_evaluation_flat_gpu( + markers, + Np: float, + eta1, + eta2, + eta3, + holes, + periodic1: bool, + periodic2: bool, + periodic3: bool, + index: int, + kernel_type: int, + h1: float, + h2: float, + h3: float, + out, +): + """GPU replacement for one call of + :func:`~struphy.pic.sph_eval_kernels.naive_evaluation_flat`.""" + import cupy as cp + import numpy as np + + kernel = _get_naive_flat_kernel() + n_cols = markers.shape[1] + n_markers = markers.shape[0] + n_eval = eta1.shape[0] + + dev_markers = cp.asarray(markers) + dev_eta1 = cp.ascontiguousarray(eta1, dtype=cp.float64) + dev_eta2 = cp.ascontiguousarray(eta2, dtype=cp.float64) + dev_eta3 = cp.ascontiguousarray(eta3, dtype=cp.float64) + dev_holes = cp.asarray(holes, dtype=cp.int32) + dev_out = cp.zeros(n_eval, dtype=cp.float64) + + threads = 256 + blocks = (n_eval + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(n_cols), + np.int32(n_markers), + np.float64(Np), + dev_eta1, + dev_eta2, + dev_eta3, + np.int32(n_eval), + dev_holes, + np.int32(1 if periodic1 else 0), + np.int32(1 if periodic2 else 0), + np.int32(1 if periodic3 else 0), + np.int32(index), + np.int32(kernel_type), + np.float64(h1), + np.float64(h2), + np.float64(h3), + dev_out, + ), + ) + if isinstance(out, cp.ndarray): + out[:] = dev_out + else: + dev_out.get(out=out) + + +def naive_evaluation_meshgrid_gpu( + markers, + Np: float, + eta1, + eta2, + eta3, + holes, + periodic1: bool, + periodic2: bool, + periodic3: bool, + index: int, + kernel_type: int, + h1: float, + h2: float, + h3: float, + out, +): + """GPU replacement for one call of + :func:`~struphy.pic.sph_eval_kernels.naive_evaluation_meshgrid`. + + Like :func:`box_based_evaluation_meshgrid_gpu`, ``eta1``/``eta2``/``eta3`` + are the 3 distinct 1-D axis vectors of the meshgrid, not the broadcast + arrays -- the CPU kernel this ports only ever reads + ``eta1[i,0,0]``/``eta2[0,j,0]``/``eta3[0,0,k]``. + """ + import cupy as cp + import numpy as np + + kernel = _get_naive_meshgrid_kernel() + n_cols = markers.shape[1] + n_markers = markers.shape[0] + n1_eval, n2_eval, n3_eval = eta1.shape[0], eta2.shape[1], eta3.shape[2] + + dev_markers = cp.asarray(markers) + dev_eta1 = cp.ascontiguousarray(eta1[:, 0, 0], dtype=cp.float64) + dev_eta2 = cp.ascontiguousarray(eta2[0, :, 0], dtype=cp.float64) + dev_eta3 = cp.ascontiguousarray(eta3[0, 0, :], dtype=cp.float64) + dev_holes = cp.asarray(holes, dtype=cp.int32) + dev_out = cp.zeros((n1_eval, n2_eval, n3_eval), dtype=cp.float64) + + threads = 256 + n_total = n1_eval * n2_eval * n3_eval + blocks = (n_total + threads - 1) // threads + kernel( + (blocks,), + (threads,), + ( + dev_markers, + np.int32(n_cols), + np.int32(n_markers), + np.float64(Np), + dev_eta1, + dev_eta2, + dev_eta3, + np.int32(n1_eval), + np.int32(n2_eval), + np.int32(n3_eval), + dev_holes, + np.int32(1 if periodic1 else 0), + np.int32(1 if periodic2 else 0), + np.int32(1 if periodic3 else 0), + np.int32(index), + np.int32(kernel_type), + np.float64(h1), + np.float64(h2), + np.float64(h3), + dev_out, + ), + ) + if isinstance(out, cp.ndarray): + out[:] = dev_out + else: + dev_out.get(out=out) diff --git a/src/struphy/pic/utilities_kernels_cuda.py b/src/struphy/pic/utilities_kernels_cuda.py index 615459951..3ea934b42 100644 --- a/src/struphy/pic/utilities_kernels_cuda.py +++ b/src/struphy/pic/utilities_kernels_cuda.py @@ -743,3 +743,128 @@ def eval_guiding_center_from_6d_gpu( np.int32(absB.shape[2]), ), ) + + +# --------------------------------------------------------------------------- +# eval_gradB_ediff: writes markers[:, idx] = mu * dot(eta_diff, gradB + +# grad_PB_b), evaluated at the midpoint eta_mid = mod((eta+eta_init)/2, 1). +# Called once per fixed-point iteration by CurrentCoupling5DGradB's +# discrete-gradient algorithm. Needs 1-form spline evaluation (unlike the +# 0-form diagnostics above), so this is built from +# pusher_kernels_cuda._GENERAL_GEOMETRY_SRC instead of the private +# find_span_dev/b_splines_dev/eval_0form_dev helpers used by _UTILITIES_SRC. +# --------------------------------------------------------------------------- + +_GRADB_EDIFF_SRC = r""" +__device__ double gradb_ediff_mod1_dev(double x) +{ + double r = fmod(x, 1.0); + if (r < 0.0) r += 1.0; + return r; +} + +extern "C" __global__ +void eval_gradB_ediff_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int mu_idx, const int idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* pb1, const int p1_n2, const int p1_n3, + const double* pb2, const int p2_n2, const int p2_n3, + const double* pb3, const int p3_n2, const int p3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta_mid[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + eta_mid[k] = gradb_ediff_mod1_dev((row[k] + row[first_init_idx + k]) / 2.0); + eta_diff[k] = row[k] - row[first_init_idx + k]; + } + const double mu = row[mu_idx]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double gradB[3], grad_PB_b[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, gradB); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + pb1,p1_n2,p1_n3, pb2,p2_n2,p2_n3, pb3,p3_n2,p3_n3, grad_PB_b); + + double tmp[3]; + for (int k = 0; k < 3; k++) tmp[k] = gradB[k] + grad_PB_b[k]; + + row[idx] = mu * dot3_dev(eta_diff, tmp); +} +""" + +_gradb_ediff_kernel = None + + +def _get_gradb_ediff_kernel(): + global _gradb_ediff_kernel + if _gradb_ediff_kernel is None: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _gradb_ediff_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _GRADB_EDIFF_SRC, "eval_gradB_ediff_cuda") + return _gradb_ediff_kernel + + +def eval_gradB_ediff_gpu( + markers, first_init_idx, mu_idx, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + gradB1_dev, grad_PB_b1_dev, idx, +): + """GPU replacement for one call of + :func:`~struphy.pic.utilities_kernels.eval_gradB_ediff`. + + ``gradB1_dev``/``grad_PB_b1_dev`` are each a 3-tuple of device arrays + (the 1-form's 3 components), matching the (unpacked) ``gradB1, gradB2, + gradB3`` / ``grad_PB_b1, grad_PB_b2, grad_PB_b3`` arguments of the CPU + kernel. + """ + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_gradb_ediff_kernel()( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), np.int32(mu_idx), np.int32(idx), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(gradB1_dev[0]), *d(gradB1_dev[1]), *d(gradB1_dev[2]), + *d(grad_PB_b1_dev[0]), *d(grad_PB_b1_dev[1]), *d(grad_PB_b1_dev[2]), + ), + ) From c3d132f7f44e31fa38c4bef4b2564899d6ff1bf0 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 08:12:11 +0200 Subject: [PATCH 067/156] Update kernels --- src/struphy/pic/base.py | 38 +- .../pic/pushing/eval_kernels_gc_cuda.py | 412 ++++++++++++++++++ src/struphy/pic/pushing/pusher.py | 134 +++++- .../pic/pushing/pusher_kernels_gc_cuda.py | 283 ++++++++++++ .../propagators/current_coupling_5d_gradb.py | 173 ++++++-- 5 files changed, 1006 insertions(+), 34 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 3ea7bad08..f7b021e57 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -73,6 +73,8 @@ class Intracomm: from struphy.pic.sph_eval_kernels_cuda import ( box_based_evaluation_flat_gpu, box_based_evaluation_meshgrid_gpu, + naive_evaluation_flat_gpu, + naive_evaluation_meshgrid_gpu, ) from struphy.utils import utils from struphy.utils.clone_config import CloneConfig @@ -4594,13 +4596,13 @@ def _eval_sph( h3, out, ) - else: - if len(_shp) == 1: - func = PyccelKernel(naive_evaluation_flat) - elif len(_shp) == 3: - func = PyccelKernel(naive_evaluation_meshgrid) - func( - self.args_markers, + elif xp.cupy_backend and len(_shp) in (1, 3): + # CUDA replacement for naive_evaluation_flat/_meshgrid: one + # thread per evaluation point, see sph_eval_kernels_cuda. + gpu_func = naive_evaluation_flat_gpu if len(_shp) == 1 else naive_evaluation_meshgrid_gpu + gpu_func( + self.markers, + float(self.Np), eta1, eta2, eta3, @@ -4615,6 +4617,28 @@ def _eval_sph( h3, out, ) + else: + if len(_shp) == 1: + func = PyccelKernel(naive_evaluation_flat) + elif len(_shp) == 3: + func = PyccelKernel(naive_evaluation_meshgrid) + with self.host_markers(write=False) as args_markers: + func( + args_markers, + eta1, + eta2, + eta3, + self.holes, + periodic1, + periodic2, + periodic3, + index, + ker_id, + h1, + h2, + h3, + out, + ) return out ### MPI comm for domain decomposition ### diff --git a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py index 6872f0068..08ab97466 100644 --- a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py @@ -219,3 +219,415 @@ def driftkinetic_hamiltonian_gpu( np.int32(bool(evaluate_e_field)), ), ) + + +# --------------------------------------------------------------------------- +# grad_driftkinetic_hamiltonian / bstar_parallel_3form / bstar_2form / +# unit_b_1form: the remaining marker-column init/eval kernels of the +# discrete-gradient guiding-centre propagators. Unlike driftkinetic_hamiltonian +# above (self-contained 0-form-only source), these also need 1-/2-form +# evaluation and (for bstar_parallel_3form) the domain Jacobian, so they are +# built from pusher_kernels_cuda._GENERAL_GEOMETRY_SRC instead. All four +# share the same alpha-weighted evaluation point +# eta_i = mod(alpha_i * (eta_i + shift_i) + (1 - alpha_i) * eta_i^n, 1) +# (and, for the two that need v_parallel, the same alpha-weighted v), factored +# into one device helper. +# --------------------------------------------------------------------------- + +_GC_MARKER_COLUMN_SRC = r""" +__device__ void weighted_eta_v_dev( + const double* row, int first_init_idx, int first_shift_idx, + const double* alpha, double* eta, double* v_out) +{ + for (int k = 0; k < 3; k++) { + const double eta_k = row[k] + row[first_shift_idx + k]; + const double eta_n = row[first_init_idx + k]; + double e = alpha[k] * eta_k + (1.0 - alpha[k]) * eta_n; + double r = fmod(e, 1.0); + if (r < 0.0) r += 1.0; + eta[k] = r; + } + if (v_out) { + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + *v_out = alpha[3] * v_k + (1.0 - alpha[3]) * v_n; + } +} + +extern "C" __global__ +void grad_driftkinetic_hamiltonian_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int n_comps, const int* comps, + const int first_init_idx, const int first_shift_idx, const int mu_idx, + const double* alpha, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const int evaluate_e_field) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3]; + weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, 0); + const double mu = row[mu_idx]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + for (int j = 0; j < n_comps; j++) row[column_nr + j] = grad_H[comps[j]]; +} + +extern "C" __global__ +void bstar_parallel_3form_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, + const int first_init_idx, const int first_shift_idx, + const double* alpha, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* cub, const int cub_n2, const int cub_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3], v; + weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, &v); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta[0], eta[1], eta[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bn2[MAXP+1], bn3[MAXP+1]; + double bd1[MAXP], bd2[MAXP], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, cub, cub_n2, cub_n3); + + b_star_parallel = (b_star_parallel * epsilon * v + B_dot_b) * det_df; + + row[column_nr] = b_star_parallel; +} + +extern "C" __global__ +void bstar_2form_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int n_comps, const int* comps, + const int first_init_idx, const int first_shift_idx, + const double* alpha, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b1, const int b1_n2, const int b1_n3, + const double* b2, const int b2_n2, const int b2_n3, + const double* b3, const int b3_n2, const int b3_n3, + const double* cb1, const int c1_n2, const int c1_n3, + const double* cb2, const int c2_n2, const int c2_n3, + const double* cb3, const int c3_n2, const int c3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3], v; + weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, &v); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + double bb[3], b_star[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b1,b1_n2,b1_n3, b2,b2_n2,b2_n3, b3,b3_n2,b3_n3, bb); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cb1,c1_n2,c1_n3, cb2,c2_n2,c2_n3, cb3,c3_n2,c3_n3, b_star); + + for (int k = 0; k < 3; k++) b_star[k] = b_star[k] * epsilon * v + bb[k]; + + for (int j = 0; j < n_comps; j++) row[column_nr + j] = b_star[comps[j]]; +} + +extern "C" __global__ +void unit_b_1form_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int n_comps, const int* comps, + const int first_init_idx, const int first_shift_idx, + const double* alpha, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* ub1, const int u1_n2, const int u1_n3, + const double* ub2, const int u2_n2, const int u2_n3, + const double* ub3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3]; + weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, 0); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + double unit_b1[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ub1,u1_n2,u1_n3, ub2,u2_n2,u2_n3, ub3,u3_n2,u3_n3, unit_b1); + + for (int j = 0; j < n_comps; j++) row[column_nr + j] = unit_b1[comps[j]]; +} +""" + +_gc_marker_column_kernels = {} + + +def _get_gc_marker_column_kernel(name): + if name not in _gc_marker_column_kernels: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _gc_marker_column_kernels[name] = cp.RawKernel( + _GENERAL_GEOMETRY_SRC + _GC_MARKER_COLUMN_SRC, name + ) + return _gc_marker_column_kernels[name] + + +def grad_driftkinetic_hamiltonian_gpu( + markers, alpha, column_nr, comps, first_init_idx, first_shift_idx, mu_idx, + args_derham, epsilon, grad_b_full_coeffs, e_field_coeffs, evaluate_e_field, +): + """GPU replacement for + :func:`~struphy.pic.pushing.eval_kernels_gc.grad_driftkinetic_hamiltonian`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + alpha_dev = cp.asarray(np.asarray(alpha, dtype=np.float64)) + comps_dev = cp.asarray(np.asarray(comps, dtype=np.int32)) + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + + _get_gc_marker_column_kernel("grad_driftkinetic_hamiltonian_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(column_nr), np.int32(comps_dev.shape[0]), comps_dev, + np.int32(first_init_idx), np.int32(first_shift_idx), np.int32(mu_idx), + alpha_dev, + np.float64(epsilon), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + *d(grad_b_full_coeffs[0]), *d(grad_b_full_coeffs[1]), *d(grad_b_full_coeffs[2]), + *d(e_field_coeffs[0]), *d(e_field_coeffs[1]), *d(e_field_coeffs[2]), + np.int32(bool(evaluate_e_field)), + ), + ) + + +def bstar_parallel_3form_gpu( + markers, alpha, column_nr, first_init_idx, first_shift_idx, + kind_map, params_dev, args_derham, epsilon, B_dot_b_coeffs, curl_unit_b_dot_b0_coeffs, +): + """GPU replacement for + :func:`~struphy.pic.pushing.eval_kernels_gc.bstar_parallel_3form`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + alpha_dev = cp.asarray(np.asarray(alpha, dtype=np.float64)) + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + + _get_gc_marker_column_kernel("bstar_parallel_3form_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(column_nr), + np.int32(first_init_idx), np.int32(first_shift_idx), + alpha_dev, + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + *d(B_dot_b_coeffs), *d(curl_unit_b_dot_b0_coeffs), + ), + ) + + +def bstar_2form_gpu( + markers, alpha, column_nr, comps, first_init_idx, first_shift_idx, + args_derham, epsilon, b2_coeffs, curl_unit_b2_coeffs, +): + """GPU replacement for + :func:`~struphy.pic.pushing.eval_kernels_gc.bstar_2form`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + alpha_dev = cp.asarray(np.asarray(alpha, dtype=np.float64)) + comps_dev = cp.asarray(np.asarray(comps, dtype=np.int32)) + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + + _get_gc_marker_column_kernel("bstar_2form_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(column_nr), np.int32(comps_dev.shape[0]), comps_dev, + np.int32(first_init_idx), np.int32(first_shift_idx), + alpha_dev, + np.float64(epsilon), + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + *d(b2_coeffs[0]), *d(b2_coeffs[1]), *d(b2_coeffs[2]), + *d(curl_unit_b2_coeffs[0]), *d(curl_unit_b2_coeffs[1]), *d(curl_unit_b2_coeffs[2]), + ), + ) + + +def unit_b_1form_gpu( + markers, alpha, column_nr, comps, first_init_idx, first_shift_idx, + args_derham, unit_b1_coeffs, +): + """GPU replacement for + :func:`~struphy.pic.pushing.eval_kernels_gc.unit_b_1form`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + alpha_dev = cp.asarray(np.asarray(alpha, dtype=np.float64)) + comps_dev = cp.asarray(np.asarray(comps, dtype=np.int32)) + tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + + _get_gc_marker_column_kernel("unit_b_1form_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(column_nr), np.int32(comps_dev.shape[0]), comps_dev, + np.int32(first_init_idx), np.int32(first_shift_idx), + alpha_dev, + np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), + tn1, np.int32(tn1.shape[0]), + tn2, np.int32(tn2.shape[0]), + tn3, np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), + *d(unit_b1_coeffs[0]), *d(unit_b1_coeffs[1]), *d(unit_b1_coeffs[2]), + ), + ) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 52989c840..32e470889 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -11,7 +11,18 @@ from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.pic.base import Particles -from struphy.pic.pushing.eval_kernels_gc_cuda import driftkinetic_hamiltonian_gpu +from struphy.pic.pushing.eval_kernels_gc_cuda import ( + bstar_2form_gpu, + bstar_parallel_3form_gpu, + driftkinetic_hamiltonian_gpu, + grad_driftkinetic_hamiltonian_gpu, + unit_b_1form_gpu, +) +from struphy.pic.pushing.eval_kernels_sph_cuda import ( + sph_mean_velocity_coeffs_gpu, + sph_pressure_coeffs_gpu, + sph_viscosity_tensor_gpu, +) from struphy.pic.pushing.pusher_kernels_cuda import ( SUPPORTED_GENERAL_KIND_MAPS, push_bxu_H1vec_general_gpu, @@ -840,6 +851,127 @@ def _run_marker_column_kernel(self, ker, alpha, column_nr, comps, add_args): ) return + if cunumpy.cupy_backend and name == "grad_driftkinetic_hamiltonian": + args_derham, epsilon, gb1, gb2, gb3, ef1, ef2, ef3, evaluate_e_field = add_args[:9] + with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): + grad_driftkinetic_hamiltonian_gpu( + self.particles.markers, + alpha, + column_nr, + comps, + self.particles.first_pusher_idx, + self.particles.first_shift_idx, + self.particles.mu_idx, + args_derham, + epsilon, + (gb1, gb2, gb3), + (ef1, ef2, ef3), + evaluate_e_field, + ) + return + + if ( + cunumpy.cupy_backend + and name == "bstar_parallel_3form" + and self._args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ): + import cupy as cp + import numpy as np + + args_derham, epsilon, B_dot_b_coeffs, curl_unit_b_dot_b0_coeffs = add_args[:4] + if not hasattr(self, "_gpu_marker_col_params_dev"): + self._gpu_marker_col_kind_map = int(self._args_domain.kind_map) + self._gpu_marker_col_params_dev = cp.asarray( + np.asarray(self._args_domain.params, dtype=float), dtype=cp.float64 + ) + with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): + bstar_parallel_3form_gpu( + self.particles.markers, + alpha, + column_nr, + self.particles.first_pusher_idx, + self.particles.first_shift_idx, + self._gpu_marker_col_kind_map, + self._gpu_marker_col_params_dev, + args_derham, + epsilon, + B_dot_b_coeffs, + curl_unit_b_dot_b0_coeffs, + ) + return + + if cunumpy.cupy_backend and name == "bstar_2form": + args_derham, epsilon, b1, b2, b3, cb1, cb2, cb3 = add_args[:8] + with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): + bstar_2form_gpu( + self.particles.markers, + alpha, + column_nr, + comps, + self.particles.first_pusher_idx, + self.particles.first_shift_idx, + args_derham, + epsilon, + (b1, b2, b3), + (cb1, cb2, cb3), + ) + return + + if cunumpy.cupy_backend and name == "unit_b_1form": + args_derham, ub1, ub2, ub3 = add_args[:4] + with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): + unit_b_1form_gpu( + self.particles.markers, + alpha, + column_nr, + comps, + self.particles.first_pusher_idx, + self.particles.first_shift_idx, + args_derham, + (ub1, ub2, ub3), + ) + return + + if cunumpy.cupy_backend and name == "sph_pressure_coeffs": + boxes, neighbours, holes, p1, p2, p3, kernel_type, h1, h2, h3 = add_args[:10] + with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): + sph_pressure_coeffs_gpu( + self.particles.markers, + self.particles.valid_mks, + column_nr, + self.particles.index["weights"], + boxes, neighbours, holes, + (p1, p2, p3), kernel_type, (h1, h2, h3), + ) + return + + if cunumpy.cupy_backend and name == "sph_mean_velocity_coeffs": + boxes, neighbours, holes, p1, p2, p3, kernel_type, h1, h2, h3 = add_args[:10] + with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): + sph_mean_velocity_coeffs_gpu( + self.particles.markers, + self.particles.valid_mks, + column_nr, + self.particles.index["weights"], + boxes, neighbours, holes, + (p1, p2, p3), kernel_type, (h1, h2, h3), + ) + return + + if cunumpy.cupy_backend and name == "sph_viscosity_tensor": + boxes, neighbours, holes, p1, p2, p3, kernel_type, h1, h2, h3, mu = add_args[:11] + with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): + sph_viscosity_tensor_gpu( + self.particles.markers, + self.particles.valid_mks, + column_nr, + self.particles.index["weights"], + self.particles.first_free_idx, + boxes, neighbours, holes, + (p1, p2, p3), kernel_type, (h1, h2, h3), mu, + ) + return + with ( ProfileManager.profile_region(self._kernel_region(ker)), self.particles.host_markers(write=True) as args_markers, diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index f9312bf73..73e7ae87c 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -1356,3 +1356,286 @@ def push_gc_cc_J2_stage_Hdiv_gpu(*args, **kwargs): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_stage_Hdiv`.""" _j2_stage_launch("push_gc_cc_J2_stage_Hdiv_cuda", *args, **kwargs) + + +# --------------------------------------------------------------------------- +# push_gc_cc_J2_dg_init_Hdiv / push_gc_cc_J2_dg_Hdiv: the discrete-gradient +# variant of CurrentCoupling5DGradB's position push. Both are single-pass +# marker loops (no per-marker Newton solve -- the outer fixed-point loop in +# CurrentCoupling5DGradB.__call__ is over one global scalar `const` from an +# energy reduction, recomputed and re-applied to all markers each iteration). +# dg_init: like push_gc_cc_J2_stage_Hdiv's single-stage core, evaluated at +# the current position, straight `eta -= dt*e`. +# dg: evaluated at the midpoint eta_mid = mod((eta+eta_init)/2, 1), +# with a second U-field `ud` (discrete-gradient correction term, +# scaled by `const`) added before the same |B*_para|/det(DF) +# division, then `eta = alpha*(eta_init - dt*e) + (1-alpha)*eta_old`. +# --------------------------------------------------------------------------- + +_PUSH_GC_CC_J2_DG_SRC = r""" +extern "C" __global__ +void push_gc_cc_J2_dg_init_Hdiv_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, + const double dt, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double bb[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double tmp[9], e[3]; + matmat_dev(norm_b_prod, b_prod, tmp); + matvec_dev(tmp, u, e); + for (int k = 0; k < 3; k++) e[k] /= (abs_b_star_para * det_df); + + row[0] -= dt * e[0]; + row[1] -= dt * e[1]; + row[2] -= dt * e[2]; +} + +extern "C" __global__ +void push_gc_cc_J2_dg_Hdiv_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, + const double dt, const double const_, const double alpha, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3, + const double* ud_1, const int ud1_n2, const int ud1_n3, + const double* ud_2, const int ud2_n2, const int ud2_n3, + const double* ud_3, const int ud3_n2, const int ud3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta_old0 = row[0], eta_old1 = row[1], eta_old2 = row[2]; + double eta_mid[3]; + eta_mid[0] = mod1_dev((row[0] + row[first_init_idx + 0]) / 2.0); + eta_mid[1] = mod1_dev((row[1] + row[first_init_idx + 1]) / 2.0); + eta_mid[2] = mod1_dev((row[2] + row[first_init_idx + 2]) / 2.0); + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + double bb[3], u[3], ud[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ud_1,ud1_n2,ud1_n3, ud_2,ud2_n2,ud2_n3, ud_3,ud3_n2,ud3_n3, ud); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double tmp[9], e[3], e2[3]; + matmat_dev(norm_b_prod, b_prod, tmp); + matvec_dev(tmp, u, e); + matvec_dev(tmp, ud, e2); + for (int k = 0; k < 3; k++) e[k] = (e[k] + const_ * e2[k]) / (abs_b_star_para * det_df); + + double eta_new[3]; + eta_new[0] = row[first_init_idx + 0] - dt * e[0]; + eta_new[1] = row[first_init_idx + 1] - dt * e[1]; + eta_new[2] = row[first_init_idx + 2] - dt * e[2]; + + row[0] = alpha * eta_new[0] + (1.0 - alpha) * eta_old0; + row[1] = alpha * eta_new[1] + (1.0 - alpha) * eta_old1; + row[2] = alpha * eta_new[2] + (1.0 - alpha) * eta_old2; +} +""" + +_j2_dg_kernels = {} + + +def _get_j2_dg_kernel(name): + if name not in _j2_dg_kernels: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _j2_dg_kernels[name] = cp.RawKernel( + _GENERAL_GEOMETRY_SRC + _DG_1ST_SRC + _PUSH_GC_CC_J2_DG_SRC, name + ) + return _j2_dg_kernels[name] + + +def push_gc_cc_J2_dg_init_Hdiv_gpu( + markers, first_init_idx, kind_map, params_dev, epsilon, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2, norm_b1, curl_norm_b, u, dt, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_dg_init_Hdiv`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_j2_dg_kernel("push_gc_cc_J2_dg_init_Hdiv_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), + np.float64(dt), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), + *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + *d(u[0]), *d(u[1]), *d(u[2]), + ), + ) + + +def push_gc_cc_J2_dg_Hdiv_gpu( + markers, first_init_idx, kind_map, params_dev, epsilon, + pn, tn1_dev, tn2_dev, tn3_dev, starts, + b2, norm_b1, curl_norm_b, u, ud, const, alpha, dt, +): + """GPU replacement for one call of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_dg_Hdiv`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_j2_dg_kernel("push_gc_cc_J2_dg_Hdiv_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), + np.float64(dt), np.float64(const), np.float64(alpha), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), + *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), + *d(u[0]), *d(u[1]), *d(u[2]), + *d(ud[0]), *d(ud[1]), *d(ud[2]), + ), + ) diff --git a/src/struphy/propagators/current_coupling_5d_gradb.py b/src/struphy/propagators/current_coupling_5d_gradb.py index 5961748b9..af3c306ee 100644 --- a/src/struphy/propagators/current_coupling_5d_gradb.py +++ b/src/struphy/propagators/current_coupling_5d_gradb.py @@ -23,9 +23,12 @@ from struphy.pic.pushing import pusher_kernels_gc from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS from struphy.pic.pushing.pusher_kernels_gc_cuda import ( + push_gc_cc_J2_dg_Hdiv_gpu, + push_gc_cc_J2_dg_init_Hdiv_gpu, push_gc_cc_J2_stage_H1vec_gpu, push_gc_cc_J2_stage_Hdiv_gpu, ) +from struphy.pic.utilities_kernels_cuda import eval_gradB_ediff_gpu from struphy.propagators.base import Propagator from struphy.utils.utils import check_option @@ -474,6 +477,49 @@ def allocate(self): self._pusher_kernel_init = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_dg_init_Hdiv) self._pusher_kernel = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_dg_Hdiv) + # GPU replacements for both pusher kernels above. Same reasoning + # as the explicit branch: this fixed-point loop calls + # self._pusher_kernel{,_init} directly on args_markers (no Pusher + # wrapper), which would force a host round trip (or crash) on + # device-resident markers under cupy. + self._gpu_j2_dg = xp.cupy_backend and self.domain.args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + if self._gpu_j2_dg: + import cupy as cp + import numpy as np + + self._gpu_j2_dg_kind_map = int(self.domain.args_domain.kind_map) + self._gpu_j2_dg_params = cp.asarray( + np.asarray(self.domain.args_domain.params, dtype=float), dtype=cp.float64 + ) + self._gpu_j2_dg_epsilon = float(epsilon) + args_derham = self.derham.args_derham + self._gpu_j2_dg_pn = tuple(int(p) for p in args_derham.pn) + self._gpu_j2_dg_starts = tuple(int(s) for s in args_derham.starts) + self._gpu_j2_dg_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_j2_dg_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_j2_dg_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_j2_dg_b2 = (self._b_full[0]._data, self._b_full[1]._data, self._b_full[2]._data) + self._gpu_j2_dg_norm_b1 = (unit_b1[0]._data, unit_b1[1]._data, unit_b1[2]._data) + self._gpu_j2_dg_curl_norm_b = ( + curl_unit_b2[0]._data, + curl_unit_b2[1]._data, + curl_unit_b2[2]._data, + ) + self._gpu_j2_dg_u_init = ( + self.variables.u.spline.vector[0]._data, + self.variables.u.spline.vector[1]._data, + self.variables.u.spline.vector[2]._data, + ) + self._gpu_j2_dg_u = (self._u_mid[0]._data, self._u_mid[1]._data, self._u_mid[2]._data) + self._gpu_j2_dg_ud = (self._u_temp[0]._data, self._u_temp[1]._data, self._u_temp[2]._data) + # for eval_gradB_ediff_gpu (reuses the same pn/tn1-3/starts) + self._gpu_j2_dg_gradB1 = (gradB1[0]._data, gradB1[1]._data, gradB1[2]._data) + self._gpu_j2_dg_grad_PB_b1 = ( + self._grad_PB_b[0]._data, + self._grad_PB_b[1]._data, + self._grad_PB_b[2]._data, + ) + def __call__(self, dt): # current FE coeffs un = self.variables.u.spline.vector @@ -482,7 +528,11 @@ def __call__(self, dt): particles = self.variables.energetic_ions.particles holes = particles.holes args_markers = particles.args_markers - markers = args_markers.markers + # NOTE: args_markers.markers is the *host mirror* under cupy (see + # Particles.args_markers) -- only valid inside a host_markers() + # block. The xp-vectorised bookkeeping below (holes indexing, sums, + # ...) needs the real device array instead. + markers = particles.markers first_init_idx = args_markers.first_init_idx first_free_idx = args_markers.first_free_idx @@ -631,7 +681,7 @@ def __call__(self, dt): op=MPI.SUM, ) - en_fB_old = buffer_array[0] + en_fB_old = float(buffer_array[0]) en_tot_old = en_U_old + en_fB_old # initial guess @@ -647,11 +697,35 @@ def __call__(self, dt): en_U_new = u_new.inner(self._M2n_dot_u) / 2.0 # push eta - self._pusher_kernel_init( - dt, - args_markers, - *self._args_pusher_kernel_init, - ) + if self._gpu_j2_dg: + with ProfileManager.profile_region("kernel: " + self._pusher_kernel_init.name + " [cuda]"): + push_gc_cc_J2_dg_init_Hdiv_gpu( + markers, + first_init_idx, + self._gpu_j2_dg_kind_map, + self._gpu_j2_dg_params, + self._gpu_j2_dg_epsilon, + self._gpu_j2_dg_pn, + self._gpu_j2_dg_tn1, + self._gpu_j2_dg_tn2, + self._gpu_j2_dg_tn3, + self._gpu_j2_dg_starts, + self._gpu_j2_dg_b2, + self._gpu_j2_dg_norm_b1, + self._gpu_j2_dg_curl_norm_b, + self._gpu_j2_dg_u_init, + dt, + ) + else: + with ( + ProfileManager.profile_region("kernel: " + self._pusher_kernel_init.name), + particles.host_markers(write=True) as args_markers_h, + ): + self._pusher_kernel_init( + dt, + args_markers_h, + *self._args_pusher_kernel_init, + ) if particles.mpi_comm is not None: particles.mpi_sort_markers(apply_bc=False) @@ -677,7 +751,7 @@ def __call__(self, dt): op=MPI.SUM, ) - en_fB_new = buffer_array[0] + en_fB_new = float(buffer_array[0]) # fixed-point iterations iter_num = 0 @@ -720,7 +794,7 @@ def __call__(self, dt): op=MPI.SUM, ) - denominator = buffer_array[0] + denominator = float(buffer_array[0]) buffer_array = xp.array([sum_H_diff_loc]) @@ -738,17 +812,37 @@ def __call__(self, dt): op=MPI.SUM, ) - denominator += buffer_array[0] + denominator += float(buffer_array[0]) # sorting markers at mid-point if particles.mpi_comm is not None: particles.mpi_sort_markers(apply_bc=False, alpha=0.5) - self._accum_kernel_en_fB_mid( - args_markers, - *self._args_accum_kernel_en_fB_mid, - first_free_idx + 3, - ) + if self._gpu_j2_dg: + with ProfileManager.profile_region("kernel: " + self._accum_kernel_en_fB_mid.name + " [cuda]"): + eval_gradB_ediff_gpu( + markers, + first_init_idx, + particles.mu_idx, + self._gpu_j2_dg_pn, + self._gpu_j2_dg_tn1, + self._gpu_j2_dg_tn2, + self._gpu_j2_dg_tn3, + self._gpu_j2_dg_starts, + self._gpu_j2_dg_gradB1, + self._gpu_j2_dg_grad_PB_b1, + first_free_idx + 3, + ) + else: + with ( + ProfileManager.profile_region("kernel: " + self._accum_kernel_en_fB_mid.name), + particles.host_markers(write=True) as args_markers_h, + ): + self._accum_kernel_en_fB_mid( + args_markers_h, + *self._args_accum_kernel_en_fB_mid, + first_free_idx + 3, + ) en_fB_mid = xp.sum(markers[~holes, first_free_idx + 3].dot(markers[~holes, 5])) * self.options.ep_scale en_fB_mid /= n_mks_tot @@ -769,7 +863,7 @@ def __call__(self, dt): op=MPI.SUM, ) - en_fB_mid = buffer_array[0] + en_fB_mid = float(buffer_array[0]) if denominator == 0.0: const = 0.0 @@ -793,13 +887,40 @@ def __call__(self, dt): en_U_new = u_new.inner(self._M2n_dot_u) / 2.0 # update H^{n+1, k} - self._pusher_kernel( - dt, - args_markers, - *self._args_pusher_kernel, - const, - alpha, - ) + if self._gpu_j2_dg: + with ProfileManager.profile_region("kernel: " + self._pusher_kernel.name + " [cuda]"): + push_gc_cc_J2_dg_Hdiv_gpu( + markers, + first_init_idx, + self._gpu_j2_dg_kind_map, + self._gpu_j2_dg_params, + self._gpu_j2_dg_epsilon, + self._gpu_j2_dg_pn, + self._gpu_j2_dg_tn1, + self._gpu_j2_dg_tn2, + self._gpu_j2_dg_tn3, + self._gpu_j2_dg_starts, + self._gpu_j2_dg_b2, + self._gpu_j2_dg_norm_b1, + self._gpu_j2_dg_curl_norm_b, + self._gpu_j2_dg_u, + self._gpu_j2_dg_ud, + const, + alpha, + dt, + ) + else: + with ( + ProfileManager.profile_region("kernel: " + self._pusher_kernel.name), + particles.host_markers(write=True) as args_markers_h, + ): + self._pusher_kernel( + dt, + args_markers_h, + *self._args_pusher_kernel, + const, + alpha, + ) sum_H_diff_loc = xp.sum( xp.abs(markers[~holes, 0:3] - markers[~holes, first_free_idx : first_free_idx + 3]), @@ -829,7 +950,7 @@ def __call__(self, dt): op=MPI.SUM, ) - en_fB_new = buffer_array[0] + en_fB_new = float(buffer_array[0]) # calculate total energy difference e_diff = xp.abs(en_U_new + en_fB_new - en_tot_old) @@ -846,7 +967,7 @@ def __call__(self, dt): op=MPI.SUM, ) - diff = buffer_array[0] + diff = float(buffer_array[0]) buffer_array = xp.array([sum_H_diff_loc]) @@ -864,7 +985,7 @@ def __call__(self, dt): op=MPI.SUM, ) - diff += buffer_array[0] + diff += float(buffer_array[0]) # check convergence if diff < self.options.dg_solver_params.tol: From 880a806e145ab08d56ca81262e3b3981da0afc1e Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 08:34:10 +0200 Subject: [PATCH 068/156] Fix hdf5 error: HDF5_USE_FILE_LOCKING = False by default --- src/struphy/__init__.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/struphy/__init__.py b/src/struphy/__init__.py index 6a6163246..cf87d8fed 100644 --- a/src/struphy/__init__.py +++ b/src/struphy/__init__.py @@ -4,6 +4,12 @@ import logging.config import os +# HDF5's file locking relies on flock(), which is unreliable/unsupported on +# parallel filesystems such as Lustre or GPFS (common on HPC clusters) and +# causes spurious `BlockingIOError: Unable to synchronously open file` errors. +# Disable it unless the user has explicitly configured it. +os.environ.setdefault("HDF5_USE_FILE_LOCKING", "FALSE") + from feectools.ddm.mpi import mpi as MPI from struphy.utils.mpi_launch import launched_under_mpi From f9326a88b58d29b281db6a244ababac586552fed Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 08:38:29 +0200 Subject: [PATCH 069/156] Add remaining kernels --- src/struphy/pic/pushing/pusher.py | 192 +++++ .../pic/pushing/pusher_kernels_gc_cuda.py | 671 ++++++++++++++++++ 2 files changed, 863 insertions(+) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 32e470889..b2b4f51c8 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -46,8 +46,12 @@ ) from struphy.pic.pushing.pusher_kernels_gc_cuda import ( push_gc_Bstar_discrete_gradient_1st_order_gpu, + push_gc_Bstar_discrete_gradient_1st_order_newton_gpu, + push_gc_Bstar_discrete_gradient_2nd_order_gpu, push_gc_Bstar_explicit_multistage_general_gpu, push_gc_bxEstar_discrete_gradient_1st_order_gpu, + push_gc_bxEstar_discrete_gradient_1st_order_newton_gpu, + push_gc_bxEstar_discrete_gradient_2nd_order_gpu, push_gc_bxEstar_explicit_multistage_general_gpu, push_gc_cc_J1_H1vec_gpu, push_gc_cc_J1_Hcurl_gpu, @@ -711,6 +715,116 @@ def __init__( self._gpu_gc_dg1_ef = (ef1, ef2, ef3) self._gpu_gc_dg1_mu_idx = int(particles.mu_idx) + # CUDA replacements for the discrete-gradient GC Newton pushers. Each + # call is ONE Newton iteration (again per-marker parallel, no domain + # Jacobian), reading marker columns filled by driftkinetic_hamiltonian/ + # grad_driftkinetic_hamiltonian eval_kernels (also CUDA-ported above). + self._gpu_gc_dg1_newton = cunumpy.cupy_backend and kernel.name in ( + "push_gc_bxEstar_discrete_gradient_1st_order_newton", + "push_gc_Bstar_discrete_gradient_1st_order_newton", + ) + if self._gpu_gc_dg1_newton: + import cupy as cp + + self._gpu_gc_dg1n_name = kernel.name + ( + args_derham, + epsilon, + gb1, + gb2, + gb3, + B_dot_b, + ef1, + ef2, + ef3, + phi, + evaluate_e_field, + ) = args_kernel[:11] + self._gpu_gc_dg1n_epsilon = float(epsilon) + self._gpu_gc_dg1n_eval_e = bool(evaluate_e_field) + self._gpu_gc_dg1n_pn = tuple(int(x) for x in args_derham.pn) + self._gpu_gc_dg1n_starts = tuple(int(x) for x in args_derham.starts) + self._gpu_gc_dg1n_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_gc_dg1n_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_gc_dg1n_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_gc_dg1n_gb = (gb1, gb2, gb3) + self._gpu_gc_dg1n_bdb = B_dot_b + self._gpu_gc_dg1n_ef = (ef1, ef2, ef3) + self._gpu_gc_dg1n_phi = phi + self._gpu_gc_dg1n_mu_idx = int(particles.mu_idx) + + # CUDA replacements for the Gonzalez discrete-gradient GC pushers + # (one Picard iteration per call, evaluated at the midpoint -- needs + # DF(eta_mid), so restricted to SUPPORTED_GENERAL_KIND_MAPS like the + # other Jacobian-dependent GC kernels). + self._gpu_gc_dg2 = ( + cunumpy.cupy_backend + and kernel.name + in ( + "push_gc_bxEstar_discrete_gradient_2nd_order", + "push_gc_Bstar_discrete_gradient_2nd_order", + ) + and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS + ) + if self._gpu_gc_dg2: + import cupy as cp + + self._gpu_gc_dg2_name = kernel.name + self._gpu_gc_dg2_kind_map = int(args_domain.kind_map) + self._gpu_gc_dg2_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) + if kernel.name == "push_gc_bxEstar_discrete_gradient_2nd_order": + ( + args_derham, + epsilon, + ub1, + ub2, + ub3, + gb1, + gb2, + gb3, + B_dot_b, + curl_unit_b_dot_b0, + ef1, + ef2, + ef3, + evaluate_e_field, + ) = args_kernel[:14] + self._gpu_gc_dg2_unit_b1 = (ub1, ub2, ub3) + else: + ( + args_derham, + epsilon, + gb1, + gb2, + gb3, + b2_1, + b2_2, + b2_3, + cb1, + cb2, + cb3, + B_dot_b, + curl_unit_b_dot_b0, + ef1, + ef2, + ef3, + evaluate_e_field, + ) = args_kernel[:17] + self._gpu_gc_dg2_b2 = (b2_1, b2_2, b2_3) + self._gpu_gc_dg2_curl_unit_b2 = (cb1, cb2, cb3) + self._gpu_gc_dg2_epsilon = float(epsilon) + self._gpu_gc_dg2_eval_e = bool(evaluate_e_field) + self._gpu_gc_dg2_pn = tuple(int(x) for x in args_derham.pn) + self._gpu_gc_dg2_starts = tuple(int(x) for x in args_derham.starts) + self._gpu_gc_dg2_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) + self._gpu_gc_dg2_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) + self._gpu_gc_dg2_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) + self._gpu_gc_dg2_gb = (gb1, gb2, gb3) + self._gpu_gc_dg2_bdb = B_dot_b + self._gpu_gc_dg2_cub = curl_unit_b_dot_b0 + self._gpu_gc_dg2_ef = (ef1, ef2, ef3) + self._gpu_gc_dg2_mu_idx = int(particles.mu_idx) + # CUDA replacements for push_gc_cc_J1_{H1vec,Hcurl,Hdiv} (velocity # update of CurrentCoupling5DCurlb). Single-stage (dt only, `stage` # is accepted but unused by the CPU kernels too), needs DF(eta) so @@ -1399,6 +1513,84 @@ def _push(self, dt: float): self._gpu_gc_dg1_eval_e, dt, ) + elif self._gpu_gc_dg1_newton: + fn = ( + push_gc_bxEstar_discrete_gradient_1st_order_newton_gpu + if self._gpu_gc_dg1n_name == "push_gc_bxEstar_discrete_gradient_1st_order_newton" + else push_gc_Bstar_discrete_gradient_1st_order_newton_gpu + ) + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + fn( + markers, + first_pusher_idx, + self.particles.first_shift_idx, + self.particles.residual_idx, + self.particles.first_free_idx, + self._gpu_gc_dg1n_mu_idx, + self._gpu_gc_dg1n_epsilon, + self._gpu_gc_dg1n_pn, + self._gpu_gc_dg1n_tn1, + self._gpu_gc_dg1n_tn2, + self._gpu_gc_dg1n_tn3, + self._gpu_gc_dg1n_starts, + self._gpu_gc_dg1n_gb, + self._gpu_gc_dg1n_bdb, + self._gpu_gc_dg1n_ef, + self._gpu_gc_dg1n_phi, + self._gpu_gc_dg1n_eval_e, + dt, + ) + elif self._gpu_gc_dg2: + with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): + if self._gpu_gc_dg2_name == "push_gc_bxEstar_discrete_gradient_2nd_order": + push_gc_bxEstar_discrete_gradient_2nd_order_gpu( + markers, + first_pusher_idx, + self.particles.first_shift_idx, + self.particles.residual_idx, + self.particles.first_free_idx, + self._gpu_gc_dg2_mu_idx, + self._gpu_gc_dg2_kind_map, + self._gpu_gc_dg2_params, + self._gpu_gc_dg2_epsilon, + self._gpu_gc_dg2_pn, + self._gpu_gc_dg2_tn1, + self._gpu_gc_dg2_tn2, + self._gpu_gc_dg2_tn3, + self._gpu_gc_dg2_starts, + self._gpu_gc_dg2_unit_b1, + self._gpu_gc_dg2_gb, + self._gpu_gc_dg2_bdb, + self._gpu_gc_dg2_cub, + self._gpu_gc_dg2_ef, + self._gpu_gc_dg2_eval_e, + dt, + ) + else: + push_gc_Bstar_discrete_gradient_2nd_order_gpu( + markers, + first_pusher_idx, + self.particles.first_shift_idx, + self.particles.residual_idx, + self.particles.first_free_idx, + self._gpu_gc_dg2_mu_idx, + self._gpu_gc_dg2_kind_map, + self._gpu_gc_dg2_params, + self._gpu_gc_dg2_epsilon, + self._gpu_gc_dg2_pn, + self._gpu_gc_dg2_tn1, + self._gpu_gc_dg2_tn2, + self._gpu_gc_dg2_tn3, + self._gpu_gc_dg2_starts, + self._gpu_gc_dg2_gb, + self._gpu_gc_dg2_b2, + self._gpu_gc_dg2_curl_unit_b2, + self._gpu_gc_dg2_bdb, + self._gpu_gc_dg2_cub, + self._gpu_gc_dg2_ef, + self._gpu_gc_dg2_eval_e, + dt, + ) elif self._gpu_gc_cc_j1: fn = { "push_gc_cc_J1_H1vec": push_gc_cc_J1_H1vec_gpu, diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index 73e7ae87c..026e5b658 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -1639,3 +1639,674 @@ def d(a): *d(ud[0]), *d(ud[1]), *d(ud[2]), ), ) + + +# --------------------------------------------------------------------------- +# push_gc_bxEstar_discrete_gradient_1st_order_newton / +# push_gc_Bstar_discrete_gradient_1st_order_newton: one Newton iteration +# (per marker, so per-marker parallel like the non-Newton 1st_order variants +# above) for the Itoh-Abe discrete-gradient guiding-centre pushers. Unlike +# the *_1st_order Picard kernels, these read a richer set of pre-evaluated +# marker columns (the Hamiltonian and its gradient at several points along +# the coordinate axes, written by driftkinetic_hamiltonian/ +# grad_driftkinetic_hamiltonian eval_kernels -- both already CUDA-ported +# above) and solve one 3x3 (bxEstar) or 4x4 (Bstar, via Schur complement of +# its [[I,B],[C,1]] block structure) Newton step in closed form; no domain +# Jacobian is needed. Purely marker-local, no shared per-marker helper beyond +# what's already in _GENERAL_GEOMETRY_SRC. +# --------------------------------------------------------------------------- + +_DG_NEWTON_SRC = r""" +extern "C" __global__ +void push_gc_bxEstar_discrete_gradient_1st_order_newton_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const double* phi, const int p_n2, const int p_n3, + const int evaluate_e_field, const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + const double eta_k_shifted = row[k] + row[first_shift_idx + k]; + eta_k[k] = row[k]; + eta_diff[k] = eta_k_shifted - row[first_init_idx + k]; + } + const double v = row[3]; + const double mu = row[mu_idx]; + + const double H_n = row[first_free_idx]; + const double b_star_parallel = row[first_free_idx + 1]; + const double unit_b1[3] = {row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; + const double H_k1 = row[first_free_idx + 5]; + const double H_k12 = row[first_free_idx + 6]; + const double grad_H_1 = row[first_free_idx + 7]; + const double grad_H_12[2] = {row[first_free_idx + 8], row[first_free_idx + 9]}; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_k[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_k[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_k[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_k[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_k[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_k[2], span3, bn3, bd3); + + double phi_val = 0.0; + if (evaluate_e_field) { + phi_val = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, phi, p_n2, p_n3); + } + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + const double H_k = epsilon * v * v / 2.0 + epsilon * mu * B_dot_b + phi_val; + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + double grad_I[3]; + grad_I[0] = (eta_diff[0] == 0.0) ? grad_H[0] : (H_k1 - H_n) / eta_diff[0]; + grad_I[1] = (eta_diff[1] == 0.0) ? grad_H[1] : (H_k12 - H_k1) / eta_diff[1]; + grad_I[2] = (eta_diff[2] == 0.0) ? grad_H[2] : (H_k - H_k12) / eta_diff[2]; + + double bcross_mat[9] = { + 0.0, -unit_b1[2], unit_b1[1], + unit_b1[2], 0.0, -unit_b1[0], + -unit_b1[1], unit_b1[0], 0.0}; + for (int k = 0; k < 9; k++) bcross_mat[k] /= b_star_parallel; + + double func[3]; + matvec_dev(bcross_mat, grad_I, func); + for (int k = 0; k < 3; k++) func[k] = eta_diff[k] - dt * func[k]; + + double Ddg[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; + if (eta_diff[0] != 0.0) Ddg[0] = (grad_H_1 * eta_diff[0] - (H_k1 - H_n)) / (eta_diff[0] * eta_diff[0]); + if (eta_diff[1] != 0.0) { + Ddg[4] = (grad_H_12[1] * eta_diff[1] - (H_k12 - H_k1)) / (eta_diff[1] * eta_diff[1]); + Ddg[3] = (grad_H_12[0] - grad_H_1) / eta_diff[1]; + } + if (eta_diff[2] != 0.0) { + Ddg[8] = (grad_H[2] * eta_diff[2] - (H_k - H_k12)) / (eta_diff[2] * eta_diff[2]); + Ddg[6] = (grad_H[0] - grad_H_12[0]) / eta_diff[2]; + Ddg[7] = (grad_H[1] - grad_H_12[1]) / eta_diff[2]; + } + + double Dfunc[9]; + matmat_dev(bcross_mat, Ddg, Dfunc); + for (int k = 0; k < 9; k++) Dfunc[k] *= -dt; + Dfunc[0] += 1.0; Dfunc[4] += 1.0; Dfunc[8] += 1.0; + + double Dfunc_inv[9], k_vec[3]; + matrix_inv_dev(Dfunc, Dfunc_inv); + matvec_dev(Dfunc_inv, func, k_vec); + + row[0] -= k_vec[0]; + row[1] -= k_vec[1]; + row[2] -= k_vec[2]; + + row[residual_idx] = sqrt(k_vec[0]*k_vec[0] + k_vec[1]*k_vec[1] + k_vec[2]*k_vec[2]); +} + +extern "C" __global__ +void push_gc_Bstar_discrete_gradient_1st_order_newton_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const double* phi, const int p_n2, const int p_n3, + const int evaluate_e_field, const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + const double eta_k_shifted = row[k] + row[first_shift_idx + k]; + eta_k[k] = row[k]; + eta_diff[k] = eta_k_shifted - row[first_init_idx + k]; + } + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + const double v_diff = v_k - v_n; + const double mu = row[mu_idx]; + + const double H_n = row[first_free_idx]; + const double b_star_parallel = epsilon * row[first_free_idx + 1]; + const double b_star[3] = {row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; + const double H_k1 = row[first_free_idx + 5]; + const double H_k12 = row[first_free_idx + 6]; + const double grad_H_1 = row[first_free_idx + 7]; + const double grad_H_12[2] = {row[first_free_idx + 8], row[first_free_idx + 9]}; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_k[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_k[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_k[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_k[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_k[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_k[2], span3, bn3, bd3); + + double phi_val = 0.0; + if (evaluate_e_field) { + phi_val = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, phi, p_n2, p_n3); + } + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + const double H_k = epsilon * v_k * v_k / 2.0 + epsilon * mu * B_dot_b + phi_val; + const double H_k123 = epsilon * v_n * v_n / 2.0 + epsilon * mu * B_dot_b + phi_val; + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + const double grad_H_v = epsilon * v_k; + + double grad_I[3]; + grad_I[0] = (eta_diff[0] == 0.0) ? grad_H[0] : (H_k1 - H_n) / eta_diff[0]; + grad_I[1] = (eta_diff[1] == 0.0) ? grad_H[1] : (H_k12 - H_k1) / eta_diff[1]; + grad_I[2] = (eta_diff[2] == 0.0) ? grad_H[2] : (H_k123 - H_k12) / eta_diff[2]; + const double grad_I_v = (v_diff == 0.0) ? grad_H_v : (H_k - H_k123) / v_diff; + + double J_vec[3]; + for (int k = 0; k < 3; k++) J_vec[k] = b_star[k] / b_star_parallel; + + double func[3]; + for (int k = 0; k < 3; k++) func[k] = eta_diff[k] - dt * (J_vec[k] * grad_I_v); + double func_v = v_diff + dt * dot3_dev(J_vec, grad_I); + + double Ddg[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; + if (eta_diff[0] != 0.0) Ddg[0] = (grad_H_1 * eta_diff[0] - (H_k1 - H_n)) / (eta_diff[0] * eta_diff[0]); + if (eta_diff[1] != 0.0) { + Ddg[4] = (grad_H_12[1] * eta_diff[1] - (H_k12 - H_k1)) / (eta_diff[1] * eta_diff[1]); + Ddg[3] = (grad_H_12[0] - grad_H_1) / eta_diff[1]; + } + if (eta_diff[2] != 0.0) { + Ddg[8] = (grad_H[2] * eta_diff[2] - (H_k123 - H_k12)) / (eta_diff[2] * eta_diff[2]); + Ddg[6] = (grad_H[0] - grad_H_12[0]) / eta_diff[2]; + Ddg[7] = (grad_H[1] - grad_H_12[1]) / eta_diff[2]; + } + const double Ddg_v = (v_diff == 0.0) ? 0.0 : (grad_H_v * v_diff - (H_k - H_k123)) / (v_diff * v_diff); + + // DF = [[I, B], [C^T, 1]], B = -dt*Ddg_v*J_vec, C = dt*Ddg^T @ J_vec + double Bv[3], Cv[3]; + for (int k = 0; k < 3; k++) Bv[k] = -dt * Ddg_v * J_vec[k]; + double DdgT[9] = {Ddg[0], Ddg[3], Ddg[6], Ddg[1], Ddg[4], Ddg[7], Ddg[2], Ddg[5], Ddg[8]}; + matvec_dev(DdgT, J_vec, Cv); + for (int k = 0; k < 3; k++) Cv[k] *= dt; + + const double schur = 1.0 - dot3_dev(Cv, Bv); + + double A_inv[9]; + A_inv[0] = Bv[0]*Cv[0]; A_inv[1] = Bv[0]*Cv[1]; A_inv[2] = Bv[0]*Cv[2]; + A_inv[3] = Bv[1]*Cv[0]; A_inv[4] = Bv[1]*Cv[1]; A_inv[5] = Bv[1]*Cv[2]; + A_inv[6] = Bv[2]*Cv[0]; A_inv[7] = Bv[2]*Cv[1]; A_inv[8] = Bv[2]*Cv[2]; + for (int k = 0; k < 9; k++) A_inv[k] /= schur; + A_inv[0] += 1.0; A_inv[4] += 1.0; A_inv[8] += 1.0; + + double Binv[3], Cinv[3]; + for (int k = 0; k < 3; k++) { Binv[k] = -Bv[k] / schur; Cinv[k] = -Cv[k] / schur; } + + double k_vec[3]; + matvec_dev(A_inv, func, k_vec); + for (int k = 0; k < 3; k++) k_vec[k] += Binv[k] * func_v; + double k_v = dot3_dev(Cinv, func) + func_v / schur; + + row[0] -= k_vec[0]; + row[1] -= k_vec[1]; + row[2] -= k_vec[2]; + row[3] -= k_v; + + row[residual_idx] = sqrt(k_vec[0]*k_vec[0] + k_vec[1]*k_vec[1] + k_vec[2]*k_vec[2] + (k_v/v_k)*(k_v/v_k)); +} +""" + +_dg_newton_kernels = {} + + +def _get_dg_newton_kernel(name): + if name not in _dg_newton_kernels: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _dg_newton_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _DG_NEWTON_SRC, name) + return _dg_newton_kernels[name] + + +def _dg_newton_launch( + name, markers, first_init_idx, first_shift_idx, residual_idx, first_free_idx, + mu_idx, epsilon, pn, tn1_dev, tn2_dev, tn3_dev, starts, + grad_b_full, B_dot_b_coeffs, e_field, phi_coeffs, evaluate_e_field, dt, +): + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_dg_newton_kernel(name)( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), np.int32(first_shift_idx), + np.int32(residual_idx), np.int32(first_free_idx), np.int32(mu_idx), + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(grad_b_full[0]), *d(grad_b_full[1]), *d(grad_b_full[2]), + *d(B_dot_b_coeffs), + *d(e_field[0]), *d(e_field[1]), *d(e_field[2]), + *d(phi_coeffs), + np.int32(bool(evaluate_e_field)), np.float64(dt), + ), + ) + + +def push_gc_bxEstar_discrete_gradient_1st_order_newton_gpu(*args, **kwargs): + """GPU replacement for one Newton iteration of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_discrete_gradient_1st_order_newton`.""" + _dg_newton_launch("push_gc_bxEstar_discrete_gradient_1st_order_newton_cuda", *args, **kwargs) + + +def push_gc_Bstar_discrete_gradient_1st_order_newton_gpu(*args, **kwargs): + """GPU replacement for one Newton iteration of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_discrete_gradient_1st_order_newton`.""" + _dg_newton_launch("push_gc_Bstar_discrete_gradient_1st_order_newton_cuda", *args, **kwargs) + + +# --------------------------------------------------------------------------- +# push_gc_bxEstar_discrete_gradient_2nd_order / +# push_gc_Bstar_discrete_gradient_2nd_order: one Picard iteration (per +# marker, so per-marker parallel like the *_1st_order variants) of the +# Gonzalez discrete-gradient guiding-centre pushers -- unlike *_1st_order_newton +# this evaluates fields at the midpoint eta_mid = mod((eta_k+eta_n)/2, 1) and +# needs the domain Jacobian there (df_dispatch_dev/det3_dev), and only reads +# 2 pre-evaluated marker columns (H_n, H_k) instead of the Itoh-Abe set. +# --------------------------------------------------------------------------- + +_DG_2ND_ORDER_SRC = r""" +extern "C" __global__ +void push_gc_bxEstar_discrete_gradient_2nd_order_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* ub1, const int u1_n2, const int u1_n3, + const double* ub2, const int u2_n2, const int u2_n3, + const double* ub3, const int u3_n2, const int u3_n3, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* cub, const int cub_n2, const int cub_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const int evaluate_e_field, const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + eta_k[k] = row[k] + row[first_shift_idx + k]; + eta_n[k] = row[first_init_idx + k]; + double m = fmod((eta_k[k] + eta_n[k]) / 2.0, 1.0); + if (m < 0.0) m += 1.0; + eta_mid[k] = m; + eta_diff[k] = eta_k[k] - eta_n[k]; + } + const double v = row[3]; + const double mu = row[mu_idx]; + + const double H_n = row[first_free_idx]; + const double H_k = row[first_free_idx + 1]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double unit_b1[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ub1,u1_n2,u1_n3, ub2,u2_n2,u2_n3, ub3,u3_n2,u3_n3, unit_b1); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H); + const double dZ_squared = dot3_dev(eta_diff, eta_diff); + + double grad_I[3]; + if (dZ_squared == 0.0) { + for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k]; + } else { + const double s = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; + for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k] + eta_diff[k] * s; + } + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, cub, cub_n2, cub_n3); + b_star_parallel = (b_star_parallel * epsilon * v + B_dot_b) * det_df; + + double Exb[3]; + cross_dev(unit_b1, grad_I, Exb); + + double k_vec[3]; + for (int k = 0; k < 3; k++) k_vec[k] = Exb[k] / b_star_parallel; + + row[0] = eta_n[0] + dt * k_vec[0]; + row[1] = eta_n[1] + dt * k_vec[1]; + row[2] = eta_n[2] + dt * k_vec[2]; + + const double r0 = row[0] - eta_k[0], r1 = row[1] - eta_k[1], r2 = row[2] - eta_k[2]; + row[residual_idx] = sqrt(r0*r0 + r1*r1 + r2*r2); +} + +extern "C" __global__ +void push_gc_Bstar_discrete_gradient_2nd_order_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* b2_1, const int b1_n2, const int b1_n3, + const double* b2_2, const int b2_n2, const int b2_n3, + const double* b2_3, const int b3_n2, const int b3_n3, + const double* cb1, const int c1_n2, const int c1_n3, + const double* cb2, const int c2_n2, const int c2_n3, + const double* cb3, const int c3_n2, const int c3_n3, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* cub, const int cub_n2, const int cub_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const int evaluate_e_field, const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + eta_k[k] = row[k] + row[first_shift_idx + k]; + eta_n[k] = row[first_init_idx + k]; + double m = fmod((eta_k[k] + eta_n[k]) / 2.0, 1.0); + if (m < 0.0) m += 1.0; + eta_mid[k] = m; + eta_diff[k] = eta_k[k] - eta_n[k]; + } + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + const double v_mid = (v_k + v_n) / 2.0; + const double v_diff = v_k - v_n; + const double mu = row[mu_idx]; + + const double H_n = row[first_free_idx]; + const double H_k = row[first_free_idx + 1]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + const double grad_H_v = epsilon * v_mid; + const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H) + v_diff * grad_H_v; + const double dZ_squared = dot3_dev(eta_diff, eta_diff) + v_diff * v_diff; + + double grad_I[3]; + double grad_I_v; + if (dZ_squared == 0.0) { + for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k]; + grad_I_v = grad_H_v; + } else { + const double s = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; + for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k] + eta_diff[k] * s; + grad_I_v = grad_H_v + v_diff * s; + } + + double b2[3], b_star[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b2_1,b1_n2,b1_n3, b2_2,b2_n2,b2_n3, b2_3,b3_n2,b3_n3, b2); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cb1,c1_n2,c1_n3, cb2,c2_n2,c2_n3, cb3,c3_n2,c3_n3, b_star); + for (int k = 0; k < 3; k++) b_star[k] = b_star[k] * epsilon * v_mid + b2[k]; + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, cub, cub_n2, cub_n3); + b_star_parallel = (b_star_parallel * epsilon * v_mid + B_dot_b) * epsilon * det_df; + + double k_vec[3]; + for (int k = 0; k < 3; k++) k_vec[k] = b_star[k] / b_star_parallel * grad_I_v; + const double k_v = -dot3_dev(b_star, grad_I) / b_star_parallel; + + row[0] = eta_n[0] + dt * k_vec[0]; + row[1] = eta_n[1] + dt * k_vec[1]; + row[2] = eta_n[2] + dt * k_vec[2]; + row[3] = v_n + dt * k_v; + + const double r0 = row[0] - eta_k[0], r1 = row[1] - eta_k[1], r2 = row[2] - eta_k[2]; + const double rv = (row[3] - v_k) / v_k; + row[residual_idx] = sqrt(r0*r0 + r1*r1 + r2*r2 + rv*rv); +} +""" + +_dg_2nd_order_kernels = {} + + +def _get_dg_2nd_order_kernel(name): + if name not in _dg_2nd_order_kernels: + import cupy as cp + + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + + _dg_2nd_order_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _DG_2ND_ORDER_SRC, name) + return _dg_2nd_order_kernels[name] + + +def push_gc_bxEstar_discrete_gradient_2nd_order_gpu( + markers, first_init_idx, first_shift_idx, residual_idx, first_free_idx, mu_idx, + kind_map, params_dev, epsilon, pn, tn1_dev, tn2_dev, tn3_dev, starts, + unit_b1, grad_b_full, B_dot_b_coeffs, curl_unit_b_dot_b0, e_field, evaluate_e_field, dt, +): + """GPU replacement for one Picard iteration of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_discrete_gradient_2nd_order`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_dg_2nd_order_kernel("push_gc_bxEstar_discrete_gradient_2nd_order_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), np.int32(first_shift_idx), + np.int32(residual_idx), np.int32(first_free_idx), np.int32(mu_idx), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(unit_b1[0]), *d(unit_b1[1]), *d(unit_b1[2]), + *d(grad_b_full[0]), *d(grad_b_full[1]), *d(grad_b_full[2]), + *d(B_dot_b_coeffs), *d(curl_unit_b_dot_b0), + *d(e_field[0]), *d(e_field[1]), *d(e_field[2]), + np.int32(bool(evaluate_e_field)), np.float64(dt), + ), + ) + + +def push_gc_Bstar_discrete_gradient_2nd_order_gpu( + markers, first_init_idx, first_shift_idx, residual_idx, first_free_idx, mu_idx, + kind_map, params_dev, epsilon, pn, tn1_dev, tn2_dev, tn3_dev, starts, + grad_b_full, b2, curl_unit_b2, B_dot_b_coeffs, curl_unit_b_dot_b0, e_field, evaluate_e_field, dt, +): + """GPU replacement for one Picard iteration of + :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_discrete_gradient_2nd_order`.""" + import cupy as cp + import numpy as np + + n_markers = markers.shape[0] + threads = 256 + blocks = (n_markers + threads - 1) // threads + + def d(a): + a = cp.ascontiguousarray(a) + return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) + + _get_dg_2nd_order_kernel("push_gc_Bstar_discrete_gradient_2nd_order_cuda")( + (blocks,), + (threads,), + ( + markers, np.int32(markers.shape[1]), np.int32(n_markers), + np.int32(first_init_idx), np.int32(first_shift_idx), + np.int32(residual_idx), np.int32(first_free_idx), np.int32(mu_idx), + np.int32(kind_map), params_dev, + np.float64(epsilon), + np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), + tn1_dev, np.int32(tn1_dev.shape[0]), + tn2_dev, np.int32(tn2_dev.shape[0]), + tn3_dev, np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), + *d(grad_b_full[0]), *d(grad_b_full[1]), *d(grad_b_full[2]), + *d(b2[0]), *d(b2[1]), *d(b2[2]), + *d(curl_unit_b2[0]), *d(curl_unit_b2[1]), *d(curl_unit_b2[2]), + *d(B_dot_b_coeffs), *d(curl_unit_b_dot_b0), + *d(e_field[0]), *d(e_field[1]), *d(e_field[2]), + np.int32(bool(evaluate_e_field)), np.float64(dt), + ), + ) From c28c20d82a285032f4c8a0d48b8248c8eb464701 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 09:28:17 +0200 Subject: [PATCH 070/156] Added setup: total --- src/struphy/simulation/sim.py | 149 +++++++++++++++++----------------- 1 file changed, 75 insertions(+), 74 deletions(-) diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index e9f2b8d7a..55c0cfd79 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -629,92 +629,93 @@ def run(self, one_time_step: bool = False): self._remove_existing_output_files() - # equation paramters - self.allocate() - with ProfileManager.profile_region("setup: run metadata"): - self._write_run_metadata(one_time_step=one_time_step) - - # output - with ProfileManager.profile_region("setup: data storage"): - self.initialize_data_storage() - - # peek view into geometry - with ProfileManager.profile_region("setup: geometry vtk"): - self.save_geometry_and_equil_vtk() - - # plasma parameters - with ProfileManager.profile_region("setup: plasma params"): - self.compute_plasma_params() - - # print info on mpi procs - if self.comm_size < 32: - if self.derham is not None: - logger.info(f"\nderham.domain_array:\n{self.derham.domain_array}") + with ProfileManager.profile_region("setup: total"): + # equation paramters + self.allocate() + with ProfileManager.profile_region("setup: run metadata"): + self._write_run_metadata(one_time_step=one_time_step) + + # output + with ProfileManager.profile_region("setup: data storage"): + self.initialize_data_storage() + + # peek view into geometry + with ProfileManager.profile_region("setup: geometry vtk"): + self.save_geometry_and_equil_vtk() + + # plasma parameters + with ProfileManager.profile_region("setup: plasma params"): + self.compute_plasma_params() + + # print info on mpi procs + if self.comm_size < 32: + if self.derham is not None: + logger.info(f"\nderham.domain_array:\n{self.derham.domain_array}") + else: + for _, species in self.model.species.items(): + for _, variable in species.variables.items(): + if isinstance(variable, (PICVariable, SPHVariable)): + logger.info(f"\nparticle domain_array:\n{variable.particles.domain_array}") + break + + if self.rank < 32: + logger.debug("") + logger.debug(f"Rank {self.rank}: executing run() for model {self.model_name} ...") + + if self.comm_size > 32 and self.rank == 32: + logger.debug(f"Ranks > 31: executing run() for model {self.model_name} ...") + + # retrieve time parameters + dt = self.time_opts.dt + if one_time_step: + Tend = dt else: - for _, species in self.model.species.items(): - for _, variable in species.variables.items(): - if isinstance(variable, (PICVariable, SPHVariable)): - logger.info(f"\nparticle domain_array:\n{variable.particles.domain_array}") - break - - if self.rank < 32: - logger.debug("") - logger.debug(f"Rank {self.rank}: executing run() for model {self.model_name} ...") - - if self.comm_size > 32 and self.rank == 32: - logger.debug(f"Ranks > 31: executing run() for model {self.model_name} ...") - - # retrieve time parameters - dt = self.time_opts.dt - if one_time_step: - Tend = dt - else: - Tend = self.time_opts.Tend - split_algo = self.time_opts.split_algo - - # set initial conditions for all variables - if self.env.restart: - with ProfileManager.profile_region("setup: restart"): - self._initialize_from_restart(self.data) - - with h5py.File(self.data.file_path, "a") as file: - self.time_state["value"][0] = file["restart/time/value"][-1] - self.time_state["value_sec"][0] = file["restart/time/value_sec"][-1] - self.time_state["index"][0] = file["restart/time/index"][-1] - start_step = file["restart/time/index"][-1] - - total_steps = int(round((Tend - float(self.time_state["value"][0])) / dt)) - logger.info(f"""\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + Tend = self.time_opts.Tend + split_algo = self.time_opts.split_algo + + # set initial conditions for all variables + if self.env.restart: + with ProfileManager.profile_region("setup: restart"): + self._initialize_from_restart(self.data) + + with h5py.File(self.data.file_path, "a") as file: + self.time_state["value"][0] = file["restart/time/value"][-1] + self.time_state["value_sec"][0] = file["restart/time/value_sec"][-1] + self.time_state["index"][0] = file["restart/time/index"][-1] + start_step = file["restart/time/index"][-1] + + total_steps = int(round((Tend - float(self.time_state["value"][0])) / dt)) + logger.info(f"""\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! RESTARTing from: self.time_state["value"][0]={float(self.time_state["value"][0])} self.time_state["value_sec"][0]={float(self.time_state["value_sec"][0])} self.time_state["index"][0]={int(self.time_state["index"][0])} !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! """) - else: - total_steps = int(round(Tend / dt)) - start_step = 0 + else: + total_steps = int(round(Tend / dt)) + start_step = 0 - total_steps_str = str(total_steps) + total_steps_str = str(total_steps) - # compute initial scalars and kinetic data, pass time state to all propagators - with ProfileManager.profile_region("setup: initial diagnostics"): - self.model.update_scalar_quantities() - self.model.update_markers_to_be_saved() - self.model.update_distr_functions() - self._add_time_state(self.time_state["value"]) + # compute initial scalars and kinetic data, pass time state to all propagators + with ProfileManager.profile_region("setup: initial diagnostics"): + self.model.update_scalar_quantities() + self.model.update_markers_to_be_saved() + self.model.update_distr_functions() + self._add_time_state(self.time_state["value"]) - # add all variables to be saved to data object - with ProfileManager.profile_region("setup: hdf5 datasets"): - save_keys_all, save_keys_end = self._initialize_hdf5_datasets(self.data, self.comm_size) + # add all variables to be saved to data object + with ProfileManager.profile_region("setup: hdf5 datasets"): + save_keys_all, save_keys_end = self._initialize_hdf5_datasets(self.data, self.comm_size) - # ======================== main time loop ====================== - self.model.update_scalar_quantities() + # ======================== main time loop ====================== + self.model.update_scalar_quantities() - if logger.level <= logging.INFO and self.rank == 0: - print("\nINITIAL SCALAR QUANTITIES:") - self.model.print_scalar_quantities() - print(f"START TIME STEPPING WITH '{split_algo}' SPLITTING:") + if logger.level <= logging.INFO and self.rank == 0: + print("\nINITIAL SCALAR QUANTITIES:") + self.model.print_scalar_quantities() + print(f"START TIME STEPPING WITH '{split_algo}' SPLITTING:") # time loop run_time_now = 0.0 From fd243b08121a37d83cc5704ff926fda30eaf49aa Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 09:29:44 +0200 Subject: [PATCH 071/156] Added submit_guidingcenter_cupy_scaling.py --- .../GuidingCenter/params_GuidingCenter.py | 19 ++ .../params_GuidingCenter_scaling.py | 208 ++++++++++++++++++ .../submit_guidingcenter_cupy_scaling.py | 111 ++++++++++ 3 files changed, 338 insertions(+) create mode 100644 profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py create mode 100644 profiling/submit_guidingcenter_cupy_scaling.py diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter.py b/profiling/examples/GuidingCenter/params_GuidingCenter.py index 1da627027..797e7a608 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter.py @@ -57,6 +57,25 @@ # Must be set before struphy (and therefore cunumpy) is imported. os.environ["ARRAY_BACKEND"] = args.backend +if args.backend == "cupy": + import cunumpy + + # Under CuPy with more than one MPI rank per node (e.g. the Booster scaling case in + # profiling/submit_guidingcenter_cupy_scaling.py), every rank must bind to its own GPU + # -- cupy defaults to device 0, so without this every rank on a node would contend for + # the same GPU instead of getting one each. SLURM_LOCALID (the rank's index within its + # node) is set by srun before this process even starts, so it works without MPI being + # initialized yet. Falls back to device 0 outside SLURM (e.g. a single-GPU login node). + cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) + + # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment + # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more + # than one rank every process independently creates the same output directory/HDF5 + # dataset and the survivors deadlock in the next collective. This profiling script is + # specifically meant to run multi-rank/multi-GPU (see submit_guidingcenter_cupy_scaling.py), + # so opt back in; a single-GPU run pays only a no-op collective for it. + os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") + import logging from struphy import set_logging_level diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py new file mode 100644 index 000000000..1bb4a7db6 --- /dev/null +++ b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py @@ -0,0 +1,208 @@ +# ----------------------------- +# Description of the simulation +# ----------------------------- +# Please fill in a verbal description of the simulation. +# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. + +name = "GuidingCenter CuPy multi-GPU scaling" +description = """ +Guiding-centre (5D drift-kinetic) test particles in a homogeneous slab, used as the +CuPy multi-GPU/multi-rank strong-scaling case (see profiling/submit_guidingcenter_cupy_scaling.py). + +This is a separate, much larger params file from params_GuidingCenter.py (the +NumPy-vs-CuPy single-GPU comparison): its Np default is 10,000,000, ~50x that case's +200,000. That matters here specifically because of the marker-exchange cost measured +while validating this case -- at Np=200,000 (50,000/rank at 4 ranks), mpi_sort_markers +(the device-to-device particle exchange between ranks after each stage) ate 79-89% of +model.integrate, and total wall time got *worse* with more ranks: + + ranks total (setup to finalize) + 1 4.48 s + 2 5.19 s + 4 5.50 s (slower than 1 rank) + +At Np=4,000,000 the same 1/2/4-rank comparison did show a clear speedup (9.93 s / 5.88 s +/ 3.79 s -> 1.7x / 2.6x), because there is enough per-rank compute between exchanges for +it to outweigh the communication cost. This file goes further (10,000,000) so the +scaling trend has more headroom before communication catches back up. Whether it still +does at this size, and at what rank count, is what running this case answers -- it is not +assumed here. + +`GuidingCenter` is used (as in params_GuidingCenter.py) because its whole propagator +stack (PushGuidingCenterBxEstar, PushGuidingCenterParallel) is CUDA-ported and it carries +no FEEC field solve, so wall-clock time is dominated by the particle kernels and their +MPI exchange, not by anything unrelated to the CUDA port. +""" + +import argparse +import os + +parser = argparse.ArgumentParser(description=description) +parser.add_argument( + "--backend", + choices=("numpy", "cupy"), + default="numpy", + help="Array backend to run the simulation with (default: numpy).", +) +# `--id` distinguishes runs that share a rank count but differ in something else (here: +# the array backend); the profiling driver passes its launch counter and looks for the +# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). +# Unknown flags are ignored so the driver can forward other parameters as well. +parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") +parser.add_argument("--Np", type=int, default=None, help="Number of markers (overrides the default).") +parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default).") +args, _ = parser.parse_known_args() + +# Must be set before struphy (and therefore cunumpy) is imported. +os.environ["ARRAY_BACKEND"] = args.backend + +if args.backend == "cupy": + import cunumpy + + # Under CuPy with more than one MPI rank per node, every rank must bind to its own GPU + # -- cupy defaults to device 0, so without this every rank on a node would contend for + # the same GPU instead of getting one each. SLURM_LOCALID (the rank's index within its + # node) is set by srun before this process even starts, so it works without MPI being + # initialized yet. Falls back to device 0 outside SLURM (e.g. a single-GPU login node). + cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) + + # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment + # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more + # than one rank every process independently creates the same output directory/HDF5 + # dataset and the survivors deadlock in the next collective. This file is specifically + # meant to run multi-rank/multi-GPU, so opt back in; a single-GPU run pays only a + # no-op collective for it. + os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") + +import logging + +from struphy import set_logging_level + +set_logging_level(logging.WARNING) + +# ------------------ +# Import Struphy API +# ------------------ + +from struphy import ( + BaseUnits, + BoundaryParameters, + DerhamOptions, + EnvironmentOptions, + LoadingParameters, + SavingParameters, + Simulation, + SortingParameters, + Time, + WeightsParameters, + domains, + equils, + grids, + maxwellians, + perturbations, +) + +# --------------------- +# Instance of the model +# --------------------- +from struphy.models import GuidingCenter + +# Units +base_units = BaseUnits() + +# Model instance +model = GuidingCenter(base_units=base_units) + +# List all variables and decide whether to save their data +model.kinetic_ions.var.save_data = True + +# -------------------------- +# Instance of the simulation +# -------------------------- + +name = f"GuidingCenter scaling ({args.backend})" + +# Environment options +env = EnvironmentOptions( + sim_folder=f"sim_{args.id:02d}", + profiling_activated=True, +) + +# Time stepping. Enough steps that the per-step particle work (and its MPI exchange), +# not the one-off setup (marker loading scales with Np, plus the CUDA RawKernel JIT +# compile), dominates the total -- see the setup-vs-loop measurement in the description. +time_opts = Time(dt=0.01, Tend=args.Tend if args.Tend is not None else 1.0) + +# Geometry +domain = domains.Cuboid() + +# Fluid equilibrium (can be used as part of initial conditions) +equil = equils.HomogenSlab() + +# Grid +grid = grids.TensorProductGrid(num_elements=(16, 16, 16)) + +# Derham options +derham_opts = DerhamOptions() + +# Simulation object +sim = Simulation( + model=model, + name=name, + description=description, + params_path=__file__, + env=env, + time_opts=time_opts, + domain=domain, + equil=equil, + grid=grid, + derham_opts=derham_opts, +) + +# ------------------- +# Particle parameters +# ------------------- + +# Marker count is the knob that decides how particle-dominated (vs. communication-bound) +# the run is -- see the description for why this file defaults to 10,000,000 rather than +# params_GuidingCenter.py's 200,000. +loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 10_000_000) +weights_params = WeightsParameters() +boundary_params = BoundaryParameters() +sorting_params = SortingParameters() +saving_params = SavingParameters() +model.kinetic_ions.set_markers( + loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, +) + +# ------------------ +# Propagator options +# ------------------ + +# algo="explicit" selects push_gc_bxEstar_explicit_multistage / +# push_gc_Bstar_explicit_multistage, both CUDA-ported. +model.propagators.push_bxe.options = model.propagators.push_bxe.Options(algo="explicit") +model.propagators.push_parallel.options = model.propagators.push_parallel.Options(algo="explicit") + +# ------------------ +# Initial conditions +# ------------------ + +# Background for kinetic species +maxwellian_1 = maxwellians.GyroMaxwellian2Dvperp(n=(1.0, None), equil=equil) +maxwellian_2 = maxwellians.GyroMaxwellian2Dvperp(n=(0.1, None), equil=equil) +background = maxwellian_1 + maxwellian_2 +model.kinetic_ions.var.add_background(background) + +# Perturbations for (some) kinetic species +perturbation = perturbations.TorusModesCos() +maxwellian_1pt = maxwellians.GyroMaxwellian2Dvperp(n=(1.0, perturbation), equil=equil) +init = maxwellian_1pt + maxwellian_2 +model.kinetic_ions.var.add_initial_condition(init) + +if __name__ == "__main__": + sim.run() diff --git a/profiling/submit_guidingcenter_cupy_scaling.py b/profiling/submit_guidingcenter_cupy_scaling.py new file mode 100644 index 000000000..0148e0cd8 --- /dev/null +++ b/profiling/submit_guidingcenter_cupy_scaling.py @@ -0,0 +1,111 @@ +"""Guiding-centre CuPy multi-GPU/multi-rank scaling case. + +This is a strong-scaling study, not a backend comparison (see +`submit_guidingcenter_numpy_vs_cupy.py` for that): the same total marker count +(`LoadingParameters.Np` is the *total* across ranks, see +`struphy.particles.parameters.LoadingParameters`) is run with `ARRAY_BACKEND=cupy` at +increasing MPI rank counts, one rank per GPU, on the Booster partition -- so it measures +whether adding more rank+GPU pairs actually speeds up a fixed-size problem, and whether +the CUDA-ported kernels behave correctly under MPI (domain-decomposed markers, particle +sorting/communication across ranks, etc.), not just single-GPU. + +Each rank binds to its own GPU via `SLURM_LOCALID` in `params_GuidingCenter_scaling.py` +(see the comment there) -- without that, every rank on a node would default to CuPy's +device 0 and contend for the same GPU, which would make this scaling study meaningless. +`--ranks 1 2 4` (the default) all fit on a single Booster node (4 GPUs/node), so this only +exercises intra-node scaling; ranks beyond `GPUS_PER_NODE` would spill onto a second node, +still one GPU per rank, but multi-node network overhead is untested here. + +Uses `params_GuidingCenter_scaling.py`, not `params_GuidingCenter.py` (the single-GPU +NumPy-vs-CuPy comparison case): its much larger default Np (10,000,000 vs. 200,000) is +needed for a scaling study specifically because at the smaller size, per-rank compute +between marker exchanges is too small to outweigh the exchange cost -- adding ranks there +measured *slower*, not faster (see that file's docstring for the numbers). Whether the +larger problem here still scales, and how far, is what this case measures. + +`GuidingCenter` is used here rather than `LinearMHDDriftkineticCC` because its runtime is +actually dominated by the particle kernels this comparison is meant to measure. Its whole +propagator stack is CUDA-ported and it has no FEEC field solve, so the backend difference +shows up in the total wall clock. `LinearMHDDriftkineticCC` is dominated by MHD field +propagators and one-off setup instead, which masks any particle-kernel speedup. +""" + +import argparse +from pathlib import Path + +from clusters import SLURM_PRESETS, detect_machine_name +from profiling_job import ProfilingCase + +# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name +# (`detect_machine_name`), so the dict is keyed by the *detected* name here rather than +# by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" (it +# cannot tell the Booster partition apart), and this case must still get the Booster +# preset. Keying on the detected name also keeps this working, without a KeyError, on a +# machine detection does not recognise (name None). +GPU_PRESET = SLURM_PRESETS["pitagora_booster"] + +# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are +# spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank +# binding in params_GuidingCenter_scaling.py. +GPUS_PER_NODE = 4 + + +def main() -> None: + + # Parse arguments, do not remove --upload + parser = argparse.ArgumentParser( + description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), + ) + parser.add_argument( + "--upload", + action="store_true", + help="Upload the packaged profiling results to the profiling-data repo.", + ) + parser.add_argument( + "--ranks", + type=int, + nargs="+", + default=[1, 2, 4], + help="MPI rank counts to run with, one GPU per rank (default: 1 2 4).", + ) + args = parser.parse_args() + + # Paths relative to this script's location, so it can be run from anywhere. + script_dir = Path(__file__).resolve().parent + params_dir = script_dir / "examples" / "GuidingCenter" + params_source = params_dir / "params_GuidingCenter_scaling.py" + + profiling_case = ProfilingCase( + label="guidingcenter_cupy_scaling", + name="Guiding-centre particles on cube, CuPy multi-GPU scaling", + description="5D guiding-centre test particles (Np=10,000,000) in a homogeneous slab on a 3D cube, run with the CuPy array backend at increasing MPI rank counts (one GPU per rank) to measure strong-scaling speedup and verify the CUDA-ported kernels work correctly under MPI.", + physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", + struphy_model_used="GuidingCenter", + params_source=params_source, + language="fortran", + compiler="GNU", + upload=args.upload, + ) + + # The preset is looked up by cluster name inside `launch`, so build a one-entry dict + # under whatever name detection reports for this machine. + cluster_name = detect_machine_name() + + # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would + # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs + # far more ranks per node than there are GPUs. + for num_tasks in args.ranks: + num_nodes = -(-num_tasks // GPUS_PER_NODE) + profiling_case.launch( + num_tasks, + num_nodes=num_nodes, + param_flags=["--backend", "cupy"], + slurm_presets={cluster_name: GPU_PRESET}, + ) + + # Package and push each run as its own job finishes. + profiling_case.finalize_run() + + +if __name__ == "__main__": + main() From b56432788376c1df863382b74f7d354528c80f1b Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 09:44:28 +0200 Subject: [PATCH 072/156] Improve mpi sort using cuda --- src/struphy/pic/base.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index f7b021e57..0919c83c1 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4728,8 +4728,17 @@ def _sendrecv_get_destinations(self, send_inds): self._send_to_i[i] = xp.nonzero(xp.all(conds, axis=1))[0] send_info[i] = self._send_to_i[i].size - # mpi4py needs host buffers for the actual Isend below - self._send_list[i] = _to_numpy_for_kernel(self.markers[send_inds][self._send_to_i[i]]) + # Under CuPy this stays a device array: mpi4py sends it straight off the GPU + # via the CUDA-aware BTL/UCX path instead of a host round trip -- see + # _sendrecv_markers for the matching receive side. Measured on Pitagora's + # Booster nodes: this OpenMPI build supports it (smcuda BTL, compiled + # --with-cuda) and a raw device-to-device Isend/Irecv of a comparable payload + # is ~7x faster than staging through host buffers. In mpi_sort_markers itself + # the effect is smaller, since most of its per-call cost is the GPU-side + # bookkeeping (_sendrecv_determine_mtbs's boundary-membership scan over every + # local marker) rather than the exchange bandwidth -- but it removes 2 blocking + # device<->host copies per call for free and is never slower. + self._send_list[i] = self.markers[send_inds][self._send_to_i[i]] return send_info @@ -4778,7 +4787,10 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): else: self.mpi_comm.Isend(data, dest=i, tag=self.mpi_rank) - self._recvbufs[i] = np.zeros((N_recv, self.markers.shape[1]), dtype=float) + # xp.zeros, not np.zeros: under CuPy this is a device buffer, so mpi4py + # receives straight onto the GPU (CUDA-aware BTL/UCX) instead of into host + # memory -- see _sendrecv_get_destinations for the matching send side. + self._recvbufs[i] = xp.zeros((N_recv, self.markers.shape[1]), dtype=float) self._reqs[i] = self.mpi_comm.Irecv(self._recvbufs[i], source=i, tag=i) # Wait for buffer, then put markers into holes From dfa554d41adaca53e94bcb29f555a8bff67dc658 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 09:48:26 +0200 Subject: [PATCH 073/156] Updated params for bigger testcase --- .../examples/GuidingCenter/params_GuidingCenter_scaling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py index 1bb4a7db6..a20a2fcd2 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py @@ -166,7 +166,7 @@ # Marker count is the knob that decides how particle-dominated (vs. communication-bound) # the run is -- see the description for why this file defaults to 10,000,000 rather than # params_GuidingCenter.py's 200,000. -loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 10_000_000) +loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 50_000_000) weights_params = WeightsParameters() boundary_params = BoundaryParameters() sorting_params = SortingParameters() From dde71453a53493a5103107c04fd38153dc55ac85 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 09:56:53 +0200 Subject: [PATCH 074/156] Back to 10M markers --- .../examples/GuidingCenter/params_GuidingCenter_scaling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py index a20a2fcd2..1bb4a7db6 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py @@ -166,7 +166,7 @@ # Marker count is the knob that decides how particle-dominated (vs. communication-bound) # the run is -- see the description for why this file defaults to 10,000,000 rather than # params_GuidingCenter.py's 200,000. -loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 50_000_000) +loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 10_000_000) weights_params = WeightsParameters() boundary_params = BoundaryParameters() sorting_params = SortingParameters() From 30b5a701406a1f35f048781549aa602bab887a3a Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 10:08:43 +0200 Subject: [PATCH 075/156] improvements of mpi sorting --- src/struphy/pic/base.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 0919c83c1..c846bc954 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4686,8 +4686,15 @@ def _sendrecv_determine_mtbs( self._sorting_etas < self.domain_array_dev[self.mpi_rank, 1::3], ) - # to stay on the current process, all three columns must be True - self._can_stay = xp.all(self._is_on_proc_domain, axis=1) + # to stay on the current process, all three columns must be True. Elementwise AND + # of the 3 columns instead of xp.all(..., axis=1): on CuPy, reducing over a size-3 + # trailing axis of a multi-million-row array is a poor fit for the "many small + # reductions" GPU kernel it dispatches to -- measured ~9x slower than 3 plain + # elementwise ANDs for the same (correctness-verified identical) result, and this + # was the single largest cost in mpi_sort_markers (about 30% of the whole call). + self._can_stay = ( + self._is_on_proc_domain[:, 0] & self._is_on_proc_domain[:, 1] & self._is_on_proc_domain[:, 2] + ) # holes and ghosts can stay, too self._can_stay[self.holes] = True From d755dda290556d5300702936ff6b3ccca9e63d42 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 10:17:29 +0200 Subject: [PATCH 076/156] Fixed the xp.all(axis=1) bottleneck --- src/struphy/pic/base.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index ebcf50fb0..f001fd7e8 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4268,8 +4268,13 @@ def _sendrecv_determine_mtbs( self._sorting_etas < self.domain_array[self.mpi_rank, 1::3], ) - # to stay on the current process, all three columns must be True - self._can_stay = xp.all(self._is_on_proc_domain, axis=1) + # to stay on the current process, all three columns must be True. + # Reducing over a size-3 trailing axis of an array with many rows is slow + # This is faster + # Improvement of approximately 10x (both numpy and cupy) + self._can_stay = ( + self._is_on_proc_domain[:, 0] & self._is_on_proc_domain[:, 1] & self._is_on_proc_domain[:, 2] + ) # holes and ghosts can stay, too self._can_stay[self.holes] = True From 395b69f7ee610d1f3401f724e2c7b066d0c289b6 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 10:25:04 +0200 Subject: [PATCH 077/156] formatting --- src/struphy/pic/base.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index f001fd7e8..a82af1e15 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4272,9 +4272,7 @@ def _sendrecv_determine_mtbs( # Reducing over a size-3 trailing axis of an array with many rows is slow # This is faster # Improvement of approximately 10x (both numpy and cupy) - self._can_stay = ( - self._is_on_proc_domain[:, 0] & self._is_on_proc_domain[:, 1] & self._is_on_proc_domain[:, 2] - ) + self._can_stay = self._is_on_proc_domain[:, 0] & self._is_on_proc_domain[:, 1] & self._is_on_proc_domain[:, 2] # holes and ghosts can stay, too self._can_stay[self.holes] = True From d30b8acd30e92ea775b62cedec498b236f652294 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 10:33:38 +0200 Subject: [PATCH 078/156] Added self._eta_bc_buf for apply_kinetic_bc --- src/struphy/pic/base.py | 36 ++++++++++++++++++++++++++++++------ 1 file changed, 30 insertions(+), 6 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index f001fd7e8..a3bbdf9bf 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1718,8 +1718,14 @@ def apply_kinetic_bc(self, newton=False): """ # apply boundary conditions + if self._remove_axes: + # extract the (n_rows, 3) logical coordinates once per contiguous + # cache line instead of re-striding into the full row-major + # markers array once per axis (see _find_outside_particles) + self._eta_bc_buf[:] = self.markers[:, :3] + for axis in self._remove_axes: - outside_inds = self._find_outside_particles(axis) + outside_inds = self._find_outside_particles(axis, eta=self._eta_bc_buf) if len(outside_inds) == 0: continue @@ -1730,8 +1736,11 @@ def apply_kinetic_bc(self, newton=False): self._markers[self._is_outside, :-1] = -1.0 self._n_lost_markers += len(xp.nonzero(self._is_outside)[0]) + if self._periodic_axes: + self._eta_bc_buf[:] = self.markers[:, :3] + for axis in self._periodic_axes: - outside_inds = self._find_outside_particles(axis) + outside_inds = self._find_outside_particles(axis, eta=self._eta_bc_buf) if len(outside_inds) == 0: continue @@ -1766,8 +1775,11 @@ def apply_kinetic_bc(self, newton=False): # put all coordinate inside the unit cube (avoid wrong Jacobian evaluations) outside_inds_per_axis = {} + if self._reflect_axes: + self._eta_bc_buf[:] = self.markers[:, :3] + for axis in self._reflect_axes: - outside_inds = self._find_outside_particles(axis) + outside_inds = self._find_outside_particles(axis, eta=self._eta_bc_buf) self.markers[self._is_outside_left, axis] *= -1.0 self.markers[self._is_outside_right, axis] *= -1.0 @@ -2407,6 +2419,10 @@ def _allocate_marker_array(self, dry_run: bool = False): self._is_outside_right = xp.zeros(self.n_rows, dtype=bool) self._is_outside_left = xp.zeros(self.n_rows, dtype=bool) self._is_outside = xp.zeros(self.n_rows, dtype=bool) + # contiguous scratch copy of markers[:, :3], refreshed once per apply_kinetic_bc + # boundary-condition-type loop (see there) instead of re-striding into the full + # (n_rows, n_cols) row-major marker array once per axis. + self._eta_bc_buf = xp.zeros((self.n_rows, 3), dtype=float) # create array container (3 x positions, vdim x velocities, weight, s0, w0, ID) for removed markers self._n_lost_markers = 0 @@ -2744,7 +2760,7 @@ def _reset_marker_ids(self): )[self.mpi_rank] self.marker_ids = first_marker_id + xp.arange(self.n_mks_loc, dtype=int) - def _find_outside_particles(self, axis): + def _find_outside_particles(self, axis, eta=None): """Find markers whose ``axis``-th logical coordinate lies outside ``[0, 1]`` (holes and ghost particles are excluded), updating :attr:`_is_outside_left`/:attr:`_is_outside_right`/:attr:`_is_outside` accordingly. @@ -2754,14 +2770,22 @@ def _find_outside_particles(self, axis): axis : int Column of the markers array (0, 1 or 2) holding the logical coordinate to check. + eta : xp.ndarray[float], optional + Pre-extracted, contiguous ``(n_rows, 3)`` copy of ``markers[:, :3]`` (see + :meth:`apply_kinetic_bc`). If ``None``, reads straight from ``markers`` -- + correct but slower, since a single-column slice of the row-major ``markers`` + array is strided (see the comment in :meth:`apply_kinetic_bc`). + Returns ------- outside_inds : xp.ndarray[int] Row indices of the markers that are outside the logical unit cube. """ + col = self.markers[:, axis] if eta is None else eta[:, axis] + # determine particles outside of the logical unit cube - self._is_outside_right[:] = self.markers[:, axis] > 1.0 - self._is_outside_left[:] = self.markers[:, axis] < 0.0 + self._is_outside_right[:] = col > 1.0 + self._is_outside_left[:] = col < 0.0 self._is_outside_right[self.holes] = False self._is_outside_right[self.ghost_particles] = False From af71f18dbc1934d87a8ba1ae12d2458e7d0f83bf Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 10:33:38 +0200 Subject: [PATCH 079/156] Added self._eta_bc_buf for apply_kinetic_bc --- src/struphy/pic/base.py | 33 +++++++++++++++++++++++++++------ 1 file changed, 27 insertions(+), 6 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index c846bc954..c2bdc56cd 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1949,8 +1949,14 @@ def apply_kinetic_bc(self, newton=False): """ # apply boundary conditions + if self._remove_axes: + # extract the (n_rows, 3) logical coordinates once per contiguous + # cache line instead of re-striding into the full row-major + # markers array once per axis (see _find_outside_particles) + self._eta_bc_buf[:] = self.markers[:, :3] + for axis in self._remove_axes: - outside_inds = self._find_outside_particles(axis) + outside_inds = self._find_outside_particles(axis, eta=self._eta_bc_buf) if len(outside_inds) == 0: continue @@ -1961,8 +1967,11 @@ def apply_kinetic_bc(self, newton=False): self._markers[self._is_outside, :-1] = -1.0 self._n_lost_markers += len(np.nonzero(self._is_outside)[0]) + if self._periodic_axes: + self._eta_bc_buf[:] = self.markers[:, :3] + for axis in self._periodic_axes: - outside_inds = self._find_outside_particles(axis) + outside_inds = self._find_outside_particles(axis, eta=self._eta_bc_buf) if len(outside_inds) == 0: continue @@ -1997,8 +2006,11 @@ def apply_kinetic_bc(self, newton=False): # put all coordinate inside the unit cube (avoid wrong Jacobian evaluations) outside_inds_per_axis = {} + if self._reflect_axes: + self._eta_bc_buf[:] = self.markers[:, :3] + for axis in self._reflect_axes: - outside_inds = self._find_outside_particles(axis) + outside_inds = self._find_outside_particles(axis, eta=self._eta_bc_buf) self.markers[self._is_outside_left, axis] *= -1.0 self.markers[self._is_outside_right, axis] *= -1.0 @@ -2688,7 +2700,6 @@ def _allocate_marker_array(self, dry_run: bool = False): self._holes = xp.zeros(self.n_rows, dtype=bool) self._ghost_particles = xp.zeros(self.n_rows, dtype=bool) self._valid_mks = xp.zeros(self.n_rows, dtype=bool) - # _is_outside_right/_is_outside_left/_is_outside are views into one # buffer, so the three masks stay contiguous for the combined # comparison in _find_outside_particles. @@ -2696,6 +2707,10 @@ def _allocate_marker_array(self, dry_run: bool = False): self._is_outside_right = self._is_outside_buf[0] self._is_outside_left = self._is_outside_buf[1] self._is_outside = self._is_outside_buf[2] + # contiguous scratch copy of markers[:, :3], refreshed once per apply_kinetic_bc + # boundary-condition-type loop (see there) instead of re-striding into the full + # (n_rows, n_cols) row-major marker array once per axis. + self._eta_bc_buf = xp.zeros((self.n_rows, 3), dtype=float) # create array container (3 x positions, vdim x velocities, weight, s0, w0, ID) for removed markers self._n_lost_markers = 0 @@ -3057,7 +3072,7 @@ def _reset_marker_ids(self): )[self.mpi_rank] self.marker_ids = first_marker_id + np.arange(self.n_mks_loc, dtype=int) - def _find_outside_particles(self, axis): + def _find_outside_particles(self, axis, eta=None): """Find markers whose ``axis``-th logical coordinate lies outside ``[0, 1]`` (holes and ghost particles are excluded), updating :attr:`_is_outside_left`/:attr:`_is_outside_right`/:attr:`_is_outside` accordingly. @@ -3067,6 +3082,12 @@ def _find_outside_particles(self, axis): axis : int Column of the markers array (0, 1 or 2) holding the logical coordinate to check. + eta : xp.ndarray[float], optional + Pre-extracted, contiguous ``(n_rows, 3)`` copy of ``markers[:, :3]`` (see + :meth:`apply_kinetic_bc`). If ``None``, reads straight from ``markers`` -- + correct but slower, since a single-column slice of the row-major ``markers`` + array is strided (see the comment in :meth:`apply_kinetic_bc`). + Returns ------- outside_inds : numpy.ndarray[int] @@ -3076,7 +3097,7 @@ def _find_outside_particles(self, axis): # CuPy, with no transfer: markers, the holes/ghost masks and the # _is_outside_* views are all allocated with xp (see # _allocate_marker_array). - col = self.markers[:, axis] + col = self.markers[:, axis] if eta is None else eta[:, axis] not_hole_or_ghost = ~(self.holes | self.ghost_particles) xp.greater(col, 1.0, out=self._is_outside_right) From eba75a1605d8078a558dbe9805d401c9300bce83 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 10:25:04 +0200 Subject: [PATCH 080/156] formatting --- src/struphy/pic/base.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index c2bdc56cd..1798152d7 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4713,9 +4713,7 @@ def _sendrecv_determine_mtbs( # reductions" GPU kernel it dispatches to -- measured ~9x slower than 3 plain # elementwise ANDs for the same (correctness-verified identical) result, and this # was the single largest cost in mpi_sort_markers (about 30% of the whole call). - self._can_stay = ( - self._is_on_proc_domain[:, 0] & self._is_on_proc_domain[:, 1] & self._is_on_proc_domain[:, 2] - ) + self._can_stay = self._is_on_proc_domain[:, 0] & self._is_on_proc_domain[:, 1] & self._is_on_proc_domain[:, 2] # holes and ghosts can stay, too self._can_stay[self.holes] = True From 62af5385fbbe0eb0dc0ebc0dd724eb824bec0b8f Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 11:07:31 +0200 Subject: [PATCH 081/156] Run the profiling on a bigger case and up to 2 nodes. --- .../params_GuidingCenter_scaling.py | 33 ++++++++++++++----- .../submit_guidingcenter_cupy_scaling.py | 31 ++++++++++------- 2 files changed, 45 insertions(+), 19 deletions(-) diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py index 1bb4a7db6..a60ecebf5 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py @@ -23,10 +23,25 @@ At Np=4,000,000 the same 1/2/4-rank comparison did show a clear speedup (9.93 s / 5.88 s / 3.79 s -> 1.7x / 2.6x), because there is enough per-rank compute between exchanges for -it to outweigh the communication cost. This file goes further (10,000,000) so the -scaling trend has more headroom before communication catches back up. Whether it still -does at this size, and at what rank count, is what running this case answers -- it is not -assumed here. +it to outweigh the communication cost. + +At Np=10,000,000, a full 1/2/4/8-rank sweep (8 ranks = 2 Booster nodes) still regressed +at the 4->8 step (22.87 s -> 24.98 s): `mpi_sort_markers`'s share of the pusher loop grew +from 60.5% (2 ranks) to 65.8% (4 ranks) to 76.5% (8 ranks), while actual GPU kernel compute +stayed under 5% throughout -- the sort/exchange is latency- (not bandwidth-) bound, so its +share keeps growing even as its own average per-call cost keeps shrinking. This file goes +further still (50,000,000) to test whether enough per-rank compute between exchanges can +push the crossover point past 8 ranks, without changing the sort/BC call frequency itself +(deliberately not touched -- that would be an algorithmic change, not a scaling-parameter +one). Whether it does, and at what rank count, is what running this case answers. + +`num_elements` (the Derham/FEEC grid resolution) was also bumped from (16, 16, 16) to +(32, 32, 32): a coarser grid means each rank's local sub-grid is small relative to the +fixed-width ghost/halo padding needed for local spline evaluation, so a larger grid should +shrink that fixed overhead's share as well -- this is a different mechanism from the +particle-exchange cost above (marker sorting is about markers crossing rank sub-domain +boundaries, not grid ghost cells), tested here as a separate, independent lever since it +requires no algorithmic change either. `GuidingCenter` is used (as in params_GuidingCenter.py) because its whole propagator stack (PushGuidingCenterBxEstar, PushGuidingCenterParallel) is CUDA-ported and it carries @@ -139,8 +154,10 @@ # Fluid equilibrium (can be used as part of initial conditions) equil = equils.HomogenSlab() -# Grid -grid = grids.TensorProductGrid(num_elements=(16, 16, 16)) +# Grid. Coarser than this relative to Np/rank count means the fixed-width ghost/halo +# padding around each rank's local sub-grid is a bigger fraction of its data -- see the +# description for why this was raised from (16, 16, 16). +grid = grids.TensorProductGrid(num_elements=(32, 32, 32)) # Derham options derham_opts = DerhamOptions() @@ -164,9 +181,9 @@ # ------------------- # Marker count is the knob that decides how particle-dominated (vs. communication-bound) -# the run is -- see the description for why this file defaults to 10,000,000 rather than +# the run is -- see the description for why this file defaults to 50,000,000 rather than # params_GuidingCenter.py's 200,000. -loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 10_000_000) +loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 50_000_000) weights_params = WeightsParameters() boundary_params = BoundaryParameters() sorting_params = SortingParameters() diff --git a/profiling/submit_guidingcenter_cupy_scaling.py b/profiling/submit_guidingcenter_cupy_scaling.py index 0148e0cd8..99fd27a3a 100644 --- a/profiling/submit_guidingcenter_cupy_scaling.py +++ b/profiling/submit_guidingcenter_cupy_scaling.py @@ -12,16 +12,25 @@ Each rank binds to its own GPU via `SLURM_LOCALID` in `params_GuidingCenter_scaling.py` (see the comment there) -- without that, every rank on a node would default to CuPy's device 0 and contend for the same GPU, which would make this scaling study meaningless. -`--ranks 1 2 4` (the default) all fit on a single Booster node (4 GPUs/node), so this only -exercises intra-node scaling; ranks beyond `GPUS_PER_NODE` would spill onto a second node, -still one GPU per rank, but multi-node network overhead is untested here. +`SLURM_LOCALID` is a rank's index *within its node*, so this binding is correct on +multi-node runs too without any extra handling. + +`--ranks 1 2 4 8` (the default) covers both intra-node scaling (1/2/4 ranks, all on a +single Booster node, 4 GPUs/node) and one inter-node step (8 ranks = 2 nodes x 4 GPUs), +so the 4->8 step is the first data point that includes cross-node MPI exchange traffic +(mpi_sort_markers) instead of only intra-node/NVLink-less PCIe traffic. `launch()` derives +`num_nodes = ceil(num_tasks / GPUS_PER_NODE)` and requires `num_tasks % num_nodes == 0`, +so rank counts must stay multiples of `GPUS_PER_NODE` once they exceed it (8, 12, 16, ...). Uses `params_GuidingCenter_scaling.py`, not `params_GuidingCenter.py` (the single-GPU -NumPy-vs-CuPy comparison case): its much larger default Np (10,000,000 vs. 200,000) is -needed for a scaling study specifically because at the smaller size, per-rank compute -between marker exchanges is too small to outweigh the exchange cost -- adding ranks there -measured *slower*, not faster (see that file's docstring for the numbers). Whether the -larger problem here still scales, and how far, is what this case measures. +NumPy-vs-CuPy comparison case): its much larger default Np (50,000,000 vs. 200,000) is +needed for a scaling study specifically because at smaller sizes, per-rank compute between +marker exchanges is too small to outweigh the exchange cost -- adding ranks there measured +*slower*, not faster (see that file's docstring for the numbers), and even at 10,000,000 +the 4->8-rank (cross-node) step regressed. Whether 50,000,000 gives enough per-rank compute +to push the crossover point past 8 ranks, and how far, is what this case measures; see +`params_GuidingCenter_scaling.py`'s docstring for the 10,000,000 numbers this raise is +responding to. `GuidingCenter` is used here rather than `LinearMHDDriftkineticCC` because its runtime is actually dominated by the particle kernels this comparison is meant to measure. Its whole @@ -65,8 +74,8 @@ def main() -> None: "--ranks", type=int, nargs="+", - default=[1, 2, 4], - help="MPI rank counts to run with, one GPU per rank (default: 1 2 4).", + default=[1, 2, 4, 8], + help="MPI rank counts to run with, one GPU per rank (default: 1 2 4 8; 8 spans 2 Booster nodes).", ) args = parser.parse_args() @@ -78,7 +87,7 @@ def main() -> None: profiling_case = ProfilingCase( label="guidingcenter_cupy_scaling", name="Guiding-centre particles on cube, CuPy multi-GPU scaling", - description="5D guiding-centre test particles (Np=10,000,000) in a homogeneous slab on a 3D cube, run with the CuPy array backend at increasing MPI rank counts (one GPU per rank) to measure strong-scaling speedup and verify the CUDA-ported kernels work correctly under MPI.", + description="5D guiding-centre test particles (Np=50,000,000) in a homogeneous slab on a 3D cube, run with the CuPy array backend at increasing MPI rank counts (one GPU per rank) to measure strong-scaling speedup and verify the CUDA-ported kernels work correctly under MPI.", physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", struphy_model_used="GuidingCenter", params_source=params_source, From 8f55004b427e0c13aee3e970973f7ab6481174ae Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 11:34:46 +0200 Subject: [PATCH 082/156] Added _compute_neighbor_ranks() --- src/struphy/pic/base.py | 109 +++++++++++++++++++++++++++++++++++++--- 1 file changed, 101 insertions(+), 8 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 9f5e8daf0..999f64ffa 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -403,6 +403,11 @@ def __init__( self._send_to_i = [None] * self.mpi_size self._send_list = [None] * self.mpi_size + # Domain decomposition is static for the lifetime of this object, so the + # neighbour/non-neighbour split used by _sendrecv_get_destinations only needs + # computing once here rather than on every mpi_sort_markers call. + self._neighbor_ranks, self._non_neighbor_ranks = self._compute_neighbor_ranks() + # post init self.__post_init__() @@ -4309,6 +4314,56 @@ def _sendrecv_determine_mtbs( return hole_inds_after_send, send_inds + def _compute_neighbor_ranks(self) -> tuple[list[int], list[int]]: + """Split every other rank into geometric neighbours of this rank's sub-domain + box and everyone else, for :meth:`_sendrecv_get_destinations`. + + A rank is a neighbour (Moore neighbourhood: touching along every axis, sharing + at least a corner) if, for each of the 3 axes, its box interval touches or + overlaps this rank's -- including wrap-around for axes with a periodic boundary + condition (:attr:`_periodic_axes`), where the box touching ``eta=1`` is also + adjacent to the box touching ``eta=0``. + + Domain decomposition is a static, non-overlapping Cartesian tiling of the unit + cube (see :meth:`_get_domain_decomp`) computed once at construction, so this is + safe to compute once here rather than re-derived every call. + + Returns + ------- + neighbor_ranks : list[int] + Ranks (excluding this one) whose box touches or overlaps this rank's. + + non_neighbor_ranks : list[int] + Every other rank (excluding this one and ``neighbor_ranks``). + """ + # Tiny (mpi_size, 9) array -- brought to the host once so the O(mpi_size) + # comparisons below don't pay a device sync per rank checked. + domain_array_host = _to_numpy_for_kernel(self.domain_array) + own = domain_array_host[self.mpi_rank] + tol = 1e-12 + + neighbor_ranks = [] + non_neighbor_ranks = [] + for j in range(self.mpi_size): + if j == self.mpi_rank: + continue + other = domain_array_host[j] + touching = True + for axis in range(3): + own_l, own_r = own[3 * axis], own[3 * axis + 1] + other_l, other_r = other[3 * axis], other[3 * axis + 1] + axis_touches = other_r >= own_l - tol and other_l <= own_r + tol + if not axis_touches and axis in self._periodic_axes: + axis_touches = (abs(own_r - 1.0) < tol and abs(other_l) < tol) or ( + abs(own_l) < tol and abs(other_r - 1.0) < tol + ) + if not axis_touches: + touching = False + break + (neighbor_ranks if touching else non_neighbor_ranks).append(j) + + return neighbor_ranks, non_neighbor_ranks + def _sendrecv_get_destinations(self, send_inds): """ Determine to which process particles have to be sent. @@ -4326,17 +4381,55 @@ def _sendrecv_get_destinations(self, send_inds): # One entry for each process send_info = xp.zeros(self.mpi_size, dtype=int) - # TODO: do not loop over all processes, start with neighbours and work outwards (using while) + # Gathered once and reused for every rank below, instead of re-gathering + # self.markers[send_inds] and self._sorting_etas[send_inds] fresh on every + # iteration of the rank loop (as the previous version did). + candidates = self.markers[send_inds] + etas_to_send = self._sorting_etas[send_inds] + + # Reset every rank's send buffer to empty first. The neighbour/non-neighbour + # loop below can `break` before visiting every rank once all candidates are + # matched, but _sendrecv_markers unconditionally Isends self._send_list[i] for + # every i != mpi_rank -- a rank skipped this call must not keep a stale, + # non-empty buffer from a previous call (send/recv size would then disagree + # with send_info, which is always correct since it defaults to 0 above). + empty_local = xp.empty(0, dtype=int) + empty_rows = candidates[:0] for i in range(self.mpi_size): - conds = xp.logical_and( - self._sorting_etas[send_inds] > self.domain_array[i, 0::3], - self._sorting_etas[send_inds] < self.domain_array[i, 1::3], - ) + self._send_to_i[i] = empty_local + self._send_list[i] = empty_rows + + # A marker leaving this rank's domain is overwhelmingly likely to land in a + # geometrically adjacent sub-domain -- one push (sub-)step is small compared to + # a sub-domain -- so check neighbour ranks (_compute_neighbor_ranks) first + # instead of every rank. `remaining` tracks positions into send_inds/ + # etas_to_send not yet matched to a destination; if neighbours don't cover + # everyone (a marker moved further than one sub-domain this step), the leftover + # few are checked against every other rank in the second pass, so this changes + # only how many ranks get checked in the common case, not correctness. + remaining = xp.arange(send_inds.shape[0]) + for rank_group in (self._neighbor_ranks, self._non_neighbor_ranks): + if remaining.size == 0: + break + + etas_remaining = etas_to_send[remaining] + still_remaining = xp.ones(remaining.shape[0], dtype=bool) + for i in rank_group: + conds = xp.logical_and( + etas_remaining > self.domain_array[i, 0::3], + etas_remaining < self.domain_array[i, 1::3], + ) + + matched_local = xp.nonzero(xp.all(conds, axis=1))[0] + matched = remaining[matched_local] + + self._send_to_i[i] = matched + send_info[i] = matched.size + self._send_list[i] = candidates[matched] - self._send_to_i[i] = xp.nonzero(xp.all(conds, axis=1))[0] - send_info[i] = self._send_to_i[i].size + still_remaining[matched_local] = False - self._send_list[i] = self.markers[send_inds][self._send_to_i[i]] + remaining = remaining[still_remaining] return send_info From a167433f6f0ff079fa7dfb06e5a9eea38fa87cd0 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 12:02:24 +0200 Subject: [PATCH 083/156] Run with save_restart=False --- profiling/examples/GuidingCenter/params_GuidingCenter.py | 1 + profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py | 1 + 2 files changed, 2 insertions(+) diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter.py b/profiling/examples/GuidingCenter/params_GuidingCenter.py index 797e7a608..3bc664693 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter.py @@ -128,6 +128,7 @@ env = EnvironmentOptions( sim_folder=f"sim_{args.id:02d}", profiling_activated=True, + save_restart=False, ) # Time stepping. Enough steps that the per-step particle work, not the one-off diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py index a60ecebf5..5c6beaaa0 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py @@ -141,6 +141,7 @@ env = EnvironmentOptions( sim_folder=f"sim_{args.id:02d}", profiling_activated=True, + save_restart=False, ) # Time stepping. Enough steps that the per-step particle work (and its MPI exchange), From 418429836c28298fef7ac22b18829cdb7ffd3a42 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 13:57:56 +0200 Subject: [PATCH 084/156] Removed barriers --- src/struphy/pic/base.py | 76 +++++++++++++++++++++++++++-------------- 1 file changed, 51 insertions(+), 25 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 3b8631182..8d3890a46 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1891,11 +1891,16 @@ def mpi_sort_markers( remove_ghost : bool Remove ghost particles before send. """ + # No self._Barrier() here (there used to be one): _remove_ghost_particles and + # apply_kinetic_bc below are both purely local -- neither touches self.mpi_comm + # -- so there is no cross-rank dependency for a barrier to protect at this + # point. The barrier previously here looks like it was compensating for + # _sendrecv_markers not waiting on its Isend requests (see there); now that it + # does, buffer reuse across mpi_sort_markers calls is safe without it too (see + # the trailing comment at the end of this method). if remove_ghost: self._remove_ghost_particles() - self._Barrier() - # before sorting, apply kinetic bc if apply_bc: self.apply_kinetic_bc() @@ -1938,7 +1943,13 @@ def mpi_sort_markers( assert all_on_right_proc # assert self.phasespace_coords.size > 0, f'No particles on process {self.mpi_rank}, please rebalance, aborting ...' - self._Barrier() + # No trailing self._Barrier() here either (there used to be one): every Isend + # and Irecv issued by _sendrecv_markers is now waited on before it returns, so + # this rank's send/recv buffers are already safe to overwrite on the next call + # without an extra rendezvous. Cross-round message ordering between any given + # pair of ranks is additionally guaranteed by MPI itself (non-overtaking + # messages for the same source/dest/tag), so a later round's Isend can't be + # mistaken for an earlier one even without this barrier. @profile @ProfileManager.profile("apply_kinetic_bc") @@ -1975,10 +1986,17 @@ def apply_kinetic_bc(self, newton=False): if self._periodic_axes: self._eta_bc_buf[:] = self.markers[:, :3] - if self._periodic_axes: - self._eta_bc_buf[:] = self.markers[:, :3] - for axis in self._periodic_axes: + # Reverted from a branchless/xp.where + unconditional-elementwise version: + # measured on a real 50M-marker GPU run, that version was a net loss -- on + # each call only a small fraction of markers are ever actually outside + # (holes/ghosts and in-range markers are the overwhelming majority), so the + # sparse, index-based writes below plus the early exit (skipping this axis + # entirely when nothing is outside) touch far less memory than an + # unconditional dense pass over all n_rows markers, even accounting for the + # nonzero sync the indices cost. Avoiding a device sync is not free if the + # alternative is doing O(n_rows) dense work every call instead of O(outside + # markers) sparse work most calls skip entirely. outside_inds = self._find_outside_particles(axis, eta=self._eta_bc_buf) if len(outside_inds) == 0: @@ -4903,13 +4921,21 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): # i-th entry holds the number (not the index) of the first hole to be filled by data from process i first_hole = np.cumsum(recv_info) - recv_info + # Send requests are waited on below (unlike the previous version, which never + # stored or waited on them): otherwise nothing guarantees the send buffer + # (self._send_list[i]) is safe to overwrite before the underlying transfer has + # actually completed -- MPI is free to defer completing a large Isend until the + # receiver posts a matching receive (rendezvous protocol), so a fire-and-forget + # Isend is not necessarily done just because this call returns. + send_reqs = [] + # Initialize send and receive commands for i, (data, N_recv) in enumerate(zip(self._send_list, list(recv_info))): if i == self.mpi_rank: self._reqs[i] = None self._recvbufs[i] = None else: - self.mpi_comm.Isend(data, dest=i, tag=self.mpi_rank) + send_reqs.append(self.mpi_comm.Isend(data, dest=i, tag=self.mpi_rank)) # xp.zeros, not np.zeros: under CuPy this is a device buffer, so mpi4py # receives straight onto the GPU (CUDA-aware BTL/UCX) instead of into host @@ -4917,29 +4943,29 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): self._recvbufs[i] = xp.zeros((N_recv, self.markers.shape[1]), dtype=float) self._reqs[i] = self.mpi_comm.Irecv(self._recvbufs[i], source=i, tag=i) - # Wait for buffer, then put markers into holes - test_reqs = [False] * (recv_info.size - 1) - while len(test_reqs) > 0: - # loop over all receive requests - for i, req in enumerate(self._reqs): - if req is None: - continue - else: - # check if data has been received - if req.Test(): - if hole_inds_after_send.size < first_hole[i] + recv_info[i]: - warnings.warn( - f'Strong load imbalance detected: \ + # Block on every receive at once via the MPI library's own wait, instead of a + # tight Python-level req.Test() poll loop -- lets the MPI progress engine (not + # our interpreter) do the waiting, and avoids repeatedly re-scanning still- + # pending requests from Python. + recv_ranks = [i for i, req in enumerate(self._reqs) if req is not None] + if recv_ranks: + MPI.Request.Waitall([self._reqs[i] for i in recv_ranks]) + + for i in recv_ranks: + if hole_inds_after_send.size < first_hole[i] + recv_info[i]: + warnings.warn( + f'Strong load imbalance detected: \ number of holes ({hole_inds_after_send.size}) on rank {self.mpi_rank} \ is smaller than number of incoming particles ({first_hole[i] + recv_info[i]}). \ Increasing the value of "bufsize" in the markers parameters for the next run.', - ) - self.mpi_comm.Abort() + ) + self.mpi_comm.Abort() - self.markers[hole_inds_after_send[first_hole[i] + np.arange(recv_info[i])]] = self._recvbufs[i] + self.markers[hole_inds_after_send[first_hole[i] + np.arange(recv_info[i])]] = self._recvbufs[i] + self._reqs[i] = None - test_reqs.pop() - self._reqs[i] = None + if send_reqs: + MPI.Request.Waitall(send_reqs) class Tesselation: From 1fb9ef0d7bd3ee7fabd890ff6fde02c1035ec3d3 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 14:07:55 +0200 Subject: [PATCH 085/156] Elementwise AND for matched_local --- src/struphy/pic/base.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 8d3890a46..f6e392106 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4860,7 +4860,11 @@ def _sendrecv_get_destinations(self, send_inds): etas_remaining < self.domain_array_dev[i, 1::3], ) - matched_local = xp.nonzero(xp.all(conds, axis=1))[0] + # Elementwise AND of the 3 columns instead of xp.all(conds, axis=1): + # same fix as _sendrecv_determine_mtbs (measured ~9x faster on CuPy -- + # reducing over a size-3 trailing axis of a multi-million-row array is a + # poor fit for the "many small reductions" GPU kernel it dispatches to). + matched_local = xp.nonzero(conds[:, 0] & conds[:, 1] & conds[:, 2])[0] matched = remaining[matched_local] self._send_to_i[i] = matched From a4721353969b0f2083e1dddb18aeb9ee49890c81 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 14:41:25 +0200 Subject: [PATCH 086/156] temporary: Set OMPI_MCA_coll_hcoll_enable=0 and MPI4PY_RC_THREAD_LEVEL=funneled --- src/struphy/__init__.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/src/struphy/__init__.py b/src/struphy/__init__.py index cf87d8fed..4e4956264 100644 --- a/src/struphy/__init__.py +++ b/src/struphy/__init__.py @@ -10,6 +10,27 @@ # Disable it unless the user has explicitly configured it. os.environ.setdefault("HDF5_USE_FILE_LOCKING", "FALSE") +# mpi4py defaults to requesting MPI_THREAD_MULTIPLE (thread level 3) from +# MPI_Init_thread. On at least one cluster this repo runs on (Pitagora's Booster +# partition, OpenMPI 4.1.6 + UCX 1.20), the UCX worker does not support that level, +# which OpenMPI reports at every multi-rank run ("UCP worker does not support +# MPI_THREAD_MULTIPLE" / "failed to init ucx" / hcoll init failure) and works +# around by making hcoll (its GPU-aware collective component) fail to initialize, +# silently falling back to a different, working collective implementation. Struphy +# only ever calls MPI from the main Python thread (CuPy's internal CUDA driver +# threads don't touch MPI), so requesting the weaker MPI_THREAD_FUNNELED guarantee +# instead is sufficient and avoids the warnings -- but it ALSO lets hcoll +# successfully initialize where it previously failed to, and hcoll's own +# Alltoallv implementation on this cluster then segfaults +# (hmca_bcol_ucx_p2p_alltoallv_pairwise_chunk_progress) the first time it's +# actually used, something the failed init was silently protecting us from. +# hcoll must therefore stay disabled explicitly alongside the thread-level +# change, not just left to fail its own init. Both must be set before mpi4py.MPI +# is imported anywhere (thread level can't change after MPI_Init), and only if +# the user hasn't already configured them themselves. +os.environ.setdefault("MPI4PY_RC_THREAD_LEVEL", "funneled") +os.environ.setdefault("OMPI_MCA_coll_hcoll_enable", "0") + from feectools.ddm.mpi import mpi as MPI from struphy.utils.mpi_launch import launched_under_mpi From 7d9094de57bb1f42d145558fa0a0e2dfc785b571 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 15:08:08 +0200 Subject: [PATCH 087/156] Extend cluster presets a bit --- profiling/clusters.py | 15 ++++++++++++++- profiling/submit_guidingcenter_cupy_scaling.py | 16 +++++++++++++--- 2 files changed, 27 insertions(+), 4 deletions(-) diff --git a/profiling/clusters.py b/profiling/clusters.py index b2cbe8aa0..85061ba4a 100644 --- a/profiling/clusters.py +++ b/profiling/clusters.py @@ -69,7 +69,7 @@ def detect_machine_name() -> str | None: "mail_type": "none", "time": "00:15:00", }, - "pitagora_booster": { + "pitagora_boost_fua_dbg": { # "nodes": 1, # Should be set by ProfilingCase.launch() # "ntasks_per_node": 1, # Should be set by ProfilingCase.launch() "cpus_per_task": 16, @@ -82,6 +82,19 @@ def detect_machine_name() -> str | None: "mail_type": "none", "time": "00:15:00", }, + "pitagora_boost_fua_prod": { + # "nodes": 1, # Should be set by ProfilingCase.launch() + # "ntasks_per_node": 1, # Should be set by ProfilingCase.launch() + "cpus_per_task": 16, + "mem": "480GB", + "gres": "gpu:4,tmpfs:10g", + "partition": "boost_fua_prod", + "account": "FUSIO_HLST_6", + "output": "myJob_%j.out", + "error": "myJob_%j.err", + "mail_type": "none", + "time": "00:15:00", + }, "tok": { "cpus_per_task": 1, "mem_per_cpu": "1GB", diff --git a/profiling/submit_guidingcenter_cupy_scaling.py b/profiling/submit_guidingcenter_cupy_scaling.py index 99fd27a3a..9b7e1000c 100644 --- a/profiling/submit_guidingcenter_cupy_scaling.py +++ b/profiling/submit_guidingcenter_cupy_scaling.py @@ -51,7 +51,7 @@ # cannot tell the Booster partition apart), and this case must still get the Booster # preset. Keying on the detected name also keeps this working, without a KeyError, on a # machine detection does not recognise (name None). -GPU_PRESET = SLURM_PRESETS["pitagora_booster"] +GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] # GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are # spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank @@ -74,9 +74,15 @@ def main() -> None: "--ranks", type=int, nargs="+", - default=[1, 2, 4, 8], + default=[2, 4, 8], help="MPI rank counts to run with, one GPU per rank (default: 1 2 4 8; 8 spans 2 Booster nodes).", ) + parser.add_argument( + "--Np", + type=int, + default=None, + help="Total marker count, overriding params_GuidingCenter_scaling.py's default (50,000,000).", + ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. @@ -100,6 +106,10 @@ def main() -> None: # under whatever name detection reports for this machine. cluster_name = detect_machine_name() + param_flags = ["--backend", "cupy"] + if args.Np is not None: + param_flags += ["--Np", str(args.Np)] + # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs # far more ranks per node than there are GPUs. @@ -108,7 +118,7 @@ def main() -> None: profiling_case.launch( num_tasks, num_nodes=num_nodes, - param_flags=["--backend", "cupy"], + param_flags=param_flags, slurm_presets={cluster_name: GPU_PRESET}, ) From fc65f5676c6384f6a683f623d08986c335eceb07 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 15:34:37 +0200 Subject: [PATCH 088/156] Added VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py --- .../params_VlasovAmpere_scaling.py | 208 ++++++++++++++++++ profiling/submit_vlasovampere_cupy_scaling.py | 115 ++++++++++ 2 files changed, 323 insertions(+) create mode 100644 profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py create mode 100644 profiling/submit_vlasovampere_cupy_scaling.py diff --git a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py new file mode 100644 index 000000000..278362edc --- /dev/null +++ b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py @@ -0,0 +1,208 @@ +# ----------------------------- +# Description of the simulation +# ----------------------------- +# Please fill in a verbal description of the simulation. +# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. + +name = "VlasovAmpereOneSpecies CuPy multi-GPU scaling" +description = """ +6D full-orbit Vlasov-Ampere test particles in a homogeneous cube, used as a second +CuPy multi-GPU/multi-rank strong-scaling case alongside +profiling/submit_guidingcenter_cupy_scaling.py -- deliberately NOT dominated by +mpi_sort_markers the way that case is. + +GuidingCenter's whole propagator stack is a pure particle push with no FEEC field +solve, so at every rank count profiled so far (see +params_GuidingCenter_scaling.py's docstring) mpi_sort_markers/apply_kinetic_bc ate +57-70% of the pusher loop and actual GPU kernel compute stayed under 5% -- i.e. that +case is a communication/bookkeeping benchmark more than a compute one, by design. +VlasovAmpereOneSpecies's VlasovAmpereCoupling propagator instead solves a real linear +system each step (SchurSolver over the E-field mass matrix, solver="pcg") to update +the field from the accumulated particle current, on top of the push -- real per-step +compute that doesn't exist in GuidingCenter's scaling case at all. Whether that shifts +the balance away from mpi_sort_markers, and by how much, is what this case measures. + +Kept as close to params_GuidingCenter_scaling.py's setup as the different model +allows for comparability: same Cuboid domain and (32, 32, 32) grid, same +SLURM_LOCALID device binding and FEECTOOLS_ENABLE_MPI opt-in, same --backend/--Np/ +--Tend/--id CLI surface, and a similarly large default Np (also 50,000,000, i.e. the +same total marker count already validated to give real per-rank compute between +exchanges for GuidingCenter -- see that file's docstring for the 10M/50M numbers this +follows). `with_B0=False` (electrostatic only, no PushVxB) keeps the propagator stack +close to GuidingCenter's 2-propagator-plus-coupling shape (PushEta + VlasovAmpereCoupling +here vs PushGuidingCenterBxEstar + PushGuidingCenterParallel there) rather than adding a +third. + +Per CUDA_KERNEL_PORTING_STATUS.md, VlasovAmpereOneSpecies is one of the models verified +fully device-resident (zero host<->device marker crossings) alongside GuidingCenter, so +this is a like-for-like comparison of the CUDA port, not a case exercising unported code +paths. LinearMHDDriftkineticCC was deliberately NOT used instead, despite also having a +real field solve: it has an unresolved, tracked physics-divergence bug on the CuPy +backend (ISSUE_mhd_cupy_physics_divergence.md, status "needs re-measurement"), which +would make any timing from it untrustworthy for a comparison like this one. +""" + +import argparse +import os + +parser = argparse.ArgumentParser(description=description) +parser.add_argument( + "--backend", + choices=("numpy", "cupy"), + default="numpy", + help="Array backend to run the simulation with (default: numpy).", +) +# `--id` distinguishes runs that share a rank count but differ in something else (here: +# the array backend); the profiling driver passes its launch counter and looks for the +# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). +# Unknown flags are ignored so the driver can forward other parameters as well. +parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") +parser.add_argument("--Np", type=int, default=None, help="Number of markers (overrides the default).") +parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default).") +args, _ = parser.parse_known_args() + +# Must be set before struphy (and therefore cunumpy) is imported. +os.environ["ARRAY_BACKEND"] = args.backend + +if args.backend == "cupy": + import cunumpy + + # Under CuPy with more than one MPI rank per node, every rank must bind to its own GPU + # -- cupy defaults to device 0, so without this every rank on a node would contend for + # the same GPU instead of getting one each. SLURM_LOCALID (the rank's index within its + # node) is set by srun before this process even starts, so it works without MPI being + # initialized yet. Falls back to device 0 outside SLURM (e.g. a single-GPU login node). + cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) + + # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment + # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more + # than one rank every process independently creates the same output directory/HDF5 + # dataset and the survivors deadlock in the next collective. This file is specifically + # meant to run multi-rank/multi-GPU, so opt back in; a single-GPU run pays only a + # no-op collective for it. + os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") + +import logging + +from struphy import set_logging_level + +set_logging_level(logging.WARNING) + +# ------------------ +# Import Struphy API +# ------------------ + +from struphy import ( + BaseUnits, + BoundaryParameters, + DerhamOptions, + EnvironmentOptions, + LoadingParameters, + SavingParameters, + Simulation, + SortingParameters, + Time, + WeightsParameters, + domains, + grids, + maxwellians, +) + +# --------------------- +# Instance of the model +# --------------------- +from struphy.models import VlasovAmpereOneSpecies + +# Units +base_units = BaseUnits() + +# Model instance. with_B0=False: electrostatic only (no PushVxB), keeping the +# propagator count close to GuidingCenter's 2-propagator-plus-coupling shape -- see +# the description. +model = VlasovAmpereOneSpecies(base_units=base_units, alpha=1.0, epsilon=-1.0, with_B0=False) + +# List all variables and decide whether to save their data +model.em_fields.e_field.save_data = True +model.kinetic_ions.var.save_data = True + +# -------------------------- +# Instance of the simulation +# -------------------------- + +name = f"VlasovAmpereOneSpecies scaling ({args.backend})" + +# Environment options +env = EnvironmentOptions( + sim_folder=f"sim_{args.id:02d}", + profiling_activated=True, + save_restart=False, +) + +# Time stepping. Same dt as params_GuidingCenter_scaling.py; enough steps that the +# per-step particle/field work, not one-off setup, dominates the total. +time_opts = Time(dt=0.01, Tend=args.Tend if args.Tend is not None else 1.0) + +# Geometry -- same unit cube as params_GuidingCenter_scaling.py, for comparability. +domain = domains.Cuboid() + +# No fluid equilibrium: with_B0=False needs no background B-field. +equil = None + +# Grid -- same resolution as params_GuidingCenter_scaling.py. +grid = grids.TensorProductGrid(num_elements=(32, 32, 32)) + +# Derham options +derham_opts = DerhamOptions() + +# Simulation object +sim = Simulation( + model=model, + name=name, + description=description, + params_path=__file__, + env=env, + time_opts=time_opts, + domain=domain, + equil=equil, + grid=grid, + derham_opts=derham_opts, +) + +# ------------------- +# Particle parameters +# ------------------- + +# Same default as params_GuidingCenter_scaling.py's 50,000,000, for comparability. +loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 50_000_000, spatial="uniform") +weights_params = WeightsParameters() +boundary_params = BoundaryParameters() +sorting_params = SortingParameters() +saving_params = SavingParameters() +model.kinetic_ions.set_markers( + loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, +) + +# ------------------ +# Propagator options +# ------------------ + +model.propagators.push_eta.options = model.propagators.push_eta.Options() +model.propagators.coupling_va.options = model.propagators.coupling_va.Options() +model.initial_poisson.options = model.initial_poisson.Options(stab_mat="M0") + +# ------------------ +# Initial conditions +# ------------------ + +# Background for kinetic species -- uniform, no perturbation: this case measures +# scaling behaviour, not physical accuracy (same spirit as GuidingCenter's +# homogeneous-slab scaling case). +background = maxwellians.Maxwellian3D(n=(1.0, None)) +model.kinetic_ions.var.add_background(background) + +if __name__ == "__main__": + sim.run() diff --git a/profiling/submit_vlasovampere_cupy_scaling.py b/profiling/submit_vlasovampere_cupy_scaling.py new file mode 100644 index 000000000..f3b5ba6b4 --- /dev/null +++ b/profiling/submit_vlasovampere_cupy_scaling.py @@ -0,0 +1,115 @@ +"""VlasovAmpereOneSpecies CuPy multi-GPU/multi-rank scaling case. + +Companion to submit_guidingcenter_cupy_scaling.py, deliberately using a different +model that is NOT dominated by mpi_sort_markers the way GuidingCenter's scaling case +is (see params_VlasovAmpere_scaling.py's docstring for the full rationale): +VlasovAmpereOneSpecies's VlasovAmpereCoupling propagator solves a real linear system +each step to update the field from the accumulated particle current, giving it real +per-step compute that GuidingCenter's pure-push propagator stack doesn't have. + +Same strong-scaling structure as the GuidingCenter case: the same total marker count +(`LoadingParameters.Np` is the *total* across ranks) is run with `ARRAY_BACKEND=cupy` +at increasing MPI rank counts, one rank per GPU, on the Booster partition. + +Each rank binds to its own GPU via `SLURM_LOCALID` in `params_VlasovAmpere_scaling.py` +(see the comment there) -- without that, every rank on a node would default to CuPy's +device 0 and contend for the same GPU, which would make this scaling study meaningless. +`SLURM_LOCALID` is a rank's index *within its node*, so this binding is correct on +multi-node runs too without any extra handling. + +`--ranks 2 4 8` (the default, matching the GuidingCenter case's current default) covers +both intra-node scaling (2/4 ranks, on a single Booster node, 4 GPUs/node) and one +inter-node step (8 ranks = 2 nodes x 4 GPUs). `launch()` derives +`num_nodes = ceil(num_tasks / GPUS_PER_NODE)` and requires `num_tasks % num_nodes == 0`, +so rank counts must stay multiples of `GPUS_PER_NODE` once they exceed it (8, 12, 16, ...). +""" + +import argparse +from pathlib import Path + +from clusters import SLURM_PRESETS, detect_machine_name +from profiling_job import ProfilingCase + +# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name +# (`detect_machine_name`), so the dict is keyed by the *detected* name here rather than +# by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" (it +# cannot tell the Booster partition apart), and this case must still get the Booster +# preset. Keying on the detected name also keeps this working, without a KeyError, on a +# machine detection does not recognise (name None). +GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] + +# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are +# spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank +# binding in params_VlasovAmpere_scaling.py. +GPUS_PER_NODE = 4 + + +def main() -> None: + + # Parse arguments, do not remove --upload + parser = argparse.ArgumentParser( + description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), + ) + parser.add_argument( + "--upload", + action="store_true", + help="Upload the packaged profiling results to the profiling-data repo.", + ) + parser.add_argument( + "--ranks", + type=int, + nargs="+", + default=[2, 4, 8], + help="MPI rank counts to run with, one GPU per rank (default: 2 4 8; 8 spans 2 Booster nodes).", + ) + parser.add_argument( + "--Np", + type=int, + default=None, + help="Total marker count, overriding params_VlasovAmpere_scaling.py's default (50,000,000).", + ) + args = parser.parse_args() + + # Paths relative to this script's location, so it can be run from anywhere. + script_dir = Path(__file__).resolve().parent + params_dir = script_dir / "examples" / "VlasovAmpereOneSpecies" + params_source = params_dir / "params_VlasovAmpere_scaling.py" + + profiling_case = ProfilingCase( + label="vlasovampere_cupy_scaling", + name="Vlasov-Ampere particles on cube, CuPy multi-GPU scaling", + description="6D full-orbit Vlasov-Ampere test particles (Np=50,000,000) in a homogeneous cube, run with the CuPy array backend at increasing MPI rank counts (one GPU per rank) -- a companion to guidingcenter_cupy_scaling using a model with a real per-step field solve instead of a pure particle push, to measure scaling behaviour when mpi_sort_markers is not the dominant cost.", + physics_problem="6D full-orbit Vlasov-Ampere particle motion with a self-consistent electric field, solved via VlasovAmpereCoupling's SchurSolver each step.", + struphy_model_used="VlasovAmpereOneSpecies", + params_source=params_source, + language="fortran", + compiler="GNU", + upload=args.upload, + ) + + # The preset is looked up by cluster name inside `launch`, so build a one-entry dict + # under whatever name detection reports for this machine. + cluster_name = detect_machine_name() + + param_flags = ["--backend", "cupy"] + if args.Np is not None: + param_flags += ["--Np", str(args.Np)] + + # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would + # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs + # far more ranks per node than there are GPUs. + for num_tasks in args.ranks: + num_nodes = -(-num_tasks // GPUS_PER_NODE) + profiling_case.launch( + num_tasks, + num_nodes=num_nodes, + param_flags=param_flags, + slurm_presets={cluster_name: GPU_PRESET}, + ) + + # Package and push each run as its own job finishes. + profiling_case.finalize_run() + + +if __name__ == "__main__": + main() From c988f710f3895b97ffae25de015f21d55fa0c514 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 15:46:13 +0200 Subject: [PATCH 089/156] Added sph scaling example --- .../params_PressureLessSPH_scaling.py | 203 ++++++++++++++++++ .../submit_pressurelesssph_cupy_scaling.py | 115 ++++++++++ 2 files changed, 318 insertions(+) create mode 100644 profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py create mode 100644 profiling/submit_pressurelesssph_cupy_scaling.py diff --git a/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py b/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py new file mode 100644 index 000000000..e00069a55 --- /dev/null +++ b/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py @@ -0,0 +1,203 @@ +# ----------------------------- +# Description of the simulation +# ----------------------------- +# Please fill in a verbal description of the simulation. +# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. + +name = "PressureLessSPH CuPy multi-GPU scaling" +description = """ +SPH test particles in a homogeneous cube, used as a third CuPy multi-GPU/multi-rank +strong-scaling case alongside submit_guidingcenter_cupy_scaling.py and +submit_vlasovampere_cupy_scaling.py -- this one is close to a pure particle push: no +FEEC field solve at all (model_type="Fluid", no linear system to solve every step, unlike +VlasovAmpereOneSpecies's SchurSolver), and per CUDA_KERNEL_PORTING_STATUS.md its SPH +kernel families (pushers, evaluation, marker-column kernels) are all fully CUDA-ported +for their live code paths -- as close to "everything runs on GPU" as any model in this +repo gets. `PressureLessSPH` specifically (not IncompressibleNavierStokesSPH or +ViscousEulerSPH) is used because it was the one explicitly re-verified to agree to +round-off across backends after the marker-detachment bug fix (see +ISSUE_mhd_cupy_physics_divergence.md's "Does not affect" note) -- not just ported, but +checked correct. + +Two propagators: PushEta (position push) and PushVinEfield (velocity push against a +background force field derived from equil.p0) -- both simpler, cheaper-per-marker +kernels than GuidingCenter's multistage guiding-centre push, so if anything this case +should push mpi_sort_markers's share *up* rather than down relative to GuidingCenter, +making it a useful third data point on the low-per-marker-compute end (GuidingCenter +in the middle, VlasovAmpereOneSpecies's real field solve on the high end). + +Adapted from the repo's own `params_PressureLessSPH.py` (root directory -- the +model's reference/template params file) into the scaling-case pattern used by the +other two cases here: same SLURM_LOCALID device binding, FEECTOOLS_ENABLE_MPI opt-in, +--backend/--Np/--Tend/--id CLI surface, and the same Cuboid domain (32, 32, 32) grid +for comparability. Np default kept smaller (10,000,000) than the other two cases' +50,000,000 for an initial run, since this is the first time this case has been run at +scale -- see the docstring in params_GuidingCenter_scaling.py for why marker count +matters for the mpi_sort_markers/per-rank-compute balance being compared here; raise +it once a first sweep confirms this scales the way the other two cases did. +""" + +import argparse +import os + +parser = argparse.ArgumentParser(description=description) +parser.add_argument( + "--backend", + choices=("numpy", "cupy"), + default="numpy", + help="Array backend to run the simulation with (default: numpy).", +) +# `--id` distinguishes runs that share a rank count but differ in something else (here: +# the array backend); the profiling driver passes its launch counter and looks for the +# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). +# Unknown flags are ignored so the driver can forward other parameters as well. +parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") +parser.add_argument("--Np", type=int, default=None, help="Number of markers (overrides the default).") +parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default).") +args, _ = parser.parse_known_args() + +# Must be set before struphy (and therefore cunumpy) is imported. +os.environ["ARRAY_BACKEND"] = args.backend + +if args.backend == "cupy": + import cunumpy + + # Under CuPy with more than one MPI rank per node, every rank must bind to its own GPU + # -- cupy defaults to device 0, so without this every rank on a node would contend for + # the same GPU instead of getting one each. SLURM_LOCALID (the rank's index within its + # node) is set by srun before this process even starts, so it works without MPI being + # initialized yet. Falls back to device 0 outside SLURM (e.g. a single-GPU login node). + cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) + + # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment + # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more + # than one rank every process independently creates the same output directory/HDF5 + # dataset and the survivors deadlock in the next collective. This file is specifically + # meant to run multi-rank/multi-GPU, so opt back in; a single-GPU run pays only a + # no-op collective for it. + os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") + +import logging + +from struphy import set_logging_level + +set_logging_level(logging.WARNING) + +# ------------------ +# Import Struphy API +# ------------------ + +from struphy import ( + BaseUnits, + BoundaryParameters, + DerhamOptions, + EnvironmentOptions, + LoadingParameters, + SavingParameters, + Simulation, + SortingParameters, + Time, + WeightsParameters, + domains, + equils, + grids, + perturbations, +) + +# --------------------- +# Instance of the model +# --------------------- +from struphy.models import PressureLessSPH + +# Units +base_units = BaseUnits() + +# Model instance +model = PressureLessSPH(base_units=base_units) + +# List all variables and decide whether to save their data +model.cold_fluid.var.save_data = True + +# -------------------------- +# Instance of the simulation +# -------------------------- + +name = f"PressureLessSPH scaling ({args.backend})" + +# Environment options +env = EnvironmentOptions( + sim_folder=f"sim_{args.id:02d}", + profiling_activated=True, + save_restart=False, +) + +# Time stepping. Same dt as the other two scaling cases; enough steps that the +# per-step particle work, not one-off setup, dominates the total. +time_opts = Time(dt=0.01, Tend=args.Tend if args.Tend is not None else 1.0) + +# Geometry -- same unit cube as the other two scaling cases, for comparability. +domain = domains.Cuboid() + +# Fluid equilibrium: PushVinEfield pushes against equil.p0 (see below). +equil = equils.HomogenSlab() + +# Grid -- same resolution as the other two scaling cases. +grid = grids.TensorProductGrid(num_elements=(32, 32, 32)) + +# Derham options +derham_opts = DerhamOptions() + +# Simulation object +sim = Simulation( + model=model, + name=name, + description=description, + params_path=__file__, + env=env, + time_opts=time_opts, + domain=domain, + equil=equil, + grid=grid, + derham_opts=derham_opts, +) + +# ------------------- +# Particle parameters +# ------------------- + +# Smaller default than the other two scaling cases' 50,000,000 -- see the description +# for why (first run of this case at scale). +loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 10_000_000, seed=1234) +weights_params = WeightsParameters() +boundary_params = BoundaryParameters() +sorting_params = SortingParameters() +saving_params = SavingParameters() +model.cold_fluid.set_markers( + loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, +) + +# ------------------ +# Propagator options +# ------------------ + +model.propagators.push_eta.options = model.propagators.push_eta.Options() +phi = equil.p0 +model.propagators.push_v.phi = phi +model.propagators.push_v.options = model.propagators.push_v.Options() + +# ------------------ +# Initial conditions +# ------------------ + +# Background for (some) sph variables -- uniform, no perturbation: this case +# measures scaling behaviour, not physical accuracy (same spirit as the other two +# scaling cases). +background = equils.ConstantVelocity() +model.cold_fluid.var.add_background(background) + +if __name__ == "__main__": + sim.run() diff --git a/profiling/submit_pressurelesssph_cupy_scaling.py b/profiling/submit_pressurelesssph_cupy_scaling.py new file mode 100644 index 000000000..ad999e8b3 --- /dev/null +++ b/profiling/submit_pressurelesssph_cupy_scaling.py @@ -0,0 +1,115 @@ +"""PressureLessSPH CuPy multi-GPU/multi-rank scaling case. + +Third companion to submit_guidingcenter_cupy_scaling.py and +submit_vlasovampere_cupy_scaling.py, using a model close to a pure particle push: no +FEEC field solve at all (see params_PressureLessSPH_scaling.py's docstring for the +full rationale), so this is the low-per-marker-compute end of the three cases -- +GuidingCenter in the middle, VlasovAmpereOneSpecies's real field solve on the high end. + +Same strong-scaling structure as the other two cases: the same total marker count +(`LoadingParameters.Np` is the *total* across ranks) is run with `ARRAY_BACKEND=cupy` +at increasing MPI rank counts, one rank per GPU, on the Booster partition. + +Each rank binds to its own GPU via `SLURM_LOCALID` in +`params_PressureLessSPH_scaling.py` (see the comment there) -- without that, every +rank on a node would default to CuPy's device 0 and contend for the same GPU, which +would make this scaling study meaningless. `SLURM_LOCALID` is a rank's index *within +its node*, so this binding is correct on multi-node runs too without any extra +handling. + +`--ranks 2 4 8` (the default, matching the other two cases' current default) covers +both intra-node scaling (2/4 ranks, on a single Booster node, 4 GPUs/node) and one +inter-node step (8 ranks = 2 nodes x 4 GPUs). `launch()` derives +`num_nodes = ceil(num_tasks / GPUS_PER_NODE)` and requires `num_tasks % num_nodes == 0`, +so rank counts must stay multiples of `GPUS_PER_NODE` once they exceed it (8, 12, 16, ...). +""" + +import argparse +from pathlib import Path + +from clusters import SLURM_PRESETS, detect_machine_name +from profiling_job import ProfilingCase + +# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name +# (`detect_machine_name`), so the dict is keyed by the *detected* name here rather than +# by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" (it +# cannot tell the Booster partition apart), and this case must still get the Booster +# preset. Keying on the detected name also keeps this working, without a KeyError, on a +# machine detection does not recognise (name None). +GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] + +# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are +# spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank +# binding in params_PressureLessSPH_scaling.py. +GPUS_PER_NODE = 4 + + +def main() -> None: + + # Parse arguments, do not remove --upload + parser = argparse.ArgumentParser( + description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), + ) + parser.add_argument( + "--upload", + action="store_true", + help="Upload the packaged profiling results to the profiling-data repo.", + ) + parser.add_argument( + "--ranks", + type=int, + nargs="+", + default=[2, 4, 8], + help="MPI rank counts to run with, one GPU per rank (default: 2 4 8; 8 spans 2 Booster nodes).", + ) + parser.add_argument( + "--Np", + type=int, + default=None, + help="Total marker count, overriding params_PressureLessSPH_scaling.py's default (10,000,000).", + ) + args = parser.parse_args() + + # Paths relative to this script's location, so it can be run from anywhere. + script_dir = Path(__file__).resolve().parent + params_dir = script_dir / "examples" / "PressureLessSPH" + params_source = params_dir / "params_PressureLessSPH_scaling.py" + + profiling_case = ProfilingCase( + label="pressurelesssph_cupy_scaling", + name="PressureLessSPH particles on cube, CuPy multi-GPU scaling", + description="SPH test particles (Np=10,000,000) in a homogeneous cube, run with the CuPy array backend at increasing MPI rank counts (one GPU per rank) -- a companion to guidingcenter_cupy_scaling and vlasovampere_cupy_scaling using a model close to a pure particle push (no FEEC field solve at all), to measure scaling behaviour at the low-per-marker-compute end.", + physics_problem="SPH-discretized pressureless Euler flow with external forcing; a position push plus a velocity push against a background force field, no field solve.", + struphy_model_used="PressureLessSPH", + params_source=params_source, + language="fortran", + compiler="GNU", + upload=args.upload, + ) + + # The preset is looked up by cluster name inside `launch`, so build a one-entry dict + # under whatever name detection reports for this machine. + cluster_name = detect_machine_name() + + param_flags = ["--backend", "cupy"] + if args.Np is not None: + param_flags += ["--Np", str(args.Np)] + + # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would + # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs + # far more ranks per node than there are GPUs. + for num_tasks in args.ranks: + num_nodes = -(-num_tasks // GPUS_PER_NODE) + profiling_case.launch( + num_tasks, + num_nodes=num_nodes, + param_flags=param_flags, + slurm_presets={cluster_name: GPU_PRESET}, + ) + + # Package and push each run as its own job finishes. + profiling_case.finalize_run() + + +if __name__ == "__main__": + main() From d4f164722f2f12ee15889af6aed4bb1d4a9f321a Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 18:05:08 +0200 Subject: [PATCH 090/156] Skip over communication if running on 1 rank --- src/struphy/pic/base.py | 56 ++++++++++++++++++++++++++++++----------- 1 file changed, 41 insertions(+), 15 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index f6e392106..b4415d7f5 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -529,7 +529,6 @@ def __init__( # CuPy). The actual mpi4py Alltoall/Isend/Irecv calls further below # take host buffers and convert explicitly at that point. self._sorting_etas = xp.zeros((self.markers.shape[0], 3), dtype=float) - self._is_on_proc_domain = xp.zeros((self.markers.shape[0], 3), dtype=bool) self._can_stay = xp.zeros(self.markers.shape[0], dtype=bool) self._reqs = [None] * self.mpi_size self._recvbufs = [None] * self.mpi_size @@ -675,7 +674,6 @@ def nbytes_local(self) -> int: nbytes = 0 nbytes += n_rows * n_cols * float_size # markers nbytes += n_rows * 3 * float_size # sorting_etas (mpi_sort_markers buffer) - nbytes += n_rows * 3 * bool_size # is_on_proc_domain nbytes += n_rows * bool_size # can_stay # holes, ghost_particles, valid_mks, is_outside_right, is_outside_left, is_outside nbytes += n_rows * bool_size * 6 @@ -1905,6 +1903,16 @@ def mpi_sort_markers( if apply_bc: self.apply_kinetic_bc() + # With a single MPI rank there is no destination calculation or + # communication to perform. Keep the bookkeeping updates that the + # exchange path performs below (boundary conditions may have created + # holes), but avoid materialising the O(n_markers) sorting masks and + # destination arrays just to discover that every marker stays local. + if self.mpi_size == 1: + self.update_holes() + self._update_ghost_particles() + return + if isinstance(alpha, int) or isinstance(alpha, float): alpha = (alpha, alpha, alpha) @@ -4690,6 +4698,7 @@ def _eval_sph( ### MPI comm for domain decomposition ### + @ProfileManager.profile("_sendrecv_determine_mtbs") def _sendrecv_determine_mtbs( self, alpha: list | tuple | xp.ndarray = (1.0, 1.0, 1.0), @@ -4727,19 +4736,32 @@ def _sendrecv_determine_mtbs( out=self._sorting_etas, ) - # check which particles are on the current process domain - self._is_on_proc_domain = xp.logical_and( - self._sorting_etas > self.domain_array_dev[self.mpi_rank, 0::3], - self._sorting_etas < self.domain_array_dev[self.mpi_rank, 1::3], - ) - - # to stay on the current process, all three columns must be True. Elementwise AND - # of the 3 columns instead of xp.all(..., axis=1): on CuPy, reducing over a size-3 - # trailing axis of a multi-million-row array is a poor fit for the "many small - # reductions" GPU kernel it dispatches to -- measured ~9x slower than 3 plain - # elementwise ANDs for the same (correctness-verified identical) result, and this - # was the single largest cost in mpi_sort_markers (about 30% of the whole call). - self._can_stay = self._is_on_proc_domain[:, 0] & self._is_on_proc_domain[:, 1] & self._is_on_proc_domain[:, 2] + if xp.cupy_backend: + # Keep the established CuPy path unchanged for now; its kernel + # characteristics differ from NumPy's allocation costs. + self._is_on_proc_domain = xp.logical_and( + self._sorting_etas > self.domain_array_dev[self.mpi_rank, 0::3], + self._sorting_etas < self.domain_array_dev[self.mpi_rank, 1::3], + ) + self._can_stay[:] = ( + self._is_on_proc_domain[:, 0] + & self._is_on_proc_domain[:, 1] + & self._is_on_proc_domain[:, 2] + ) + else: + # Build only the one-dimensional result needed by the exchange + # path; retaining a temporary (n_markers, 3) boolean array adds + # another full marker-sized allocation and memory pass on NumPy. + bounds = self.domain_array_dev[self.mpi_rank] + eta = self._sorting_etas + self._can_stay[:] = ( + (eta[:, 0] > bounds[0]) + & (eta[:, 0] < bounds[1]) + & (eta[:, 1] > bounds[3]) + & (eta[:, 1] < bounds[4]) + & (eta[:, 2] > bounds[6]) + & (eta[:, 2] < bounds[7]) + ) # holes and ghosts can stay, too self._can_stay[self.holes] = True @@ -4752,6 +4774,7 @@ def _sendrecv_determine_mtbs( return hole_inds_after_send, send_inds + @ProfileManager.profile("_compute_neighbor_ranks") def _compute_neighbor_ranks(self) -> tuple[list[int], list[int]]: """Split every other rank into geometric neighbours of this rank's sub-domain box and everyone else, for :meth:`_sendrecv_get_destinations`. @@ -4802,6 +4825,7 @@ def _compute_neighbor_ranks(self) -> tuple[list[int], list[int]]: return neighbor_ranks, non_neighbor_ranks + @ProfileManager.profile("_sendrecv_get_destinations") def _sendrecv_get_destinations(self, send_inds): """ Determine to which process particles have to be sent. @@ -4888,6 +4912,7 @@ def _sendrecv_get_destinations(self, send_inds): return send_info + @ProfileManager.profile("_sendrecv_all_to_all") def _sendrecv_all_to_all(self, send_info): """ Distribute info on how many markers will be sent/received to/from each process via all-to-all. @@ -4909,6 +4934,7 @@ def _sendrecv_all_to_all(self, send_info): return recv_info + @ProfileManager.profile("_sendrecv_markers") def _sendrecv_markers(self, recv_info, hole_inds_after_send): """ Use non-blocking communication. In-place modification of markers From bf225c51be0e2b812fca6ceba67407aa875d80e6 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 19:05:13 +0200 Subject: [PATCH 091/156] Some micro optimization --- src/struphy/pic/base.py | 78 +++++++++++++++++++++++------------------ 1 file changed, 44 insertions(+), 34 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index b4415d7f5..d5efc7125 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4729,12 +4729,24 @@ def _sendrecv_determine_mtbs( assert alpha.size == 3 assert xp.all(alpha >= 0.0) and xp.all(alpha <= 1.0) bi = self.first_pusher_idx - xp.mod( - alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) - + (1.0 - alpha) * self.markers[:, bi : bi + 3], - 1.0, - out=self._sorting_etas, - ) + _y = alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + ( + 1.0 - alpha + ) * self.markers[:, bi : bi + 3] + + # y - floor(y), not xp.mod(y, 1.0): mathematically identical for a modulus of 1 + # (verified bit-for-bit equal), but xp.mod dispatches to a true floating-point + # remainder (fmod-like). On NumPy that fmod path is ~15x slower than plain + # floor+subtract (313k x 3: ~15ms vs ~1ms), making this single line the + # dominant cost of the whole function before this fix. On CuPy it is the other + # way around: cp.mod is one fused kernel launch, while floor+subtract is two, + # and kernel-launch overhead dominates at this array size (~0.07ms vs ~0.17ms) + # -- so this is backend-conditional rather than a universal fix. xp.floor + # (unlike xp.subtract/xp.mod) does not accept out= in the array-api-compat + # wrapper, so the NumPy path gets its own temporary for it. + if xp.cupy_backend: + xp.mod(_y, 1.0, out=self._sorting_etas) + else: + xp.subtract(_y, xp.floor(_y), out=self._sorting_etas) if xp.cupy_backend: # Keep the established CuPy path unchanged for now; its kernel @@ -4843,10 +4855,14 @@ def _sendrecv_get_destinations(self, send_inds): # One entry for each process send_info = np.zeros(self.mpi_size, dtype=int) - # Gathered once and reused for every rank below, instead of re-gathering - # self.markers[send_inds] and self._sorting_etas[send_inds] fresh on every - # iteration of the rank loop (as the previous version did). - candidates = self.markers[send_inds] + # etas_to_send is gathered once and reused for every rank below (cheap: 3 + # columns wide). The full marker payload is deliberately NOT gathered into a + # "candidates" intermediate here: that would gather all of send_inds' rows + # (every column) once, only to re-gather a subset of that copy per matched + # rank below -- two gather passes moving comparable total data. Composing the + # index instead (self.markers[send_inds[matched]] per rank, below) does the + # same total row selection in one gather pass per rank instead of two overall + # -- measured ~30% faster for this function on NumPy at Np=1e6/4 ranks. etas_to_send = self._sorting_etas[send_inds] # Reset every rank's send buffer to empty first. The neighbour/non-neighbour @@ -4856,7 +4872,7 @@ def _sendrecv_get_destinations(self, send_inds): # non-empty buffer from a previous call (send/recv size would then disagree # with send_info, which is always correct since it defaults to 0 above). empty_local = xp.empty(0, dtype=int) - empty_rows = candidates[:0] + empty_rows = self._markers[:0] for i in range(self.mpi_size): self._send_to_i[i] = empty_local self._send_list[i] = empty_rows @@ -4869,6 +4885,14 @@ def _sendrecv_get_destinations(self, send_inds): # everyone (a marker moved further than one sub-domain this step), the leftover # few are checked against every other rank in the second pass, so this changes # only how many ranks get checked in the common case, not correctness. + # A batched, per-pass variant of this loop (one broadcasted compare + one + # argsort per pass instead of one xp.nonzero per rank) was tried here to cut + # CuPy device syncs from O(n_group) to O(1) per pass. Measured net loss on + # BOTH backends at mpi_size=4 (NumPy: no sync cost to amortize against the + # extra broadcast memory traffic; CuPy: too few ranks per group -- 3 here -- + # for the eliminated syncs to outweigh the broadcast/argsort/searchsorted + # overhead). Might still win at much larger rank counts (more neighbours per + # group), but not re-added without measuring that regime first. remaining = xp.arange(send_inds.shape[0]) for rank_group in (self._neighbor_ranks, self._non_neighbor_ranks): if remaining.size == 0: @@ -4877,34 +4901,16 @@ def _sendrecv_get_destinations(self, send_inds): etas_remaining = etas_to_send[remaining] still_remaining = xp.ones(remaining.shape[0], dtype=bool) for i in rank_group: - # domain_array_dev, not domain_array: under CuPy this stays a device - # array, avoiding a host round trip on every rank checked here. conds = xp.logical_and( etas_remaining > self.domain_array_dev[i, 0::3], etas_remaining < self.domain_array_dev[i, 1::3], ) - - # Elementwise AND of the 3 columns instead of xp.all(conds, axis=1): - # same fix as _sendrecv_determine_mtbs (measured ~9x faster on CuPy -- - # reducing over a size-3 trailing axis of a multi-million-row array is a - # poor fit for the "many small reductions" GPU kernel it dispatches to). matched_local = xp.nonzero(conds[:, 0] & conds[:, 1] & conds[:, 2])[0] matched = remaining[matched_local] self._send_to_i[i] = matched send_info[i] = matched.size - - # Under CuPy this stays a device array: mpi4py sends it straight off the - # GPU via the CUDA-aware BTL/UCX path instead of a host round trip -- see - # _sendrecv_markers for the matching receive side. Measured on Pitagora's - # Booster nodes: this OpenMPI build supports it (smcuda BTL, compiled - # --with-cuda) and a raw device-to-device Isend/Irecv of a comparable - # payload is ~7x faster than staging through host buffers. In - # mpi_sort_markers itself the effect is smaller, since most of its - # per-call cost is the GPU-side bookkeeping rather than the exchange - # bandwidth -- but it removes 2 blocking device<->host copies per call - # for free and is never slower. - self._send_list[i] = candidates[matched] + self._send_list[i] = self._markers[send_inds[matched]] still_remaining[matched_local] = False @@ -4967,10 +4973,14 @@ def _sendrecv_markers(self, recv_info, hole_inds_after_send): else: send_reqs.append(self.mpi_comm.Isend(data, dest=i, tag=self.mpi_rank)) - # xp.zeros, not np.zeros: under CuPy this is a device buffer, so mpi4py - # receives straight onto the GPU (CUDA-aware BTL/UCX) instead of into host - # memory -- see _sendrecv_get_destinations for the matching send side. - self._recvbufs[i] = xp.zeros((N_recv, self.markers.shape[1]), dtype=float) + # xp.empty, not xp.zeros: Irecv below overwrites the buffer completely + # (exactly N_recv rows, matching what the sender sent -- see the + # send/recv size cross-check in _sendrecv_get_destinations/_sendrecv_ + # all_to_all), so zero-initializing it first is wasted work. Under CuPy + # this is a device buffer, so mpi4py receives straight onto the GPU + # (CUDA-aware BTL/UCX) instead of into host memory -- see + # _sendrecv_get_destinations for the matching send side. + self._recvbufs[i] = xp.empty((N_recv, self.markers.shape[1]), dtype=float) self._reqs[i] = self.mpi_comm.Irecv(self._recvbufs[i], source=i, tag=i) # Block on every receive at once via the MPI library's own wait, instead of a From 4f503dca0cd87e93319c0ef262d43944130bd166 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 19:19:58 +0200 Subject: [PATCH 092/156] Added src/struphy/pic/tests/bench_mpi_sort_markers.py --- .../pic/tests/bench_mpi_sort_markers.py | 220 ++++++++++++++++++ 1 file changed, 220 insertions(+) create mode 100644 src/struphy/pic/tests/bench_mpi_sort_markers.py diff --git a/src/struphy/pic/tests/bench_mpi_sort_markers.py b/src/struphy/pic/tests/bench_mpi_sort_markers.py new file mode 100644 index 000000000..ae7e28596 --- /dev/null +++ b/src/struphy/pic/tests/bench_mpi_sort_markers.py @@ -0,0 +1,220 @@ +"""Standalone MPI benchmark for :meth:`Particles.mpi_sort_markers`. + +The benchmark does not construct or run a Struphy simulation. It creates a +mesh-less :class:`~struphy.pic.particles.Particles6D` instance, redistributes +uniformly placed markers, and measures the complete marker exchange. Marker +IDs are checked after every measured call, so this is useful while changing +the implementation of ``mpi_sort_markers``. + +Run with MPI (one process per rank/GPU), for example:: + + mpiexec -n 4 python src/struphy/pic/tests/bench_mpi_sort_markers.py \ + --sizes 10000,100000,1000000 --repeats 10 + +For the NumPy baseline, set ``ARRAY_BACKEND`` before Python imports +``cunumpy`` (the benchmark does not change the backend at runtime):: + + ARRAY_BACKEND=numpy python src/struphy/pic/tests/bench_mpi_sort_markers.py \ + --sizes 100000,1000000 --repeats 10 + +For CuPy, use instead (one rank per GPU; device binding and the MPI opt-in for the +CuPy backend are handled automatically, see below):: + + ARRAY_BACKEND=cupy mpiexec -n 4 python src/struphy/pic/tests/bench_mpi_sort_markers.py + +The reported time is the maximum wall time over ranks (the useful MPI step +time). GPU streams are synchronized around the timed region. +""" + +import argparse +import os +import time + +import numpy as np + +# cunumpy does not import feectools.ddm.mpi (verified), so this is safe to import +# first and do CUDA-only, MPI-independent setup (device binding, the MPI opt-in env +# var below) before struphy -- which does transitively import feectools.ddm.mpi as +# part of its own __init__ -- gets imported next. +import cunumpy as xp + +# Under CuPy with more than one MPI rank per node, every rank must bind to its own +# GPU -- cunumpy defaults to device 0, so without this every rank on a node would +# contend for the same GPU instead of getting one each (same pattern as the +# profiling/examples/*/params_*_scaling.py cases). SLURM_LOCALID (the rank's index +# within its node) is set by srun before this process starts, so it works without +# MPI being initialized yet. Falls back to device 0 outside SLURM. +if xp.cupy_backend: + xp.set_device(int(os.environ.get("SLURM_LOCALID", 0))) + + # feectools.ddm.mpi disables MPI by default on the CuPy backend; this benchmark + # is specifically meant to exercise the real multi-rank exchange, so opt back in. + # Must be set before struphy (hence feectools.ddm.mpi) is imported below -- + # feectools.ddm.mpi reads this env var once, at its own import time, so setting + # it any later would silently have no effect. + os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") + +# struphy must be imported before feectools.ddm.mpi is imported anywhere else: +# struphy/__init__.py sets MPI4PY_RC_THREAD_LEVEL=funneled and disables hcoll +# *before* mpi4py's MPI_Init_thread runs, which is required to avoid a +# hcoll/Alltoallv segfault on this cluster (see the comment there). struphy itself +# imports feectools.ddm.mpi as part of this same import, so this line satisfies +# both ordering requirements at once. +from struphy import BoundaryParameters, LoadingParameters, SortingParameters +from feectools.ddm.mpi import mpi as MPI + +from struphy.pic.particles import Particles6D + + +def _sync_device(): + if xp.cupy_backend: + import cupy as cp + + cp.cuda.Stream.null.synchronize() + + +def _host(a): + """Convert either backend's array to a NumPy array.""" + if xp.cupy_backend: + import cupy as cp + + return cp.asnumpy(a) + return np.asarray(a) + + +def _id_signature(comm, local_ids): + """Return global count/sum/sum-of-squares using scalar MPI reductions. + + ``feectools`` supplies a singleton ``MockComm`` when the script is run + without ``mpiexec``; unlike mpi4py, its lowercase ``allgather`` does not + return a Python list. Scalar reductions work for both communicators and + avoid any CuPy/NumPy array dispatch in the validation path. + + The sum-of-squares must be accumulated as Python (arbitrary-precision) + ints, not float64: at Np in the millions the sum of squared IDs reaches + ~1e17-1e20, past float64's exact-integer range (2^53 ~= 9e15), so + summing the same values in a different order -- which is exactly what + happens here, since mpi_sort_markers regroups which rank holds which + IDs -- rounds to a different (both inexact) result and produces a + false-positive "lost or duplicated" mismatch despite the exchange being + exact. Python ints have no such limit and integer addition is exactly + associative, so this is order-independent regardless of Np. + """ + ids = _host(local_ids).astype(np.int64, copy=False) + local_sumsq = sum(int(v) * int(v) for v in ids.tolist()) + if comm.Get_size() == 1: + return int(ids.size), int(ids.sum(dtype=np.int64)), local_sumsq + return ( + int(comm.allreduce(int(ids.size), op=MPI.SUM)), + int(comm.allreduce(int(ids.sum(dtype=np.int64)), op=MPI.SUM)), + int(comm.allreduce(local_sumsq, op=MPI.SUM)), + ) + + +def _global_max(comm, value): + return value if comm.Get_size() == 1 else comm.allreduce(value, op=MPI.MAX) + + +def _randomize_positions(particles, seed): + # Keep marker rows/IDs intact; only move valid markers to create traffic. + xp.random.seed(seed) + # ``n_mks_loc`` is a backend scalar under CuPy; shape tuples require a + # native Python integer. + n_local = int(particles.n_mks_loc) + particles.positions = xp.random.random((n_local, 3)) + + +def _check(particles, comm, expected_ids): + got_ids = _id_signature(comm, particles.markers[particles.valid_mks, -1]) + if got_ids != expected_ids: + raise AssertionError("mpi_sort_markers lost or duplicated marker IDs") + + # Check ownership on the host, avoiding a device-to-host synchronization + # inside the timed region. Every real marker must be strictly inside its + # rank's three-dimensional subdomain. + positions = _host(particles.positions) + bounds = np.asarray(particles.domain_array[particles.mpi_rank]).reshape(3, 3) + if positions.size: + inside = np.all((positions > bounds[:, 0]) & (positions < bounds[:, 1]), axis=1) + if not np.all(inside): + raise AssertionError("mpi_sort_markers left markers on the wrong rank") + + +def _one_size(comm, np_global, repeats, seed, check): + loading = LoadingParameters( + Np=np_global, + seed=seed, + moments=(0.0, 0.0, 0.0, 1.0, 1.0, 1.0), + spatial="uniform", + ) + sorting = SortingParameters(boxes_per_dim=None) + boundary = BoundaryParameters(bc=["periodic", "periodic", "periodic"]) + particles = Particles6D( + comm_world=comm, + loading_params=loading, + sorting_params=sorting, + boundary_params=boundary, + ) + # Loading is intentionally unsorted: this gives every rank a global, + # uniform sample that must be exchanged by the first sort. + particles.draw_markers(sort=False) + comm.Barrier() + _sync_device() + + expected_ids = _id_signature(comm, particles.markers[particles.valid_mks, -1]) + + def prepare(i): + _randomize_positions(particles, seed + 1009 * (i + 1) + comm.Get_rank()) + _sync_device() + comm.Barrier() + + # Warm up MPI requests, allocation paths, and CuPy kernels. + prepare(-1) + particles.mpi_sort_markers(apply_bc=False, do_test=False) + _sync_device() + comm.Barrier() + if check: + _check(particles, comm, expected_ids) + + samples = [] + for i in range(repeats): + prepare(i) + _sync_device() + t0 = time.perf_counter() + particles.mpi_sort_markers(apply_bc=False, do_test=False) + _sync_device() + elapsed = time.perf_counter() - t0 + samples.append(_global_max(comm, elapsed)) + if check: + _check(particles, comm, expected_ids) + + return float(np.median(samples)), float(np.percentile(samples, q=95)) + + +def main(): + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--sizes", default="10000,100000,1000000", help="global marker counts to sweep") + parser.add_argument("--repeats", type=int, default=10, help="measured calls per size") + parser.add_argument("--seed", type=int, default=1607) + parser.add_argument("--no-check", action="store_true", help="skip ID/ownership checks after each call") + args = parser.parse_args() + + comm = MPI.COMM_WORLD + sizes = [int(value) for value in args.sizes.split(",") if value] + if args.repeats < 1 or any(size < comm.Get_size() for size in sizes): + raise ValueError("repeats must be positive and every size must be at least the MPI rank count") + + if comm.Get_rank() == 0: + backend = "cupy" if xp.cupy_backend else "numpy" + print(f"mpi_sort_markers benchmark: ranks={comm.Get_size()}, backend={backend}") + print(f"{'Np (global)':>14} {'median [ms]':>14} {'p95 [ms]':>14} {'markers/s':>16}") + + for size in sizes: + median, p95 = _one_size(comm, size, args.repeats, args.seed, not args.no_check) + if comm.Get_rank() == 0: + rate = size / median if median else float("inf") + print(f"{size:>14d} {median * 1e3:>14.3f} {p95 * 1e3:>14.3f} {rate:>16.3e}") + + +if __name__ == "__main__": + main() From ab4309e83904ccc4ff421e7cfefcb2a59a129655 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 20:03:43 +0200 Subject: [PATCH 093/156] Optimization, special case for alpha=1 --- src/struphy/pic/base.py | 51 +++++++++++++++++++++++++++++++---------- 1 file changed, 39 insertions(+), 12 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index d5efc7125..cac09e3f5 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4722,16 +4722,41 @@ def _sendrecv_determine_mtbs( sorting_etas : array[float] Eta-values of shape (n_send, :) according to which the sorting is performed. """ - # position that determines the sorting (including periodic shift of boundary conditions). - # Runs on the backend the markers live on; alpha is a 3-element - # weighting, not physics data, so it is converted to match. - alpha = xp.asarray(alpha, dtype=float) - assert alpha.size == 3 - assert xp.all(alpha >= 0.0) and xp.all(alpha <= 1.0) + # Fast path: alpha == 1 collapses alpha*(A + B) + (1 - alpha)*C to exactly A + B + # (1*x == x and 0*C == 0 for the finite phase-space values C holds in practice, + # so this is not an approximation -- verified bit-for-bit identical to the + # general formula below). Checked in plain Python against the raw, unconverted + # argument -- most callers pass the default (a Python tuple/float), so this + # costs nothing when it doesn't apply and never touches a device array or + # forces a sync just to decide. Only an xp.ndarray alpha (the dynamic, + # per-kernel case in pusher.py) skips the check and always takes the general + # path below, since its value isn't known without a sync anyway. + alpha_is_one = ( + (isinstance(alpha, (int, float)) and alpha == 1.0) + or ( + not isinstance(alpha, xp.ndarray) + and hasattr(alpha, "__iter__") + and all(a == 1.0 for a in alpha) + ) + ) bi = self.first_pusher_idx - _y = alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + ( - 1.0 - alpha - ) * self.markers[:, bi : bi + 3] + if alpha_is_one: + # Measured ~2.2x faster than the general formula below on CuPy at + # Np_local=12.5M (2.9ms -> 1.3ms): the general path is 2 strided column + # reads, an add, 2 multiplies and a second add -- several separate kernel + # launches over non-contiguous (strided) column slices. This fast path + # keeps only the one unavoidable add. + _y = self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3] + else: + # position that determines the sorting (including periodic shift of + # boundary conditions). Runs on the backend the markers live on; alpha is + # a 3-element weighting, not physics data, so it is converted to match. + alpha = xp.asarray(alpha, dtype=float) + assert alpha.size == 3 + assert xp.all(alpha >= 0.0) and xp.all(alpha <= 1.0) + _y = alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + ( + 1.0 - alpha + ) * self.markers[:, bi : bi + 3] # y - floor(y), not xp.mod(y, 1.0): mathematically identical for a modulus of 1 # (verified bit-for-bit equal), but xp.mod dispatches to a true floating-point @@ -4775,9 +4800,11 @@ def _sendrecv_determine_mtbs( & (eta[:, 2] < bounds[7]) ) - # holes and ghosts can stay, too - self._can_stay[self.holes] = True - self._can_stay[self.ghost_particles] = True + # holes and ghosts can stay, too. One merged boolean-mask assignment instead + # of two separate ones -- measured ~23% faster on CuPy at Np_local=12.5M + # (0.40ms -> 0.30ms): setting the same rows to True twice costs a second + # kernel launch + mask read for no benefit, since "True" is idempotent. + self._can_stay[self.holes | self.ghost_particles] = True # True values can stay on the process, False must be sent, already empty rows (-1) cannot be sent send_inds = xp.nonzero(~self._can_stay)[0] From 74808a1f8b15fe7ee11ddab349a6b1aba9067d3e Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 20:26:02 +0200 Subject: [PATCH 094/156] Reusing of not_hole_or_ghost --- src/struphy/pic/base.py | 27 ++++++++++++++++++++++++--- 1 file changed, 24 insertions(+), 3 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index cac09e3f5..b60541f91 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1994,6 +1994,14 @@ def apply_kinetic_bc(self, newton=False): if self._periodic_axes: self._eta_bc_buf[:] = self.markers[:, :3] + # Computed once and reused for every axis below, instead of recomputing + # inside _find_outside_particles on each iteration: nothing in this loop + # body creates a hole or a ghost particle (it only wraps positions and + # updates the shift buffer), so the holes/ghost_particles masks -- and + # therefore this derived mask -- cannot change between axes. Measured + # ~9.5% faster on CuPy for the 3-axis periodic loop at Np_local=12.5M. + periodic_not_hole_or_ghost = ~(self.holes | self.ghost_particles) + for axis in self._periodic_axes: # Reverted from a branchless/xp.where + unconditional-elementwise version: # measured on a real 50M-marker GPU run, that version was a net loss -- on @@ -2005,7 +2013,11 @@ def apply_kinetic_bc(self, newton=False): # nonzero sync the indices cost. Avoiding a device sync is not free if the # alternative is doing O(n_rows) dense work every call instead of O(outside # markers) sparse work most calls skip entirely. - outside_inds = self._find_outside_particles(axis, eta=self._eta_bc_buf) + outside_inds = self._find_outside_particles( + axis, + eta=self._eta_bc_buf, + not_hole_or_ghost=periodic_not_hole_or_ghost, + ) if len(outside_inds) == 0: continue @@ -3106,7 +3118,7 @@ def _reset_marker_ids(self): )[self.mpi_rank] self.marker_ids = first_marker_id + np.arange(self.n_mks_loc, dtype=int) - def _find_outside_particles(self, axis, eta=None): + def _find_outside_particles(self, axis, eta=None, not_hole_or_ghost=None): """Find markers whose ``axis``-th logical coordinate lies outside ``[0, 1]`` (holes and ghost particles are excluded), updating :attr:`_is_outside_left`/:attr:`_is_outside_right`/:attr:`_is_outside` accordingly. @@ -3122,6 +3134,14 @@ def _find_outside_particles(self, axis, eta=None): correct but slower, since a single-column slice of the row-major ``markers`` array is strided (see the comment in :meth:`apply_kinetic_bc`). + not_hole_or_ghost : xp.ndarray[bool], optional + Pre-computed ``~(self.holes | self.ghost_particles)``, for callers that loop + over several axes without any hole/ghost-changing operation in between (e.g. + the periodic-axis loop in :meth:`apply_kinetic_bc`, which only wraps + positions) -- recomputing this per axis is redundant there. If ``None``, + computed fresh (correct default for callers that may create holes/ghosts + between axes, e.g. the remove-axis loop). + Returns ------- outside_inds : numpy.ndarray[int] @@ -3132,7 +3152,8 @@ def _find_outside_particles(self, axis, eta=None): # _is_outside_* views are all allocated with xp (see # _allocate_marker_array). col = self.markers[:, axis] if eta is None else eta[:, axis] - not_hole_or_ghost = ~(self.holes | self.ghost_particles) + if not_hole_or_ghost is None: + not_hole_or_ghost = ~(self.holes | self.ghost_particles) xp.greater(col, 1.0, out=self._is_outside_right) self._is_outside_right &= not_hole_or_ghost From d210b9aaca834d6adb7e0dd01a56d7a515988d42 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 20:30:16 +0200 Subject: [PATCH 095/156] Update scope-profiler --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index d6fb97e72..2950a065f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,7 @@ dependencies = [ "pytest-testmon<=2.2.0", "ruff==0.15.0, <=0.16.0", "line_profiler<=5.0.2", - "scope-profiler<=0.2.8", + "scope-profiler<=0.3.1", ] [project.license] From 879e6959af555c016856469bd1e1e5d3c4d29765 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 22:04:40 +0200 Subject: [PATCH 096/156] Updated update_holes(self, update_valid_mks: bool = True) --- src/struphy/pic/base.py | 47 ++++++++++++++++++++++++++++------------- 1 file changed, 32 insertions(+), 15 deletions(-) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index b60541f91..0059417b5 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -1360,7 +1360,7 @@ def draw_markers( self._markers[n_mks_load_loc:] = -1.0 # number of holes and markers on process - self.update_holes() + self.update_holes(update_valid_mks=False) self._update_ghost_particles() # cumulative sum of number of markers on each process at loading stage. @@ -1909,7 +1909,7 @@ def mpi_sort_markers( # holes), but avoid materialising the O(n_markers) sorting masks and # destination arrays just to discover that every marker stays local. if self.mpi_size == 1: - self.update_holes() + self.update_holes(update_valid_mks=False) self._update_ghost_particles() return @@ -1931,12 +1931,11 @@ def mpi_sort_markers( # send and receive markers self._sendrecv_markers(recv_info, hole_inds_after_send) - # new holes and new number of holes and markers on process - self.update_holes() - - # refresh ghost mask: received markers may land in rows that previously held - # ghost particles. update_holes alone recomputes valid_mks from a stale - # _ghost_particles mask, which would wrongly exclude these incoming real markers. + # new holes and new number of holes and markers on process. update_valid_mks + # deferred to _update_ghost_particles below (see its parameter docstring) -- + # this alone recomputing valid_mks would use a stale _ghost_particles mask + # anyway (received markers may land in rows that previously held ghosts). + self.update_holes(update_valid_mks=False) self._update_ghost_particles() # check if all markers are on the right process after sorting @@ -2091,13 +2090,24 @@ def apply_kinetic_bc(self, newton=False): axis, ) - def update_holes(self): + def update_holes(self, update_valid_mks: bool = True): """Recompute the :attr:`~struphy.pic.base.Particles.holes` mask (rows with ``markers[:, 0] == -1``) and, from it, refresh :attr:`~struphy.pic.base.Particles.valid_mks`. Must be called after any operation that creates, removes or moves markers - (e.g. sorting, boundary conditions, refilling), since holes are tracked per row index.""" + (e.g. sorting, boundary conditions, refilling), since holes are tracked per row index. + + Parameters + ---------- + update_valid_mks : bool + Set to False when the caller is about to call :meth:`_update_ghost_particles` + immediately afterwards (which also refreshes ``valid_mks``, from fresh + holes *and* fresh ghosts) -- refreshing it here first would just be + overwritten a moment later with the ghost mask still stale, a full-array + pass wasted on every call to a very hot path (called every substep from + :meth:`mpi_sort_markers`).""" self._holes[:] = self.markers[:, 0] == -1.0 - self._update_valid_mks() + if update_valid_mks: + self._update_valid_mks() def set_velocities_comp(self, velocity, comp): """Set one or several velocity components to the same constant value, for all valid markers. @@ -2203,9 +2213,11 @@ def do_sort(self, use_numpy_argsort=None): # The marker rows have just been reordered. The masks are row-based, # so they must be rebuilt before any later use of valid_mks/f_coords. - self.update_holes() + # _update_ghost_particles already refreshes valid_mks from fresh holes and + # fresh ghosts -- update_holes redoing it first, then a third explicit call + # redoing it again right after, were both pure repeats of the same result. + self.update_holes(update_valid_mks=False) self._update_ghost_particles() - self._update_valid_mks() def eval_density( self, @@ -4515,12 +4527,17 @@ def _communicate_boxes(self): self._prepare_ghost_particles() self._get_destinations_box() self._self_communication_boxes() - self.update_holes() + # Both update_holes calls below defer valid_mks to the final + # _update_ghost_particles call, which is always the last of these to run + # (whichever update_holes call precedes it) and refreshes it from fresh + # holes and fresh ghosts in one pass, instead of every intermediate call + # redoing the same full-array work with a ghost mask that's about to change. + self.update_holes(update_valid_mks=False) if self.mpi_comm is not None: self._Barrier() self._sendrecv_all_to_all_boxes() self._sendrecv_markers_boxes() - self.update_holes() + self.update_holes(update_valid_mks=False) self._update_ghost_particles() # if verbose: From bd0de01fe01172486b66ca6bd9f0ea2d5a611028 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 17 Aug 2026 22:45:28 +0200 Subject: [PATCH 097/156] Use xp.to_numpy instead of .get() manually --- feectools | 2 +- src/struphy/feec/psydac_derham.py | 23 ++++++++++++++------ src/struphy/geometry/base.py | 13 +++++++---- src/struphy/io/output_handling.py | 10 ++------- src/struphy/pic/base.py | 15 ++++++++----- src/struphy/pic/tests/test_accum_vec_H1.py | 3 +-- src/struphy/pic/tests/test_mat_vec_filler.py | 3 ++- src/struphy/simulation/sim.py | 7 +++--- 8 files changed, 45 insertions(+), 31 deletions(-) diff --git a/feectools b/feectools index 920695172..01e9f1da2 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 920695172104fa2ce225063ce9b9d1d73e14a839 +Subproject commit 01e9f1da28557f7afe1a9485e9c098fc8a33c8c9 diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index fea11675c..2e3378d8e 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -72,11 +72,16 @@ def _to_numpy_for_kernel(value): - """Convert CuPy arrays to NumPy for compiled kernel calls.""" - if hasattr(value, "get"): - # This is a CuPy array - return value.get() - return value + """Convert CuPy arrays to NumPy for compiled kernel calls. + + xp.is_gpu, not xp.to_numpy: some callers pass plain Python scalars (e.g. a + degree or index) that must reach the compiled kernel unchanged, and + xp.to_numpy would wrap those into 0-d NumPy arrays via np.asarray -- a type + the kernel signature does not expect. xp.is_gpu leaves anything that isn't + actually a CuPy array untouched, matching the original hasattr(value, "get") + passthrough behaviour exactly. + """ + return value.get() if xp.is_gpu(value) else value class DiscreteDerham: @@ -3478,7 +3483,7 @@ def get_pts_and_wts(space_1d, start, end, n_quad=None, polar_shift=False): # make sure that greville points used for interpolation are in [0, 1] # Use numpy for comparison since greville points are NumPy arrays - greville_loc_np = greville_loc.get() if hasattr(greville_loc, "get") else greville_loc + greville_loc_np = xp.to_numpy(greville_loc) assert np.all(np.logical_and(greville_loc_np >= 0.0, greville_loc_np <= 1.0)) # interpolation @@ -3542,7 +3547,11 @@ def get_pts_and_wts(space_1d, start, end, n_quad=None, polar_shift=False): pts_loc, wts_loc = np.polynomial.legendre.leggauss(n_quad) - if "cupy" in xp.__name__: + # xp.cupy_backend, not "cupy" in xp.__name__: xp is `cunumpy` here, so + # xp.__name__ is always the literal string "cunumpy" -- which does not + # contain "cupy" as a substring -- so that check was dead code, never + # true even when the active backend actually is CuPy. + if xp.cupy_backend: import cupy as cp pts_loc = cp.array(pts_loc) diff --git a/src/struphy/geometry/base.py b/src/struphy/geometry/base.py index b8a15e29b..6011fa1a1 100644 --- a/src/struphy/geometry/base.py +++ b/src/struphy/geometry/base.py @@ -24,10 +24,15 @@ def _to_numpy_for_kernel(value): - """Convert CuPy arrays to NumPy for passing to compiled kernels.""" - if hasattr(value, "get"): # CuPy array - return value.get() - return value + """Convert CuPy arrays to NumPy for passing to compiled kernels. + + xp.is_gpu, not xp.to_numpy: some callers pass plain Python scalars that must + reach the compiled kernel unchanged, and xp.to_numpy would wrap those into + 0-d NumPy arrays via np.asarray -- a type the kernel signature does not + expect. xp.is_gpu leaves anything that isn't actually a CuPy array + untouched, matching the original hasattr(value, "get") passthrough exactly. + """ + return value.get() if xp.is_gpu(value) else value class DomainMeta(ABCMeta): diff --git a/src/struphy/io/output_handling.py b/src/struphy/io/output_handling.py index d906ad100..f9151309d 100644 --- a/src/struphy/io/output_handling.py +++ b/src/struphy/io/output_handling.py @@ -3,6 +3,7 @@ import os import h5py +import cunumpy as xp import numpy as np logger = logging.getLogger("struphy") @@ -79,14 +80,7 @@ def dset_dict(self): @staticmethod def _as_numpy_array(val): """Return a NumPy view/copy suitable for h5py writes.""" - if isinstance(val, np.ndarray): - return val - - get = getattr(val, "get", None) - if callable(get) and "cupy" in val.__class__.__module__: - return get() - - return np.asarray(val) + return xp.to_numpy(val) def add_data(self, data_dict): """ diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 0059417b5..ccaeef474 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -100,11 +100,16 @@ def _array_types(): def _to_numpy_for_kernel(value): - """Convert CuPy arrays to NumPy for compiled kernel calls.""" - if hasattr(value, "get"): - # This is a CuPy array - return value.get() - return value + """Convert CuPy arrays to NumPy for compiled kernel calls. + + xp.is_gpu, not xp.to_numpy: some callers pass plain Python scalars (e.g. + self.Np, self.vdim) that must reach MarkerArguments unchanged, and + xp.to_numpy would wrap those into 0-d NumPy arrays via np.asarray -- not + the plain int MarkerArguments' typed fields expect. xp.is_gpu leaves + anything that isn't actually a CuPy array untouched, matching the original + hasattr(value, "get") passthrough behaviour exactly. + """ + return value.get() if xp.is_gpu(value) else value def _dev(*arrays): diff --git a/src/struphy/pic/tests/test_accum_vec_H1.py b/src/struphy/pic/tests/test_accum_vec_H1.py index 7aa682a50..da4de9a84 100644 --- a/src/struphy/pic/tests/test_accum_vec_H1.py +++ b/src/struphy/pic/tests/test_accum_vec_H1.py @@ -473,8 +473,7 @@ def u_xyz(x, y, z): # of this file) follows the active array backend; convert both ways here. eta = particles.positions n_vals = n_xyz(xp.asarray(eta[:, 0]), xp.asarray(eta[:, 1]), xp.asarray(eta[:, 2])) - if hasattr(n_vals, "get"): - n_vals = n_vals.get() + n_vals = xp.to_numpy(n_vals) particles.markers[particles.valid_mks, particles.first_free_idx] = n_vals # ------------------------------------------------------------------ # diff --git a/src/struphy/pic/tests/test_mat_vec_filler.py b/src/struphy/pic/tests/test_mat_vec_filler.py index 5692a20cc..3f61a5a37 100644 --- a/src/struphy/pic/tests/test_mat_vec_filler.py +++ b/src/struphy/pic/tests/test_mat_vec_filler.py @@ -1,5 +1,6 @@ import logging +import cunumpy as xp import numpy as np import pytest @@ -76,7 +77,7 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): # host-only scenario, so _data is brought to the host right after # construction. def _host(a): - return a.get() if hasattr(a, "get") else a + return xp.to_numpy(a) # _data of StencilMatrices/Vectors mat = {} diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index 82707e087..d12ab1c75 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -77,9 +77,10 @@ class CuPyJSONEncoder(json.JSONEncoder): """JSON encoder that handles CuPy arrays and NumPy arrays.""" def default(self, obj): - # Check if it has a .get() method (CuPy array) - if hasattr(obj, "get"): - return obj.get().tolist() if hasattr(obj.get(), "tolist") else obj.get() + if xp.is_gpu(obj): + # xp.to_numpy(obj) is always a NumPy array (CuPy's .get()), which always + # has .tolist(), so no further hasattr check is needed here. + return xp.to_numpy(obj).tolist() # Handle NumPy arrays and scalars import numpy as np From 0b6eb6f7c113ce2691383ef551b94fc1ade6a615 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 18 Aug 2026 13:08:25 +0200 Subject: [PATCH 098/156] Use numpy for small arrays --- src/struphy/fields_background/equils.py | 43 +++++++++++++++++++------ src/struphy/pic/accumulation/filter.py | 32 ++++++++++++------ src/struphy/pic/sobol_seq.py | 12 ++++++- 3 files changed, 66 insertions(+), 21 deletions(-) diff --git a/src/struphy/fields_background/equils.py b/src/struphy/fields_background/equils.py index e1b4bf3b1..d532b067c 100644 --- a/src/struphy/fields_background/equils.py +++ b/src/struphy/fields_background/equils.py @@ -3,6 +3,7 @@ import copy import importlib.util import logging +import math import os import sys import warnings @@ -10,6 +11,7 @@ from typing import TYPE_CHECKING import cunumpy as xp +import numpy as np from line_profiler import profile from scipy.integrate import odeint, quad from scipy.interpolate import RectBivariateSpline, UnivariateSpline @@ -978,12 +980,20 @@ def __init__( self._p_i = None elif self.params["q_kind"] == 1 or self.params["q_kind"] == 2: - r_i = xp.linspace(0.0, self.params["a"], self.params["psi_nel"] + 1) + # Built entirely with plain NumPy/math, never xp: this is a ~200-point, + # one-off host-side interpolation setup feeding scipy.integrate.quad and + # scipy.interpolate.UnivariateSpline, both host-only. Under the CuPy backend + # xp.sqrt() on a plain Python float returns a 0-d CuPy array rather than a + # float (see the comment in psi_r()), which quad's integrand cannot return + # and UnivariateSpline cannot accept -- using np/math here instead of xp + # sidesteps that entirely, at no cost since this never runs on the device + # regardless of backend. + r_i = np.linspace(0.0, self.params["a"], self.params["psi_nel"] + 1) def dpsi_dr(r): - return self.params["B0"] * r / (self.q_r(r) * xp.sqrt(1 - r**2 / self.params["R0"] ** 2)) + return self.params["B0"] * r / (self.q_r(r) * math.sqrt(1 - r**2 / self.params["R0"] ** 2)) - psis = xp.zeros_like(r_i) + psis = np.zeros_like(r_i) for i, rr in enumerate(r_i): psis[i] = quad(dpsi_dr, 0.0, rr)[0] @@ -1006,7 +1016,7 @@ def dp_dr(r): * (2 * self.q_r(r) - r * self.q_r(r, der=1)) ) - ps = xp.zeros_like(r_i) + ps = np.zeros_like(r_i) for i, rr in enumerate(r_i): ps[i] = quad(dp_dr, 0.0, rr)[0] @@ -1083,12 +1093,21 @@ def psi_r(self, r, der=0): # alternative profile (interpolated) elif self.params["q_kind"] == 1 or self.params["q_kind"] == 2: - out = self._psi_i(r, nu=der) + # UnivariateSpline is a host-only scipy object: a plain float still works + # directly, but under the CuPy backend even a "scalar" r may already be a + # 0-d CuPy array (e.g. from xp.sqrt() on a Python float in psi()), and a + # genuine array r may live on the device -- both need an explicit host copy + # first (scipy raises on an implicit CuPy->NumPy conversion), converted back + # afterwards so device callers still get a device array back. + was_gpu = xp.is_gpu(r) + r_np = r if isinstance(r, (int, float)) else xp.to_numpy(r) + out = self._psi_i(r_np, nu=der) # remove all "dimensions" for point-wise evaluation - if isinstance(r, (int, float)): - assert out.ndim == 0 + if isinstance(r, (int, float)) or (hasattr(r, "ndim") and r.ndim == 0): out = out.item() + elif was_gpu: + out = xp.asarray(out) return out @@ -1212,12 +1231,16 @@ def p_r(self, r): # alternative profile elif self.params["q_kind"] == 1: - pout = self._p_i(r) + # see the matching comment in psi_r() for why r needs a host copy here. + was_gpu = xp.is_gpu(r) + r_np = r if isinstance(r, (int, float)) else xp.to_numpy(r) + pout = self._p_i(r_np) # remove all "dimensions" for point-wise evaluation - if isinstance(r, (int, float)): - assert pout.ndim == 0 + if isinstance(r, (int, float)) or (hasattr(r, "ndim") and r.ndim == 0): pout = pout.item() + elif was_gpu: + pout = xp.asarray(pout) # ad-hoc profile elif self.params["p_kind"] == 1: diff --git a/src/struphy/pic/accumulation/filter.py b/src/struphy/pic/accumulation/filter.py index 82430abd7..2c8b0dd67 100644 --- a/src/struphy/pic/accumulation/filter.py +++ b/src/struphy/pic/accumulation/filter.py @@ -1,6 +1,7 @@ from dataclasses import dataclass import cunumpy as xp +import numpy as np from scipy.fft import irfft, rfft from struphy.feec.psydac_derham import Derham @@ -149,24 +150,33 @@ def _apply_toroidal_fourier_filter(self, vec, modes: tuple[int, ...]): Mode numbers which are not filtered out. """ + # Host-only throughout: this is a per-(i,j)-line loop over scipy.fft calls (no + # batched-2D API used here), never vectorized even under NumPy, so `modes`/`pn`/ + # `ir` are plain NumPy/Python (used only as Python loop bounds and fancy-index + # arrays -- under the CuPy backend `xp.empty(...)` would make `ir` a CuPy array, + # and `range(ir[0])` on a device scalar raises TypeError). tor_num_elements = self.derham.num_elements[2] - modes = xp.asarray(modes, dtype=int) + modes = np.asarray(modes, dtype=int) - assert tor_num_elements >= 2 * int(xp.max(modes)), "num_elements[2] must be at least 2*max(modes)" + assert tor_num_elements >= 2 * int(modes.max()), "num_elements[2] must be at least 2*max(modes)" assert self.derham.domain_decomposition.nprocs[2] == 1, "No domain decomposition along toroidal direction" - pn = xp.asarray(self.derham.degree, dtype=int) - ir = xp.empty(3, dtype=int) + pn = np.asarray(self.derham.degree, dtype=int) # rfft output length if (tor_num_elements % 2) == 0: - vec_temp = xp.zeros(int(tor_num_elements / 2) + 1, dtype=complex) + vec_temp = np.zeros(int(tor_num_elements / 2) + 1, dtype=complex) else: - vec_temp = xp.zeros(int((tor_num_elements - 1) / 2) + 1, dtype=complex) + vec_temp = np.zeros(int((tor_num_elements - 1) / 2) + 1, dtype=complex) for _, comp, starts, ends in self._yield_dir_components(vec): - for i in range(3): - ir[i] = int(ends[i] + 1 - starts[i]) + ir = [int(ends[i] + 1 - starts[i]) for i in range(3)] + + # Under the CuPy backend, comp._data lives on the device; pulled to host once + # per component and pushed back once, rather than paying a device round-trip + # on every one of the ir[0]*ir[1] lines below (also correct on the NumPy + # backend, where to_numpy/asarray are no-ops). + data = xp.to_numpy(comp._data) # filter along toroidal index (k direction) for i in range(ir[0]): @@ -175,11 +185,13 @@ def _apply_toroidal_fourier_filter(self, vec, modes: tuple[int, ...]): jj = pn[1] + j # forward FFT along toroidal line - line = rfft(comp._data[ii, jj, pn[2] : pn[2] + ir[2]]) + line = rfft(data[ii, jj, pn[2] : pn[2] + ir[2]]) vec_temp[:] = 0 vec_temp[modes] = line[modes] # keep selected modes only # inverse FFT back to real space, write in-place - comp._data[ii, jj, pn[2] : pn[2] + ir[2]] = irfft(vec_temp, n=tor_num_elements) + data[ii, jj, pn[2] : pn[2] + ir[2]] = irfft(vec_temp, n=tor_num_elements) + + comp._data[...] = xp.asarray(data) comp.update_ghost_regions() diff --git a/src/struphy/pic/sobol_seq.py b/src/struphy/pic/sobol_seq.py index e24193949..dc607ff01 100644 --- a/src/struphy/pic/sobol_seq.py +++ b/src/struphy/pic/sobol_seq.py @@ -19,7 +19,17 @@ import logging -import cunumpy as xp +# Deliberately plain NumPy, not cunumpy: this Sobol-sequence generator is a +# direct port of a scalar Fortran/MATLAB algorithm (bit shifts, per-element +# int()/bitwise_xor in Python loops, see i4_sobol below) with no vectorized +# hot loop to move to the device -- forcing it onto the CuPy backend only adds +# per-scalar host<->device sync overhead and, worse, breaks outright wherever +# a plain Python int/float is threaded through (xp.transpose on a list, +# xp.bitwise_xor expecting device arrays, etc.). The small (n, dim_num) array +# it produces is consumed via boolean/fancy-index assignment into the marker +# array (see Particles.phasespace_coords's setter in pic/base.py), which +# accepts a NumPy source even when the destination is a CuPy array. +import numpy as xp from scipy.stats import norm logger = logging.getLogger("struphy") From 162873934cb969cd9983e0e49897b3acbad55bd0 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 18 Aug 2026 13:56:48 +0200 Subject: [PATCH 099/156] Added DirectSolver(InverseLinearOperator) --- feectools | 2 +- .../submit_guidingcenter_numpy_vs_cupy.py | 2 +- src/struphy/feec/linear_operators.py | 35 +++++++- src/struphy/feec/mass.py | 80 +++++++++++++++++-- src/struphy/io/options.py | 2 +- 5 files changed, 106 insertions(+), 15 deletions(-) diff --git a/feectools b/feectools index 01e9f1da2..9a8807ee9 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 01e9f1da28557f7afe1a9485e9c098fc8a33c8c9 +Subproject commit 9a8807ee97cfba3ece24ddc5a877a3550a497b02 diff --git a/profiling/submit_guidingcenter_numpy_vs_cupy.py b/profiling/submit_guidingcenter_numpy_vs_cupy.py index 9ad382e51..db25523fd 100644 --- a/profiling/submit_guidingcenter_numpy_vs_cupy.py +++ b/profiling/submit_guidingcenter_numpy_vs_cupy.py @@ -29,7 +29,7 @@ # GPU run must still get the Booster preset. Keying on the detected name also keeps this # working, without a KeyError, on a machine detection does not recognise (name None). CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] -GPU_PRESET = SLURM_PRESETS["pitagora_booster"] +GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] BACKEND_PRESETS = { "numpy": CPU_PRESET, diff --git a/src/struphy/feec/linear_operators.py b/src/struphy/feec/linear_operators.py index 940bcd4d5..ca9908686 100644 --- a/src/struphy/feec/linear_operators.py +++ b/src/struphy/feec/linear_operators.py @@ -2,6 +2,7 @@ from abc import abstractmethod import cunumpy as xp +import numpy as np from feectools.ddm.mpi import MockComm from feectools.ddm.mpi import mpi as MPI from feectools.linalg.basic import LinearOperator, Vector, VectorSpace @@ -446,13 +447,39 @@ def codomain(self): def dtype(self): return self._dtype - @property def tosparse(self): - raise NotImplementedError() + """Convert to a sparse (diagonal) matrix. + + `dot()` is exactly a copy followed by an elementwise zero-mask + (`apply_essential_bc_to_array`), so the operator is diagonal with 0/1 entries -- + applying it once to an all-ones vector directly gives that diagonal, far cheaper + than a generic basis-vector sweep (see AverageOperator.tosparse for that + approach, used where the operator isn't diagonal). Serial (single MPI rank) + only, meant for feectools.linalg.solvers.DirectSolver's factor-once use (see its + docstring). + """ + + def _stencil_diag_flat(v): + idx = tuple(slice(m * p, -m * p) if p != 0 else slice(0, None) for p, m in zip(v.pads, v.space.shifts)) + return xp.to_numpy(v._data[idx]).reshape(-1) + + ones = self.domain.zeros() + if isinstance(self._domain, StencilVectorSpace): + ones._data[:] = 1.0 + else: + for block in ones.blocks: + block._data[:] = 1.0 + diag_vec = self.dot(ones) + + if isinstance(self._domain, StencilVectorSpace): + diag_flat = _stencil_diag_flat(diag_vec) + else: + diag_flat = np.concatenate([_stencil_diag_flat(block) for block in diag_vec.blocks]) + + return sparse.diags(diag_flat, format="csr") - @property def toarray(self): - raise NotImplementedError() + return self.tosparse().toarray() @property def bc(self): diff --git a/src/struphy/feec/mass.py b/src/struphy/feec/mass.py index 14a04381f..5e2e807e8 100644 --- a/src/struphy/feec/mass.py +++ b/src/struphy/feec/mass.py @@ -4,6 +4,7 @@ from typing import Callable import cunumpy as xp +import numpy as np from cunumpy import PyccelKernel from feectools.api.settings import PSYDAC_BACKEND_GPYCCEL from feectools.ddm.mpi import MockComm @@ -3214,6 +3215,22 @@ def __call__( return self.solve(self.get_dofs(fun, dofs=dofs, apply_bc=apply_bc), out=out) +def _einsum_out(subscripts, *operands, out): + """``xp.einsum(subscripts, *operands, out=out)``, working on both backends. + + CuPy's ``einsum`` (unlike NumPy's) does not accept an ``out=`` keyword at all -- it + raises ``TypeError`` rather than silently ignoring it -- so under the CuPy backend + this falls back to an unfused call plus an explicit copy into ``out``. NumPy keeps + the single fused call, which avoids the extra allocation/copy that the CuPy fallback + cannot. + """ + if xp.cupy_backend: + out[...] = xp.einsum(subscripts, *operands) + else: + xp.einsum(subscripts, *operands, out=out) + return out + + class AverageOperator(LinOpWithTransp): r""" Class for quadrature operators, performs the average of a `FeecVariable` along a given direction. @@ -3336,7 +3353,13 @@ def allocate(self): i_begin += self.derham.degree[self._directions[0]] i_end += self.derham.degree[self._directions[0]] # General formula for any distribution of knots for the integral of a B-spline function, thus works with periodic and clamped boundary conditions : - self._weights[:] = (knots[i_begin + degree + 1 : i_end + degree + 1] - knots[i_begin:i_end]) / (degree + 1) + # `knots` (derham.args_derham's host knot vector) is always NumPy, while + # `self._weights` may be a CuPy array under the CuPy backend -- a full-slice + # assignment (unlike a boolean/fancy-indexed one) does not auto-convert a NumPy + # source, so it is wrapped explicitly. + self._weights[:] = xp.asarray( + (knots[i_begin + degree + 1 : i_end + degree + 1] - knots[i_begin:i_end]) / (degree + 1) + ) @property def domain(self): @@ -3361,13 +3384,54 @@ def nquads(self): else: return self._nquads - @property def tosparse(self): - raise NotImplementedError() + """Convert to a sparse matrix, built directly from the closed-form averaging + formula rather than a basis-vector sweep. + + `dot()`'s math (see the class docstring) is + ``out[..., i_d0, ...] = sum_o weights[o] * x[..., o, ...]`` for every index along + the averaged axis `d0` (`self._directions[0]`) -- i.e. output row + `(i0, i1, i2)` (any value at index `d0`) depends only on the `n_d0` input columns + that share its other two indices, with coefficient `weights[o]`. That is fully + vectorizable with `numpy`, unlike a basis-vector sweep (one `dot()` call per + domain DOF, i.e. per grid point): that would mean thousands of individual CuPy + kernel launches with a device sync each under the CuPy backend -- exactly the + per-iteration launch/sync overhead + feectools.linalg.solvers.DirectSolver exists to eliminate from the *solve* path, + reappearing in its one-time *setup* path instead. This builds the sparse matrix + entirely on the host regardless of backend (`self._weights` is the only device + array involved, and is tiny -- one value per grid point along `d0`). + + Serial (single MPI rank) only, like `DirectSolver` itself. + """ + from scipy.sparse import coo_matrix + + e = self.domain.zeros() + idx = tuple(slice(m * p, -m * p) if p != 0 else slice(0, None) for p, m in zip(e.pads, e.space.shifts)) + shape = e._data[idx].shape + d0 = self._directions[0] + weights_np = xp.to_numpy(self._weights) + assert weights_np.shape[0] == shape[d0] + + grids = np.meshgrid(*[np.arange(s) for s in shape], indexing="ij") + row = np.ravel_multi_index(grids, shape).reshape(-1) + + rows, cols, data = [], [], [] + for o in range(shape[d0]): + col_idx = [g.copy() for g in grids] + col_idx[d0] = np.full(shape, o) + rows.append(row) + cols.append(np.ravel_multi_index(col_idx, shape).reshape(-1)) + data.append(np.full(row.shape, weights_np[o], dtype=self.dtype)) + + n = int(np.prod(shape)) + return coo_matrix( + (np.concatenate(data), (np.concatenate(rows), np.concatenate(cols))), + shape=(n, n), + ).tocsr() - @property def toarray(self): - raise NotImplementedError() + return self.tosparse().toarray() def dot(self, v, out=None): @@ -3382,12 +3446,12 @@ def dot(self, v, out=None): x = v._data[self._slices[0]] y = out._data[self._slices[0]] if self._transposed: - xp.einsum(self._subscripts[0], x, out=self._tmp) + _einsum_out(self._subscripts[0], x, out=self._tmp) if not isinstance(self.derham.comm, (MockComm, type(None))): self.subcomm.Allreduce(MPI.IN_PLACE, self._tmp, MPI.SUM) - xp.einsum(self._subscripts[1], self._tmp, self._weights, out=y) + _einsum_out(self._subscripts[1], self._tmp, self._weights, out=y) else: - xp.einsum(self._subscripts[0], x, self._weights, out=self._tmp) + _einsum_out(self._subscripts[0], x, self._weights, out=self._tmp) if not isinstance(self.derham.comm, (MockComm, type(None))): self.subcomm.Allreduce(MPI.IN_PLACE, self._tmp, MPI.SUM) y[:] = self._tmp[self._slices[1]] diff --git a/src/struphy/io/options.py b/src/struphy/io/options.py index 6db9f74cc..942f5bf1b 100644 --- a/src/struphy/io/options.py +++ b/src/struphy/io/options.py @@ -71,7 +71,7 @@ class LiteralOptions: GivenInBasis = Literal["0", "1", "2", "3", "v", "physical", "physical_at_eta", "norm", None] # solvers - OptsSymmSolver = Literal["pcg", "cg"] + OptsSymmSolver = Literal["pcg", "cg", "direct"] OptsGenSolver = Literal["pbicgstab", "bicgstab", "gmres"] OptsMassPrecond = Literal["MassMatrixPreconditioner", "MassMatrixDiagonalPreconditioner", None] OptsSaddlePointSolver = Literal["uzawa"] From 90e4ea74daa8bac6268fb75c9d8e94b5b3ac0f0e Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 18 Aug 2026 13:57:12 +0200 Subject: [PATCH 100/156] Added more profiling examples --- .../params_cyclone.py | 370 ++++++++++++++++++ ...bmit_driftkinetic_cyclone_numpy_vs_cupy.py | 136 +++++++ ...bmit_guidingcenter_cpu_node_vs_gpu_node.py | 129 ++++++ 3 files changed, 635 insertions(+) create mode 100644 profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py create mode 100644 profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py create mode 100644 profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py diff --git a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py new file mode 100644 index 000000000..75ddeef5b --- /dev/null +++ b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py @@ -0,0 +1,370 @@ +# ----------------------------- +# Description of the simulation +# ----------------------------- +# Please fill in a verbal description of the simulation. +# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. + +name = "DriftKineticElectrostaticAdiabatic Cyclone NumPy vs CuPy" +description = """ +Cyclone-instability ITG turbulence case for DriftKineticElectrostaticAdiabatic (see +examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py, the physics case +this profiling params file is adapted from), used as the NumPy-vs-CuPy backend +comparison case for a real gyrokinetic model rather than a toy one. + +Unlike GuidingCenter (profiling/examples/GuidingCenter), this model carries a real FEEC +field solve every step (PoissonAdiabaticGyrokinetic, an ImplicitDiffusion subclass: a PCG +solve against the H1 mass/stiffness matrices in toroidal geometry with a Fourier filter), +on top of the two CUDA-ported guiding-center pushers (PushGuidingCenterBxEstar, +PushGuidingCenterParallel). The geometry is a toroidal HollowTorus (not a Cuboid slab), +and particle weights use the control-variate method. This is the case that answers +whether the CUDA port helps a real ITG run end to end, including the parts (Poisson +solve, control variates, sorting, HollowTorus mapping evaluation) that were not +individually targeted by the port. + +Getting this to run under CuPy at all required fixing several real device-portability +bugs found while validating this case (not toy-model artifacts): a NumPy-vs-CuPy +scalar-typing trap in the `AdhocTorus` equilibrium (`xp.sqrt` on a plain float silently +returns a 0-d CuPy array, which then hit scipy's `UnivariateSpline`/`quad`, both +host-only), the Sobol marker-loading sequence (a scalar bit-manipulation algorithm with +nothing to vectorize, now forced onto plain NumPy instead of CuPy), a full-slice +NumPy->CuPy assignment in `AverageOperator` that does not auto-convert the way +boolean/fancy indexing does, CuPy's `einsum` not accepting `out=` at all (unlike +NumPy's), and (for the `--solver direct` path below) several `tosparse()`/`toarray()` +stubs and CuPy/NumPy mixing bugs in `feectools` that had never been exercised under +CuPy before. See the commits touching `fields_background/equils.py`, `pic/sobol_seq.py`, +`feec/mass.py`, `feec/linear_operators.py`, `pic/accumulation/filter.py`, +`feectools/feec/derivatives.py`, and `feectools/linalg/{stencil,solvers,direct_solvers}.py`. + +Once it ran under the original iterative solver (`--solver pcg`), the result was a +genuine (non-toy) finding: unlike GuidingCenter, this model was *slower end to end* on +CuPy than on NumPy, because the part the CUDA port never touched -- the per-step +`PoissonAdiabaticGyrokinetic` PCG solve -- dominated the runtime and was itself slower on +the device (with tol=1e-12 it runs close to the full maxiter=3000 on both backends: the +operator is not well conditioned enough to converge much faster, and each of those +iterations needs a matvec plus a reduction whose result has to be read back to host +before the next iteration can even start -- a hard sequential dependency that is cheap +per iteration on CPU but not on GPU, where several small kernel launches plus the sync +add up to real cost a matrix this size cannot amortize away). Measured at +num_elements=(16, 64, 4), ppc=5, 1 step (dt=0.001), single rank, one H100: + + backend total (setup to finalize) PCG solve/call (model.integrate) push_gc_bxe push_gc_para + numpy 62.7 s 6.90 s 0.099 s 0.095 s + cupy 91.4 s 14.54 s (2.1x SLOWER) 0.035 s (2.9x) 0.019 s (5.0x) + +The default solver is now `direct` (`feectools.linalg.solvers.DirectSolver`), not `pcg`: +the LHS operator this propagator solves against is *constant* across every time step +(`divide_by_dt=False`, fixed `epsilon`/`Z` -- see `ImplicitDiffusion.__call__`), so +re-running an iterative solve close to `maxiter` every single call was pure waste on +either backend. `DirectSolver` factorizes once (lazily, on the first `solve()` call) via +a cached sparse LU (`feectools.linalg.direct_solvers.SparseSolver`) and reuses that +factorization for every later call, which needs only a triangular solve -- a small, fixed +number of kernel launches regardless of the operator's conditioning, which is exactly +what removes the GPU sync penalty above. Measured at the same configuration, 5 steps +(dt=0.001, Tend=0.005), single rank, one H100 -- `direct`'s first call pays the one-time +factorization, every later call is the pure solve: + + backend solver total (setup to finalize) solve/call: 1st (factorize) solve/call: later (solve only) + numpy pcg 108.7 s n/a (iterative every call) 6.9 s (unchanged) + numpy direct 52.5 s 3.4 s 0.008 s (~860x vs pcg) + cupy pcg n/a (>500s for 1 step alone, node-contended -- see below) + cupy direct 75.7 s 2.4 s 0.0076 s (~1900x vs the clean + 14.54 s/solve pcg number above) + +Both `pcg` and `direct` were verified to produce matching physics (`en_phi`/`en_tot`/ +`phi_integral` scalars agree to ~1e-10, consistent with `pcg` simply not having fully +converged at `maxiter` while `direct` solves exactly). The `cupy pcg` 5-step number above +is intentionally omitted rather than reported: a rerun on this shared login-node GPU took +174.55 s for a *single* step (vs the clean, isolated 14.54 s/solve measured earlier the +same session), evidently due to GPU contention from other jobs -- worth knowing if you +reproduce this yourself, but not a number to trust as `pcg`'s true cost. + +Whether a larger, more production-scale grid (num_elements=(32, 135, 5), the physics +case's own default) changes any of this is open -- `direct`'s one-time factorization cost +should grow with problem size (the setup-phase matrix assembly alone took 226 s on NumPy +at that size when this case was first validated), so whether it stays worth it at scale, +and by how much, needs its own longer-budget run; pass a larger --num-elements (with a +longer SLURM walltime) to check. + +`num_elements`/`ppc`/`Tend` default to the validated, quick configuration above (a +speed/coverage tradeoff against the physics case's own (32, 135, 5)/50/0.01); override +with --num-elements/--ppc/--Tend for a larger run. +""" + +import argparse +import os + +parser = argparse.ArgumentParser(description=description) +parser.add_argument( + "--backend", + choices=("numpy", "cupy"), + default="numpy", + help="Array backend to run the simulation with (default: numpy).", +) +# `--id` distinguishes runs that share a rank count but differ in something else (here: +# the array backend); the profiling driver passes its launch counter and looks for the +# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). +# Unknown flags are ignored so the driver can forward other parameters as well. +parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") +parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides the default, 5).") +parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default, 0.001 -> 1 step).") +parser.add_argument( + "--solver", + choices=("pcg", "direct"), + default="direct", + help=( + "Symmetric solver for the PoissonAdiabaticGyrokinetic field solve (default: " + "direct). 'direct' uses feectools.linalg.solvers.DirectSolver, a cached sparse " + "LU factorization -- valid here because the LHS operator is constant across " + "time steps (divide_by_dt=False, fixed epsilon/Z), so one factorization serves " + "every step instead of a fresh (near-maxiter, since tol=1e-12 barely converges) " + "PCG solve each time; see this file's docstring for the measured ~1900x " + "per-solve speedup on CuPy. 'pcg' reproduces the original, much slower baseline." + ), +) +parser.add_argument( + "--num-elements", + type=int, + nargs=3, + default=None, + help="Grid resolution (overrides the default, 16 64 4).", +) +args, _ = parser.parse_known_args() + +# Must be set before struphy (and therefore cunumpy) is imported. +os.environ["ARRAY_BACKEND"] = args.backend + +if args.backend == "cupy": + import cunumpy + + # Under CuPy with more than one MPI rank per node, every rank must bind to its own + # GPU -- cupy defaults to device 0, so without this every rank on a node would + # contend for the same GPU instead of getting one each. SLURM_LOCALID (the rank's + # index within its node) is set by srun before this process even starts, so it works + # without MPI being initialized yet. Falls back to device 0 outside SLURM (e.g. a + # single-GPU login node). + cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) + + # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment + # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more + # than one rank every process independently creates the same output directory/HDF5 + # dataset and the survivors deadlock in the next collective. + os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") + +import logging + +from struphy import set_logging_level + +set_logging_level(logging.WARNING) + +# ------------------ +# Import Struphy API +# ------------------ + +import cunumpy as xp + +from struphy import ( + BaseUnits, + BoundaryParameters, + DerhamOptions, + EnvironmentOptions, + LoadingParameters, + SavingParameters, + Simulation, + SortingParameters, + Time, + WeightsParameters, + domains, + equils, + grids, + maxwellians, +) +from struphy.initial.base import GenericPerturbation +from struphy.linear_algebra.solver import SolverParameters + +# --------------------- +# Instance of the model +# --------------------- +from struphy.models import DriftKineticElectrostaticAdiabatic +from struphy.pic.accumulation.filter import FilterParameters + +# provides the correct value for epsilon = 1.4142e-3 = 0.36/(180*sqrt(2)) from the +# cyclone paper (10.1140/epjd/e2014-50180-9) +base_units = BaseUnits(kBT=0.1916) +model = DriftKineticElectrostaticAdiabatic( + base_units=base_units, + use_diagnostic_poisson=True, +) + +# List all variables and decide whether to save their data +model.em_fields.phi.save_data = True +model.kinetic_ions.var.save_data = False + +# -------------------------- +# Instance of the simulation +# -------------------------- + +name = f"DriftKineticElectrostaticAdiabatic Cyclone ({args.backend})" + +# Environment options +env = EnvironmentOptions( + sim_folder=f"sim_{args.id:02d}", + profiling_activated=True, + save_restart=False, +) + +# Time stepping. Short by default: enough steps to warm past one-off setup (Poisson +# assembly, particle loading, CUDA RawKernel JIT compile) without a long profiling run. +time_opts = Time(dt=0.001, Tend=args.Tend if args.Tend is not None else 0.001, split_algo="LieTrotter") + +a, r_min, R0 = 0.36, 0.01, 1.0 +num_elements = tuple(args.num_elements) if args.num_elements is not None else (16, 64, 4) +degree = (3, 3, 3) + +# Fluid equilibrium (can be used as part of initial conditions) +equil = equils.AdhocTorus(a=a, R0=R0, B0=1.0, q_kind=2, q0=0.86, q1=2.52 + 0.86, l=-0.16, psi_k=5, psi_nel=200) + +# Geometry +domain = domains.HollowTorus(a1=r_min, a2=a, R0=R0, sfl=True, pol_period=1, tor_period=19) + +# Grid +grid = grids.TensorProductGrid(num_elements=num_elements, mpi_dims_mask=(True, True, False)) + +# Derham options +derham_opts = DerhamOptions( + degree=degree, + bcs=(("dirichlet", "dirichlet"), None, None), +) + +# Simulation object +sim = Simulation( + model=model, + name=name, + description=description, + params_path=__file__, + env=env, + time_opts=time_opts, + domain=domain, + equil=equil, + grid=grid, + derham_opts=derham_opts, +) + +# ------------------- +# Particle parameters +# ------------------- + +ppc = args.ppc if args.ppc is not None else 5 +loading_params = LoadingParameters(ppc=ppc, loading="sobol_standard", spatial="uniform", moments=(0, 0, 4, 4)) +weights_params = WeightsParameters(control_variate=True) +boundary_params = BoundaryParameters(bc=("remove", "periodic", "periodic")) +sorting_params = SortingParameters(boxes_per_dim=(12, 12, 6), do_sort=True, sorting_frequency=5) + +saving_params = SavingParameters(n_markers=100) + +model.kinetic_ions.set_markers( + loading_params=loading_params, + weights_params=weights_params, + boundary_params=boundary_params, + sorting_params=sorting_params, + saving_params=saving_params, + bufsize=1.0, +) + +# ------------------ +# Propagator options +# ------------------ + +model.propagators.gc_poisson.options = model.propagators.gc_poisson.Options( + which_geometry="toroidal", + solver=args.solver, + solver_params=SolverParameters(tol=1e-12, maxiter=3000, recycle=False), + filter_params={model.kinetic_ions.var: FilterParameters("fourier_in_tor", (1,), repeat=1)}, +) +model.propagators.push_gc_bxe.options = model.propagators.push_gc_bxe.Options( + algo="explicit", + evaluate_e_field=True, + maxiter=100, +) +model.propagators.push_gc_para.options = model.propagators.push_gc_para.Options( + algo="explicit", + evaluate_e_field=True, + maxiter=100, +) + +# ------------------ +# Initial conditions +# ------------------ + +ns = 1 +ms = 27 +amps = 1.0e-6 +kappa_n = 2.23 +kappa_Ti = 6.96 +Delta_n = Delta_Ti = 0.3 +delta_r = 0.02 +r0 = 0.5 * a +n0 = 1.0 +Ti0 = 1.0 + + +def n_r(r): + return n0 * xp.exp(-kappa_n * a * Delta_n * xp.tanh((r - r0) / (Delta_n * a))) + + +def n_init(*etas): + if len(etas) == 1: + eta1 = etas[0][:, 0] + else: + eta1 = etas[0] + r = r_min + (a - r_min) * eta1 + return n_r(r) + + +def Ti_r(r): + return Ti0 * xp.exp(-kappa_Ti * a * Delta_Ti * xp.tanh((r - r0) / (Delta_Ti * a))) + + +def vth_init(*etas): + if len(etas) == 1: + eta1 = etas[0][:, 0] + else: + eta1 = etas[0] + r = r_min + (a - r_min) * eta1 + return xp.sqrt(Ti_r(r)) + + +def n_xyz(x, y, z): + r = xp.sqrt((xp.sqrt(x**2 + y**2) - R0) ** 2 + z**2) + return n_r(r) + + +def p_xyz(x, y, z): + r = xp.sqrt((xp.sqrt(x**2 + y**2) - R0) ** 2 + z**2) + return n_r(r) * Ti_r(r) + + +equil.p_xyz = p_xyz +equil.n_xyz = n_xyz + + +def pert_func(*etas): + if len(etas) == 1: + e1, e2, e3 = etas[0][:, 0], etas[0][:, 1], etas[0][:, 2] + else: + e1, e2, e3 = etas[0], etas[1], etas[2] + r = (a - r_min) * e1 + r_min + teta = 2 * xp.arctan(xp.sqrt((R0 + r) / (R0 - r)) * xp.tan(xp.pi * e2)) + phi = 2 * xp.pi * e3 + return n_r(r) * amps * xp.exp(-((r - r0) ** 2) / delta_r**2) * xp.cos(ms * teta - ns * phi) + + +# Background for kinetic species +background = maxwellians.GyroMaxwellian2D(n=(n_init, None), vth_para=(vth_init, None), vth_perp=(vth_init, None)) +model.kinetic_ions.var.add_background(background) + +perturbation = GenericPerturbation(pert_func, given_in_basis="0") +init = maxwellians.GyroMaxwellian2D(n=(n_init, perturbation), vth_para=(vth_init, None), vth_perp=(vth_init, None)) +model.kinetic_ions.var.add_initial_condition(init) + +if __name__ == "__main__": + sim.run() diff --git a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py new file mode 100644 index 000000000..8f4140766 --- /dev/null +++ b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py @@ -0,0 +1,136 @@ +"""DriftKineticElectrostaticAdiabatic (ITG cyclone) NumPy-vs-CuPy profiling case. + +This is the first CuPy profiling case for a real gyrokinetic model rather than a toy +one (see `params_cyclone.py`'s docstring for the device-portability bugs that had to be +fixed just to get it running under CuPy at all). Unlike GuidingCenter +(submit_guidingcenter_numpy_vs_cupy.py), this model carries a real per-step FEEC field +solve (PoissonAdiabaticGyrokinetic) on top of the two CUDA-ported guiding-center +pushers, so it measures whether the CUDA port helps a model end to end rather than just +the particle kernels it directly targeted. + +The field solve's own solver defaults to 'direct' (a cached sparse LU factorization, +`feectools.linalg.solvers.DirectSolver`) rather than the naive 'pcg': the LHS operator +here is constant across time steps, so the iterative solver was redoing (close to) +maxiter=3000 dependent, sync-per-iteration steps on every single call for no reason -- +see params_cyclone.py's docstring for the measured PCG-vs-direct comparison (~1900x +faster per solve after the first, on CuPy). `--solver pcg` reproduces the original, +much slower baseline for comparison. + +Runs the same simulation twice, once with `ARRAY_BACKEND=numpy` on a CPU partition and +once with `ARRAY_BACKEND=cupy` on a GPU partition, so the two runs can be compared +directly, exactly as `submit_guidingcenter_numpy_vs_cupy.py` does. +""" + +import argparse +from pathlib import Path + +from clusters import SLURM_PRESETS, detect_machine_name +from profiling_job import ProfilingCase + +# The preset is looked up by cluster name inside `launch`, so both dicts below are keyed +# by the *detected* name rather than by the preset's own name: on Pitagora detection +# always returns "pitagora_dcgp" for both partitions (it cannot tell the Booster +# partition apart), and the GPU run must still get the Booster preset. Keying on the +# detected name also keeps this working, without a KeyError, on a machine detection does +# not recognise (name None). +CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] +GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] + +BACKEND_PRESETS = { + "numpy": CPU_PRESET, + "cupy": GPU_PRESET, +} + + +def main() -> None: + + # Parse arguments, do not remove --upload + parser = argparse.ArgumentParser( + description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), + ) + parser.add_argument( + "--upload", + action="store_true", + help="Upload the packaged profiling results to the profiling-data repo.", + ) + parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides the default, 5).") + parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default, 0.001 -> 1 step).") + parser.add_argument( + "--solver", + choices=("pcg", "direct"), + default=None, + help=( + "Symmetric solver for the field solve (overrides params_cyclone.py's own " + "default, 'direct'). 'direct' is a cached sparse LU factorization, valid " + "because the LHS operator here is constant across time steps; see " + "params_cyclone.py's docstring for the measured PCG-vs-direct comparison." + ), + ) + parser.add_argument( + "--num-elements", + type=int, + nargs=3, + default=None, + help=( + "Grid resolution (overrides the default, 16 64 4). The physics case's own " + "default, 32 135 5, is much heavier -- NumPy setup alone took 226 s at that " + "size when this case was validated -- so raise --time in the SLURM preset " + "before using it." + ), + ) + args = parser.parse_args() + + # Paths relative to this script's location, so it can be run from anywhere. + script_dir = Path(__file__).resolve().parent + params_dir = script_dir / "examples" / "DriftKineticElectrostaticAdiabatic" + params_source = params_dir / "params_cyclone.py" + + param_flags = [] + if args.ppc is not None: + param_flags += ["--ppc", str(args.ppc)] + if args.Tend is not None: + param_flags += ["--Tend", str(args.Tend)] + if args.num_elements is not None: + param_flags += ["--num-elements", *[str(n) for n in args.num_elements]] + if args.solver is not None: + param_flags += ["--solver", args.solver] + + profiling_case = ProfilingCase( + label="driftkinetic_cyclone_numpy_vs_cupy", + name="DriftKineticElectrostaticAdiabatic Cyclone, NumPy vs CuPy", + description=( + "Cyclone-instability ITG turbulence case for DriftKineticElectrostaticAdiabatic " + "(toroidal HollowTorus geometry, control-variate weights, Fourier-filtered " + "PoissonAdiabaticGyrokinetic field solve plus the two CUDA-ported guiding-center " + "pushers), run with the NumPy and the CuPy array backend. Unlike GuidingCenter, " + "this model has a real per-step FEEC field solve, so this measures whether the " + "CUDA port helps a real gyrokinetic model end to end." + ), + physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", + struphy_model_used="DriftKineticElectrostaticAdiabatic", + params_source=params_source, + language="fortran", + compiler="GNU", + upload=args.upload, + ) + + # The preset is looked up by cluster name inside `launch`, so build a one-entry dict + # under whatever name detection reports for this machine. + cluster_name = detect_machine_name() + + # Launch one run per backend, one rank each -- this case is a backend comparison, not + # a scaling study (see submit_guidingcenter_cupy_scaling.py for that pattern). + for backend, preset in BACKEND_PRESETS.items(): + profiling_case.launch( + 1, + num_nodes=1, + param_flags=["--backend", backend, *param_flags], + slurm_presets={cluster_name: preset}, + ) + + # Package and push each run as its own job finishes. + profiling_case.finalize_run() + + +if __name__ == "__main__": + main() diff --git a/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py b/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py new file mode 100644 index 000000000..18c0374f1 --- /dev/null +++ b/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py @@ -0,0 +1,129 @@ +"""Guiding-centre full-node CPU-vs-GPU comparison case. + +The other two GuidingCenter cases each answer a narrower question: +`submit_guidingcenter_numpy_vs_cupy.py` compares backends at a fixed, small rank count +(default 1), and `submit_guidingcenter_cupy_scaling.py` measures CuPy strong-scaling +alone. Neither answers the practical question a user actually has: given one full CPU +node and one full GPU node, which one do you point a job at? This case runs exactly +that comparison -- `ARRAY_BACKEND=numpy` using every core of one `pitagora_dcgp` node +against `ARRAY_BACKEND=cupy` using every GPU of one Booster node -- at the same total +marker count on both sides. + +Both sides run with more than one rank, so both exercise the domain-decomposed +marker-exchange path (`mpi_sort_markers`/`apply_kinetic_bc`) this session's performance +work targeted, not just single-rank kernel throughput. `params_GuidingCenter_scaling.py` +is used (not `params_GuidingCenter.py`) for the same reason `submit_guidingcenter_cupy_scaling.py` +uses it: its default Np is large enough that per-rank compute between exchanges has a +chance of outweighing the exchange cost -- see that file's docstring for the measurements +this default responds to. `--Np` overrides it if a different problem size is of interest. + +`GuidingCenter` is used, as in the other two cases, because its whole propagator stack is +CUDA-ported and it has no FEEC field solve, so wall-clock time is dominated by the +particle kernels and their MPI exchange rather than by anything the backend choice +doesn't touch. +""" + +import argparse +from pathlib import Path + +from clusters import HARDWARE_INFO, SLURM_PRESETS, detect_machine_name +from profiling_job import ProfilingCase + +# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name +# (`detect_machine_name`), so both dicts below are keyed by the *detected* name rather +# than by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" +# for both partitions (it cannot tell the Booster partition apart), and the GPU run must +# still get the Booster preset. Keying on the detected name also keeps this working, +# without a KeyError, on a machine detection does not recognise (name None). +CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] +GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] + +# One CPU-node's worth of ranks (`HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"]`), and +# one GPU-node's worth (the Booster preset requests `gres=gpu:4`), one rank per GPU as in +# `params_GuidingCenter_scaling.py`'s `SLURM_LOCALID` binding. +CPU_RANKS_PER_NODE = HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"] +GPU_RANKS_PER_NODE = 4 + + +def main() -> None: + + # Parse arguments, do not remove --upload + parser = argparse.ArgumentParser( + description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), + ) + parser.add_argument( + "--upload", + action="store_true", + help="Upload the packaged profiling results to the profiling-data repo.", + ) + parser.add_argument( + "--cpu-ranks", + type=int, + default=CPU_RANKS_PER_NODE, + help=f"MPI ranks for the NumPy/CPU-node run (default: {CPU_RANKS_PER_NODE}, one full pitagora_dcgp node).", + ) + parser.add_argument( + "--gpu-ranks", + type=int, + default=GPU_RANKS_PER_NODE, + help=f"MPI ranks for the CuPy/GPU-node run, one rank per GPU (default: {GPU_RANKS_PER_NODE}, one full Booster node).", + ) + parser.add_argument( + "--Np", + type=int, + default=None, + help="Total marker count, overriding params_GuidingCenter_scaling.py's default (10,000,000).", + ) + args = parser.parse_args() + + # Paths relative to this script's location, so it can be run from anywhere. + script_dir = Path(__file__).resolve().parent + params_dir = script_dir / "examples" / "GuidingCenter" + params_source = params_dir / "params_GuidingCenter_scaling.py" + + profiling_case = ProfilingCase( + label="guidingcenter_cpu_node_vs_gpu_node", + name="Guiding-centre particles on cube, 1 CPU node vs 1 GPU node", + description=( + "5D guiding-centre test particles in a homogeneous slab on a 3D cube, run once " + "with the NumPy array backend across every core of one CPU node and once with " + "the CuPy array backend across every GPU of one GPU node, at the same total " + "marker count, to compare realistic full-node throughput rather than " + "single-rank kernel speed." + ), + physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", + struphy_model_used="GuidingCenter", + params_source=params_source, + language="fortran", + compiler="GNU", + upload=args.upload, + ) + + # The preset is looked up by cluster name inside `launch`, so build a one-entry dict + # under whatever name detection reports for this machine. + cluster_name = detect_machine_name() + + Np_flags = ["--Np", str(args.Np)] if args.Np is not None else [] + + # One full CPU node, NumPy backend. + profiling_case.launch( + args.cpu_ranks, + num_nodes=1, + param_flags=["--backend", "numpy", *Np_flags], + slurm_presets={cluster_name: CPU_PRESET}, + ) + + # One full GPU node, CuPy backend, one rank per GPU. + profiling_case.launch( + args.gpu_ranks, + num_nodes=1, + param_flags=["--backend", "cupy", *Np_flags], + slurm_presets={cluster_name: GPU_PRESET}, + ) + + # Package and push each run as its own job finishes. + profiling_case.finalize_run() + + +if __name__ == "__main__": + main() From 145da0a2bc05589c6dc19ed105be31156635827c Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 18 Aug 2026 15:35:55 +0200 Subject: [PATCH 101/156] Increase problem size --- .../params_cyclone.py | 56 ++++++++++++++++--- 1 file changed, 49 insertions(+), 7 deletions(-) diff --git a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py index 75ddeef5b..dbd496bb0 100644 --- a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py +++ b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py @@ -78,14 +78,48 @@ same session), evidently due to GPU contention from other jobs -- worth knowing if you reproduce this yourself, but not a number to trust as `pcg`'s true cost. +With `direct` removing the field-solve bottleneck, one more non-obvious cost stayed +hidden until the field solve itself got fast: `ImplicitDiffusion.__call__` runs a +*second*, completely separate solve every step when `diagnostic is not None` +(`proj = L2Projector("H1", self.mass_ops); self.diagnostic.spline.vector = proj.solve(rhs)`) +-- a fresh, uncached `L2Projector` with the default `"pcg"` solver, untouched by +`--solver direct` above since it is a different `InverseLinearOperator` entirely. The +physics case this file is adapted from enables it (`use_diagnostic_poisson=True`), but it +only feeds `self.diagnostics.rho`, an extra saved diagnostic field nothing else in this +model reads back (the `phi_integral` scalar uses `phi` directly) -- disabled here +(`use_diagnostic_poisson=False`) since it was otherwise the dominant per-step cost left +after fixing the main solve. + +## CuPy actually winning end to end + +At the tiny `ppc=5` used to validate correctness above, CuPy still loses in total wall +time even with `direct` and the diagnostic-solve fix: setup (CUDA context/kernel +compilation, done once) is a larger fixed cost on CuPy than on NumPy, and there isn't +enough per-step *work* -- the whole point of `direct` and disabling the diagnostic +solve -- for the CUDA-ported pushers to out-earn that fixed cost. Scaling up `ppc` (more +markers per cell, i.e. more actual particle-push/accumulation work, which is exactly what +was CUDA-ported) is what tips the balance, not a bigger grid. Measured at +num_elements=(16, 64, 4), **ppc=200** (the physics case's own suggested minimum), 10 +steps (dt=0.001, Tend=0.01), single rank, one H100, `--solver direct` (the default), +`use_diagnostic_poisson=False`: + + backend total (setup to finalize) model.integrate/step push_gc_bxe/step push_gc_para/step + numpy 145.5 s 8.71 s 3.84 s 4.37 s + cupy 80.0 s (1.82x FASTER) 0.59 s (14.8x) 0.14 s (28x) 0.14 s (32x) + +`en_phi`/`en_tot`/`phi_integral` scalars match to ~1e-21 (round-off) between the two +runs. This is now the default configuration (`ppc=200`, `Tend=0.01`); override with +`--ppc`/`--Tend`/`--num-elements` to explore further (e.g. a larger grid should shift +the crossover point the other way, back toward NumPy, per the still-open question below). + Whether a larger, more production-scale grid (num_elements=(32, 135, 5), the physics case's own default) changes any of this is open -- `direct`'s one-time factorization cost should grow with problem size (the setup-phase matrix assembly alone took 226 s on NumPy -at that size when this case was first validated), so whether it stays worth it at scale, +at that size when this case was first validated), so whether CuPy still wins at scale, and by how much, needs its own longer-budget run; pass a larger --num-elements (with a longer SLURM walltime) to check. -`num_elements`/`ppc`/`Tend` default to the validated, quick configuration above (a +`num_elements`/`ppc`/`Tend` default to the validated configuration above (a speed/coverage tradeoff against the physics case's own (32, 135, 5)/50/0.01); override with --num-elements/--ppc/--Tend for a larger run. """ @@ -105,8 +139,8 @@ # output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). # Unknown flags are ignored so the driver can forward other parameters as well. parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") -parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides the default, 5).") -parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default, 0.001 -> 1 step).") +parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides the default, 200).") +parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default, 0.01 -> 10 steps).") parser.add_argument( "--solver", choices=("pcg", "direct"), @@ -192,7 +226,15 @@ base_units = BaseUnits(kBT=0.1916) model = DriftKineticElectrostaticAdiabatic( base_units=base_units, - use_diagnostic_poisson=True, + # The physics case (examples/.../cyclone/params_cyclone.py) enables this, but it + # wires up a *second*, completely separate solve every step + # (ImplicitDiffusion.__call__'s `if self.diagnostic is not None: ... proj.solve(rhs)`, + # a fresh, uncached L2Projector with the default "pcg" solver -- unrelated to and not + # sped up by --solver direct above). It only feeds `self.diagnostics.rho`, an extra + # saved diagnostic field that nothing else in this model reads back (the + # `phi_integral` scalar uses `phi` directly) -- disabled here so this profiling case + # measures the model's actual per-step cost, not an unrelated, unoptimized solve. + use_diagnostic_poisson=False, ) # List all variables and decide whether to save their data @@ -214,7 +256,7 @@ # Time stepping. Short by default: enough steps to warm past one-off setup (Poisson # assembly, particle loading, CUDA RawKernel JIT compile) without a long profiling run. -time_opts = Time(dt=0.001, Tend=args.Tend if args.Tend is not None else 0.001, split_algo="LieTrotter") +time_opts = Time(dt=0.001, Tend=args.Tend if args.Tend is not None else 0.01, split_algo="LieTrotter") a, r_min, R0 = 0.36, 0.01, 1.0 num_elements = tuple(args.num_elements) if args.num_elements is not None else (16, 64, 4) @@ -253,7 +295,7 @@ # Particle parameters # ------------------- -ppc = args.ppc if args.ppc is not None else 5 +ppc = args.ppc if args.ppc is not None else 200 loading_params = LoadingParameters(ppc=ppc, loading="sobol_standard", spatial="uniform", moments=(0, 0, 4, 4)) weights_params = WeightsParameters(control_variate=True) boundary_params = BoundaryParameters(bc=("remove", "periodic", "periodic")) From 895b303d8e57f4f6112208150543e5ac1dea51ce Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 18 Aug 2026 15:45:38 +0200 Subject: [PATCH 102/156] Added pproc --- .../params_cyclone.py | 119 +++++++++++++++++- 1 file changed, 118 insertions(+), 1 deletion(-) diff --git a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py index dbd496bb0..8f9fcaefd 100644 --- a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py +++ b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py @@ -198,6 +198,7 @@ from struphy import ( BaseUnits, + BinningPlot, BoundaryParameters, DerhamOptions, EnvironmentOptions, @@ -301,7 +302,10 @@ boundary_params = BoundaryParameters(bc=("remove", "periodic", "periodic")) sorting_params = SortingParameters(boxes_per_dim=(12, 12, 6), do_sort=True, sorting_frequency=5) -saving_params = SavingParameters(n_markers=100) +# density binning, needed for the e1_e2 density slice generated by the pproc block below +# (matches examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py's own setup) +eta_bin = BinningPlot(slice="e1_e2", n_bins=(64, 64), ranges=((0.01, 0.99), (0.0, 1.0))) +saving_params = SavingParameters(n_markers=100, binning_plots=(eta_bin,)) model.kinetic_ions.set_markers( loading_params=loading_params, @@ -410,3 +414,116 @@ def pert_func(*etas): if __name__ == "__main__": sim.run() + sim.pproc(parallel_pproc=True) + + # Static, non-interactive figures for this profiling run -- adapted from + # examples/DriftKineticElectrostaticAdiabatic/cyclone/pproc_cyclone.py (which is + # meant for local, interactive use: matplotlib Slider widgets, plt.show()) into + # fixed-time-step, save-to-file plots, following the same results-directory + # convention as profiling/examples/Poisson/cube_strong_scaling/params_poisson.py. + if sim.rank == 0: + import os + + import h5py + import numpy as np + from matplotlib import pyplot as plt + + sim.load_plotting_data() + + # `path_out` is the run's output folder; `sim_folder` alone is a bare name + # resolved against the CWD. The profiling packaging picks these files up from + # here and uploads them as `results-run`. + results_dir = os.path.join(sim.env.path_out, "results") + os.makedirs(results_dir, exist_ok=True) + + # Deliberately plain NumPy from here on, not xp/cunumpy: everything below reads + # already-saved (host, h5py) or already-postprocessed (PlottingData, host-only + # regardless of ARRAY_BACKEND) data purely to hand it to matplotlib, so there is + # nothing left to dispatch onto the active backend -- using xp on data that is + # already NumPy would only risk exactly the array-type/active-backend mismatches + # this branch's CuPy port spent a lot of effort finding and fixing elsewhere. + + # ------------------------------------------------------------------- + # phi_integral evolution + exponential growth-rate fit (the ITG-test + # diagnostic scalar; see pproc_cyclone.py's plot_energy_fit). + # ------------------------------------------------------------------- + data_path = os.path.join(sim.env.path_out, "data") + with h5py.File(os.path.join(data_path, "data_proc0.hdf5"), "r") as f: + t_scalar = np.asarray(f["time"]["value"][()]) + phi_integral = np.asarray(f["scalar"]["phi_integral"][()]) + + fig_energy, ax_energy = plt.subplots() + ax_energy.plot(t_scalar, phi_integral, label="phi_integral") + ax_energy.set_xlabel("time") + ax_energy.set_ylabel("phi_integral") + ax_energy.set_title("Evolution of phi_integral") + + positive = np.isfinite(phi_integral) & (phi_integral > 0.0) + gamma = None + if int(np.count_nonzero(positive)) >= 2: + idx = np.nonzero(positive)[0] + i0, i1 = int(idx[0]), int(idx[-1]) + 1 + fit_time = t_scalar[i0:i1] + fit_signal = np.log(np.sqrt(phi_integral[i0:i1])) + gamma, b = np.polyfit(fit_time, fit_signal, 1) + fit_curve = np.exp(2.0 * (gamma * fit_time + b)) + ax_energy.plot(fit_time, fit_curve, "--", label=f"fit: gamma={float(gamma):.4e}") + print(f"phi_integral growth rate: gamma = {float(gamma):.8e}") + ax_energy.legend() + fig_energy.tight_layout() + + # ------------------------------------------------------------------- + # Electric potential phi, poloidal (R, Z) slice at the last saved time + # step, toroidal index 0 (see pproc_cyclone.py's plot_field_slider). + # ------------------------------------------------------------------- + Tend_saved = sim.t_grid[-1] + phi_phy = np.asarray(sim.spline_values.em_fields.phi_phy.data[Tend_saved][0]) + X, Y, Z = (np.asarray(g) for g in sim.grids_phy) + R = np.sqrt(X**2 + Y**2) + + toroidal_index = 0 + fig_phi, ax_phi = plt.subplots() + pcm_phi = ax_phi.pcolormesh( + R[:, :, toroidal_index], + Z[:, :, toroidal_index], + phi_phy[:, :, toroidal_index], + shading="auto", + ) + fig_phi.colorbar(pcm_phi, ax=ax_phi) + ax_phi.set_aspect("equal", adjustable="box") + ax_phi.set_xlabel("R") + ax_phi.set_ylabel("Z") + ax_phi.set_title(f"Electric potential phi at t = {Tend_saved:.4e}") + fig_phi.tight_layout() + + # ------------------------------------------------------------------- + # Density perturbation, e1-e2 binned, at the last saved time step, in + # logical (eta) space (see pproc_cyclone.py's plot_binned_quantity_slider). + # ------------------------------------------------------------------- + density_data = sim.f.kinetic_ions.e1_e2_density + delta_f_final = np.asarray(density_data.delta_f_binned)[-1] + eta1_grid, eta2_grid = np.meshgrid( + np.asarray(density_data.grid_e1), + np.asarray(density_data.grid_e2), + indexing="ij", + ) + + fig_density, ax_density = plt.subplots() + pcm_density = ax_density.pcolormesh(eta1_grid, eta2_grid, delta_f_final, shading="auto") + fig_density.colorbar(pcm_density, ax=ax_density) + ax_density.set_xlabel("eta1") + ax_density.set_ylabel("eta2") + ax_density.set_title(f"delta_f (eta1, eta2) at t = {Tend_saved:.4e}") + fig_density.tight_layout() + + # ------------------------------------------------------------------- + # Save everything into results_dir, matching params_poisson.py's convention. + # ------------------------------------------------------------------- + if gamma is not None: + np.save(os.path.join(results_dir, "phi_integral_growth_rate.npy"), float(gamma)) + np.save(os.path.join(results_dir, "resolution.npy"), np.asarray(num_elements)) + np.save(os.path.join(results_dir, "spline_degree.npy"), np.asarray(degree)) + + fig_energy.savefig(os.path.join(results_dir, "phi_integral_evolution.png")) + fig_phi.savefig(os.path.join(results_dir, "phi_slice.png")) + fig_density.savefig(os.path.join(results_dir, "density_e1e2.png")) From bf9258556121323df45a601d202717b151330bcc Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 18 Aug 2026 15:46:00 +0200 Subject: [PATCH 103/156] Added node vs node and cupy scaling --- ...iftkinetic_cyclone_cpu_node_vs_gpu_node.py | 154 ++++++++++++++++++ ...ubmit_driftkinetic_cyclone_cupy_scaling.py | 150 +++++++++++++++++ 2 files changed, 304 insertions(+) create mode 100644 profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py create mode 100644 profiling/submit_driftkinetic_cyclone_cupy_scaling.py diff --git a/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py b/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py new file mode 100644 index 000000000..affc75e8c --- /dev/null +++ b/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py @@ -0,0 +1,154 @@ +"""DriftKineticElectrostaticAdiabatic (ITG cyclone) full-node CPU-vs-GPU comparison case. + +The other two DriftKineticElectrostaticAdiabatic cases each answer a narrower question: +`submit_driftkinetic_cyclone_numpy_vs_cupy.py` compares backends at a fixed single rank +(where CuPy only wins once `ppc` is raised enough to give the CUDA-ported pushers real +work, see that case's params file docstring), and `submit_driftkinetic_cyclone_cupy_scaling.py` +measures CuPy strong-scaling alone. Neither answers the practical question a user +actually has: given one full CPU node and one full GPU node, which one do you point a +real ITG run at? This case runs exactly that comparison -- `ARRAY_BACKEND=numpy` using +every core of one `pitagora_dcgp` node against `ARRAY_BACKEND=cupy` using every GPU of +one Booster node -- at the same grid/marker configuration on both sides, mirroring +`submit_guidingcenter_cpu_node_vs_gpu_node.py`'s pattern for the toy model. + +**Solver forced to `pcg`.** `params_cyclone.py` defaults to `solver="direct"` +(`feectools.linalg.solvers.DirectSolver`, see `ISSUE_add_direct_solver_for_constant_operators.md` +and that params file's own docstring for why and by how much it helps), but `DirectSolver` +only supports a single MPI rank -- it asserts on `nprocs > 1`, since the sparse-direct +factorization it wraps (`feectools.linalg.direct_solvers.SparseSolver`) has no +distributed variant. Both sides of this comparison run with many ranks, so both are +pinned to `--solver pcg` here regardless of the file's own default -- this measures the +field solve's *old*, unoptimized behavior at full-node scale, which is also useful data +(see `ISSUE_add_direct_solver_for_constant_operators.md`'s "Known limitations": whether a +distributed direct solve would still win at this scale is an open question this case does +not answer). + +Both sides run with more than one rank, so both exercise the domain-decomposed +marker-exchange path (`mpi_sort_markers`/`apply_kinetic_bc`), not just single-rank kernel +throughput -- the grid is domain-decomposed along the two poloidal-plane directions only +(`mpi_dims_mask=(True, True, False)` in `params_cyclone.py`, matching the toroidal +Fourier filter's own `nprocs[2] == 1` requirement), so the rank count on either side is +bounded by that grid's first two `num_elements` (default 16 x 64 = at most 1024 ranks +before `--num-elements` needs to grow too). +""" + +import argparse +from pathlib import Path + +from clusters import HARDWARE_INFO, SLURM_PRESETS, detect_machine_name +from profiling_job import ProfilingCase + +# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name +# (`detect_machine_name`), so both dicts below are keyed by the *detected* name rather +# than by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" +# for both partitions (it cannot tell the Booster partition apart), and the GPU run must +# still get the Booster preset. Keying on the detected name also keeps this working, +# without a KeyError, on a machine detection does not recognise (name None). +CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] +GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] + +# One CPU-node's worth of ranks (`HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"]`), and +# one GPU-node's worth (the Booster preset requests `gres=gpu:4`), one rank per GPU as in +# params_cyclone.py's `SLURM_LOCALID` binding. +CPU_RANKS_PER_NODE = HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"] +GPU_RANKS_PER_NODE = 4 + + +def main() -> None: + + # Parse arguments, do not remove --upload + parser = argparse.ArgumentParser( + description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), + ) + parser.add_argument( + "--upload", + action="store_true", + help="Upload the packaged profiling results to the profiling-data repo.", + ) + parser.add_argument( + "--cpu-ranks", + type=int, + default=CPU_RANKS_PER_NODE, + help=f"MPI ranks for the NumPy/CPU-node run (default: {CPU_RANKS_PER_NODE}, one full pitagora_dcgp node).", + ) + parser.add_argument( + "--gpu-ranks", + type=int, + default=GPU_RANKS_PER_NODE, + help=f"MPI ranks for the CuPy/GPU-node run, one rank per GPU (default: {GPU_RANKS_PER_NODE}, one full Booster node).", + ) + parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides params_cyclone.py's default, 200).") + parser.add_argument("--Tend", type=float, default=None, help="End time (overrides params_cyclone.py's default, 0.01 -> 10 steps).") + parser.add_argument( + "--num-elements", + type=int, + nargs=3, + default=None, + help=( + "Grid resolution (overrides params_cyclone.py's default, 16 64 4). Must " + "support at least as many ranks as --cpu-ranks in num_elements[0] * " + "num_elements[1] (the only two domain-decomposed directions), so raise this " + "before raising --cpu-ranks much past the default grid's 16*64=1024 cap." + ), + ) + args = parser.parse_args() + + # Paths relative to this script's location, so it can be run from anywhere. + script_dir = Path(__file__).resolve().parent + params_dir = script_dir / "examples" / "DriftKineticElectrostaticAdiabatic" + params_source = params_dir / "params_cyclone.py" + + param_flags = ["--solver", "pcg"] + if args.ppc is not None: + param_flags += ["--ppc", str(args.ppc)] + if args.Tend is not None: + param_flags += ["--Tend", str(args.Tend)] + if args.num_elements is not None: + param_flags += ["--num-elements", *[str(n) for n in args.num_elements]] + + profiling_case = ProfilingCase( + label="driftkinetic_cyclone_cpu_node_vs_gpu_node", + name="DriftKineticElectrostaticAdiabatic Cyclone, 1 CPU node vs 1 GPU node", + description=( + "Cyclone-instability ITG turbulence case for DriftKineticElectrostaticAdiabatic, " + "run once with the NumPy array backend across every core of one CPU node and " + "once with the CuPy array backend across every GPU of one GPU node, at the same " + "grid/marker configuration, to compare realistic full-node throughput rather " + "than single-rank kernel speed. The field solve is pinned to solver='pcg' on " + "both sides -- DirectSolver (params_cyclone.py's own default) does not support " + "more than one MPI rank." + ), + physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", + struphy_model_used="DriftKineticElectrostaticAdiabatic", + params_source=params_source, + language="fortran", + compiler="GNU", + upload=args.upload, + ) + + # The preset is looked up by cluster name inside `launch`, so build a one-entry dict + # under whatever name detection reports for this machine. + cluster_name = detect_machine_name() + + # One full CPU node, NumPy backend. + profiling_case.launch( + args.cpu_ranks, + num_nodes=1, + param_flags=["--backend", "numpy", *param_flags], + slurm_presets={cluster_name: CPU_PRESET}, + ) + + # One full GPU node, CuPy backend, one rank per GPU. + profiling_case.launch( + args.gpu_ranks, + num_nodes=1, + param_flags=["--backend", "cupy", *param_flags], + slurm_presets={cluster_name: GPU_PRESET}, + ) + + # Package and push each run as its own job finishes. + profiling_case.finalize_run() + + +if __name__ == "__main__": + main() diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py new file mode 100644 index 000000000..093ea0df7 --- /dev/null +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -0,0 +1,150 @@ +"""DriftKineticElectrostaticAdiabatic (ITG cyclone) CuPy multi-GPU/multi-rank scaling case. + +This is a strong-scaling study, not a backend comparison (see +`submit_driftkinetic_cyclone_numpy_vs_cupy.py` for that, and +`submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py` for the full-node version): the +same grid/marker configuration is run with `ARRAY_BACKEND=cupy` at increasing MPI rank +counts, one rank per GPU, on the Booster partition -- so it measures whether adding more +rank+GPU pairs actually speeds up this fixed-size ITG run, and whether the CUDA-ported +kernels behave correctly under MPI for this model (domain-decomposed markers, particle +sorting/communication, the toroidal Fourier filter's `nprocs[2] == 1` requirement, etc.), +mirroring `submit_guidingcenter_cupy_scaling.py`'s pattern for the toy model. + +Each rank binds to its own GPU via `SLURM_LOCALID` in `params_cyclone.py` (see the +comment there) -- without that, every rank on a node would default to CuPy's device 0 +and contend for the same GPU, which would make this scaling study meaningless. +`SLURM_LOCALID` is a rank's index *within its node*, so this binding is correct on +multi-node runs too without any extra handling. + +`--ranks 1 2 4 8` (the default) covers both intra-node scaling (1/2/4 ranks, all on a +single Booster node, 4 GPUs/node) and one inter-node step (8 ranks = 2 nodes x 4 GPUs), +so the 4->8 step is the first data point that includes cross-node MPI exchange traffic +instead of only intra-node/NVLink-less PCIe traffic. `launch()` derives +`num_nodes = ceil(num_tasks / GPUS_PER_NODE)` and requires `num_tasks % num_nodes == 0`, +so rank counts must stay multiples of `GPUS_PER_NODE` once they exceed it (8, 12, 16, ...). + +The grid is only domain-decomposed along the two poloidal-plane directions +(`mpi_dims_mask=(True, True, False)` in `params_cyclone.py`, matching the toroidal +Fourier filter's own `nprocs[2] == 1` requirement), so the rank count is bounded by +`num_elements[0] * num_elements[1]` (default 16 x 64 = at most 1024) -- pass a larger +`--num-elements` before pushing `--ranks` much higher than the default. + +**Solver forced to `pcg`.** `params_cyclone.py` defaults to `solver="direct"` +(`feectools.linalg.solvers.DirectSolver`, see +`ISSUE_add_direct_solver_for_constant_operators.md`), but `DirectSolver` only supports a +single MPI rank -- it has no distributed sparse-direct factorization. Since this case +compares rank counts against each other, using the same solver at every rank count +matters more than using the fastest one available only at rank 1, so `--solver pcg` is +forced across the whole sweep (including at `--ranks 1`) for a consistent, apples-to-apples +comparison. Whether a (currently nonexistent) distributed direct solve would change this +picture is an open question -- see the ISSUE file's "Known limitations". +""" + +import argparse +from pathlib import Path + +from clusters import SLURM_PRESETS, detect_machine_name +from profiling_job import ProfilingCase + +# The preset is looked up by cluster name inside `launch`, so the dict is keyed by the +# *detected* name here rather than by the preset's own name: on Pitagora detection +# always returns "pitagora_dcgp" (it cannot tell the Booster partition apart), and this +# case must still get the Booster preset. Keying on the detected name also keeps this +# working, without a KeyError, on a machine detection does not recognise (name None). +GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] + +# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are +# spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank +# binding in params_cyclone.py. +GPUS_PER_NODE = 4 + + +def main() -> None: + + # Parse arguments, do not remove --upload + parser = argparse.ArgumentParser( + description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), + ) + parser.add_argument( + "--upload", + action="store_true", + help="Upload the packaged profiling results to the profiling-data repo.", + ) + parser.add_argument( + "--ranks", + type=int, + nargs="+", + default=[1, 2, 4, 8], + help="MPI rank counts to run with, one GPU per rank (default: 1 2 4 8; 8 spans 2 Booster nodes).", + ) + parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides params_cyclone.py's default, 200).") + parser.add_argument("--Tend", type=float, default=None, help="End time (overrides params_cyclone.py's default, 0.01 -> 10 steps).") + parser.add_argument( + "--num-elements", + type=int, + nargs=3, + default=None, + help=( + "Grid resolution (overrides params_cyclone.py's default, 16 64 4). Must " + "support at least as many ranks as the largest --ranks value in " + "num_elements[0] * num_elements[1] (the only two domain-decomposed " + "directions)." + ), + ) + args = parser.parse_args() + + # Paths relative to this script's location, so it can be run from anywhere. + script_dir = Path(__file__).resolve().parent + params_dir = script_dir / "examples" / "DriftKineticElectrostaticAdiabatic" + params_source = params_dir / "params_cyclone.py" + + profiling_case = ProfilingCase( + label="driftkinetic_cyclone_cupy_scaling", + name="DriftKineticElectrostaticAdiabatic Cyclone, CuPy multi-GPU scaling", + description=( + "Cyclone-instability ITG turbulence case for DriftKineticElectrostaticAdiabatic, " + "run with the CuPy array backend at increasing MPI rank counts (one GPU per " + "rank) on a fixed grid/marker configuration, to measure strong-scaling " + "speedup and verify the CUDA-ported kernels and MPI exchange paths work " + "correctly for this model under multi-rank/multi-GPU. The field solve is " + "pinned to solver='pcg' at every rank count -- DirectSolver " + "(params_cyclone.py's own default) does not support more than one MPI rank." + ), + physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", + struphy_model_used="DriftKineticElectrostaticAdiabatic", + params_source=params_source, + language="fortran", + compiler="GNU", + upload=args.upload, + ) + + # The preset is looked up by cluster name inside `launch`, so build a one-entry dict + # under whatever name detection reports for this machine. + cluster_name = detect_machine_name() + + param_flags = ["--backend", "cupy", "--solver", "pcg"] + if args.ppc is not None: + param_flags += ["--ppc", str(args.ppc)] + if args.Tend is not None: + param_flags += ["--Tend", str(args.Tend)] + if args.num_elements is not None: + param_flags += ["--num-elements", *[str(n) for n in args.num_elements]] + + # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would + # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs + # far more ranks per node than there are GPUs. + for num_tasks in args.ranks: + num_nodes = -(-num_tasks // GPUS_PER_NODE) + profiling_case.launch( + num_tasks, + num_nodes=num_nodes, + param_flags=param_flags, + slurm_presets={cluster_name: GPU_PRESET}, + ) + + # Package and push each run as its own job finishes. + profiling_case.finalize_run() + + +if __name__ == "__main__": + main() From db1ae2421a9ff45b56012ab5aa08ade29ec87878 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 18 Aug 2026 16:10:39 +0200 Subject: [PATCH 104/156] cpus_per_task=1 for gpu --- profiling/clusters.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/profiling/clusters.py b/profiling/clusters.py index 85061ba4a..030f5a152 100644 --- a/profiling/clusters.py +++ b/profiling/clusters.py @@ -85,7 +85,7 @@ def detect_machine_name() -> str | None: "pitagora_boost_fua_prod": { # "nodes": 1, # Should be set by ProfilingCase.launch() # "ntasks_per_node": 1, # Should be set by ProfilingCase.launch() - "cpus_per_task": 16, + "cpus_per_task": 1, "mem": "480GB", "gres": "gpu:4,tmpfs:10g", "partition": "boost_fua_prod", From 71e771fe320d2eeacf3c140296678e14755b1ed4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 18 Aug 2026 16:52:34 +0200 Subject: [PATCH 105/156] Updated params --- profiling/clusters.py | 2 +- ...iftkinetic_cyclone_cpu_node_vs_gpu_node.py | 48 +++++++++++++++---- ...ubmit_driftkinetic_cyclone_cupy_scaling.py | 2 +- 3 files changed, 40 insertions(+), 12 deletions(-) diff --git a/profiling/clusters.py b/profiling/clusters.py index 030f5a152..7602dcabd 100644 --- a/profiling/clusters.py +++ b/profiling/clusters.py @@ -72,7 +72,7 @@ def detect_machine_name() -> str | None: "pitagora_boost_fua_dbg": { # "nodes": 1, # Should be set by ProfilingCase.launch() # "ntasks_per_node": 1, # Should be set by ProfilingCase.launch() - "cpus_per_task": 16, + "cpus_per_task": 1, "mem": "480GB", "gres": "gpu:4,tmpfs:10g", "partition": "boost_fua_dbg", diff --git a/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py b/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py index affc75e8c..006182631 100644 --- a/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py +++ b/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py @@ -27,9 +27,19 @@ marker-exchange path (`mpi_sort_markers`/`apply_kinetic_bc`), not just single-rank kernel throughput -- the grid is domain-decomposed along the two poloidal-plane directions only (`mpi_dims_mask=(True, True, False)` in `params_cyclone.py`, matching the toroidal -Fourier filter's own `nprocs[2] == 1` requirement), so the rank count on either side is -bounded by that grid's first two `num_elements` (default 16 x 64 = at most 1024 ranks -before `--num-elements` needs to grow too). +Fourier filter's own `nprocs[2] == 1` requirement). + +**Grid size, and why it's much bigger than `params_cyclone.py`'s own default.** +`feectools.fem.partitioning.partition_coefficients` requires at least `degree` elements +*per rank* (not just in total) in each decomposed direction, so the actual rank cap is +`(num_elements[0] // degree) * (num_elements[1] // degree)`. `params_cyclone.py`'s own +default grid, `(16, 64, 4)` at `degree=3`, caps out around `(16 // 3) * (64 // 3) = 105` +ranks -- nowhere near a full `pitagora_dcgp` node's 256 cores, and running this case at +that default grid fails outright with an assertion from `partition_coefficients` ("Local +number of elements ... is to small for spline degree"). `--num-elements` therefore +defaults here to `(96, 256, 4)` instead (cap around `32 * 85 = 2720`, comfortably above +256 with margin for an uneven decomposition split) -- see the `--num-elements` help text +for the exact math if you need to raise `--cpu-ranks` further. """ import argparse @@ -44,8 +54,17 @@ # for both partitions (it cannot tell the Booster partition apart), and the GPU run must # still get the Booster preset. Keying on the detected name also keeps this working, # without a KeyError, on a machine detection does not recognise (name None). -CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] -GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] +# +# `time` is bumped from the presets' own 15 min to 30 min, the actual hard cap of the +# "*_fua_dbg" debug partitions both presets use (a longer request just gets clamped by +# SLURM) -- the much larger grid below (needed for the domain decomposition to support +# 256 ranks in the first place, see the module docstring) means setup (matrix assembly, +# with `solver="pcg"` forced) has real room to run past the original 15 min budget. If +# 30 min still isn't enough, the partition itself needs to change (the "_fua_prod" +# presets in clusters.py allow up to 24h, at the cost of a probably much longer queue +# wait) -- that tradeoff is left to the caller rather than made here. +CPU_PRESET = {**SLURM_PRESETS["pitagora_dcgp"], "time": "00:30:00"} +GPU_PRESET = {**SLURM_PRESETS["pitagora_boost_fua_dbg"], "time": "00:30:00"} # One CPU-node's worth of ranks (`HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"]`), and # one GPU-node's worth (the Booster preset requests `gres=gpu:4`), one rank per GPU as in @@ -83,12 +102,21 @@ def main() -> None: "--num-elements", type=int, nargs=3, - default=None, + default=[64, 128, 4], help=( - "Grid resolution (overrides params_cyclone.py's default, 16 64 4). Must " - "support at least as many ranks as --cpu-ranks in num_elements[0] * " - "num_elements[1] (the only two domain-decomposed directions), so raise this " - "before raising --cpu-ranks much past the default grid's 16*64=1024 cap." + "Grid resolution (default: 96 256 4 -- deliberately much larger than " + "params_cyclone.py's own default, 16 64 4, which only supports up to " + "~105 ranks at degree 3; see the note below). Domain decomposition " + "(feectools.fem.partitioning.partition_coefficients) requires at least " + "`degree` elements *per rank* in each decomposed direction, not just " + "`degree` elements total, so the usable rank cap is " + "`(num_elements[0] // degree) * (num_elements[1] // degree)` (the only two " + "decomposed directions, degree=3 by default) -- 16 64 4 caps out around " + "(16//3)*(64//3) = 5*21 = 105 ranks, well under a full pitagora_dcgp node's " + "256 cores, and fails with an assertion from partition_coefficients (\"Local " + "number of elements ... is to small for spline degree\") past that. The " + "default here, 96 256 4, caps out around (96//3)*(256//3) = 32*85 = 2720, " + "comfortably above 256 with margin for an uneven decomposition split." ), ) args = parser.parse_args() diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py index 093ea0df7..18b5e13f3 100644 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -74,7 +74,7 @@ def main() -> None: "--ranks", type=int, nargs="+", - default=[1, 2, 4, 8], + default=[1, 2], #, 4, 8], help="MPI rank counts to run with, one GPU per rank (default: 1 2 4 8; 8 spans 2 Booster nodes).", ) parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides params_cyclone.py's default, 200).") From 06da69a9c7a664babf6699b59d5c92062f6901d2 Mon Sep 17 00:00:00 2001 From: Max Date: Tue, 18 Aug 2026 16:59:39 +0200 Subject: [PATCH 106/156] Added tests --- .github/workflows/reusable-unit-testing.yml | 4 +- .github/workflows/test-PR-unit-mpi.yml | 2 +- src/struphy/pic/tests/test_neighbor_ranks.py | 104 +++++++++++++++++++ 3 files changed, 107 insertions(+), 3 deletions(-) create mode 100644 src/struphy/pic/tests/test_neighbor_ranks.py diff --git a/.github/workflows/reusable-unit-testing.yml b/.github/workflows/reusable-unit-testing.yml index 6fbf21f07..3910378aa 100644 --- a/.github/workflows/reusable-unit-testing.yml +++ b/.github/workflows/reusable-unit-testing.yml @@ -81,9 +81,9 @@ jobs: run: | { echo 'TESTMON_KEY=testmon-unit-mpi' - echo 'RUN_MAXWELL=mpirun -n 1 pytest -m single --testmon-forceselect -v --with-mpi --model-name Maxwell' + echo 'RUN_MAXWELL=mpirun --oversubscribe -n 1 pytest -m single --testmon-forceselect -v --with-mpi --model-name Maxwell' echo 'UNINSTALL_MPI=' - echo 'PYTEST_CMD=mpirun -n ${{ inputs.n-procs }} pytest -v --testmon --with-mpi' + echo 'PYTEST_CMD=mpirun --oversubscribe -n ${{ inputs.n-procs }} pytest -v --testmon --with-mpi' } >> "$GITHUB_ENV" - name: Check .testmondata 1 diff --git a/.github/workflows/test-PR-unit-mpi.yml b/.github/workflows/test-PR-unit-mpi.yml index 0d7a1ab45..a7db45f10 100644 --- a/.github/workflows/test-PR-unit-mpi.yml +++ b/.github/workflows/test-PR-unit-mpi.yml @@ -18,6 +18,6 @@ jobs: uses: ./.github/workflows/reusable-unit-testing.yml with: os: ubuntu-latest - n-procs: 2 + n-procs: 4 secrets: ghcr-token: ${{ secrets.GHCR_TOKEN }} \ No newline at end of file diff --git a/src/struphy/pic/tests/test_neighbor_ranks.py b/src/struphy/pic/tests/test_neighbor_ranks.py new file mode 100644 index 000000000..f5ed8be7a --- /dev/null +++ b/src/struphy/pic/tests/test_neighbor_ranks.py @@ -0,0 +1,104 @@ +from types import SimpleNamespace + +import pytest +from feectools.ddm.mpi import mpi as MPI + +from struphy.pic.base import Particles + + +def make_stub(comm, periodic_axes=()): + """A Particles stub exposing only the attributes touched by + ``_get_domain_decomp`` / ``_compute_neighbor_ranks``, decomposed over ``comm``.""" + stub = SimpleNamespace( + mpi_size=comm.Get_size(), + mpi_rank=comm.Get_rank(), + boxes_per_dim=None, + _periodic_axes=list(periodic_axes), + ) + domain_array, nprocs = Particles._get_domain_decomp(stub, mpi_dims_mask=None) + stub.domain_array = domain_array + return stub, nprocs + + +def rank_ijk(rank, nprocs): + """Inverse of the (i, j, k) -> rank flattening used in ``_get_domain_decomp``.""" + i = rank // (nprocs[1] * nprocs[2]) + nn = rank % (nprocs[1] * nprocs[2]) + j = nn // nprocs[2] + k = nn % nprocs[2] + return i, j, k + + +def ijk_rank(i, j, k, nprocs): + return i * (nprocs[1] * nprocs[2]) + j * nprocs[2] + k + + +@pytest.mark.mpi(min_size=2) +@pytest.mark.parametrize("periodic_axes", [(), (0,), (0, 1, 2)]) +def test_neighbor_ranks_partition_all_other_ranks(periodic_axes): + """Every other rank must end up in exactly one of the two output lists.""" + comm = MPI.COMM_WORLD + stub, _ = make_stub(comm, periodic_axes=periodic_axes) + + neighbor_ranks, non_neighbor_ranks = Particles._compute_neighbor_ranks(stub) + + rank = comm.Get_rank() + all_others = set(range(comm.Get_size())) - {rank} + assert set(neighbor_ranks) | set(non_neighbor_ranks) == all_others + assert set(neighbor_ranks).isdisjoint(non_neighbor_ranks) + assert rank not in neighbor_ranks + assert rank not in non_neighbor_ranks + + +@pytest.mark.mpi(min_size=2) +@pytest.mark.parametrize("periodic_axes", [(), (0,), (0, 1, 2)]) +def test_neighbor_relation_is_symmetric(periodic_axes): + """If rank j is a neighbour of rank i, rank i must be a neighbour of rank j. + + Both ranks classify the same pair of boxes, so a one-sided result would + mean the touching check disagrees with itself depending on which side asks. + """ + comm = MPI.COMM_WORLD + stub, _ = make_stub(comm, periodic_axes=periodic_axes) + + neighbor_ranks, _ = Particles._compute_neighbor_ranks(stub) + + all_neighbor_sets = comm.allgather(set(neighbor_ranks)) + rank = comm.Get_rank() + for j in range(comm.Get_size()): + if j == rank: + continue + assert (j in all_neighbor_sets[rank]) == (rank in all_neighbor_sets[j]), ( + f"neighbour relation between rank {rank} and rank {j} is not symmetric " + f"(periodic_axes={periodic_axes})" + ) + + +@pytest.mark.mpi(min_size=2) +@pytest.mark.parametrize("periodic_axes", [(), (0,), (1,), (0, 1, 2)]) +def test_face_adjacent_ranks_are_always_neighbors(periodic_axes): + """Ranks whose process-grid index differs by 1 along a single axis share a + face, so they must always be classified as neighbours -- including across + the periodic wrap when that axis is periodic.""" + comm = MPI.COMM_WORLD + stub, nprocs = make_stub(comm, periodic_axes=periodic_axes) + + neighbor_ranks, _ = Particles._compute_neighbor_ranks(stub) + + rank = comm.Get_rank() + ijk = rank_ijk(rank, nprocs) + for axis, n_axis in enumerate(nprocs): + for step in (-1, 1): + idx = list(ijk) + idx[axis] += step + if idx[axis] < 0 or idx[axis] >= n_axis: + if axis not in periodic_axes or n_axis == 1: + continue + idx[axis] %= n_axis + other_rank = ijk_rank(*idx, nprocs) + if other_rank == rank: + continue + assert other_rank in neighbor_ranks, ( + f"rank {rank} at grid index {ijk} expected face-neighbour {other_rank} " + f"at {tuple(idx)} along axis {axis} (periodic_axes={periodic_axes})" + ) From d708b8bf3ebdd9b0715cd08085aaa42c50b95944 Mon Sep 17 00:00:00 2001 From: Max Date: Tue, 18 Aug 2026 17:00:03 +0200 Subject: [PATCH 107/156] formatting --- src/struphy/pic/tests/test_neighbor_ranks.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/struphy/pic/tests/test_neighbor_ranks.py b/src/struphy/pic/tests/test_neighbor_ranks.py index f5ed8be7a..818f84d39 100644 --- a/src/struphy/pic/tests/test_neighbor_ranks.py +++ b/src/struphy/pic/tests/test_neighbor_ranks.py @@ -69,8 +69,7 @@ def test_neighbor_relation_is_symmetric(periodic_axes): if j == rank: continue assert (j in all_neighbor_sets[rank]) == (rank in all_neighbor_sets[j]), ( - f"neighbour relation between rank {rank} and rank {j} is not symmetric " - f"(periodic_axes={periodic_axes})" + f"neighbour relation between rank {rank} and rank {j} is not symmetric (periodic_axes={periodic_axes})" ) From 42136e0ef2e1bdcf66d8d22fd15fafc31dc48243 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 09:09:28 +0200 Subject: [PATCH 108/156] Updated profiling params --- .../params_cyclone.py | 35 +++++++++++-------- ...ubmit_driftkinetic_cyclone_cupy_scaling.py | 25 ++++++++++--- 2 files changed, 42 insertions(+), 18 deletions(-) diff --git a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py index 8f9fcaefd..79244580c 100644 --- a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py +++ b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py @@ -300,7 +300,7 @@ loading_params = LoadingParameters(ppc=ppc, loading="sobol_standard", spatial="uniform", moments=(0, 0, 4, 4)) weights_params = WeightsParameters(control_variate=True) boundary_params = BoundaryParameters(bc=("remove", "periodic", "periodic")) -sorting_params = SortingParameters(boxes_per_dim=(12, 12, 6), do_sort=True, sorting_frequency=5) +sorting_params = SortingParameters(boxes_per_dim=(16, 16, 6), do_sort=True, sorting_frequency=5) # density binning, needed for the e1_e2 density slice generated by the pproc block below # (matches examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py's own setup) @@ -414,7 +414,7 @@ def pert_func(*etas): if __name__ == "__main__": sim.run() - sim.pproc(parallel_pproc=True) + sim.pproc(parallel_pproc=True, physical=True) # Static, non-interactive figures for this profiling run -- adapted from # examples/DriftKineticElectrostaticAdiabatic/cyclone/pproc_cyclone.py (which is @@ -436,12 +436,11 @@ def pert_func(*etas): results_dir = os.path.join(sim.env.path_out, "results") os.makedirs(results_dir, exist_ok=True) - # Deliberately plain NumPy from here on, not xp/cunumpy: everything below reads - # already-saved (host, h5py) or already-postprocessed (PlottingData, host-only - # regardless of ARRAY_BACKEND) data purely to hand it to matplotlib, so there is - # nothing left to dispatch onto the active backend -- using xp on data that is - # already NumPy would only risk exactly the array-type/active-backend mismatches - # this branch's CuPy port spent a lot of effort finding and fixing elsewhere. + # Deliberately plain NumPy from here on, not xp/cunumpy, for data that really is + # already host-side (h5py reads). spline_values/grids_phy/PlottingData attributes + # are NOT host-only under ARRAY_BACKEND=cupy despite being "post-processed" -- + # they stay on-device, so those are pulled to host explicitly via xp.to_numpy() + # below rather than np.asarray(), which CuPy refuses as an implicit conversion. # ------------------------------------------------------------------- # phi_integral evolution + exponential growth-rate fit (the ITG-test @@ -476,9 +475,14 @@ def pert_func(*etas): # Electric potential phi, poloidal (R, Z) slice at the last saved time # step, toroidal index 0 (see pproc_cyclone.py's plot_field_slider). # ------------------------------------------------------------------- - Tend_saved = sim.t_grid[-1] - phi_phy = np.asarray(sim.spline_values.em_fields.phi_phy.data[Tend_saved][0]) - X, Y, Z = (np.asarray(g) for g in sim.grids_phy) + # float(): dict keys of `.data` are plain Python floats, and t_grid may be a + # 0-d CuPy array here (ARRAY_BACKEND=cupy), which is unhashable. + Tend_saved = float(sim.t_grid[-1]) + # xp.to_numpy(), not np.asarray(): unlike the PlottingData read above, + # spline_values/grids_phy stay on-device under ARRAY_BACKEND=cupy, and CuPy + # arrays refuse implicit conversion via np.asarray(). + phi_phy = xp.to_numpy(sim.spline_values.em_fields.phi_phy.data[Tend_saved][0]) + X, Y, Z = (xp.to_numpy(g) for g in sim.grids_phy) R = np.sqrt(X**2 + Y**2) toroidal_index = 0 @@ -501,10 +505,13 @@ def pert_func(*etas): # logical (eta) space (see pproc_cyclone.py's plot_binned_quantity_slider). # ------------------------------------------------------------------- density_data = sim.f.kinetic_ions.e1_e2_density - delta_f_final = np.asarray(density_data.delta_f_binned)[-1] + # xp.to_numpy(): same on-device-under-cupy issue as phi_phy/grids_phy above -- + # the "PlottingData is host-only regardless of backend" premise noted at the top + # of this function does not hold for these attributes. + delta_f_final = xp.to_numpy(density_data.delta_f_binned)[-1] eta1_grid, eta2_grid = np.meshgrid( - np.asarray(density_data.grid_e1), - np.asarray(density_data.grid_e2), + xp.to_numpy(density_data.grid_e1), + xp.to_numpy(density_data.grid_e2), indexing="ij", ) diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py index 18b5e13f3..0fc912787 100644 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -38,6 +38,16 @@ forced across the whole sweep (including at `--ranks 1`) for a consistent, apples-to-apples comparison. Whether a (currently nonexistent) distributed direct solve would change this picture is an open question -- see the ISSUE file's "Known limitations". + +**`Tend` shortened to 3 steps.** `params_cyclone.py`'s own default (`Tend=0.01`, `dt=0.001` +-> 10 steps) was written for the `direct`-solver comparison, where the field solve is +essentially free after the first call. Here `--solver pcg` is forced instead, and the +case's own profiling notes (`driftkinetic_cyclone_numpy_vs_cupy`'s run metadata) recorded +a single CuPy `pcg` step taking anywhere from ~14.5 s (clean, isolated GPU) up to 174 s +under GPU contention on this shared partition -- 10 such steps plus ~90 s of CUDA +setup/compile do not reliably fit in `pitagora_boost_fua_dbg`'s 15-30 min walltime (its +partition-enforced cap). 3 steps is enough to warm up and get a stable per-step timing for +the scaling comparison this case cares about; pass `--Tend` to override. """ import argparse @@ -78,7 +88,16 @@ def main() -> None: help="MPI rank counts to run with, one GPU per rank (default: 1 2 4 8; 8 spans 2 Booster nodes).", ) parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides params_cyclone.py's default, 200).") - parser.add_argument("--Tend", type=float, default=None, help="End time (overrides params_cyclone.py's default, 0.01 -> 10 steps).") + parser.add_argument( + "--Tend", + type=float, + default=0.003, + help=( + "End time (default: 0.003 -> 3 steps, shortened from params_cyclone.py's own " + "0.01/10 steps so the forced pcg solver reliably fits in the debug partition's " + "walltime; see the module docstring)." + ), + ) parser.add_argument( "--num-elements", type=int, @@ -122,11 +141,9 @@ def main() -> None: # under whatever name detection reports for this machine. cluster_name = detect_machine_name() - param_flags = ["--backend", "cupy", "--solver", "pcg"] + param_flags = ["--backend", "cupy", "--solver", "pcg", "--Tend", str(args.Tend)] if args.ppc is not None: param_flags += ["--ppc", str(args.ppc)] - if args.Tend is not None: - param_flags += ["--Tend", str(args.Tend)] if args.num_elements is not None: param_flags += ["--num-elements", *[str(n) for n in args.num_elements]] From 3db41d826578a018bf103486262851921d90dd3f Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 09:10:11 +0200 Subject: [PATCH 109/156] Use xp.union1d since it works with both numpy and cupy --- src/struphy/post_processing/post_processing_tools.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/struphy/post_processing/post_processing_tools.py b/src/struphy/post_processing/post_processing_tools.py index 69906ac48..f26925401 100644 --- a/src/struphy/post_processing/post_processing_tools.py +++ b/src/struphy/post_processing/post_processing_tools.py @@ -1017,7 +1017,10 @@ def _post_process_markers( ids = temp[:, -1].astype("int") ids_lost_particles = xp.setdiff1d(xp.arange(n_markers), ids) ids_removed_particles = xp.nonzero(temp[:, 0] == -1.0)[0] - ids_lost_particles = xp.array(list(set(ids_lost_particles) | set(ids_removed_particles)), dtype=int) + # xp.union1d (not Python set()) stays backend-safe: iterating a CuPy + # array yields 0-d CuPy arrays, which are unhashable, unlike NumPy + # scalars (same class of issue as the xp.sort note below). + ids_lost_particles = xp.union1d(ids_lost_particles, ids_removed_particles).astype(int) lost_particles_mask[:] = False lost_particles_mask[ids_lost_particles] = True From 53f2133505b25fa6bc99ffc58d32eeddca68a2ef Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 09:46:24 +0200 Subject: [PATCH 110/156] updated params_LinearMHDDriftkineticCC.py --- params_LinearMHDDriftkineticCC.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/params_LinearMHDDriftkineticCC.py b/params_LinearMHDDriftkineticCC.py index eb6e3efb3..536dde36a 100644 --- a/params_LinearMHDDriftkineticCC.py +++ b/params_LinearMHDDriftkineticCC.py @@ -90,7 +90,7 @@ ) # Time stepping -time_opts = Time() +time_opts = Time(dt=0.01, Tend=0.05) # Geometry domain = domains.Cuboid() @@ -122,7 +122,7 @@ # Particle parameters # ------------------- -loading_params = LoadingParameters() +loading_params = LoadingParameters(seed=1234) weights_params = WeightsParameters() boundary_params = BoundaryParameters() sorting_params = SortingParameters() @@ -166,14 +166,14 @@ # For kinetic species the perturbations are added to the moments of the distribution function (defined as tuples). # Background for kinetic species -maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None), equil=equil) -maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None), equil=equil) +maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None)) +maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None)) background = maxwellian_1 + maxwellian_2 model.energetic_ions.var.add_background(background) # Perturbations for (some) kinetic species perturbation = perturbations.TorusModesCos() -maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation), equil=equil) +maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation)) init = maxwellian_1pt + maxwellian_2 model.energetic_ions.var.add_initial_condition(init) From 375c7391f131f498974ebc6e87bf0ea72d46f8f4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 09:46:51 +0200 Subject: [PATCH 111/156] Run driftkinetic_cyclone_cupy_scaling with 1,2,4,8 GPUs --- profiling/submit_driftkinetic_cyclone_cupy_scaling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py index 0fc912787..212f697a7 100644 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -84,7 +84,7 @@ def main() -> None: "--ranks", type=int, nargs="+", - default=[1, 2], #, 4, 8], + default=[1, 2, 4, 8], help="MPI rank counts to run with, one GPU per rank (default: 1 2 4 8; 8 spans 2 Booster nodes).", ) parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides params_cyclone.py's default, 200).") From 365d19b91b03d13dbdb3e5a5e93ffd03ed7b0768 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 12:11:11 +0200 Subject: [PATCH 112/156] Updated feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 9a8807ee9..2f0cd1651 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 9a8807ee97cfba3ece24ddc5a877a3550a497b02 +Subproject commit 2f0cd1651c8f8dfbd7fca81aa53b207857eac848 From bd33ae14595578d9def49e3191a7cfb289dcaa44 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 12:27:03 +0200 Subject: [PATCH 113/156] update feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 2f0cd1651..8a175c5dc 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 2f0cd1651c8f8dfbd7fca81aa53b207857eac848 +Subproject commit 8a175c5dca280285939320c28819aefa8bd28c71 From aea1ab15480cc289e4d8c59e75e2d1447c309cfd Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 13:43:01 +0200 Subject: [PATCH 114/156] update feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 8a175c5dc..737d440c6 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 8a175c5dca280285939320c28819aefa8bd28c71 +Subproject commit 737d440c6f534e3a5cf789855b052955a9ab11a0 From 045ef7ca70f52e7cbd85b6c9bf23bd6350d90d33 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 14:10:03 +0200 Subject: [PATCH 115/156] Cleanup --- .../params_cyclone.py | 112 ----------- .../GuidingCenter/params_GuidingCenter.py | 29 +-- .../params_GuidingCenter_scaling.py | 45 +---- .../params_PressureLessSPH_scaling.py | 30 +-- .../params_VlasovAmpere_scaling.py | 38 +--- ...iftkinetic_cyclone_cpu_node_vs_gpu_node.py | 182 ------------------ ...ubmit_driftkinetic_cyclone_cupy_scaling.py | 97 +++------- ...bmit_driftkinetic_cyclone_numpy_vs_cupy.py | 33 +--- ...bmit_guidingcenter_cpu_node_vs_gpu_node.py | 39 +--- .../submit_guidingcenter_cupy_scaling.py | 48 +---- .../submit_guidingcenter_numpy_vs_cupy.py | 21 +- .../submit_pressurelesssph_cupy_scaling.py | 31 +-- profiling/submit_vlasovampere_cupy_scaling.py | 31 +-- 13 files changed, 82 insertions(+), 654 deletions(-) delete mode 100644 profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py diff --git a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py index 79244580c..d14008e81 100644 --- a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py +++ b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py @@ -10,118 +10,6 @@ examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py, the physics case this profiling params file is adapted from), used as the NumPy-vs-CuPy backend comparison case for a real gyrokinetic model rather than a toy one. - -Unlike GuidingCenter (profiling/examples/GuidingCenter), this model carries a real FEEC -field solve every step (PoissonAdiabaticGyrokinetic, an ImplicitDiffusion subclass: a PCG -solve against the H1 mass/stiffness matrices in toroidal geometry with a Fourier filter), -on top of the two CUDA-ported guiding-center pushers (PushGuidingCenterBxEstar, -PushGuidingCenterParallel). The geometry is a toroidal HollowTorus (not a Cuboid slab), -and particle weights use the control-variate method. This is the case that answers -whether the CUDA port helps a real ITG run end to end, including the parts (Poisson -solve, control variates, sorting, HollowTorus mapping evaluation) that were not -individually targeted by the port. - -Getting this to run under CuPy at all required fixing several real device-portability -bugs found while validating this case (not toy-model artifacts): a NumPy-vs-CuPy -scalar-typing trap in the `AdhocTorus` equilibrium (`xp.sqrt` on a plain float silently -returns a 0-d CuPy array, which then hit scipy's `UnivariateSpline`/`quad`, both -host-only), the Sobol marker-loading sequence (a scalar bit-manipulation algorithm with -nothing to vectorize, now forced onto plain NumPy instead of CuPy), a full-slice -NumPy->CuPy assignment in `AverageOperator` that does not auto-convert the way -boolean/fancy indexing does, CuPy's `einsum` not accepting `out=` at all (unlike -NumPy's), and (for the `--solver direct` path below) several `tosparse()`/`toarray()` -stubs and CuPy/NumPy mixing bugs in `feectools` that had never been exercised under -CuPy before. See the commits touching `fields_background/equils.py`, `pic/sobol_seq.py`, -`feec/mass.py`, `feec/linear_operators.py`, `pic/accumulation/filter.py`, -`feectools/feec/derivatives.py`, and `feectools/linalg/{stencil,solvers,direct_solvers}.py`. - -Once it ran under the original iterative solver (`--solver pcg`), the result was a -genuine (non-toy) finding: unlike GuidingCenter, this model was *slower end to end* on -CuPy than on NumPy, because the part the CUDA port never touched -- the per-step -`PoissonAdiabaticGyrokinetic` PCG solve -- dominated the runtime and was itself slower on -the device (with tol=1e-12 it runs close to the full maxiter=3000 on both backends: the -operator is not well conditioned enough to converge much faster, and each of those -iterations needs a matvec plus a reduction whose result has to be read back to host -before the next iteration can even start -- a hard sequential dependency that is cheap -per iteration on CPU but not on GPU, where several small kernel launches plus the sync -add up to real cost a matrix this size cannot amortize away). Measured at -num_elements=(16, 64, 4), ppc=5, 1 step (dt=0.001), single rank, one H100: - - backend total (setup to finalize) PCG solve/call (model.integrate) push_gc_bxe push_gc_para - numpy 62.7 s 6.90 s 0.099 s 0.095 s - cupy 91.4 s 14.54 s (2.1x SLOWER) 0.035 s (2.9x) 0.019 s (5.0x) - -The default solver is now `direct` (`feectools.linalg.solvers.DirectSolver`), not `pcg`: -the LHS operator this propagator solves against is *constant* across every time step -(`divide_by_dt=False`, fixed `epsilon`/`Z` -- see `ImplicitDiffusion.__call__`), so -re-running an iterative solve close to `maxiter` every single call was pure waste on -either backend. `DirectSolver` factorizes once (lazily, on the first `solve()` call) via -a cached sparse LU (`feectools.linalg.direct_solvers.SparseSolver`) and reuses that -factorization for every later call, which needs only a triangular solve -- a small, fixed -number of kernel launches regardless of the operator's conditioning, which is exactly -what removes the GPU sync penalty above. Measured at the same configuration, 5 steps -(dt=0.001, Tend=0.005), single rank, one H100 -- `direct`'s first call pays the one-time -factorization, every later call is the pure solve: - - backend solver total (setup to finalize) solve/call: 1st (factorize) solve/call: later (solve only) - numpy pcg 108.7 s n/a (iterative every call) 6.9 s (unchanged) - numpy direct 52.5 s 3.4 s 0.008 s (~860x vs pcg) - cupy pcg n/a (>500s for 1 step alone, node-contended -- see below) - cupy direct 75.7 s 2.4 s 0.0076 s (~1900x vs the clean - 14.54 s/solve pcg number above) - -Both `pcg` and `direct` were verified to produce matching physics (`en_phi`/`en_tot`/ -`phi_integral` scalars agree to ~1e-10, consistent with `pcg` simply not having fully -converged at `maxiter` while `direct` solves exactly). The `cupy pcg` 5-step number above -is intentionally omitted rather than reported: a rerun on this shared login-node GPU took -174.55 s for a *single* step (vs the clean, isolated 14.54 s/solve measured earlier the -same session), evidently due to GPU contention from other jobs -- worth knowing if you -reproduce this yourself, but not a number to trust as `pcg`'s true cost. - -With `direct` removing the field-solve bottleneck, one more non-obvious cost stayed -hidden until the field solve itself got fast: `ImplicitDiffusion.__call__` runs a -*second*, completely separate solve every step when `diagnostic is not None` -(`proj = L2Projector("H1", self.mass_ops); self.diagnostic.spline.vector = proj.solve(rhs)`) --- a fresh, uncached `L2Projector` with the default `"pcg"` solver, untouched by -`--solver direct` above since it is a different `InverseLinearOperator` entirely. The -physics case this file is adapted from enables it (`use_diagnostic_poisson=True`), but it -only feeds `self.diagnostics.rho`, an extra saved diagnostic field nothing else in this -model reads back (the `phi_integral` scalar uses `phi` directly) -- disabled here -(`use_diagnostic_poisson=False`) since it was otherwise the dominant per-step cost left -after fixing the main solve. - -## CuPy actually winning end to end - -At the tiny `ppc=5` used to validate correctness above, CuPy still loses in total wall -time even with `direct` and the diagnostic-solve fix: setup (CUDA context/kernel -compilation, done once) is a larger fixed cost on CuPy than on NumPy, and there isn't -enough per-step *work* -- the whole point of `direct` and disabling the diagnostic -solve -- for the CUDA-ported pushers to out-earn that fixed cost. Scaling up `ppc` (more -markers per cell, i.e. more actual particle-push/accumulation work, which is exactly what -was CUDA-ported) is what tips the balance, not a bigger grid. Measured at -num_elements=(16, 64, 4), **ppc=200** (the physics case's own suggested minimum), 10 -steps (dt=0.001, Tend=0.01), single rank, one H100, `--solver direct` (the default), -`use_diagnostic_poisson=False`: - - backend total (setup to finalize) model.integrate/step push_gc_bxe/step push_gc_para/step - numpy 145.5 s 8.71 s 3.84 s 4.37 s - cupy 80.0 s (1.82x FASTER) 0.59 s (14.8x) 0.14 s (28x) 0.14 s (32x) - -`en_phi`/`en_tot`/`phi_integral` scalars match to ~1e-21 (round-off) between the two -runs. This is now the default configuration (`ppc=200`, `Tend=0.01`); override with -`--ppc`/`--Tend`/`--num-elements` to explore further (e.g. a larger grid should shift -the crossover point the other way, back toward NumPy, per the still-open question below). - -Whether a larger, more production-scale grid (num_elements=(32, 135, 5), the physics -case's own default) changes any of this is open -- `direct`'s one-time factorization cost -should grow with problem size (the setup-phase matrix assembly alone took 226 s on NumPy -at that size when this case was first validated), so whether CuPy still wins at scale, -and by how much, needs its own longer-budget run; pass a larger --num-elements (with a -longer SLURM walltime) to check. - -`num_elements`/`ppc`/`Tend` default to the validated configuration above (a -speed/coverage tradeoff against the physics case's own (32, 135, 5)/50/0.01); override -with --num-elements/--ppc/--Tend for a larger run. """ import argparse diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter.py b/profiling/examples/GuidingCenter/params_GuidingCenter.py index 3bc664693..82536c7a1 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter.py @@ -7,32 +7,9 @@ name = "GuidingCenter NumPy vs CuPy" description = """ Guiding-centre (5D drift-kinetic) test particles in a homogeneous slab, used as the -NumPy-vs-CuPy backend comparison case. - -This model is chosen for the backend comparison because its entire propagator stack -(PushGuidingCenterBxEstar, PushGuidingCenterParallel) is backed by kernels that have a -hand-written CUDA implementation, and it carries no FEEC field solve. The measured -wall-clock time is therefore dominated by the particle kernels themselves, which is what -the GPU port is meant to accelerate -- unlike e.g. LinearMHDDriftkineticCC, whose runtime -is dominated by MHD field propagators and one-off setup, so that particle-kernel speedups -are invisible in the total. - -Both propagators are run with algo="explicit"; the default -("discrete_gradient_1st_order") is also CUDA-ported, but the explicit scheme keeps the -comparison to a single kernel call per stage and avoids the outer Picard loop, whose -iteration count can differ between runs. - -Measured at the defaults below (Np=200000, 100 steps, single rank, one A100): - - backend total (setup to finalize) - numpy 126.2 s - cupy 9.7 s -> 13.0x - - region numpy cupy speedup - prop: PushGuidingCenterParallel 46.87 s 0.685 s 68x - prop: PushGuidingCenterBxEstar 42.16 s 0.556 s 76x - kernel: push_gc_Bstar_explicit_multistage 33.48 s 0.050 s 665x - kernel: push_gc_bxEstar_explicit_multistage 29.66 s 0.051 s 581x +NumPy-vs-CuPy backend comparison case. Its whole propagator stack is CUDA-ported and it +has no FEEC field solve, so wall-clock time is dominated by the particle kernels the GPU +port targets. """ import argparse diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py index 5c6beaaa0..0c05d488b 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py @@ -6,47 +6,10 @@ name = "GuidingCenter CuPy multi-GPU scaling" description = """ -Guiding-centre (5D drift-kinetic) test particles in a homogeneous slab, used as the -CuPy multi-GPU/multi-rank strong-scaling case (see profiling/submit_guidingcenter_cupy_scaling.py). - -This is a separate, much larger params file from params_GuidingCenter.py (the -NumPy-vs-CuPy single-GPU comparison): its Np default is 10,000,000, ~50x that case's -200,000. That matters here specifically because of the marker-exchange cost measured -while validating this case -- at Np=200,000 (50,000/rank at 4 ranks), mpi_sort_markers -(the device-to-device particle exchange between ranks after each stage) ate 79-89% of -model.integrate, and total wall time got *worse* with more ranks: - - ranks total (setup to finalize) - 1 4.48 s - 2 5.19 s - 4 5.50 s (slower than 1 rank) - -At Np=4,000,000 the same 1/2/4-rank comparison did show a clear speedup (9.93 s / 5.88 s -/ 3.79 s -> 1.7x / 2.6x), because there is enough per-rank compute between exchanges for -it to outweigh the communication cost. - -At Np=10,000,000, a full 1/2/4/8-rank sweep (8 ranks = 2 Booster nodes) still regressed -at the 4->8 step (22.87 s -> 24.98 s): `mpi_sort_markers`'s share of the pusher loop grew -from 60.5% (2 ranks) to 65.8% (4 ranks) to 76.5% (8 ranks), while actual GPU kernel compute -stayed under 5% throughout -- the sort/exchange is latency- (not bandwidth-) bound, so its -share keeps growing even as its own average per-call cost keeps shrinking. This file goes -further still (50,000,000) to test whether enough per-rank compute between exchanges can -push the crossover point past 8 ranks, without changing the sort/BC call frequency itself -(deliberately not touched -- that would be an algorithmic change, not a scaling-parameter -one). Whether it does, and at what rank count, is what running this case answers. - -`num_elements` (the Derham/FEEC grid resolution) was also bumped from (16, 16, 16) to -(32, 32, 32): a coarser grid means each rank's local sub-grid is small relative to the -fixed-width ghost/halo padding needed for local spline evaluation, so a larger grid should -shrink that fixed overhead's share as well -- this is a different mechanism from the -particle-exchange cost above (marker sorting is about markers crossing rank sub-domain -boundaries, not grid ghost cells), tested here as a separate, independent lever since it -requires no algorithmic change either. - -`GuidingCenter` is used (as in params_GuidingCenter.py) because its whole propagator -stack (PushGuidingCenterBxEstar, PushGuidingCenterParallel) is CUDA-ported and it carries -no FEEC field solve, so wall-clock time is dominated by the particle kernels and their -MPI exchange, not by anything unrelated to the CUDA port. +Guiding-centre (5D drift-kinetic) test particles in a homogeneous slab, used as the CuPy +multi-GPU/multi-rank strong-scaling case. Np is much larger than params_GuidingCenter.py's +(50,000,000 vs 200,000) so there's enough per-rank compute between marker-exchange calls +for scaling to actually pay off, rather than being dominated by mpi_sort_markers. """ import argparse diff --git a/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py b/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py index e00069a55..69b1b1422 100644 --- a/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py +++ b/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py @@ -7,34 +7,8 @@ name = "PressureLessSPH CuPy multi-GPU scaling" description = """ SPH test particles in a homogeneous cube, used as a third CuPy multi-GPU/multi-rank -strong-scaling case alongside submit_guidingcenter_cupy_scaling.py and -submit_vlasovampere_cupy_scaling.py -- this one is close to a pure particle push: no -FEEC field solve at all (model_type="Fluid", no linear system to solve every step, unlike -VlasovAmpereOneSpecies's SchurSolver), and per CUDA_KERNEL_PORTING_STATUS.md its SPH -kernel families (pushers, evaluation, marker-column kernels) are all fully CUDA-ported -for their live code paths -- as close to "everything runs on GPU" as any model in this -repo gets. `PressureLessSPH` specifically (not IncompressibleNavierStokesSPH or -ViscousEulerSPH) is used because it was the one explicitly re-verified to agree to -round-off across backends after the marker-detachment bug fix (see -ISSUE_mhd_cupy_physics_divergence.md's "Does not affect" note) -- not just ported, but -checked correct. - -Two propagators: PushEta (position push) and PushVinEfield (velocity push against a -background force field derived from equil.p0) -- both simpler, cheaper-per-marker -kernels than GuidingCenter's multistage guiding-centre push, so if anything this case -should push mpi_sort_markers's share *up* rather than down relative to GuidingCenter, -making it a useful third data point on the low-per-marker-compute end (GuidingCenter -in the middle, VlasovAmpereOneSpecies's real field solve on the high end). - -Adapted from the repo's own `params_PressureLessSPH.py` (root directory -- the -model's reference/template params file) into the scaling-case pattern used by the -other two cases here: same SLURM_LOCALID device binding, FEECTOOLS_ENABLE_MPI opt-in, ---backend/--Np/--Tend/--id CLI surface, and the same Cuboid domain (32, 32, 32) grid -for comparability. Np default kept smaller (10,000,000) than the other two cases' -50,000,000 for an initial run, since this is the first time this case has been run at -scale -- see the docstring in params_GuidingCenter_scaling.py for why marker count -matters for the mpi_sort_markers/per-rank-compute balance being compared here; raise -it once a first sweep confirms this scales the way the other two cases did. +strong-scaling case alongside GuidingCenter and VlasovAmpereOneSpecies -- this one has no +FEEC field solve at all, the low-per-marker-compute end of the three. """ import argparse diff --git a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py index 278362edc..e2e1cb2e4 100644 --- a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py +++ b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py @@ -6,40 +6,10 @@ name = "VlasovAmpereOneSpecies CuPy multi-GPU scaling" description = """ -6D full-orbit Vlasov-Ampere test particles in a homogeneous cube, used as a second -CuPy multi-GPU/multi-rank strong-scaling case alongside -profiling/submit_guidingcenter_cupy_scaling.py -- deliberately NOT dominated by -mpi_sort_markers the way that case is. - -GuidingCenter's whole propagator stack is a pure particle push with no FEEC field -solve, so at every rank count profiled so far (see -params_GuidingCenter_scaling.py's docstring) mpi_sort_markers/apply_kinetic_bc ate -57-70% of the pusher loop and actual GPU kernel compute stayed under 5% -- i.e. that -case is a communication/bookkeeping benchmark more than a compute one, by design. -VlasovAmpereOneSpecies's VlasovAmpereCoupling propagator instead solves a real linear -system each step (SchurSolver over the E-field mass matrix, solver="pcg") to update -the field from the accumulated particle current, on top of the push -- real per-step -compute that doesn't exist in GuidingCenter's scaling case at all. Whether that shifts -the balance away from mpi_sort_markers, and by how much, is what this case measures. - -Kept as close to params_GuidingCenter_scaling.py's setup as the different model -allows for comparability: same Cuboid domain and (32, 32, 32) grid, same -SLURM_LOCALID device binding and FEECTOOLS_ENABLE_MPI opt-in, same --backend/--Np/ ---Tend/--id CLI surface, and a similarly large default Np (also 50,000,000, i.e. the -same total marker count already validated to give real per-rank compute between -exchanges for GuidingCenter -- see that file's docstring for the 10M/50M numbers this -follows). `with_B0=False` (electrostatic only, no PushVxB) keeps the propagator stack -close to GuidingCenter's 2-propagator-plus-coupling shape (PushEta + VlasovAmpereCoupling -here vs PushGuidingCenterBxEstar + PushGuidingCenterParallel there) rather than adding a -third. - -Per CUDA_KERNEL_PORTING_STATUS.md, VlasovAmpereOneSpecies is one of the models verified -fully device-resident (zero host<->device marker crossings) alongside GuidingCenter, so -this is a like-for-like comparison of the CUDA port, not a case exercising unported code -paths. LinearMHDDriftkineticCC was deliberately NOT used instead, despite also having a -real field solve: it has an unresolved, tracked physics-divergence bug on the CuPy -backend (ISSUE_mhd_cupy_physics_divergence.md, status "needs re-measurement"), which -would make any timing from it untrustworthy for a comparison like this one. +6D full-orbit Vlasov-Ampere test particles in a homogeneous cube, used as a second CuPy +multi-GPU/multi-rank strong-scaling case alongside GuidingCenter -- deliberately not +dominated by mpi_sort_markers, since VlasovAmpereCoupling solves a real linear system +each step on top of the particle push. """ import argparse diff --git a/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py b/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py deleted file mode 100644 index 006182631..000000000 --- a/profiling/submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py +++ /dev/null @@ -1,182 +0,0 @@ -"""DriftKineticElectrostaticAdiabatic (ITG cyclone) full-node CPU-vs-GPU comparison case. - -The other two DriftKineticElectrostaticAdiabatic cases each answer a narrower question: -`submit_driftkinetic_cyclone_numpy_vs_cupy.py` compares backends at a fixed single rank -(where CuPy only wins once `ppc` is raised enough to give the CUDA-ported pushers real -work, see that case's params file docstring), and `submit_driftkinetic_cyclone_cupy_scaling.py` -measures CuPy strong-scaling alone. Neither answers the practical question a user -actually has: given one full CPU node and one full GPU node, which one do you point a -real ITG run at? This case runs exactly that comparison -- `ARRAY_BACKEND=numpy` using -every core of one `pitagora_dcgp` node against `ARRAY_BACKEND=cupy` using every GPU of -one Booster node -- at the same grid/marker configuration on both sides, mirroring -`submit_guidingcenter_cpu_node_vs_gpu_node.py`'s pattern for the toy model. - -**Solver forced to `pcg`.** `params_cyclone.py` defaults to `solver="direct"` -(`feectools.linalg.solvers.DirectSolver`, see `ISSUE_add_direct_solver_for_constant_operators.md` -and that params file's own docstring for why and by how much it helps), but `DirectSolver` -only supports a single MPI rank -- it asserts on `nprocs > 1`, since the sparse-direct -factorization it wraps (`feectools.linalg.direct_solvers.SparseSolver`) has no -distributed variant. Both sides of this comparison run with many ranks, so both are -pinned to `--solver pcg` here regardless of the file's own default -- this measures the -field solve's *old*, unoptimized behavior at full-node scale, which is also useful data -(see `ISSUE_add_direct_solver_for_constant_operators.md`'s "Known limitations": whether a -distributed direct solve would still win at this scale is an open question this case does -not answer). - -Both sides run with more than one rank, so both exercise the domain-decomposed -marker-exchange path (`mpi_sort_markers`/`apply_kinetic_bc`), not just single-rank kernel -throughput -- the grid is domain-decomposed along the two poloidal-plane directions only -(`mpi_dims_mask=(True, True, False)` in `params_cyclone.py`, matching the toroidal -Fourier filter's own `nprocs[2] == 1` requirement). - -**Grid size, and why it's much bigger than `params_cyclone.py`'s own default.** -`feectools.fem.partitioning.partition_coefficients` requires at least `degree` elements -*per rank* (not just in total) in each decomposed direction, so the actual rank cap is -`(num_elements[0] // degree) * (num_elements[1] // degree)`. `params_cyclone.py`'s own -default grid, `(16, 64, 4)` at `degree=3`, caps out around `(16 // 3) * (64 // 3) = 105` -ranks -- nowhere near a full `pitagora_dcgp` node's 256 cores, and running this case at -that default grid fails outright with an assertion from `partition_coefficients` ("Local -number of elements ... is to small for spline degree"). `--num-elements` therefore -defaults here to `(96, 256, 4)` instead (cap around `32 * 85 = 2720`, comfortably above -256 with margin for an uneven decomposition split) -- see the `--num-elements` help text -for the exact math if you need to raise `--cpu-ranks` further. -""" - -import argparse -from pathlib import Path - -from clusters import HARDWARE_INFO, SLURM_PRESETS, detect_machine_name -from profiling_job import ProfilingCase - -# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name -# (`detect_machine_name`), so both dicts below are keyed by the *detected* name rather -# than by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" -# for both partitions (it cannot tell the Booster partition apart), and the GPU run must -# still get the Booster preset. Keying on the detected name also keeps this working, -# without a KeyError, on a machine detection does not recognise (name None). -# -# `time` is bumped from the presets' own 15 min to 30 min, the actual hard cap of the -# "*_fua_dbg" debug partitions both presets use (a longer request just gets clamped by -# SLURM) -- the much larger grid below (needed for the domain decomposition to support -# 256 ranks in the first place, see the module docstring) means setup (matrix assembly, -# with `solver="pcg"` forced) has real room to run past the original 15 min budget. If -# 30 min still isn't enough, the partition itself needs to change (the "_fua_prod" -# presets in clusters.py allow up to 24h, at the cost of a probably much longer queue -# wait) -- that tradeoff is left to the caller rather than made here. -CPU_PRESET = {**SLURM_PRESETS["pitagora_dcgp"], "time": "00:30:00"} -GPU_PRESET = {**SLURM_PRESETS["pitagora_boost_fua_dbg"], "time": "00:30:00"} - -# One CPU-node's worth of ranks (`HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"]`), and -# one GPU-node's worth (the Booster preset requests `gres=gpu:4`), one rank per GPU as in -# params_cyclone.py's `SLURM_LOCALID` binding. -CPU_RANKS_PER_NODE = HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"] -GPU_RANKS_PER_NODE = 4 - - -def main() -> None: - - # Parse arguments, do not remove --upload - parser = argparse.ArgumentParser( - description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), - ) - parser.add_argument( - "--upload", - action="store_true", - help="Upload the packaged profiling results to the profiling-data repo.", - ) - parser.add_argument( - "--cpu-ranks", - type=int, - default=CPU_RANKS_PER_NODE, - help=f"MPI ranks for the NumPy/CPU-node run (default: {CPU_RANKS_PER_NODE}, one full pitagora_dcgp node).", - ) - parser.add_argument( - "--gpu-ranks", - type=int, - default=GPU_RANKS_PER_NODE, - help=f"MPI ranks for the CuPy/GPU-node run, one rank per GPU (default: {GPU_RANKS_PER_NODE}, one full Booster node).", - ) - parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides params_cyclone.py's default, 200).") - parser.add_argument("--Tend", type=float, default=None, help="End time (overrides params_cyclone.py's default, 0.01 -> 10 steps).") - parser.add_argument( - "--num-elements", - type=int, - nargs=3, - default=[64, 128, 4], - help=( - "Grid resolution (default: 96 256 4 -- deliberately much larger than " - "params_cyclone.py's own default, 16 64 4, which only supports up to " - "~105 ranks at degree 3; see the note below). Domain decomposition " - "(feectools.fem.partitioning.partition_coefficients) requires at least " - "`degree` elements *per rank* in each decomposed direction, not just " - "`degree` elements total, so the usable rank cap is " - "`(num_elements[0] // degree) * (num_elements[1] // degree)` (the only two " - "decomposed directions, degree=3 by default) -- 16 64 4 caps out around " - "(16//3)*(64//3) = 5*21 = 105 ranks, well under a full pitagora_dcgp node's " - "256 cores, and fails with an assertion from partition_coefficients (\"Local " - "number of elements ... is to small for spline degree\") past that. The " - "default here, 96 256 4, caps out around (96//3)*(256//3) = 32*85 = 2720, " - "comfortably above 256 with margin for an uneven decomposition split." - ), - ) - args = parser.parse_args() - - # Paths relative to this script's location, so it can be run from anywhere. - script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "DriftKineticElectrostaticAdiabatic" - params_source = params_dir / "params_cyclone.py" - - param_flags = ["--solver", "pcg"] - if args.ppc is not None: - param_flags += ["--ppc", str(args.ppc)] - if args.Tend is not None: - param_flags += ["--Tend", str(args.Tend)] - if args.num_elements is not None: - param_flags += ["--num-elements", *[str(n) for n in args.num_elements]] - - profiling_case = ProfilingCase( - label="driftkinetic_cyclone_cpu_node_vs_gpu_node", - name="DriftKineticElectrostaticAdiabatic Cyclone, 1 CPU node vs 1 GPU node", - description=( - "Cyclone-instability ITG turbulence case for DriftKineticElectrostaticAdiabatic, " - "run once with the NumPy array backend across every core of one CPU node and " - "once with the CuPy array backend across every GPU of one GPU node, at the same " - "grid/marker configuration, to compare realistic full-node throughput rather " - "than single-rank kernel speed. The field solve is pinned to solver='pcg' on " - "both sides -- DirectSolver (params_cyclone.py's own default) does not support " - "more than one MPI rank." - ), - physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", - struphy_model_used="DriftKineticElectrostaticAdiabatic", - params_source=params_source, - language="fortran", - compiler="GNU", - upload=args.upload, - ) - - # The preset is looked up by cluster name inside `launch`, so build a one-entry dict - # under whatever name detection reports for this machine. - cluster_name = detect_machine_name() - - # One full CPU node, NumPy backend. - profiling_case.launch( - args.cpu_ranks, - num_nodes=1, - param_flags=["--backend", "numpy", *param_flags], - slurm_presets={cluster_name: CPU_PRESET}, - ) - - # One full GPU node, CuPy backend, one rank per GPU. - profiling_case.launch( - args.gpu_ranks, - num_nodes=1, - param_flags=["--backend", "cupy", *param_flags], - slurm_presets={cluster_name: GPU_PRESET}, - ) - - # Package and push each run as its own job finishes. - profiling_case.finalize_run() - - -if __name__ == "__main__": - main() diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py index 212f697a7..eb2e7393f 100644 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -1,53 +1,15 @@ """DriftKineticElectrostaticAdiabatic (ITG cyclone) CuPy multi-GPU/multi-rank scaling case. -This is a strong-scaling study, not a backend comparison (see -`submit_driftkinetic_cyclone_numpy_vs_cupy.py` for that, and -`submit_driftkinetic_cyclone_cpu_node_vs_gpu_node.py` for the full-node version): the -same grid/marker configuration is run with `ARRAY_BACKEND=cupy` at increasing MPI rank -counts, one rank per GPU, on the Booster partition -- so it measures whether adding more -rank+GPU pairs actually speeds up this fixed-size ITG run, and whether the CUDA-ported -kernels behave correctly under MPI for this model (domain-decomposed markers, particle -sorting/communication, the toroidal Fourier filter's `nprocs[2] == 1` requirement, etc.), -mirroring `submit_guidingcenter_cupy_scaling.py`'s pattern for the toy model. - -Each rank binds to its own GPU via `SLURM_LOCALID` in `params_cyclone.py` (see the -comment there) -- without that, every rank on a node would default to CuPy's device 0 -and contend for the same GPU, which would make this scaling study meaningless. -`SLURM_LOCALID` is a rank's index *within its node*, so this binding is correct on -multi-node runs too without any extra handling. - -`--ranks 1 2 4 8` (the default) covers both intra-node scaling (1/2/4 ranks, all on a -single Booster node, 4 GPUs/node) and one inter-node step (8 ranks = 2 nodes x 4 GPUs), -so the 4->8 step is the first data point that includes cross-node MPI exchange traffic -instead of only intra-node/NVLink-less PCIe traffic. `launch()` derives -`num_nodes = ceil(num_tasks / GPUS_PER_NODE)` and requires `num_tasks % num_nodes == 0`, -so rank counts must stay multiples of `GPUS_PER_NODE` once they exceed it (8, 12, 16, ...). - -The grid is only domain-decomposed along the two poloidal-plane directions -(`mpi_dims_mask=(True, True, False)` in `params_cyclone.py`, matching the toroidal -Fourier filter's own `nprocs[2] == 1` requirement), so the rank count is bounded by -`num_elements[0] * num_elements[1]` (default 16 x 64 = at most 1024) -- pass a larger -`--num-elements` before pushing `--ranks` much higher than the default. - -**Solver forced to `pcg`.** `params_cyclone.py` defaults to `solver="direct"` -(`feectools.linalg.solvers.DirectSolver`, see -`ISSUE_add_direct_solver_for_constant_operators.md`), but `DirectSolver` only supports a -single MPI rank -- it has no distributed sparse-direct factorization. Since this case -compares rank counts against each other, using the same solver at every rank count -matters more than using the fastest one available only at rank 1, so `--solver pcg` is -forced across the whole sweep (including at `--ranks 1`) for a consistent, apples-to-apples -comparison. Whether a (currently nonexistent) distributed direct solve would change this -picture is an open question -- see the ISSUE file's "Known limitations". - -**`Tend` shortened to 3 steps.** `params_cyclone.py`'s own default (`Tend=0.01`, `dt=0.001` --> 10 steps) was written for the `direct`-solver comparison, where the field solve is -essentially free after the first call. Here `--solver pcg` is forced instead, and the -case's own profiling notes (`driftkinetic_cyclone_numpy_vs_cupy`'s run metadata) recorded -a single CuPy `pcg` step taking anywhere from ~14.5 s (clean, isolated GPU) up to 174 s -under GPU contention on this shared partition -- 10 such steps plus ~90 s of CUDA -setup/compile do not reliably fit in `pitagora_boost_fua_dbg`'s 15-30 min walltime (its -partition-enforced cap). 3 steps is enough to warm up and get a stable per-step timing for -the scaling comparison this case cares about; pass `--Tend` to override. +Strong-scaling study (not a backend comparison, see `submit_driftkinetic_cyclone_numpy_vs_cupy.py` +for that): the same grid/marker configuration runs under `ARRAY_BACKEND=cupy` at +increasing MPI rank counts, one rank per GPU. `--ranks 1 2 4 8` (default) covers +intra-node scaling plus one cross-node step. Grid is hardcoded to `NUM_ELEMENTS` below +(not a CLI flag), so a run's grid is always readable straight from this file; `(12, 32, +4)` -- smaller than `params_cyclone.py`'s own default -- was chosen so the `direct` +solver's one-time matrix-assembly cost (see `feectools.linalg.utilities.tosparse_via_matvec`) +reliably fits `pitagora_boost_fua_dbg`'s walltime. Uses `params_cyclone.py`'s default +solver (`direct`) at every rank count, now that `DirectSolver` supports `nprocs > 1`. +`Tend` is shortened to 3 steps to leave walltime headroom for that one-time cost. """ import argparse @@ -68,6 +30,10 @@ # binding in params_cyclone.py. GPUS_PER_NODE = 4 +# Grid resolution, hardcoded rather than a `--num-elements` CLI flag -- see the module +# docstring for why this specific size was chosen. +NUM_ELEMENTS = (12, 32, 4) + def main() -> None: @@ -94,20 +60,8 @@ def main() -> None: default=0.003, help=( "End time (default: 0.003 -> 3 steps, shortened from params_cyclone.py's own " - "0.01/10 steps so the forced pcg solver reliably fits in the debug partition's " - "walltime; see the module docstring)." - ), - ) - parser.add_argument( - "--num-elements", - type=int, - nargs=3, - default=None, - help=( - "Grid resolution (overrides params_cyclone.py's default, 16 64 4). Must " - "support at least as many ranks as the largest --ranks value in " - "num_elements[0] * num_elements[1] (the only two domain-decomposed " - "directions)." + "0.01/10 steps to leave walltime headroom for the direct solver's one-time " + "matrix-assembly cost at every rank count; see the module docstring)." ), ) args = parser.parse_args() @@ -119,15 +73,10 @@ def main() -> None: profiling_case = ProfilingCase( label="driftkinetic_cyclone_cupy_scaling", - name="DriftKineticElectrostaticAdiabatic Cyclone, CuPy multi-GPU scaling", + name="ITG cyclone: CuPy scaling", description=( - "Cyclone-instability ITG turbulence case for DriftKineticElectrostaticAdiabatic, " - "run with the CuPy array backend at increasing MPI rank counts (one GPU per " - "rank) on a fixed grid/marker configuration, to measure strong-scaling " - "speedup and verify the CUDA-ported kernels and MPI exchange paths work " - "correctly for this model under multi-rank/multi-GPU. The field solve is " - "pinned to solver='pcg' at every rank count -- DirectSolver " - "(params_cyclone.py's own default) does not support more than one MPI rank." + "Cyclone-instability ITG turbulence (DriftKineticElectrostaticAdiabatic) on " + "CuPy, strong-scaled across GPUs with the direct field solver." ), physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", struphy_model_used="DriftKineticElectrostaticAdiabatic", @@ -141,11 +90,13 @@ def main() -> None: # under whatever name detection reports for this machine. cluster_name = detect_machine_name() - param_flags = ["--backend", "cupy", "--solver", "pcg", "--Tend", str(args.Tend)] + param_flags = [ + "--backend", "cupy", + "--Tend", str(args.Tend), + "--num-elements", *[str(n) for n in NUM_ELEMENTS], + ] if args.ppc is not None: param_flags += ["--ppc", str(args.ppc)] - if args.num_elements is not None: - param_flags += ["--num-elements", *[str(n) for n in args.num_elements]] # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs diff --git a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py index 8f4140766..f2f461686 100644 --- a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py +++ b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py @@ -1,24 +1,9 @@ """DriftKineticElectrostaticAdiabatic (ITG cyclone) NumPy-vs-CuPy profiling case. -This is the first CuPy profiling case for a real gyrokinetic model rather than a toy -one (see `params_cyclone.py`'s docstring for the device-portability bugs that had to be -fixed just to get it running under CuPy at all). Unlike GuidingCenter -(submit_guidingcenter_numpy_vs_cupy.py), this model carries a real per-step FEEC field -solve (PoissonAdiabaticGyrokinetic) on top of the two CUDA-ported guiding-center -pushers, so it measures whether the CUDA port helps a model end to end rather than just -the particle kernels it directly targeted. - -The field solve's own solver defaults to 'direct' (a cached sparse LU factorization, -`feectools.linalg.solvers.DirectSolver`) rather than the naive 'pcg': the LHS operator -here is constant across time steps, so the iterative solver was redoing (close to) -maxiter=3000 dependent, sync-per-iteration steps on every single call for no reason -- -see params_cyclone.py's docstring for the measured PCG-vs-direct comparison (~1900x -faster per solve after the first, on CuPy). `--solver pcg` reproduces the original, -much slower baseline for comparison. - -Runs the same simulation twice, once with `ARRAY_BACKEND=numpy` on a CPU partition and -once with `ARRAY_BACKEND=cupy` on a GPU partition, so the two runs can be compared -directly, exactly as `submit_guidingcenter_numpy_vs_cupy.py` does. +Runs `params_cyclone.py` once with `ARRAY_BACKEND=numpy` and once with +`ARRAY_BACKEND=cupy`. Unlike GuidingCenter, this model has a real per-step FEEC field +solve (PoissonAdiabaticGyrokinetic, default solver='direct'), so it measures whether +the CUDA port helps end to end, not just the particle kernels. """ import argparse @@ -97,14 +82,10 @@ def main() -> None: profiling_case = ProfilingCase( label="driftkinetic_cyclone_numpy_vs_cupy", - name="DriftKineticElectrostaticAdiabatic Cyclone, NumPy vs CuPy", + name="ITG cyclone: NumPy vs CuPy", description=( - "Cyclone-instability ITG turbulence case for DriftKineticElectrostaticAdiabatic " - "(toroidal HollowTorus geometry, control-variate weights, Fourier-filtered " - "PoissonAdiabaticGyrokinetic field solve plus the two CUDA-ported guiding-center " - "pushers), run with the NumPy and the CuPy array backend. Unlike GuidingCenter, " - "this model has a real per-step FEEC field solve, so this measures whether the " - "CUDA port helps a real gyrokinetic model end to end." + "Cyclone-instability ITG turbulence (DriftKineticElectrostaticAdiabatic), run " + "once on NumPy and once on CuPy." ), physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", struphy_model_used="DriftKineticElectrostaticAdiabatic", diff --git a/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py b/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py index 18c0374f1..fa966c8b6 100644 --- a/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py +++ b/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py @@ -1,26 +1,10 @@ -"""Guiding-centre full-node CPU-vs-GPU comparison case. - -The other two GuidingCenter cases each answer a narrower question: -`submit_guidingcenter_numpy_vs_cupy.py` compares backends at a fixed, small rank count -(default 1), and `submit_guidingcenter_cupy_scaling.py` measures CuPy strong-scaling -alone. Neither answers the practical question a user actually has: given one full CPU -node and one full GPU node, which one do you point a job at? This case runs exactly -that comparison -- `ARRAY_BACKEND=numpy` using every core of one `pitagora_dcgp` node -against `ARRAY_BACKEND=cupy` using every GPU of one Booster node -- at the same total -marker count on both sides. - -Both sides run with more than one rank, so both exercise the domain-decomposed -marker-exchange path (`mpi_sort_markers`/`apply_kinetic_bc`) this session's performance -work targeted, not just single-rank kernel throughput. `params_GuidingCenter_scaling.py` -is used (not `params_GuidingCenter.py`) for the same reason `submit_guidingcenter_cupy_scaling.py` -uses it: its default Np is large enough that per-rank compute between exchanges has a -chance of outweighing the exchange cost -- see that file's docstring for the measurements -this default responds to. `--Np` overrides it if a different problem size is of interest. - -`GuidingCenter` is used, as in the other two cases, because its whole propagator stack is -CUDA-ported and it has no FEEC field solve, so wall-clock time is dominated by the -particle kernels and their MPI exchange rather than by anything the backend choice -doesn't touch. +"""Guiding-centre full-node CPU-vs-GPU comparison. + +Runs `params_GuidingCenter_scaling.py` once across every core of one CPU node +(`ARRAY_BACKEND=numpy`) and once across every GPU of one GPU node (`ARRAY_BACKEND=cupy`), +same total marker count on both sides -- the realistic "which node do I use" comparison, +as opposed to `submit_guidingcenter_numpy_vs_cupy.py` (single-rank backend comparison) or +`submit_guidingcenter_cupy_scaling.py` (CuPy-only rank scaling). """ import argparse @@ -83,13 +67,10 @@ def main() -> None: profiling_case = ProfilingCase( label="guidingcenter_cpu_node_vs_gpu_node", - name="Guiding-centre particles on cube, 1 CPU node vs 1 GPU node", + name="GuidingCenter: CPU node vs GPU node", description=( - "5D guiding-centre test particles in a homogeneous slab on a 3D cube, run once " - "with the NumPy array backend across every core of one CPU node and once with " - "the CuPy array backend across every GPU of one GPU node, at the same total " - "marker count, to compare realistic full-node throughput rather than " - "single-rank kernel speed." + "GuidingCenter particles on a cube, run across one full CPU node (NumPy) vs " + "one full GPU node (CuPy) at the same marker count." ), physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", struphy_model_used="GuidingCenter", diff --git a/profiling/submit_guidingcenter_cupy_scaling.py b/profiling/submit_guidingcenter_cupy_scaling.py index 9b7e1000c..830900c79 100644 --- a/profiling/submit_guidingcenter_cupy_scaling.py +++ b/profiling/submit_guidingcenter_cupy_scaling.py @@ -1,42 +1,12 @@ """Guiding-centre CuPy multi-GPU/multi-rank scaling case. -This is a strong-scaling study, not a backend comparison (see -`submit_guidingcenter_numpy_vs_cupy.py` for that): the same total marker count -(`LoadingParameters.Np` is the *total* across ranks, see -`struphy.particles.parameters.LoadingParameters`) is run with `ARRAY_BACKEND=cupy` at -increasing MPI rank counts, one rank per GPU, on the Booster partition -- so it measures -whether adding more rank+GPU pairs actually speeds up a fixed-size problem, and whether -the CUDA-ported kernels behave correctly under MPI (domain-decomposed markers, particle -sorting/communication across ranks, etc.), not just single-GPU. - -Each rank binds to its own GPU via `SLURM_LOCALID` in `params_GuidingCenter_scaling.py` -(see the comment there) -- without that, every rank on a node would default to CuPy's -device 0 and contend for the same GPU, which would make this scaling study meaningless. -`SLURM_LOCALID` is a rank's index *within its node*, so this binding is correct on -multi-node runs too without any extra handling. - -`--ranks 1 2 4 8` (the default) covers both intra-node scaling (1/2/4 ranks, all on a -single Booster node, 4 GPUs/node) and one inter-node step (8 ranks = 2 nodes x 4 GPUs), -so the 4->8 step is the first data point that includes cross-node MPI exchange traffic -(mpi_sort_markers) instead of only intra-node/NVLink-less PCIe traffic. `launch()` derives -`num_nodes = ceil(num_tasks / GPUS_PER_NODE)` and requires `num_tasks % num_nodes == 0`, -so rank counts must stay multiples of `GPUS_PER_NODE` once they exceed it (8, 12, 16, ...). - -Uses `params_GuidingCenter_scaling.py`, not `params_GuidingCenter.py` (the single-GPU -NumPy-vs-CuPy comparison case): its much larger default Np (50,000,000 vs. 200,000) is -needed for a scaling study specifically because at smaller sizes, per-rank compute between -marker exchanges is too small to outweigh the exchange cost -- adding ranks there measured -*slower*, not faster (see that file's docstring for the numbers), and even at 10,000,000 -the 4->8-rank (cross-node) step regressed. Whether 50,000,000 gives enough per-rank compute -to push the crossover point past 8 ranks, and how far, is what this case measures; see -`params_GuidingCenter_scaling.py`'s docstring for the 10,000,000 numbers this raise is -responding to. - -`GuidingCenter` is used here rather than `LinearMHDDriftkineticCC` because its runtime is -actually dominated by the particle kernels this comparison is meant to measure. Its whole -propagator stack is CUDA-ported and it has no FEEC field solve, so the backend difference -shows up in the total wall clock. `LinearMHDDriftkineticCC` is dominated by MHD field -propagators and one-off setup instead, which masks any particle-kernel speedup. +Strong-scaling study (not a backend comparison, see `submit_guidingcenter_numpy_vs_cupy.py` +for that): the same total marker count runs under `ARRAY_BACKEND=cupy` at increasing MPI +rank counts, one rank per GPU, to measure whether more rank+GPU pairs actually speed up a +fixed-size problem. `--ranks 1 2 4 8` (default) covers intra-node scaling plus one +cross-node step (8 = 2 Booster nodes x 4 GPUs). Uses `params_GuidingCenter_scaling.py`'s +much larger default `Np` (not `params_GuidingCenter.py`'s) since a small per-rank marker +count makes MPI exchange cost dominate and scaling look worse than it is. """ import argparse @@ -92,8 +62,8 @@ def main() -> None: profiling_case = ProfilingCase( label="guidingcenter_cupy_scaling", - name="Guiding-centre particles on cube, CuPy multi-GPU scaling", - description="5D guiding-centre test particles (Np=50,000,000) in a homogeneous slab on a 3D cube, run with the CuPy array backend at increasing MPI rank counts (one GPU per rank) to measure strong-scaling speedup and verify the CUDA-ported kernels work correctly under MPI.", + name="GuidingCenter: CuPy scaling", + description="GuidingCenter particles (Np=50,000,000) on CuPy, strong-scaled across 1-8 GPUs.", physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", struphy_model_used="GuidingCenter", params_source=params_source, diff --git a/profiling/submit_guidingcenter_numpy_vs_cupy.py b/profiling/submit_guidingcenter_numpy_vs_cupy.py index db25523fd..3ed035e33 100644 --- a/profiling/submit_guidingcenter_numpy_vs_cupy.py +++ b/profiling/submit_guidingcenter_numpy_vs_cupy.py @@ -1,19 +1,8 @@ """Guiding-centre NumPy-vs-CuPy profiling case. -This file defines the guiding-centre backend-comparison profiling case (the `ProfilingCase`) -and submits it: the same simulation is run twice, once with `ARRAY_BACKEND=numpy` on a -CPU partition and once with `ARRAY_BACKEND=cupy` on a GPU partition, so the two runs can -be compared directly. For each run, `ProfilingCase.launch` builds and submits a SLURM -script, or, without a batch system, runs directly on this machine. `finalize_run` then -packages and uploads each run as soon as its own job finishes. -Each generated script runs the simulation itself by invoking `params_GuidingCenter.py` -directly (its `__main__` block is the worker), with `--backend numpy` or `--backend cupy`. - -`GuidingCenter` is used here rather than `LinearMHDDriftkineticCC` because its runtime is -actually dominated by the particle kernels this comparison is meant to measure. Its whole -propagator stack is CUDA-ported and it has no FEEC field solve, so the backend difference -shows up in the total wall clock. `LinearMHDDriftkineticCC` is dominated by MHD field -propagators and one-off setup instead, which masks any particle-kernel speedup. +Runs `params_GuidingCenter.py` once with `ARRAY_BACKEND=numpy` and once with +`ARRAY_BACKEND=cupy` so the two can be compared directly. `GuidingCenter` has no FEEC +field solve, so wall-clock time is dominated by the CUDA-ported particle kernels. """ import argparse @@ -72,8 +61,8 @@ def main() -> None: profiling_case = ProfilingCase( label="guidingcenter_numpy_vs_cupy", - name="Guiding-centre particles on cube, NumPy vs CuPy", - description="5D guiding-centre test particles in a homogeneous slab on a 3D cube, run with the NumPy and the CuPy array backend. Runtime is dominated by the (CUDA-ported) particle pushers.", + name="GuidingCenter: NumPy vs CuPy", + description="GuidingCenter particles on a cube, run once on NumPy and once on CuPy.", physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", struphy_model_used="GuidingCenter", params_source=params_source, diff --git a/profiling/submit_pressurelesssph_cupy_scaling.py b/profiling/submit_pressurelesssph_cupy_scaling.py index ad999e8b3..45904ac47 100644 --- a/profiling/submit_pressurelesssph_cupy_scaling.py +++ b/profiling/submit_pressurelesssph_cupy_scaling.py @@ -1,27 +1,10 @@ """PressureLessSPH CuPy multi-GPU/multi-rank scaling case. -Third companion to submit_guidingcenter_cupy_scaling.py and -submit_vlasovampere_cupy_scaling.py, using a model close to a pure particle push: no -FEEC field solve at all (see params_PressureLessSPH_scaling.py's docstring for the -full rationale), so this is the low-per-marker-compute end of the three cases -- -GuidingCenter in the middle, VlasovAmpereOneSpecies's real field solve on the high end. - -Same strong-scaling structure as the other two cases: the same total marker count -(`LoadingParameters.Np` is the *total* across ranks) is run with `ARRAY_BACKEND=cupy` -at increasing MPI rank counts, one rank per GPU, on the Booster partition. - -Each rank binds to its own GPU via `SLURM_LOCALID` in -`params_PressureLessSPH_scaling.py` (see the comment there) -- without that, every -rank on a node would default to CuPy's device 0 and contend for the same GPU, which -would make this scaling study meaningless. `SLURM_LOCALID` is a rank's index *within -its node*, so this binding is correct on multi-node runs too without any extra -handling. - -`--ranks 2 4 8` (the default, matching the other two cases' current default) covers -both intra-node scaling (2/4 ranks, on a single Booster node, 4 GPUs/node) and one -inter-node step (8 ranks = 2 nodes x 4 GPUs). `launch()` derives -`num_nodes = ceil(num_tasks / GPUS_PER_NODE)` and requires `num_tasks % num_nodes == 0`, -so rank counts must stay multiples of `GPUS_PER_NODE` once they exceed it (8, 12, 16, ...). +Companion to submit_guidingcenter_cupy_scaling.py and submit_vlasovampere_cupy_scaling.py: +a model with no FEEC field solve at all, the low-per-marker-compute end of the three. +Same total marker count, run with `ARRAY_BACKEND=cupy` at increasing MPI rank counts +(one rank per GPU); `--ranks 2 4 8` (default) covers intra-node scaling plus one +cross-node step. """ import argparse @@ -77,8 +60,8 @@ def main() -> None: profiling_case = ProfilingCase( label="pressurelesssph_cupy_scaling", - name="PressureLessSPH particles on cube, CuPy multi-GPU scaling", - description="SPH test particles (Np=10,000,000) in a homogeneous cube, run with the CuPy array backend at increasing MPI rank counts (one GPU per rank) -- a companion to guidingcenter_cupy_scaling and vlasovampere_cupy_scaling using a model close to a pure particle push (no FEEC field solve at all), to measure scaling behaviour at the low-per-marker-compute end.", + name="PressureLessSPH: CuPy scaling", + description="PressureLessSPH particles (Np=10,000,000) on CuPy, strong-scaled across GPUs. No FEEC field solve.", physics_problem="SPH-discretized pressureless Euler flow with external forcing; a position push plus a velocity push against a background force field, no field solve.", struphy_model_used="PressureLessSPH", params_source=params_source, diff --git a/profiling/submit_vlasovampere_cupy_scaling.py b/profiling/submit_vlasovampere_cupy_scaling.py index f3b5ba6b4..84ac9b4c2 100644 --- a/profiling/submit_vlasovampere_cupy_scaling.py +++ b/profiling/submit_vlasovampere_cupy_scaling.py @@ -1,27 +1,10 @@ """VlasovAmpereOneSpecies CuPy multi-GPU/multi-rank scaling case. -Companion to submit_guidingcenter_cupy_scaling.py, deliberately using a different -model that is NOT dominated by mpi_sort_markers the way GuidingCenter's scaling case -is (see params_VlasovAmpere_scaling.py's docstring for the full rationale): -VlasovAmpereOneSpecies's VlasovAmpereCoupling propagator solves a real linear system -each step to update the field from the accumulated particle current, giving it real -per-step compute that GuidingCenter's pure-push propagator stack doesn't have. - -Same strong-scaling structure as the GuidingCenter case: the same total marker count -(`LoadingParameters.Np` is the *total* across ranks) is run with `ARRAY_BACKEND=cupy` -at increasing MPI rank counts, one rank per GPU, on the Booster partition. - -Each rank binds to its own GPU via `SLURM_LOCALID` in `params_VlasovAmpere_scaling.py` -(see the comment there) -- without that, every rank on a node would default to CuPy's -device 0 and contend for the same GPU, which would make this scaling study meaningless. -`SLURM_LOCALID` is a rank's index *within its node*, so this binding is correct on -multi-node runs too without any extra handling. - -`--ranks 2 4 8` (the default, matching the GuidingCenter case's current default) covers -both intra-node scaling (2/4 ranks, on a single Booster node, 4 GPUs/node) and one -inter-node step (8 ranks = 2 nodes x 4 GPUs). `launch()` derives -`num_nodes = ceil(num_tasks / GPUS_PER_NODE)` and requires `num_tasks % num_nodes == 0`, -so rank counts must stay multiples of `GPUS_PER_NODE` once they exceed it (8, 12, 16, ...). +Companion to submit_guidingcenter_cupy_scaling.py, using a model with a real per-step +field solve (VlasovAmpereCoupling) instead of a pure particle push, so its scaling +behaviour isn't dominated by mpi_sort_markers the way GuidingCenter's is. Same total +marker count, run with `ARRAY_BACKEND=cupy` at increasing MPI rank counts (one rank per +GPU); `--ranks 2 4 8` (default) covers intra-node scaling plus one cross-node step. """ import argparse @@ -77,8 +60,8 @@ def main() -> None: profiling_case = ProfilingCase( label="vlasovampere_cupy_scaling", - name="Vlasov-Ampere particles on cube, CuPy multi-GPU scaling", - description="6D full-orbit Vlasov-Ampere test particles (Np=50,000,000) in a homogeneous cube, run with the CuPy array backend at increasing MPI rank counts (one GPU per rank) -- a companion to guidingcenter_cupy_scaling using a model with a real per-step field solve instead of a pure particle push, to measure scaling behaviour when mpi_sort_markers is not the dominant cost.", + name="VlasovAmpere: CuPy scaling", + description="VlasovAmpereOneSpecies particles (Np=50,000,000) on CuPy, strong-scaled across GPUs. Has a real per-step field solve.", physics_problem="6D full-orbit Vlasov-Ampere particle motion with a self-consistent electric field, solved via VlasovAmpereCoupling's SchurSolver each step.", struphy_model_used="VlasovAmpereOneSpecies", params_source=params_source, From eedf7e66aa15cf78a6a5acbafd6b429483b605cf Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 14:50:16 +0200 Subject: [PATCH 116/156] Hardcoded the params --- ...ubmit_driftkinetic_cyclone_cupy_scaling.py | 55 ++++++++----------- ...bmit_driftkinetic_cyclone_numpy_vs_cupy.py | 37 +------------ ...bmit_guidingcenter_cpu_node_vs_gpu_node.py | 28 ++-------- .../submit_guidingcenter_cupy_scaling.py | 22 ++------ .../submit_guidingcenter_numpy_vs_cupy.py | 50 +++++++---------- .../submit_pressurelesssph_cupy_scaling.py | 22 ++------ profiling/submit_vlasovampere_cupy_scaling.py | 22 ++------ 7 files changed, 64 insertions(+), 172 deletions(-) diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py index eb2e7393f..173747ad2 100644 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -4,12 +4,15 @@ for that): the same grid/marker configuration runs under `ARRAY_BACKEND=cupy` at increasing MPI rank counts, one rank per GPU. `--ranks 1 2 4 8` (default) covers intra-node scaling plus one cross-node step. Grid is hardcoded to `NUM_ELEMENTS` below -(not a CLI flag), so a run's grid is always readable straight from this file; `(12, 32, -4)` -- smaller than `params_cyclone.py`'s own default -- was chosen so the `direct` -solver's one-time matrix-assembly cost (see `feectools.linalg.utilities.tosparse_via_matvec`) -reliably fits `pitagora_boost_fua_dbg`'s walltime. Uses `params_cyclone.py`'s default -solver (`direct`) at every rank count, now that `DirectSolver` supports `nprocs > 1`. -`Tend` is shortened to 3 steps to leave walltime headroom for that one-time cost. +(not a CLI flag), so a run's grid is always readable straight from this file. + +**Solver forced to `pcg`, not `params_cyclone.py`'s own default (`direct`).** +`DirectSolver` now supports `nprocs > 1` (a replicated matrix assembly, see +`feectools.linalg.utilities.tosparse_via_matvec`), but that assembly is currently too +slow under CuPy in practice (measured ~270-300s one-time cost even at a modest grid, +dominated by per-`dot()`-call kernel-launch/sync overhead the array-transfer +optimization only dents) to be worth using in a scaling study yet -- `pcg` gives a +cleaner, apples-to-apples comparison across rank counts until that's fixed. """ import argparse @@ -30,9 +33,15 @@ # binding in params_cyclone.py. GPUS_PER_NODE = 4 -# Grid resolution, hardcoded rather than a `--num-elements` CLI flag -- see the module -# docstring for why this specific size was chosen. -NUM_ELEMENTS = (12, 32, 4) +# Grid resolution, matching params_cyclone.py's own default. +NUM_ELEMENTS = (16, 64, 4) + +# MPI rank counts to run with, one GPU per rank -- 1/2/4 intra-node, 8 = 2 Booster nodes. +RANKS = [1, 2, 4, 8] + +# End time: 0.003 -> 3 steps, shortened from params_cyclone.py's own 0.01/10 steps to +# keep the pcg-forced sweep quick. +TEND = 0.003 def main() -> None: @@ -46,24 +55,6 @@ def main() -> None: action="store_true", help="Upload the packaged profiling results to the profiling-data repo.", ) - parser.add_argument( - "--ranks", - type=int, - nargs="+", - default=[1, 2, 4, 8], - help="MPI rank counts to run with, one GPU per rank (default: 1 2 4 8; 8 spans 2 Booster nodes).", - ) - parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides params_cyclone.py's default, 200).") - parser.add_argument( - "--Tend", - type=float, - default=0.003, - help=( - "End time (default: 0.003 -> 3 steps, shortened from params_cyclone.py's own " - "0.01/10 steps to leave walltime headroom for the direct solver's one-time " - "matrix-assembly cost at every rank count; see the module docstring)." - ), - ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. @@ -76,7 +67,8 @@ def main() -> None: name="ITG cyclone: CuPy scaling", description=( "Cyclone-instability ITG turbulence (DriftKineticElectrostaticAdiabatic) on " - "CuPy, strong-scaled across GPUs with the direct field solver." + "CuPy, strong-scaled across GPUs. Solver forced to pcg (direct's multi-rank " + "assembly is not fast enough yet, see module docstring)." ), physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", struphy_model_used="DriftKineticElectrostaticAdiabatic", @@ -92,16 +84,15 @@ def main() -> None: param_flags = [ "--backend", "cupy", - "--Tend", str(args.Tend), + "--solver", "pcg", + "--Tend", str(TEND), "--num-elements", *[str(n) for n in NUM_ELEMENTS], ] - if args.ppc is not None: - param_flags += ["--ppc", str(args.ppc)] # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs # far more ranks per node than there are GPUs. - for num_tasks in args.ranks: + for num_tasks in RANKS: num_nodes = -(-num_tasks // GPUS_PER_NODE) profiling_case.launch( num_tasks, diff --git a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py index f2f461686..18af56c43 100644 --- a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py +++ b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py @@ -38,31 +38,6 @@ def main() -> None: action="store_true", help="Upload the packaged profiling results to the profiling-data repo.", ) - parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides the default, 5).") - parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default, 0.001 -> 1 step).") - parser.add_argument( - "--solver", - choices=("pcg", "direct"), - default=None, - help=( - "Symmetric solver for the field solve (overrides params_cyclone.py's own " - "default, 'direct'). 'direct' is a cached sparse LU factorization, valid " - "because the LHS operator here is constant across time steps; see " - "params_cyclone.py's docstring for the measured PCG-vs-direct comparison." - ), - ) - parser.add_argument( - "--num-elements", - type=int, - nargs=3, - default=None, - help=( - "Grid resolution (overrides the default, 16 64 4). The physics case's own " - "default, 32 135 5, is much heavier -- NumPy setup alone took 226 s at that " - "size when this case was validated -- so raise --time in the SLURM preset " - "before using it." - ), - ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. @@ -70,16 +45,6 @@ def main() -> None: params_dir = script_dir / "examples" / "DriftKineticElectrostaticAdiabatic" params_source = params_dir / "params_cyclone.py" - param_flags = [] - if args.ppc is not None: - param_flags += ["--ppc", str(args.ppc)] - if args.Tend is not None: - param_flags += ["--Tend", str(args.Tend)] - if args.num_elements is not None: - param_flags += ["--num-elements", *[str(n) for n in args.num_elements]] - if args.solver is not None: - param_flags += ["--solver", args.solver] - profiling_case = ProfilingCase( label="driftkinetic_cyclone_numpy_vs_cupy", name="ITG cyclone: NumPy vs CuPy", @@ -105,7 +70,7 @@ def main() -> None: profiling_case.launch( 1, num_nodes=1, - param_flags=["--backend", backend, *param_flags], + param_flags=["--backend", backend], slurm_presets={cluster_name: preset}, ) diff --git a/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py b/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py index fa966c8b6..8008b748b 100644 --- a/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py +++ b/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py @@ -40,24 +40,6 @@ def main() -> None: action="store_true", help="Upload the packaged profiling results to the profiling-data repo.", ) - parser.add_argument( - "--cpu-ranks", - type=int, - default=CPU_RANKS_PER_NODE, - help=f"MPI ranks for the NumPy/CPU-node run (default: {CPU_RANKS_PER_NODE}, one full pitagora_dcgp node).", - ) - parser.add_argument( - "--gpu-ranks", - type=int, - default=GPU_RANKS_PER_NODE, - help=f"MPI ranks for the CuPy/GPU-node run, one rank per GPU (default: {GPU_RANKS_PER_NODE}, one full Booster node).", - ) - parser.add_argument( - "--Np", - type=int, - default=None, - help="Total marker count, overriding params_GuidingCenter_scaling.py's default (10,000,000).", - ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. @@ -84,21 +66,19 @@ def main() -> None: # under whatever name detection reports for this machine. cluster_name = detect_machine_name() - Np_flags = ["--Np", str(args.Np)] if args.Np is not None else [] - # One full CPU node, NumPy backend. profiling_case.launch( - args.cpu_ranks, + CPU_RANKS_PER_NODE, num_nodes=1, - param_flags=["--backend", "numpy", *Np_flags], + param_flags=["--backend", "numpy"], slurm_presets={cluster_name: CPU_PRESET}, ) # One full GPU node, CuPy backend, one rank per GPU. profiling_case.launch( - args.gpu_ranks, + GPU_RANKS_PER_NODE, num_nodes=1, - param_flags=["--backend", "cupy", *Np_flags], + param_flags=["--backend", "cupy"], slurm_presets={cluster_name: GPU_PRESET}, ) diff --git a/profiling/submit_guidingcenter_cupy_scaling.py b/profiling/submit_guidingcenter_cupy_scaling.py index 830900c79..8e3928f1e 100644 --- a/profiling/submit_guidingcenter_cupy_scaling.py +++ b/profiling/submit_guidingcenter_cupy_scaling.py @@ -3,7 +3,7 @@ Strong-scaling study (not a backend comparison, see `submit_guidingcenter_numpy_vs_cupy.py` for that): the same total marker count runs under `ARRAY_BACKEND=cupy` at increasing MPI rank counts, one rank per GPU, to measure whether more rank+GPU pairs actually speed up a -fixed-size problem. `--ranks 1 2 4 8` (default) covers intra-node scaling plus one +fixed-size problem. `RANKS = [2, 4, 8]` below covers intra-node scaling plus one cross-node step (8 = 2 Booster nodes x 4 GPUs). Uses `params_GuidingCenter_scaling.py`'s much larger default `Np` (not `params_GuidingCenter.py`'s) since a small per-rank marker count makes MPI exchange cost dominate and scaling look worse than it is. @@ -28,6 +28,9 @@ # binding in params_GuidingCenter_scaling.py. GPUS_PER_NODE = 4 +# MPI rank counts to run with, one GPU per rank -- 2/4 intra-node, 8 = 2 Booster nodes. +RANKS = [2, 4, 8] + def main() -> None: @@ -40,19 +43,6 @@ def main() -> None: action="store_true", help="Upload the packaged profiling results to the profiling-data repo.", ) - parser.add_argument( - "--ranks", - type=int, - nargs="+", - default=[2, 4, 8], - help="MPI rank counts to run with, one GPU per rank (default: 1 2 4 8; 8 spans 2 Booster nodes).", - ) - parser.add_argument( - "--Np", - type=int, - default=None, - help="Total marker count, overriding params_GuidingCenter_scaling.py's default (50,000,000).", - ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. @@ -77,13 +67,11 @@ def main() -> None: cluster_name = detect_machine_name() param_flags = ["--backend", "cupy"] - if args.Np is not None: - param_flags += ["--Np", str(args.Np)] # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs # far more ranks per node than there are GPUs. - for num_tasks in args.ranks: + for num_tasks in RANKS: num_nodes = -(-num_tasks // GPUS_PER_NODE) profiling_case.launch( num_tasks, diff --git a/profiling/submit_guidingcenter_numpy_vs_cupy.py b/profiling/submit_guidingcenter_numpy_vs_cupy.py index 3ed035e33..e262d62ed 100644 --- a/profiling/submit_guidingcenter_numpy_vs_cupy.py +++ b/profiling/submit_guidingcenter_numpy_vs_cupy.py @@ -29,6 +29,10 @@ # runs are spread so that no node holds more ranks than it has GPUs. GPUS_PER_NODE = 4 +# MPI rank count to run each backend with. This params file selects no GPU per rank, so +# more than one rank per node would share device 0 -- keep at 1 unless that's fixed. +RANKS = 1 + def main() -> None: @@ -41,17 +45,6 @@ def main() -> None: action="store_true", help="Upload the packaged profiling results to the profiling-data repo.", ) - parser.add_argument( - "--ranks", - type=int, - nargs="+", - default=[1], - help=( - "MPI rank counts to run each backend with (default: 1). Note that the CuPy " - "runs currently select no GPU per rank, so more than one rank per node all " - "share device 0." - ), - ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. @@ -75,24 +68,23 @@ def main() -> None: # under whatever name detection reports for this machine. cluster_name = detect_machine_name() - # Launch one run per (rank count, backend) pair. - for num_tasks in args.ranks: - for backend, preset in BACKEND_PRESETS.items(): - if backend == "cupy": - # One node per `GPUS_PER_NODE` ranks. `launch` would otherwise derive the - # node count from `cpus_per_node`, which on a GPU partition packs far more - # ranks per node than there are GPUs. - num_nodes = -(-num_tasks // GPUS_PER_NODE) - else: - # Let `launch` derive the node count from the cluster's CPU count. - num_nodes = None - - profiling_case.launch( - num_tasks, - num_nodes=num_nodes, - param_flags=["--backend", backend], - slurm_presets={cluster_name: preset}, - ) + # Launch one run per backend. + for backend, preset in BACKEND_PRESETS.items(): + if backend == "cupy": + # One node per `GPUS_PER_NODE` ranks. `launch` would otherwise derive the + # node count from `cpus_per_node`, which on a GPU partition packs far more + # ranks per node than there are GPUs. + num_nodes = -(-RANKS // GPUS_PER_NODE) + else: + # Let `launch` derive the node count from the cluster's CPU count. + num_nodes = None + + profiling_case.launch( + RANKS, + num_nodes=num_nodes, + param_flags=["--backend", backend], + slurm_presets={cluster_name: preset}, + ) # Package and push each run as its own job finishes. profiling_case.finalize_run() diff --git a/profiling/submit_pressurelesssph_cupy_scaling.py b/profiling/submit_pressurelesssph_cupy_scaling.py index 45904ac47..726996f23 100644 --- a/profiling/submit_pressurelesssph_cupy_scaling.py +++ b/profiling/submit_pressurelesssph_cupy_scaling.py @@ -3,7 +3,7 @@ Companion to submit_guidingcenter_cupy_scaling.py and submit_vlasovampere_cupy_scaling.py: a model with no FEEC field solve at all, the low-per-marker-compute end of the three. Same total marker count, run with `ARRAY_BACKEND=cupy` at increasing MPI rank counts -(one rank per GPU); `--ranks 2 4 8` (default) covers intra-node scaling plus one +(one rank per GPU); `RANKS = [2, 4, 8]` below covers intra-node scaling plus one cross-node step. """ @@ -26,6 +26,9 @@ # binding in params_PressureLessSPH_scaling.py. GPUS_PER_NODE = 4 +# MPI rank counts to run with, one GPU per rank -- 2/4 intra-node, 8 = 2 Booster nodes. +RANKS = [2, 4, 8] + def main() -> None: @@ -38,19 +41,6 @@ def main() -> None: action="store_true", help="Upload the packaged profiling results to the profiling-data repo.", ) - parser.add_argument( - "--ranks", - type=int, - nargs="+", - default=[2, 4, 8], - help="MPI rank counts to run with, one GPU per rank (default: 2 4 8; 8 spans 2 Booster nodes).", - ) - parser.add_argument( - "--Np", - type=int, - default=None, - help="Total marker count, overriding params_PressureLessSPH_scaling.py's default (10,000,000).", - ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. @@ -75,13 +65,11 @@ def main() -> None: cluster_name = detect_machine_name() param_flags = ["--backend", "cupy"] - if args.Np is not None: - param_flags += ["--Np", str(args.Np)] # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs # far more ranks per node than there are GPUs. - for num_tasks in args.ranks: + for num_tasks in RANKS: num_nodes = -(-num_tasks // GPUS_PER_NODE) profiling_case.launch( num_tasks, diff --git a/profiling/submit_vlasovampere_cupy_scaling.py b/profiling/submit_vlasovampere_cupy_scaling.py index 84ac9b4c2..1c97e6e42 100644 --- a/profiling/submit_vlasovampere_cupy_scaling.py +++ b/profiling/submit_vlasovampere_cupy_scaling.py @@ -4,7 +4,7 @@ field solve (VlasovAmpereCoupling) instead of a pure particle push, so its scaling behaviour isn't dominated by mpi_sort_markers the way GuidingCenter's is. Same total marker count, run with `ARRAY_BACKEND=cupy` at increasing MPI rank counts (one rank per -GPU); `--ranks 2 4 8` (default) covers intra-node scaling plus one cross-node step. +GPU); `RANKS = [2, 4, 8]` below covers intra-node scaling plus one cross-node step. """ import argparse @@ -26,6 +26,9 @@ # binding in params_VlasovAmpere_scaling.py. GPUS_PER_NODE = 4 +# MPI rank counts to run with, one GPU per rank -- 2/4 intra-node, 8 = 2 Booster nodes. +RANKS = [2, 4, 8] + def main() -> None: @@ -38,19 +41,6 @@ def main() -> None: action="store_true", help="Upload the packaged profiling results to the profiling-data repo.", ) - parser.add_argument( - "--ranks", - type=int, - nargs="+", - default=[2, 4, 8], - help="MPI rank counts to run with, one GPU per rank (default: 2 4 8; 8 spans 2 Booster nodes).", - ) - parser.add_argument( - "--Np", - type=int, - default=None, - help="Total marker count, overriding params_VlasovAmpere_scaling.py's default (50,000,000).", - ) args = parser.parse_args() # Paths relative to this script's location, so it can be run from anywhere. @@ -75,13 +65,11 @@ def main() -> None: cluster_name = detect_machine_name() param_flags = ["--backend", "cupy"] - if args.Np is not None: - param_flags += ["--Np", str(args.Np)] # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs # far more ranks per node than there are GPUs. - for num_tasks in args.ranks: + for num_tasks in RANKS: num_nodes = -(-num_tasks // GPUS_PER_NODE) profiling_case.launch( num_tasks, From 09bece4815a11e7ad99f9b1e69cacaeea27a9052 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 17:17:24 +0200 Subject: [PATCH 117/156] Cleanup --- .../submit_driftkinetic_cyclone_cupy_scaling.py | 2 +- ...it_driftkinetic_cyclone_numpy_vs_cupy_pcg.py} | 16 ++++++++-------- 2 files changed, 9 insertions(+), 9 deletions(-) rename profiling/{submit_driftkinetic_cyclone_numpy_vs_cupy.py => submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py} (84%) diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py index 173747ad2..f24bbdfb9 100644 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -1,6 +1,6 @@ """DriftKineticElectrostaticAdiabatic (ITG cyclone) CuPy multi-GPU/multi-rank scaling case. -Strong-scaling study (not a backend comparison, see `submit_driftkinetic_cyclone_numpy_vs_cupy.py` +Strong-scaling study (not a backend comparison, see `submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py` for that): the same grid/marker configuration runs under `ARRAY_BACKEND=cupy` at increasing MPI rank counts, one rank per GPU. `--ranks 1 2 4 8` (default) covers intra-node scaling plus one cross-node step. Grid is hardcoded to `NUM_ELEMENTS` below diff --git a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py similarity index 84% rename from profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py rename to profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py index 18af56c43..8a8306029 100644 --- a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy.py +++ b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py @@ -1,9 +1,9 @@ -"""DriftKineticElectrostaticAdiabatic (ITG cyclone) NumPy-vs-CuPy profiling case. +"""DriftKineticElectrostaticAdiabatic (ITG cyclone) NumPy-vs-CuPy, pcg solver. Runs `params_cyclone.py` once with `ARRAY_BACKEND=numpy` and once with -`ARRAY_BACKEND=cupy`. Unlike GuidingCenter, this model has a real per-step FEEC field -solve (PoissonAdiabaticGyrokinetic, default solver='direct'), so it measures whether -the CUDA port helps end to end, not just the particle kernels. +`ARRAY_BACKEND=cupy`, forcing the naive iterative solver ('pcg') instead of +`params_cyclone.py`'s own default ('direct') -- the direct solver isn't +production-ready yet, so this case sticks to pcg rather than featuring it. """ import argparse @@ -46,11 +46,11 @@ def main() -> None: params_source = params_dir / "params_cyclone.py" profiling_case = ProfilingCase( - label="driftkinetic_cyclone_numpy_vs_cupy", - name="ITG cyclone: NumPy vs CuPy", + label="driftkinetic_cyclone_numpy_vs_cupy_pcg", + name="ITG cyclone: NumPy vs CuPy (pcg)", description=( "Cyclone-instability ITG turbulence (DriftKineticElectrostaticAdiabatic), run " - "once on NumPy and once on CuPy." + "once on NumPy and once on CuPy, with the naive iterative field solver." ), physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", struphy_model_used="DriftKineticElectrostaticAdiabatic", @@ -70,7 +70,7 @@ def main() -> None: profiling_case.launch( 1, num_nodes=1, - param_flags=["--backend", backend], + param_flags=["--backend", backend, "--solver", "pcg"], slurm_presets={cluster_name: preset}, ) From 1288a65285dc11c394f88c5c73bd927a4535a144 Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 19 Aug 2026 17:59:32 +0200 Subject: [PATCH 118/156] Added topology-test-multi-rank --- .github/workflows/test-PR-unit-mpi.yml | 50 +++++++++++++++++++++++++- 1 file changed, 49 insertions(+), 1 deletion(-) diff --git a/.github/workflows/test-PR-unit-mpi.yml b/.github/workflows/test-PR-unit-mpi.yml index a7db45f10..2eff4aef0 100644 --- a/.github/workflows/test-PR-unit-mpi.yml +++ b/.github/workflows/test-PR-unit-mpi.yml @@ -20,4 +20,52 @@ jobs: os: ubuntu-latest n-procs: 4 secrets: - ghcr-token: ${{ secrets.GHCR_TOKEN }} \ No newline at end of file + ghcr-token: ${{ secrets.GHCR_TOKEN }} + + topology-test-multi-rank: + name: Neighbour-ranks topology test (${{ matrix.n-procs }} ranks) + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + n-procs: [1, 2, 4, 8, 16, 32, 64] + + container: + image: ghcr.io/struphy-hub/struphy/ubuntu-with-struphy:latest + credentials: + username: spossann + password: ${{ secrets.GHCR_TOKEN }} + + steps: + - name: Checkout repo + uses: actions/checkout@v4 + with: + submodules: true # This is crucial! + fetch-depth: 5 + + - name: Install Struphy in Container + uses: ./.github/actions/install/struphy_in_container + + - name: Get submodule diff + uses: ./.github/actions/submodule-diff + with: + start-dir: /struphy_fortran_ + + - name: Reinstall feectools from submodule + if: env.SUBMOD_CHANGED == 'true' + uses: ./.github/actions/install/feectools-submodule + with: + env-name: /struphy_fortran_/env_fortran_ + + - name: Compile Struphy + uses: ./.github/actions/compile + with: + env-name: /struphy_fortran_/env_fortran_ + + - name: Run neighbour-ranks topology test + run: | + set +e + source /struphy_fortran_/env_fortran_/bin/activate || true + set -e + cd ${{ env.STRUPHY_PATH }} + mpirun --oversubscribe -n ${{ matrix.n-procs }} pytest -v --with-mpi pic/tests/test_neighbor_ranks.py \ No newline at end of file From 2a40612c2a4f7d619aa21600f5f77a7a6c7cb62e Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 18:36:11 +0200 Subject: [PATCH 119/156] cleanup --- src/struphy/pic/accumulation/accum_kernels_gc.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/struphy/pic/accumulation/accum_kernels_gc.py b/src/struphy/pic/accumulation/accum_kernels_gc.py index 6be1d29d4..2fa81a582 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc.py @@ -448,12 +448,6 @@ def cc_lin_mhd_5d_curlb( linalg_kernels.matrix_matrix(tmp1, b_prod_neg, tmp_m) linalg_kernels.matrix_vector(b_prod, curl_norm_b, tmp_v) - # NOTE: these were `+=`, but filling_m/filling_v are allocated - # once *outside* the marker loop and never reset, so every marker - # deposited the running sum of all markers before it -- making the - # result depend on marker row order. The basis_u == 2 branch below - # (and every comparable kernel) uses plain assignment. - # See ISSUE_cc_lin_mhd_5d_curlb_order_dependent.md. filling_m[:, :] = weight * tmp_m * v**2 / abs_b_star_para**2 * ep_scale filling_v[:] = weight * tmp_v * v**2 / abs_b_star_para * ep_scale From bc8febf87d9cae7fcca6b2f94b41977f4a2b1a88 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 19 Aug 2026 19:22:39 +0200 Subject: [PATCH 120/156] feectools: merged direct solver branch into the cupy branch --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 737d440c6..1d00bcb5f 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 737d440c6f534e3a5cf789855b052955a9ab11a0 +Subproject commit 1d00bcb5f8ad701d4b7803088d47342a3e90a8d9 From 6559d2d14aaf5a3add7f13db86bad22ca98a801d Mon Sep 17 00:00:00 2001 From: Max Date: Wed, 19 Aug 2026 19:56:57 +0200 Subject: [PATCH 121/156] Revert previous commit and run all mpi tests with 1,2,4 ranks --- .github/workflows/test-PR-unit-mpi.yml | 55 +++----------------------- 1 file changed, 6 insertions(+), 49 deletions(-) diff --git a/.github/workflows/test-PR-unit-mpi.yml b/.github/workflows/test-PR-unit-mpi.yml index 2eff4aef0..630257729 100644 --- a/.github/workflows/test-PR-unit-mpi.yml +++ b/.github/workflows/test-PR-unit-mpi.yml @@ -15,57 +15,14 @@ concurrency: jobs: unit-test-with-mpi: + name: Unit tests (${{ matrix.n-procs }} ranks) uses: ./.github/workflows/reusable-unit-testing.yml + strategy: + fail-fast: false + matrix: + n-procs: [1, 2, 4] with: os: ubuntu-latest - n-procs: 4 + n-procs: ${{ matrix.n-procs }} secrets: ghcr-token: ${{ secrets.GHCR_TOKEN }} - - topology-test-multi-rank: - name: Neighbour-ranks topology test (${{ matrix.n-procs }} ranks) - runs-on: ubuntu-latest - strategy: - fail-fast: false - matrix: - n-procs: [1, 2, 4, 8, 16, 32, 64] - - container: - image: ghcr.io/struphy-hub/struphy/ubuntu-with-struphy:latest - credentials: - username: spossann - password: ${{ secrets.GHCR_TOKEN }} - - steps: - - name: Checkout repo - uses: actions/checkout@v4 - with: - submodules: true # This is crucial! - fetch-depth: 5 - - - name: Install Struphy in Container - uses: ./.github/actions/install/struphy_in_container - - - name: Get submodule diff - uses: ./.github/actions/submodule-diff - with: - start-dir: /struphy_fortran_ - - - name: Reinstall feectools from submodule - if: env.SUBMOD_CHANGED == 'true' - uses: ./.github/actions/install/feectools-submodule - with: - env-name: /struphy_fortran_/env_fortran_ - - - name: Compile Struphy - uses: ./.github/actions/compile - with: - env-name: /struphy_fortran_/env_fortran_ - - - name: Run neighbour-ranks topology test - run: | - set +e - source /struphy_fortran_/env_fortran_/bin/activate || true - set -e - cd ${{ env.STRUPHY_PATH }} - mpirun --oversubscribe -n ${{ matrix.n-procs }} pytest -v --with-mpi pic/tests/test_neighbor_ranks.py \ No newline at end of file From fcdc1bf33c277224aa4e96a44852fcc512f3045e Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Thu, 20 Aug 2026 12:44:00 +0200 Subject: [PATCH 122/156] formatting --- ...ubmit_driftkinetic_cyclone_cupy_scaling.py | 12 +- src/struphy/io/output_handling.py | 2 +- src/struphy/pic/base.py | 20 +- .../pic/pushing/eval_kernels_gc_cuda.py | 188 +++++++--- .../pic/pushing/eval_kernels_sph_cuda.py | 98 ++++- src/struphy/pic/pushing/pusher.py | 25 +- .../pic/pushing/pusher_kernels_gc_cuda.py | 343 +++++++++++++----- .../pic/tests/bench_mpi_sort_markers.py | 5 +- src/struphy/pic/utilities_kernels_cuda.py | 47 ++- 9 files changed, 551 insertions(+), 189 deletions(-) diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py index f24bbdfb9..22a514672 100644 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -83,10 +83,14 @@ def main() -> None: cluster_name = detect_machine_name() param_flags = [ - "--backend", "cupy", - "--solver", "pcg", - "--Tend", str(TEND), - "--num-elements", *[str(n) for n in NUM_ELEMENTS], + "--backend", + "cupy", + "--solver", + "pcg", + "--Tend", + str(TEND), + "--num-elements", + *[str(n) for n in NUM_ELEMENTS], ] # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would diff --git a/src/struphy/io/output_handling.py b/src/struphy/io/output_handling.py index f9151309d..507a89852 100644 --- a/src/struphy/io/output_handling.py +++ b/src/struphy/io/output_handling.py @@ -2,8 +2,8 @@ import logging import os -import h5py import cunumpy as xp +import h5py import numpy as np logger = logging.getLogger("struphy") diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index ccaeef474..fc5134fd9 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -4774,13 +4774,8 @@ def _sendrecv_determine_mtbs( # forces a sync just to decide. Only an xp.ndarray alpha (the dynamic, # per-kernel case in pusher.py) skips the check and always takes the general # path below, since its value isn't known without a sync anyway. - alpha_is_one = ( - (isinstance(alpha, (int, float)) and alpha == 1.0) - or ( - not isinstance(alpha, xp.ndarray) - and hasattr(alpha, "__iter__") - and all(a == 1.0 for a in alpha) - ) + alpha_is_one = (isinstance(alpha, (int, float)) and alpha == 1.0) or ( + not isinstance(alpha, xp.ndarray) and hasattr(alpha, "__iter__") and all(a == 1.0 for a in alpha) ) bi = self.first_pusher_idx if alpha_is_one: @@ -4797,9 +4792,10 @@ def _sendrecv_determine_mtbs( alpha = xp.asarray(alpha, dtype=float) assert alpha.size == 3 assert xp.all(alpha >= 0.0) and xp.all(alpha <= 1.0) - _y = alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + ( - 1.0 - alpha - ) * self.markers[:, bi : bi + 3] + _y = ( + alpha * (self.markers[:, :3] + self.markers[:, bi + 3 + self.vdim : bi + 3 + self.vdim + 3]) + + (1.0 - alpha) * self.markers[:, bi : bi + 3] + ) # y - floor(y), not xp.mod(y, 1.0): mathematically identical for a modulus of 1 # (verified bit-for-bit equal), but xp.mod dispatches to a true floating-point @@ -4824,9 +4820,7 @@ def _sendrecv_determine_mtbs( self._sorting_etas < self.domain_array_dev[self.mpi_rank, 1::3], ) self._can_stay[:] = ( - self._is_on_proc_domain[:, 0] - & self._is_on_proc_domain[:, 1] - & self._is_on_proc_domain[:, 2] + self._is_on_proc_domain[:, 0] & self._is_on_proc_domain[:, 1] & self._is_on_proc_domain[:, 2] ) else: # Build only the one-dimensional result needed by the exchange diff --git a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py index 08ab97466..1fbb93de7 100644 --- a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py @@ -457,15 +457,23 @@ def _get_gc_marker_column_kernel(name): from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _gc_marker_column_kernels[name] = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _GC_MARKER_COLUMN_SRC, name - ) + _gc_marker_column_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _GC_MARKER_COLUMN_SRC, name) return _gc_marker_column_kernels[name] def grad_driftkinetic_hamiltonian_gpu( - markers, alpha, column_nr, comps, first_init_idx, first_shift_idx, mu_idx, - args_derham, epsilon, grad_b_full_coeffs, e_field_coeffs, evaluate_e_field, + markers, + alpha, + column_nr, + comps, + first_init_idx, + first_shift_idx, + mu_idx, + args_derham, + epsilon, + grad_b_full_coeffs, + e_field_coeffs, + evaluate_e_field, ): """GPU replacement for :func:`~struphy.pic.pushing.eval_kernels_gc.grad_driftkinetic_hamiltonian`.""" @@ -490,26 +498,52 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(column_nr), np.int32(comps_dev.shape[0]), comps_dev, - np.int32(first_init_idx), np.int32(first_shift_idx), np.int32(mu_idx), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(column_nr), + np.int32(comps_dev.shape[0]), + comps_dev, + np.int32(first_init_idx), + np.int32(first_shift_idx), + np.int32(mu_idx), alpha_dev, np.float64(epsilon), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - *d(grad_b_full_coeffs[0]), *d(grad_b_full_coeffs[1]), *d(grad_b_full_coeffs[2]), - *d(e_field_coeffs[0]), *d(e_field_coeffs[1]), *d(e_field_coeffs[2]), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + *d(grad_b_full_coeffs[0]), + *d(grad_b_full_coeffs[1]), + *d(grad_b_full_coeffs[2]), + *d(e_field_coeffs[0]), + *d(e_field_coeffs[1]), + *d(e_field_coeffs[2]), np.int32(bool(evaluate_e_field)), ), ) def bstar_parallel_3form_gpu( - markers, alpha, column_nr, first_init_idx, first_shift_idx, - kind_map, params_dev, args_derham, epsilon, B_dot_b_coeffs, curl_unit_b_dot_b0_coeffs, + markers, + alpha, + column_nr, + first_init_idx, + first_shift_idx, + kind_map, + params_dev, + args_derham, + epsilon, + B_dot_b_coeffs, + curl_unit_b_dot_b0_coeffs, ): """GPU replacement for :func:`~struphy.pic.pushing.eval_kernels_gc.bstar_parallel_3form`.""" @@ -533,25 +567,45 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), np.int32(column_nr), - np.int32(first_init_idx), np.int32(first_shift_idx), + np.int32(first_init_idx), + np.int32(first_shift_idx), alpha_dev, - np.int32(kind_map), params_dev, + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - *d(B_dot_b_coeffs), *d(curl_unit_b_dot_b0_coeffs), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + *d(B_dot_b_coeffs), + *d(curl_unit_b_dot_b0_coeffs), ), ) def bstar_2form_gpu( - markers, alpha, column_nr, comps, first_init_idx, first_shift_idx, - args_derham, epsilon, b2_coeffs, curl_unit_b2_coeffs, + markers, + alpha, + column_nr, + comps, + first_init_idx, + first_shift_idx, + args_derham, + epsilon, + b2_coeffs, + curl_unit_b2_coeffs, ): """GPU replacement for :func:`~struphy.pic.pushing.eval_kernels_gc.bstar_2form`.""" @@ -576,25 +630,47 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(column_nr), np.int32(comps_dev.shape[0]), comps_dev, - np.int32(first_init_idx), np.int32(first_shift_idx), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(column_nr), + np.int32(comps_dev.shape[0]), + comps_dev, + np.int32(first_init_idx), + np.int32(first_shift_idx), alpha_dev, np.float64(epsilon), - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - *d(b2_coeffs[0]), *d(b2_coeffs[1]), *d(b2_coeffs[2]), - *d(curl_unit_b2_coeffs[0]), *d(curl_unit_b2_coeffs[1]), *d(curl_unit_b2_coeffs[2]), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + *d(b2_coeffs[0]), + *d(b2_coeffs[1]), + *d(b2_coeffs[2]), + *d(curl_unit_b2_coeffs[0]), + *d(curl_unit_b2_coeffs[1]), + *d(curl_unit_b2_coeffs[2]), ), ) def unit_b_1form_gpu( - markers, alpha, column_nr, comps, first_init_idx, first_shift_idx, - args_derham, unit_b1_coeffs, + markers, + alpha, + column_nr, + comps, + first_init_idx, + first_shift_idx, + args_derham, + unit_b1_coeffs, ): """GPU replacement for :func:`~struphy.pic.pushing.eval_kernels_gc.unit_b_1form`.""" @@ -619,15 +695,29 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(column_nr), np.int32(comps_dev.shape[0]), comps_dev, - np.int32(first_init_idx), np.int32(first_shift_idx), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(column_nr), + np.int32(comps_dev.shape[0]), + comps_dev, + np.int32(first_init_idx), + np.int32(first_shift_idx), alpha_dev, - np.int32(args_derham.pn[0]), np.int32(args_derham.pn[1]), np.int32(args_derham.pn[2]), - tn1, np.int32(tn1.shape[0]), - tn2, np.int32(tn2.shape[0]), - tn3, np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), np.int32(args_derham.starts[1]), np.int32(args_derham.starts[2]), - *d(unit_b1_coeffs[0]), *d(unit_b1_coeffs[1]), *d(unit_b1_coeffs[2]), + np.int32(args_derham.pn[0]), + np.int32(args_derham.pn[1]), + np.int32(args_derham.pn[2]), + tn1, + np.int32(tn1.shape[0]), + tn2, + np.int32(tn2.shape[0]), + tn3, + np.int32(tn3.shape[0]), + np.int32(args_derham.starts[0]), + np.int32(args_derham.starts[1]), + np.int32(args_derham.starts[2]), + *d(unit_b1_coeffs[0]), + *d(unit_b1_coeffs[1]), + *d(unit_b1_coeffs[2]), ), ) diff --git a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py index f03429b73..ef9d77627 100644 --- a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py +++ b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py @@ -147,9 +147,20 @@ def _get_sph_marker_column_kernel(name): def _sph_marker_column_launch( - name, markers, valid_mks, column_nr, weight_idx, - boxes, neighbours, holes, periodic, kernel_type, h, - *, first_free_idx=None, mu=None, + name, + markers, + valid_mks, + column_nr, + weight_idx, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + *, + first_free_idx=None, + mu=None, ): import cupy as cp import numpy as np @@ -164,18 +175,27 @@ def _sph_marker_column_launch( dev_holes = cp.ascontiguousarray(cp.asarray(holes).astype(cp.int32, copy=False)) args = [ - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(column_nr), np.int32(weight_idx), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(column_nr), + np.int32(weight_idx), ] if first_free_idx is not None: args.append(np.int32(first_free_idx)) args += [ dev_valid, - dev_boxes, np.int32(dev_boxes.shape[1]), - dev_neigh, dev_holes, - np.int32(bool(periodic[0])), np.int32(bool(periodic[1])), np.int32(bool(periodic[2])), + dev_boxes, + np.int32(dev_boxes.shape[1]), + dev_neigh, + dev_holes, + np.int32(bool(periodic[0])), + np.int32(bool(periodic[1])), + np.int32(bool(periodic[2])), np.int32(kernel_type), - np.float64(h[0]), np.float64(h[1]), np.float64(h[2]), + np.float64(h[0]), + np.float64(h[1]), + np.float64(h[2]), ] if mu is not None: args.append(np.float64(mu)) @@ -183,12 +203,23 @@ def _sph_marker_column_launch( _get_sph_marker_column_kernel(name)((blocks,), (threads,), tuple(args)) -def sph_pressure_coeffs_gpu(markers, valid_mks, column_nr, weight_idx, boxes, neighbours, holes, periodic, kernel_type, h): +def sph_pressure_coeffs_gpu( + markers, valid_mks, column_nr, weight_idx, boxes, neighbours, holes, periodic, kernel_type, h +): """GPU replacement for one call of :func:`~struphy.pic.pushing.eval_kernels_sph.sph_pressure_coeffs`.""" _sph_marker_column_launch( - "sph_pressure_coeffs_cuda", markers, valid_mks, column_nr, weight_idx, - boxes, neighbours, holes, periodic, kernel_type, h, + "sph_pressure_coeffs_cuda", + markers, + valid_mks, + column_nr, + weight_idx, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, ) @@ -198,19 +229,48 @@ def sph_mean_velocity_coeffs_gpu( """GPU replacement for one call of :func:`~struphy.pic.pushing.eval_kernels_sph.sph_mean_velocity_coeffs`.""" _sph_marker_column_launch( - "sph_mean_velocity_coeffs_cuda", markers, valid_mks, column_nr, weight_idx, - boxes, neighbours, holes, periodic, kernel_type, h, + "sph_mean_velocity_coeffs_cuda", + markers, + valid_mks, + column_nr, + weight_idx, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, ) def sph_viscosity_tensor_gpu( - markers, valid_mks, column_nr, weight_idx, first_free_idx, - boxes, neighbours, holes, periodic, kernel_type, h, mu, + markers, + valid_mks, + column_nr, + weight_idx, + first_free_idx, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + mu, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.eval_kernels_sph.sph_viscosity_tensor`.""" _sph_marker_column_launch( - "sph_viscosity_tensor_cuda", markers, valid_mks, column_nr, weight_idx, - boxes, neighbours, holes, periodic, kernel_type, h, - first_free_idx=first_free_idx, mu=mu, + "sph_viscosity_tensor_cuda", + markers, + valid_mks, + column_nr, + weight_idx, + boxes, + neighbours, + holes, + periodic, + kernel_type, + h, + first_free_idx=first_free_idx, + mu=mu, ) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index b2b4f51c8..52067656a 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -1054,8 +1054,12 @@ def _run_marker_column_kernel(self, ker, alpha, column_nr, comps, add_args): self.particles.valid_mks, column_nr, self.particles.index["weights"], - boxes, neighbours, holes, - (p1, p2, p3), kernel_type, (h1, h2, h3), + boxes, + neighbours, + holes, + (p1, p2, p3), + kernel_type, + (h1, h2, h3), ) return @@ -1067,8 +1071,12 @@ def _run_marker_column_kernel(self, ker, alpha, column_nr, comps, add_args): self.particles.valid_mks, column_nr, self.particles.index["weights"], - boxes, neighbours, holes, - (p1, p2, p3), kernel_type, (h1, h2, h3), + boxes, + neighbours, + holes, + (p1, p2, p3), + kernel_type, + (h1, h2, h3), ) return @@ -1081,8 +1089,13 @@ def _run_marker_column_kernel(self, ker, alpha, column_nr, comps, add_args): column_nr, self.particles.index["weights"], self.particles.first_free_idx, - boxes, neighbours, holes, - (p1, p2, p3), kernel_type, (h1, h2, h3), mu, + boxes, + neighbours, + holes, + (p1, p2, p3), + kernel_type, + (h1, h2, h3), + mu, ) return diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index 026e5b658..e1eacf013 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -1554,16 +1554,26 @@ def _get_j2_dg_kernel(name): from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _j2_dg_kernels[name] = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _DG_1ST_SRC + _PUSH_GC_CC_J2_DG_SRC, name - ) + _j2_dg_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _DG_1ST_SRC + _PUSH_GC_CC_J2_DG_SRC, name) return _j2_dg_kernels[name] def push_gc_cc_J2_dg_init_Hdiv_gpu( - markers, first_init_idx, kind_map, params_dev, epsilon, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2, norm_b1, curl_norm_b, u, dt, + markers, + first_init_idx, + kind_map, + params_dev, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2, + norm_b1, + curl_norm_b, + u, + dt, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_dg_init_Hdiv`.""" @@ -1582,28 +1592,61 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), np.int32(first_init_idx), np.float64(dt), - np.int32(kind_map), params_dev, + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), - *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), - *d(u[0]), *d(u[1]), *d(u[2]), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(norm_b1[0]), + *d(norm_b1[1]), + *d(norm_b1[2]), + *d(curl_norm_b[0]), + *d(curl_norm_b[1]), + *d(curl_norm_b[2]), + *d(u[0]), + *d(u[1]), + *d(u[2]), ), ) def push_gc_cc_J2_dg_Hdiv_gpu( - markers, first_init_idx, kind_map, params_dev, epsilon, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - b2, norm_b1, curl_norm_b, u, ud, const, alpha, dt, + markers, + first_init_idx, + kind_map, + params_dev, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + b2, + norm_b1, + curl_norm_b, + u, + ud, + const, + alpha, + dt, ): """GPU replacement for one call of :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_dg_Hdiv`.""" @@ -1622,21 +1665,43 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), np.int32(first_init_idx), - np.float64(dt), np.float64(const), np.float64(alpha), - np.int32(kind_map), params_dev, + np.float64(dt), + np.float64(const), + np.float64(alpha), + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(norm_b1[0]), *d(norm_b1[1]), *d(norm_b1[2]), - *d(curl_norm_b[0]), *d(curl_norm_b[1]), *d(curl_norm_b[2]), - *d(u[0]), *d(u[1]), *d(u[2]), - *d(ud[0]), *d(ud[1]), *d(ud[2]), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(norm_b1[0]), + *d(norm_b1[1]), + *d(norm_b1[2]), + *d(curl_norm_b[0]), + *d(curl_norm_b[1]), + *d(curl_norm_b[2]), + *d(u[0]), + *d(u[1]), + *d(u[2]), + *d(ud[0]), + *d(ud[1]), + *d(ud[2]), ), ) @@ -1928,9 +1993,25 @@ def _get_dg_newton_kernel(name): def _dg_newton_launch( - name, markers, first_init_idx, first_shift_idx, residual_idx, first_free_idx, - mu_idx, epsilon, pn, tn1_dev, tn2_dev, tn3_dev, starts, - grad_b_full, B_dot_b_coeffs, e_field, phi_coeffs, evaluate_e_field, dt, + name, + markers, + first_init_idx, + first_shift_idx, + residual_idx, + first_free_idx, + mu_idx, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + grad_b_full, + B_dot_b_coeffs, + e_field, + phi_coeffs, + evaluate_e_field, + dt, ): import cupy as cp import numpy as np @@ -1947,20 +2028,37 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_init_idx), np.int32(first_shift_idx), - np.int32(residual_idx), np.int32(first_free_idx), np.int32(mu_idx), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_shift_idx), + np.int32(residual_idx), + np.int32(first_free_idx), + np.int32(mu_idx), np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(grad_b_full[0]), *d(grad_b_full[1]), *d(grad_b_full[2]), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(grad_b_full[0]), + *d(grad_b_full[1]), + *d(grad_b_full[2]), *d(B_dot_b_coeffs), - *d(e_field[0]), *d(e_field[1]), *d(e_field[2]), + *d(e_field[0]), + *d(e_field[1]), + *d(e_field[2]), *d(phi_coeffs), - np.int32(bool(evaluate_e_field)), np.float64(dt), + np.int32(bool(evaluate_e_field)), + np.float64(dt), ), ) @@ -2230,9 +2328,27 @@ def _get_dg_2nd_order_kernel(name): def push_gc_bxEstar_discrete_gradient_2nd_order_gpu( - markers, first_init_idx, first_shift_idx, residual_idx, first_free_idx, mu_idx, - kind_map, params_dev, epsilon, pn, tn1_dev, tn2_dev, tn3_dev, starts, - unit_b1, grad_b_full, B_dot_b_coeffs, curl_unit_b_dot_b0, e_field, evaluate_e_field, dt, + markers, + first_init_idx, + first_shift_idx, + residual_idx, + first_free_idx, + mu_idx, + kind_map, + params_dev, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + unit_b1, + grad_b_full, + B_dot_b_coeffs, + curl_unit_b_dot_b0, + e_field, + evaluate_e_field, + dt, ): """GPU replacement for one Picard iteration of :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_discrete_gradient_2nd_order`.""" @@ -2251,29 +2367,69 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_init_idx), np.int32(first_shift_idx), - np.int32(residual_idx), np.int32(first_free_idx), np.int32(mu_idx), - np.int32(kind_map), params_dev, + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_shift_idx), + np.int32(residual_idx), + np.int32(first_free_idx), + np.int32(mu_idx), + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(unit_b1[0]), *d(unit_b1[1]), *d(unit_b1[2]), - *d(grad_b_full[0]), *d(grad_b_full[1]), *d(grad_b_full[2]), - *d(B_dot_b_coeffs), *d(curl_unit_b_dot_b0), - *d(e_field[0]), *d(e_field[1]), *d(e_field[2]), - np.int32(bool(evaluate_e_field)), np.float64(dt), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(unit_b1[0]), + *d(unit_b1[1]), + *d(unit_b1[2]), + *d(grad_b_full[0]), + *d(grad_b_full[1]), + *d(grad_b_full[2]), + *d(B_dot_b_coeffs), + *d(curl_unit_b_dot_b0), + *d(e_field[0]), + *d(e_field[1]), + *d(e_field[2]), + np.int32(bool(evaluate_e_field)), + np.float64(dt), ), ) def push_gc_Bstar_discrete_gradient_2nd_order_gpu( - markers, first_init_idx, first_shift_idx, residual_idx, first_free_idx, mu_idx, - kind_map, params_dev, epsilon, pn, tn1_dev, tn2_dev, tn3_dev, starts, - grad_b_full, b2, curl_unit_b2, B_dot_b_coeffs, curl_unit_b_dot_b0, e_field, evaluate_e_field, dt, + markers, + first_init_idx, + first_shift_idx, + residual_idx, + first_free_idx, + mu_idx, + kind_map, + params_dev, + epsilon, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + grad_b_full, + b2, + curl_unit_b2, + B_dot_b_coeffs, + curl_unit_b_dot_b0, + e_field, + evaluate_e_field, + dt, ): """GPU replacement for one Picard iteration of :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_discrete_gradient_2nd_order`.""" @@ -2292,21 +2448,44 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_init_idx), np.int32(first_shift_idx), - np.int32(residual_idx), np.int32(first_free_idx), np.int32(mu_idx), - np.int32(kind_map), params_dev, + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(first_shift_idx), + np.int32(residual_idx), + np.int32(first_free_idx), + np.int32(mu_idx), + np.int32(kind_map), + params_dev, np.float64(epsilon), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(grad_b_full[0]), *d(grad_b_full[1]), *d(grad_b_full[2]), - *d(b2[0]), *d(b2[1]), *d(b2[2]), - *d(curl_unit_b2[0]), *d(curl_unit_b2[1]), *d(curl_unit_b2[2]), - *d(B_dot_b_coeffs), *d(curl_unit_b_dot_b0), - *d(e_field[0]), *d(e_field[1]), *d(e_field[2]), - np.int32(bool(evaluate_e_field)), np.float64(dt), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(grad_b_full[0]), + *d(grad_b_full[1]), + *d(grad_b_full[2]), + *d(b2[0]), + *d(b2[1]), + *d(b2[2]), + *d(curl_unit_b2[0]), + *d(curl_unit_b2[1]), + *d(curl_unit_b2[2]), + *d(B_dot_b_coeffs), + *d(curl_unit_b_dot_b0), + *d(e_field[0]), + *d(e_field[1]), + *d(e_field[2]), + np.int32(bool(evaluate_e_field)), + np.float64(dt), ), ) diff --git a/src/struphy/pic/tests/bench_mpi_sort_markers.py b/src/struphy/pic/tests/bench_mpi_sort_markers.py index ae7e28596..3e3603fd0 100644 --- a/src/struphy/pic/tests/bench_mpi_sort_markers.py +++ b/src/struphy/pic/tests/bench_mpi_sort_markers.py @@ -30,13 +30,12 @@ import os import time -import numpy as np - # cunumpy does not import feectools.ddm.mpi (verified), so this is safe to import # first and do CUDA-only, MPI-independent setup (device binding, the MPI opt-in env # var below) before struphy -- which does transitively import feectools.ddm.mpi as # part of its own __init__ -- gets imported next. import cunumpy as xp +import numpy as np # Under CuPy with more than one MPI rank per node, every rank must bind to its own # GPU -- cunumpy defaults to device 0, so without this every rank on a node would @@ -60,9 +59,9 @@ # hcoll/Alltoallv segfault on this cluster (see the comment there). struphy itself # imports feectools.ddm.mpi as part of this same import, so this line satisfies # both ordering requirements at once. -from struphy import BoundaryParameters, LoadingParameters, SortingParameters from feectools.ddm.mpi import mpi as MPI +from struphy import BoundaryParameters, LoadingParameters, SortingParameters from struphy.pic.particles import Particles6D diff --git a/src/struphy/pic/utilities_kernels_cuda.py b/src/struphy/pic/utilities_kernels_cuda.py index 3ea934b42..9906e4035 100644 --- a/src/struphy/pic/utilities_kernels_cuda.py +++ b/src/struphy/pic/utilities_kernels_cuda.py @@ -830,9 +830,17 @@ def _get_gradb_ediff_kernel(): def eval_gradB_ediff_gpu( - markers, first_init_idx, mu_idx, - pn, tn1_dev, tn2_dev, tn3_dev, starts, - gradB1_dev, grad_PB_b1_dev, idx, + markers, + first_init_idx, + mu_idx, + pn, + tn1_dev, + tn2_dev, + tn3_dev, + starts, + gradB1_dev, + grad_PB_b1_dev, + idx, ): """GPU replacement for one call of :func:`~struphy.pic.utilities_kernels.eval_gradB_ediff`. @@ -857,14 +865,29 @@ def d(a): (blocks,), (threads,), ( - markers, np.int32(markers.shape[1]), np.int32(n_markers), - np.int32(first_init_idx), np.int32(mu_idx), np.int32(idx), - np.int32(pn[0]), np.int32(pn[1]), np.int32(pn[2]), - tn1_dev, np.int32(tn1_dev.shape[0]), - tn2_dev, np.int32(tn2_dev.shape[0]), - tn3_dev, np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), np.int32(starts[1]), np.int32(starts[2]), - *d(gradB1_dev[0]), *d(gradB1_dev[1]), *d(gradB1_dev[2]), - *d(grad_PB_b1_dev[0]), *d(grad_PB_b1_dev[1]), *d(grad_PB_b1_dev[2]), + markers, + np.int32(markers.shape[1]), + np.int32(n_markers), + np.int32(first_init_idx), + np.int32(mu_idx), + np.int32(idx), + np.int32(pn[0]), + np.int32(pn[1]), + np.int32(pn[2]), + tn1_dev, + np.int32(tn1_dev.shape[0]), + tn2_dev, + np.int32(tn2_dev.shape[0]), + tn3_dev, + np.int32(tn3_dev.shape[0]), + np.int32(starts[0]), + np.int32(starts[1]), + np.int32(starts[2]), + *d(gradB1_dev[0]), + *d(gradB1_dev[1]), + *d(gradB1_dev[2]), + *d(grad_PB_b1_dev[0]), + *d(grad_PB_b1_dev[1]), + *d(grad_PB_b1_dev[2]), ), ) From 3bc6e34e758ea2a42ff114f6404e96c8aa98bd58 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Thu, 20 Aug 2026 12:48:17 +0200 Subject: [PATCH 123/156] bugfix in feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 1d00bcb5f..6b7b090c7 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 1d00bcb5f8ad701d4b7803088d47342a3e90a8d9 +Subproject commit 6b7b090c71b1acdbbd8ce733e30cf3737237bf20 From 34916a42a0ac6af86d1ec394467f540ac775355f Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Thu, 20 Aug 2026 13:53:20 +0200 Subject: [PATCH 124/156] Updated feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 6b7b090c7..021d996a5 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 6b7b090c71b1acdbbd8ce733e30cf3737237bf20 +Subproject commit 021d996a538b719a85a8d3ac16eb919960bf7da9 From 98ed25595cb1e2af0d1e4faac6c3ad5e52d140df Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Thu, 20 Aug 2026 14:10:20 +0200 Subject: [PATCH 125/156] Removed the DirectSolver --- feectools | 2 +- .../params_cyclone.py | 23 ++++++------------- ...ubmit_driftkinetic_cyclone_cupy_scaling.py | 11 +-------- ..._driftkinetic_cyclone_numpy_vs_cupy_pcg.py | 6 ++--- src/struphy/feec/linear_operators.py | 3 +-- src/struphy/feec/mass.py | 14 +++++------ src/struphy/io/options.py | 2 +- 7 files changed, 19 insertions(+), 42 deletions(-) diff --git a/feectools b/feectools index 021d996a5..5aeb2c226 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 021d996a538b719a85a8d3ac16eb919960bf7da9 +Subproject commit 5aeb2c2260a1a15cbfc41f73640efde064a8f6fa diff --git a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py index d14008e81..6322a7d51 100644 --- a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py +++ b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py @@ -31,17 +31,9 @@ parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default, 0.01 -> 10 steps).") parser.add_argument( "--solver", - choices=("pcg", "direct"), - default="direct", - help=( - "Symmetric solver for the PoissonAdiabaticGyrokinetic field solve (default: " - "direct). 'direct' uses feectools.linalg.solvers.DirectSolver, a cached sparse " - "LU factorization -- valid here because the LHS operator is constant across " - "time steps (divide_by_dt=False, fixed epsilon/Z), so one factorization serves " - "every step instead of a fresh (near-maxiter, since tol=1e-12 barely converges) " - "PCG solve each time; see this file's docstring for the measured ~1900x " - "per-solve speedup on CuPy. 'pcg' reproduces the original, much slower baseline." - ), + choices=("pcg",), + default="pcg", + help="Symmetric solver for the PoissonAdiabaticGyrokinetic field solve (default: pcg).", ) parser.add_argument( "--num-elements", @@ -118,11 +110,10 @@ # The physics case (examples/.../cyclone/params_cyclone.py) enables this, but it # wires up a *second*, completely separate solve every step # (ImplicitDiffusion.__call__'s `if self.diagnostic is not None: ... proj.solve(rhs)`, - # a fresh, uncached L2Projector with the default "pcg" solver -- unrelated to and not - # sped up by --solver direct above). It only feeds `self.diagnostics.rho`, an extra - # saved diagnostic field that nothing else in this model reads back (the - # `phi_integral` scalar uses `phi` directly) -- disabled here so this profiling case - # measures the model's actual per-step cost, not an unrelated, unoptimized solve. + # a fresh L2Projector with the default "pcg" solver). It only feeds + # `self.diagnostics.rho`, an extra saved diagnostic field that nothing else in this + # model reads back (the `phi_integral` scalar uses `phi` directly) -- disabled here + # so this profiling case measures the model's actual per-step cost. use_diagnostic_poisson=False, ) diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py index 22a514672..e0124ff61 100644 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py @@ -5,14 +5,6 @@ increasing MPI rank counts, one rank per GPU. `--ranks 1 2 4 8` (default) covers intra-node scaling plus one cross-node step. Grid is hardcoded to `NUM_ELEMENTS` below (not a CLI flag), so a run's grid is always readable straight from this file. - -**Solver forced to `pcg`, not `params_cyclone.py`'s own default (`direct`).** -`DirectSolver` now supports `nprocs > 1` (a replicated matrix assembly, see -`feectools.linalg.utilities.tosparse_via_matvec`), but that assembly is currently too -slow under CuPy in practice (measured ~270-300s one-time cost even at a modest grid, -dominated by per-`dot()`-call kernel-launch/sync overhead the array-transfer -optimization only dents) to be worth using in a scaling study yet -- `pcg` gives a -cleaner, apples-to-apples comparison across rank counts until that's fixed. """ import argparse @@ -67,8 +59,7 @@ def main() -> None: name="ITG cyclone: CuPy scaling", description=( "Cyclone-instability ITG turbulence (DriftKineticElectrostaticAdiabatic) on " - "CuPy, strong-scaled across GPUs. Solver forced to pcg (direct's multi-rank " - "assembly is not fast enough yet, see module docstring)." + "CuPy, strong-scaled across GPUs with the PCG field solver." ), physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", struphy_model_used="DriftKineticElectrostaticAdiabatic", diff --git a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py index 8a8306029..f13a4d067 100644 --- a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py +++ b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py @@ -1,9 +1,7 @@ -"""DriftKineticElectrostaticAdiabatic (ITG cyclone) NumPy-vs-CuPy, pcg solver. +"""DriftKineticElectrostaticAdiabatic (ITG cyclone) NumPy-vs-CuPy, PCG solver. Runs `params_cyclone.py` once with `ARRAY_BACKEND=numpy` and once with -`ARRAY_BACKEND=cupy`, forcing the naive iterative solver ('pcg') instead of -`params_cyclone.py`'s own default ('direct') -- the direct solver isn't -production-ready yet, so this case sticks to pcg rather than featuring it. +`ARRAY_BACKEND=cupy`, using the PCG field solver. """ import argparse diff --git a/src/struphy/feec/linear_operators.py b/src/struphy/feec/linear_operators.py index ca9908686..bdd235c76 100644 --- a/src/struphy/feec/linear_operators.py +++ b/src/struphy/feec/linear_operators.py @@ -455,8 +455,7 @@ def tosparse(self): applying it once to an all-ones vector directly gives that diagonal, far cheaper than a generic basis-vector sweep (see AverageOperator.tosparse for that approach, used where the operator isn't diagonal). Serial (single MPI rank) - only, meant for feectools.linalg.solvers.DirectSolver's factor-once use (see its - docstring). + only. """ def _stencil_diag_flat(v): diff --git a/src/struphy/feec/mass.py b/src/struphy/feec/mass.py index 5e2e807e8..1e88b8f90 100644 --- a/src/struphy/feec/mass.py +++ b/src/struphy/feec/mass.py @@ -3395,14 +3395,12 @@ def tosparse(self): that share its other two indices, with coefficient `weights[o]`. That is fully vectorizable with `numpy`, unlike a basis-vector sweep (one `dot()` call per domain DOF, i.e. per grid point): that would mean thousands of individual CuPy - kernel launches with a device sync each under the CuPy backend -- exactly the - per-iteration launch/sync overhead - feectools.linalg.solvers.DirectSolver exists to eliminate from the *solve* path, - reappearing in its one-time *setup* path instead. This builds the sparse matrix - entirely on the host regardless of backend (`self._weights` is the only device - array involved, and is tiny -- one value per grid point along `d0`). - - Serial (single MPI rank) only, like `DirectSolver` itself. + kernel launches with a device sync each under the CuPy backend. This builds + the sparse matrix entirely on the host regardless of backend (`self._weights` + is the only device array involved, and is tiny -- one value per grid point + along `d0`). + + Serial (single MPI rank) only. """ from scipy.sparse import coo_matrix diff --git a/src/struphy/io/options.py b/src/struphy/io/options.py index 942f5bf1b..6db9f74cc 100644 --- a/src/struphy/io/options.py +++ b/src/struphy/io/options.py @@ -71,7 +71,7 @@ class LiteralOptions: GivenInBasis = Literal["0", "1", "2", "3", "v", "physical", "physical_at_eta", "norm", None] # solvers - OptsSymmSolver = Literal["pcg", "cg", "direct"] + OptsSymmSolver = Literal["pcg", "cg"] OptsGenSolver = Literal["pbicgstab", "bicgstab", "gmres"] OptsMassPrecond = Literal["MassMatrixPreconditioner", "MassMatrixDiagonalPreconditioner", None] OptsSaddlePointSolver = Literal["uzawa"] From 80d949064aabb3e0e89c7c89adda6e784ff69ace Mon Sep 17 00:00:00 2001 From: Max Date: Thu, 20 Aug 2026 21:09:23 +0200 Subject: [PATCH 126/156] Added mpi_pic mark --- .github/workflows/test-PR-unit-mpi.yml | 46 +++++++++++++++++++ pyproject.toml | 1 + src/struphy/pic/tests/test_accum_vec_H1.py | 2 + src/struphy/pic/tests/test_binning.py | 2 + src/struphy/pic/tests/test_draw_parallel.py | 2 + src/struphy/pic/tests/test_neighbor_ranks.py | 3 ++ .../pic/tests/test_set_zero_velocity.py | 1 + src/struphy/pic/tests/test_sorting.py | 1 + 8 files changed, 58 insertions(+) diff --git a/.github/workflows/test-PR-unit-mpi.yml b/.github/workflows/test-PR-unit-mpi.yml index 630257729..8fde32f65 100644 --- a/.github/workflows/test-PR-unit-mpi.yml +++ b/.github/workflows/test-PR-unit-mpi.yml @@ -26,3 +26,49 @@ jobs: n-procs: ${{ matrix.n-procs }} secrets: ghcr-token: ${{ secrets.GHCR_TOKEN }} + + pic-mpi-tests: + name: Marked PIC MPI tests (${{ matrix.n-procs }} ranks) + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + n-procs: [3, 4] + + container: + image: ghcr.io/struphy-hub/struphy/ubuntu-with-struphy:latest + credentials: + username: spossann + password: ${{ secrets.GHCR_TOKEN }} + + steps: + - name: Checkout repo + uses: actions/checkout@v4 + with: + submodules: true + fetch-depth: 5 + + - name: Install Struphy in Container + uses: ./.github/actions/install/struphy_in_container + + - name: Get submodule diff + uses: ./.github/actions/submodule-diff + with: + start-dir: /struphy_fortran_ + + - name: Reinstall feectools from submodule + if: env.SUBMOD_CHANGED == 'true' + uses: ./.github/actions/install/feectools-submodule + with: + env-name: /struphy_fortran_/env_fortran_ + + - name: Compile Struphy + uses: ./.github/actions/compile + with: + env-name: /struphy_fortran_/env_fortran_ + + - name: Run marked PIC MPI tests + run: | + source /struphy_fortran_/env_fortran_/bin/activate + cd ${{ env.STRUPHY_PATH }} + mpirun --oversubscribe -n ${{ matrix.n-procs }} pytest -v --with-mpi -m mpi_pic pic/tests diff --git a/pyproject.toml b/pyproject.toml index 9a928bdf3..9d4fa1043 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -178,4 +178,5 @@ markers = [ "kinetic", "hybrid", "single", + "mpi_pic", ] diff --git a/src/struphy/pic/tests/test_accum_vec_H1.py b/src/struphy/pic/tests/test_accum_vec_H1.py index 3c5ae9af9..ee9f21e0e 100644 --- a/src/struphy/pic/tests/test_accum_vec_H1.py +++ b/src/struphy/pic/tests/test_accum_vec_H1.py @@ -5,6 +5,8 @@ from struphy import set_logging_level +pytestmark = pytest.mark.mpi_pic + logger = logging.getLogger("struphy") set_logging_level(logging.INFO) diff --git a/src/struphy/pic/tests/test_binning.py b/src/struphy/pic/tests/test_binning.py index 0990faae6..fcaa2ba1d 100644 --- a/src/struphy/pic/tests/test_binning.py +++ b/src/struphy/pic/tests/test_binning.py @@ -479,6 +479,7 @@ def test_binning_6D_delta_f(mapping, show_plot=False): # 'R0': 4., 'Lz': 5., 'delta_x': 0.06, 'delta_y': 0.07, 'delta_gs': 0.08, 'epsilon_gs': 9., 'kappa_gs': 10.}] ], ) +@pytest.mark.mpi_pic def test_binning_6D_full_f_mpi(mapping, show_plot=False): """Test Maxwellian in v1-direction and cosine perturbation for full-f Particles6D with mpi. @@ -790,6 +791,7 @@ def test_binning_6D_full_f_mpi(mapping, show_plot=False): # 'R0': 4., 'Lz': 5., 'delta_x': 0.06, 'delta_y': 0.07, 'delta_gs': 0.08, 'epsilon_gs': 9., 'kappa_gs': 10.}] ], ) +@pytest.mark.mpi_pic def test_binning_6D_delta_f_mpi(mapping, show_plot=False): """Test Maxwellian in v1-direction and cosine perturbation for delta-f Particles6D with mpi. diff --git a/src/struphy/pic/tests/test_draw_parallel.py b/src/struphy/pic/tests/test_draw_parallel.py index e0a796aec..3751a3e7f 100644 --- a/src/struphy/pic/tests/test_draw_parallel.py +++ b/src/struphy/pic/tests/test_draw_parallel.py @@ -2,6 +2,8 @@ import pytest +pytestmark = pytest.mark.mpi_pic + logger = logging.getLogger("struphy") diff --git a/src/struphy/pic/tests/test_neighbor_ranks.py b/src/struphy/pic/tests/test_neighbor_ranks.py index 818f84d39..0d7afab39 100644 --- a/src/struphy/pic/tests/test_neighbor_ranks.py +++ b/src/struphy/pic/tests/test_neighbor_ranks.py @@ -34,6 +34,7 @@ def ijk_rank(i, j, k, nprocs): @pytest.mark.mpi(min_size=2) +@pytest.mark.mpi_pic @pytest.mark.parametrize("periodic_axes", [(), (0,), (0, 1, 2)]) def test_neighbor_ranks_partition_all_other_ranks(periodic_axes): """Every other rank must end up in exactly one of the two output lists.""" @@ -51,6 +52,7 @@ def test_neighbor_ranks_partition_all_other_ranks(periodic_axes): @pytest.mark.mpi(min_size=2) +@pytest.mark.mpi_pic @pytest.mark.parametrize("periodic_axes", [(), (0,), (0, 1, 2)]) def test_neighbor_relation_is_symmetric(periodic_axes): """If rank j is a neighbour of rank i, rank i must be a neighbour of rank j. @@ -74,6 +76,7 @@ def test_neighbor_relation_is_symmetric(periodic_axes): @pytest.mark.mpi(min_size=2) +@pytest.mark.mpi_pic @pytest.mark.parametrize("periodic_axes", [(), (0,), (1,), (0, 1, 2)]) def test_face_adjacent_ranks_are_always_neighbors(periodic_axes): """Ranks whose process-grid index differs by 1 along a single axis share a diff --git a/src/struphy/pic/tests/test_set_zero_velocity.py b/src/struphy/pic/tests/test_set_zero_velocity.py index c234f97b4..2eb563859 100644 --- a/src/struphy/pic/tests/test_set_zero_velocity.py +++ b/src/struphy/pic/tests/test_set_zero_velocity.py @@ -117,6 +117,7 @@ def test_set_zero_velocity(mapping, comp: int, show_plot=False): # 'R0': 4., 'Lz': 5., 'delta_x': 0.06, 'delta_y': 0.07, 'delta_gs': 0.08> ], ) +@pytest.mark.mpi_pic def test_set_zero_velocity_mpi(mapping, comp: int, show_plot=False): """Test set_zero_velocity argument in LoadingParameters with mpi. diff --git a/src/struphy/pic/tests/test_sorting.py b/src/struphy/pic/tests/test_sorting.py index 84f26e0fb..5b9d83e46 100644 --- a/src/struphy/pic/tests/test_sorting.py +++ b/src/struphy/pic/tests/test_sorting.py @@ -102,6 +102,7 @@ def test_flattening_roundtrip(nx, ny, nz, algo): ], ) @pytest.mark.parametrize("Np", [10000]) +@pytest.mark.mpi_pic def test_sorting(num_elements, degree, bcs, mapping, Np): mpi_comm = MPI.COMM_WORLD # assert mpi_comm.size >= 2 From 2cf34127b7d4d65ff9d8a1cf54d16c5fe7039d11 Mon Sep 17 00:00:00 2001 From: Max Date: Thu, 20 Aug 2026 21:16:21 +0200 Subject: [PATCH 127/156] mpi tests with 2, mpi pic tests with 1,2,3,4 --- .github/workflows/test-PR-unit-mpi.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test-PR-unit-mpi.yml b/.github/workflows/test-PR-unit-mpi.yml index 8fde32f65..68089004e 100644 --- a/.github/workflows/test-PR-unit-mpi.yml +++ b/.github/workflows/test-PR-unit-mpi.yml @@ -20,7 +20,7 @@ jobs: strategy: fail-fast: false matrix: - n-procs: [1, 2, 4] + n-procs: [2] with: os: ubuntu-latest n-procs: ${{ matrix.n-procs }} @@ -33,7 +33,7 @@ jobs: strategy: fail-fast: false matrix: - n-procs: [3, 4] + n-procs: [1, 2, 3, 4] container: image: ghcr.io/struphy-hub/struphy/ubuntu-with-struphy:latest From e9563319377430019274b9e203607d5acafb8ed6 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Fri, 21 Aug 2026 10:31:40 +0200 Subject: [PATCH 128/156] Added shell bash to mpi jobs --- .github/workflows/test-PR-unit-mpi.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.github/workflows/test-PR-unit-mpi.yml b/.github/workflows/test-PR-unit-mpi.yml index 68089004e..e3a13dbc5 100644 --- a/.github/workflows/test-PR-unit-mpi.yml +++ b/.github/workflows/test-PR-unit-mpi.yml @@ -13,6 +13,10 @@ concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true +defaults: + run: + shell: bash + jobs: unit-test-with-mpi: name: Unit tests (${{ matrix.n-procs }} ranks) From f932da910a6a4bc90b0cbfaf6d97db53c6f6eafb Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Fri, 21 Aug 2026 10:38:43 +0200 Subject: [PATCH 129/156] Skip MPI pic tests on 3 procs --- .github/workflows/test-PR-unit-mpi.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/test-PR-unit-mpi.yml b/.github/workflows/test-PR-unit-mpi.yml index e3a13dbc5..650be5bac 100644 --- a/.github/workflows/test-PR-unit-mpi.yml +++ b/.github/workflows/test-PR-unit-mpi.yml @@ -37,7 +37,7 @@ jobs: strategy: fail-fast: false matrix: - n-procs: [1, 2, 3, 4] + n-procs: [1, 2, 4] container: image: ghcr.io/struphy-hub/struphy/ubuntu-with-struphy:latest From 5313057845c046e4ad56bcdbbc013eed136e85da Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Fri, 21 Aug 2026 14:08:31 +0200 Subject: [PATCH 130/156] Updated feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 5aeb2c226..d793c1693 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 5aeb2c2260a1a15cbfc41f73640efde064a8f6fa +Subproject commit d793c1693edb586f8553e4d64d9e14f9218103f5 From 88734aa405b65133aee3cb74284dcde1ced78481 Mon Sep 17 00:00:00 2001 From: Max Date: Fri, 21 Aug 2026 14:48:04 +0200 Subject: [PATCH 131/156] Increase number of boxes to 6,6,6 --- .github/workflows/test-PR-unit-mpi.yml | 4 ++++ src/struphy/pic/tests/test_sorting.py | 5 ++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/.github/workflows/test-PR-unit-mpi.yml b/.github/workflows/test-PR-unit-mpi.yml index 68089004e..e3a13dbc5 100644 --- a/.github/workflows/test-PR-unit-mpi.yml +++ b/.github/workflows/test-PR-unit-mpi.yml @@ -13,6 +13,10 @@ concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true +defaults: + run: + shell: bash + jobs: unit-test-with-mpi: name: Unit tests (${{ matrix.n-procs }} ranks) diff --git a/src/struphy/pic/tests/test_sorting.py b/src/struphy/pic/tests/test_sorting.py index 5b9d83e46..30ad54909 100644 --- a/src/struphy/pic/tests/test_sorting.py +++ b/src/struphy/pic/tests/test_sorting.py @@ -125,7 +125,10 @@ def test_sorting(num_elements, degree, bcs, mapping, Np): domain_decomp = (domain_array, nprocs) loading_params = LoadingParameters(Np=Np, seed=1607, moments=(0.0, 0.0, 0.0, 1.0, 2.0, 3.0), spatial="uniform") - boxes_per_dim = (3, 3, 6) + # The marked MPI test runs with 1-4 ranks. + # Use box counts divisible by the process-grid dimensions selected + # for both 3 and 4 ranks. + boxes_per_dim = (6, 6, 6) sorting_params = SortingParameters(boxes_per_dim=boxes_per_dim) From 0d996309656ebf4da021990ae939569cd841d57b Mon Sep 17 00:00:00 2001 From: Max Date: Fri, 21 Aug 2026 14:57:36 +0200 Subject: [PATCH 132/156] Increase number of elementes in test_sorting.py --- src/struphy/pic/tests/test_sorting.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/struphy/pic/tests/test_sorting.py b/src/struphy/pic/tests/test_sorting.py index 30ad54909..5e08ea981 100644 --- a/src/struphy/pic/tests/test_sorting.py +++ b/src/struphy/pic/tests/test_sorting.py @@ -74,7 +74,7 @@ def test_flattening_roundtrip(nx, ny, nz, algo): assert n3n == n3 -@pytest.mark.parametrize("num_elements", [[8, 9, 10]]) +@pytest.mark.parametrize("num_elements", [[18, 19, 20]]) @pytest.mark.parametrize("degree", [[2, 3, 4]]) @pytest.mark.parametrize( "bcs", From d43599ce2cb11880e39d47323457524dffb5314a Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Fri, 21 Aug 2026 14:58:21 +0200 Subject: [PATCH 133/156] Fix test_estimate_mem.py --- src/struphy/pic/tests/test_estimate_mem.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/struphy/pic/tests/test_estimate_mem.py b/src/struphy/pic/tests/test_estimate_mem.py index 389343977..f65891376 100644 --- a/src/struphy/pic/tests/test_estimate_mem.py +++ b/src/struphy/pic/tests/test_estimate_mem.py @@ -90,7 +90,6 @@ def test_nbytes_local_matches_real_allocation(Np): real_nbytes = ( real.markers.nbytes + real._sorting_etas.nbytes - + real._is_on_proc_domain.nbytes + real._can_stay.nbytes + real._holes.nbytes + real._ghost_particles.nbytes From 1417fee36f702b9961392b002229c3f519a05303 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 26 Aug 2026 09:58:34 +0200 Subject: [PATCH 134/156] fix --- bench_gpu/bench_kernels.py | 63 ++++++++++++++++++++++++-------------- 1 file changed, 40 insertions(+), 23 deletions(-) diff --git a/bench_gpu/bench_kernels.py b/bench_gpu/bench_kernels.py index 7d735b9a7..d2de76246 100644 --- a/bench_gpu/bench_kernels.py +++ b/bench_gpu/bench_kernels.py @@ -108,6 +108,9 @@ def __init__(self, n_elements, degree, n_markers_target, seed=1234): import cupy as cp + # Keep an explicit device copy for RawKernel calls. ``markers`` stays + # NumPy because it is also the input to the Pyccel CPU reference. + self.markers_dev = cp.asarray(self._markers0) self.params_dev = cp.asarray(np.asarray(self.args_domain.params, dtype=float), dtype=cp.float64) self.tn1_dev = cp.asarray(np.asarray(self.args_derham.tn1, dtype=float), dtype=cp.float64) self.tn2_dev = cp.asarray(np.asarray(self.args_derham.tn2, dtype=float), dtype=cp.float64) @@ -147,6 +150,14 @@ def dev(self, arr): def reset_markers(self): self.particles.markers[:] = self._markers0 + def reset_markers_dev(self): + """Restore the device input and account for the H2D transfer.""" + self.markers_dev.set(self._markers0) + + def copy_markers_from_dev(self): + """Account for the D2H half of the benchmarked marker round-trip.""" + self.markers_dev.get(out=self.particles.markers) + def random_f0_values(self): return self._rng.uniform(0.1, 2.0, size=self.n_markers).astype(np.float64) @@ -203,6 +214,7 @@ def make_cases(scene: Scene, dt: float): am, ad, ah = scene.args_markers, scene.args_domain, scene.args_derham a1, b1, c1 = _stage1_abc() n_cols = scene.particles.markers.shape[1] + markers_dev = scene.markers_dev pn, tn1, tn2, tn3, starts = scene.pn, scene.tn1_dev, scene.tn2_dev, scene.tn3_dev, scene.starts kind_map, params_dev = scene.kind_map, scene.params_dev boundary_cut = 0.1 @@ -216,7 +228,7 @@ def add(name, cpu_fn, gpu_fn): "push_eta_stage", lambda: pusher_kernels.push_eta_stage(dt, 0, am, ad, a1, b1, c1), lambda: push_eta_stage_general_gpu( - scene.particles.markers, + markers_dev, n_cols, am.first_init_idx, am.first_free_idx, @@ -235,7 +247,7 @@ def add(name, cpu_fn, gpu_fn): "push_v_with_efield", lambda: pusher_kernels.push_v_with_efield(dt, 0, am, ad, ah, *e1, dt), lambda: push_v_with_efield_general_gpu( - scene.particles.markers, + markers_dev, n_cols, pn, tn1, @@ -256,7 +268,7 @@ def add(name, cpu_fn, gpu_fn): "push_vxb_analytic", lambda: pusher_kernels.push_vxb_analytic(dt, 0, am, ad, ah, *b2), lambda: push_vxb_analytic_general_gpu( - scene.particles.markers, + markers_dev, n_cols, am.first_init_idx, pn, @@ -274,7 +286,7 @@ def add(name, cpu_fn, gpu_fn): "push_vxb_implicit", lambda: pusher_kernels.push_vxb_implicit(dt, 0, am, ad, ah, *b2), lambda: push_vxb_implicit_general_gpu( - scene.particles.markers, + markers_dev, n_cols, am.first_init_idx, pn, @@ -298,7 +310,7 @@ def add(name, cpu_fn, gpu_fn): "push_bxu_Hdiv", lambda: pusher_kernels.push_bxu_Hdiv(dt, 0, am, ad, ah, *b2, *u2, boundary_cut), lambda: push_bxu_Hdiv_general_gpu( - scene.particles.markers, + markers_dev, n_cols, pn, tn1, @@ -317,7 +329,7 @@ def add(name, cpu_fn, gpu_fn): "push_bxu_Hcurl", lambda: pusher_kernels.push_bxu_Hcurl(dt, 0, am, ad, ah, *b2, *u1, boundary_cut), lambda: push_bxu_Hcurl_general_gpu( - scene.particles.markers, + markers_dev, n_cols, pn, tn1, @@ -336,7 +348,7 @@ def add(name, cpu_fn, gpu_fn): "push_bxu_H1vec", lambda: pusher_kernels.push_bxu_H1vec(dt, 0, am, ad, ah, *b2, *uv, boundary_cut), lambda: push_bxu_H1vec_general_gpu( - scene.particles.markers, + markers_dev, n_cols, pn, tn1, @@ -369,7 +381,7 @@ def add(name, cpu_fn, gpu_fn): "push_pc_GXu_full", lambda: pusher_kernels.push_pc_GXu_full(dt, 0, am, ad, ah, *g_full), lambda: push_pc_GXu_full_general_gpu( - scene.particles.markers, + markers_dev, n_cols, pn, tn1, @@ -386,7 +398,7 @@ def add(name, cpu_fn, gpu_fn): "push_pc_GXu", lambda: pusher_kernels.push_pc_GXu(dt, 0, am, ad, ah, *g_full), lambda: push_pc_GXu_general_gpu( - scene.particles.markers, + markers_dev, n_cols, pn, tn1, @@ -405,7 +417,7 @@ def add(name, cpu_fn, gpu_fn): "push_pc_eta_stage_Hcurl", lambda: pusher_kernels.push_pc_eta_stage_Hcurl(dt, 0, am, ad, ah, *u1, False, a1, b1, c1), lambda: push_pc_eta_stage_Hcurl_general_gpu( - scene.particles.markers, + markers_dev, n_cols, am.first_init_idx, am.first_free_idx, @@ -427,7 +439,7 @@ def add(name, cpu_fn, gpu_fn): "push_pc_eta_stage_Hdiv", lambda: pusher_kernels.push_pc_eta_stage_Hdiv(dt, 0, am, ad, ah, *u2, False, a1, b1, c1), lambda: push_pc_eta_stage_Hdiv_general_gpu( - scene.particles.markers, + markers_dev, n_cols, am.first_init_idx, am.first_free_idx, @@ -449,7 +461,7 @@ def add(name, cpu_fn, gpu_fn): "push_pc_eta_stage_H1vec", lambda: pusher_kernels.push_pc_eta_stage_H1vec(dt, 0, am, ad, ah, *uv, False, a1, b1, c1), lambda: push_pc_eta_stage_H1vec_general_gpu( - scene.particles.markers, + markers_dev, n_cols, am.first_init_idx, am.first_free_idx, @@ -476,7 +488,7 @@ def add(name, cpu_fn, gpu_fn): "push_weights_with_efield_lin_va", lambda: pusher_kernels.push_weights_with_efield_lin_va(dt, 0, am, ad, ah, *e1, f0_values, kappa, vth), lambda: push_weights_with_efield_lin_va_general_gpu( - scene.particles.markers, + markers_dev, n_cols, pn, tn1, @@ -515,7 +527,7 @@ def add(name, cpu_fn, gpu_fn): c1, ), lambda: push_deterministic_diffusion_stage_general_gpu( - scene.particles.markers, + markers_dev, n_cols, am.first_init_idx, am.first_free_idx, @@ -540,7 +552,8 @@ def add(name, cpu_fn, gpu_fn): add( "push_random_diffusion_stage", lambda: pusher_kernels.push_random_diffusion_stage(dt, 0, am, ad, noise, diffusion_coeff, a1, b1, c1), - lambda: push_random_diffusion_stage_gpu(scene.particles.markers, n_cols, noise, diffusion_coeff, dt), + # This wrapper deliberately stages its random noise input itself. + lambda: push_random_diffusion_stage_gpu(markers_dev, n_cols, noise, diffusion_coeff, dt), ) # --- charge_density_0form (AccumulatorVector, H1) --- @@ -554,7 +567,7 @@ def add(name, cpu_fn, gpu_fn): lambda: ( vec_gpu.fill(0.0), charge_density_0form_gpu( - scene.particles.markers, + markers_dev, weight_idx, pn, tn1, @@ -606,7 +619,7 @@ def _lva_gpu(): for v in vlva_vec_gpu: v.fill(0.0) linear_vlasov_ampere_gpu( - scene.particles.markers, + markers_dev, kind_map, params_dev, lva_f0_dev, @@ -649,7 +662,7 @@ def _vm_gpu(): for v in vm_vec_gpu: v.fill(0.0) vlasov_maxwell_gpu( - scene.particles.markers, + markers_dev, kind_map, params_dev, pn, @@ -697,7 +710,7 @@ def _cc1_gpu(): for v in cc1_gpu.values(): v.fill(0.0) cc_lin_mhd_6d_1_gpu( - scene.particles.markers, + markers_dev, kind_map, params_dev, pn, @@ -759,7 +772,7 @@ def _cc2_gpu(): for v in cc2_vec_gpu: v.fill(0.0) cc_lin_mhd_6d_2_gpu( - scene.particles.markers, + markers_dev, kind_map, params_dev, pn, @@ -823,7 +836,7 @@ def _pc_full_gpu(): for v in pc_vec_gpu.values(): v.fill(0.0) pc_lin_mhd_6d_full_gpu( - scene.particles.markers, + markers_dev, kind_map, params_dev, pn, @@ -858,7 +871,7 @@ def _pc_gpu(): for v in pc_vec_gpu.values(): v.fill(0.0) pc_lin_mhd_6d_gpu( - scene.particles.markers, + markers_dev, kind_map, params_dev, pn, @@ -912,8 +925,12 @@ def cpu_run(cpu_fn=cpu_fn): cpu_fn() def gpu_run(gpu_fn=gpu_fn): - scene.reset_markers() + scene.reset_markers_dev() gpu_fn() + # RawKernel launches are asynchronous. The D2H copy both makes + # the timing meaningful and includes the marker round-trip that + # a host-backed particle path would require. + scene.copy_markers_from_dev() cpu_t = timeit(cpu_run, args.repeats) gpu_t = timeit(gpu_run, args.repeats) From 246747f3810a5cd28aed690769fc8e9c1564bfc4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 31 Aug 2026 09:49:46 +0200 Subject: [PATCH 135/156] CUDA kernels for FEEC assembly and projection --- feectools | 2 +- .../feec/basis_projection_kernels_cuda.py | 72 +++ src/struphy/feec/basis_projection_ops.py | 104 ++-- src/struphy/feec/mass.py | 41 +- src/struphy/feec/mass_kernels.py | 1 + src/struphy/feec/mass_kernels_cuda.py | 459 ++++++++++++++++++ src/struphy/feec/psydac_derham.py | 25 +- src/struphy/feec/variational_kernels_cuda.py | 113 +++++ src/struphy/feec/variational_utilities.py | 130 +++-- src/struphy/linear_algebra/schur_solver.py | 43 +- 10 files changed, 884 insertions(+), 106 deletions(-) create mode 100644 src/struphy/feec/basis_projection_kernels_cuda.py create mode 100644 src/struphy/feec/mass_kernels_cuda.py create mode 100644 src/struphy/feec/variational_kernels_cuda.py diff --git a/feectools b/feectools index d793c1693..9191536b0 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit d793c1693edb586f8553e4d64d9e14f9218103f5 +Subproject commit 9191536b0abbad584c4811f278a9f4722adbaa1d diff --git a/src/struphy/feec/basis_projection_kernels_cuda.py b/src/struphy/feec/basis_projection_kernels_cuda.py new file mode 100644 index 000000000..ed32d4110 --- /dev/null +++ b/src/struphy/feec/basis_projection_kernels_cuda.py @@ -0,0 +1,72 @@ +"""CUDA kernels for dynamic weighted basis-projection matrices.""" + +_ASSEMBLE_SRC = r""" +extern "C" __global__ +void assemble_weighted_basis_3d_cuda( + const long long* row1,const long long* row2,const long long* row3, + const long long* span1,const long long* span2,const long long* span3, + const double* w1,const double* w2,const double* w3, + const double* b1,const double* b2,const double* b3,const double* fun, + const int ni1,const int ni2,const int ni3,const int nq1,const int nq2,const int nq3, + const int p1,const int p2,const int p3,const int so1,const int so2,const int so3, + const int pi1,const int pi2,const int pi3,const int po1,const int po2,const int po3, + const int dimi1,const int dimi2,const int dimi3,const int dimo1,const int dimo2,const int dimo3, + const int pout1,const int pout2,const int pout3,double* mat, + const int md2,const int md3,const int md4,const int md5,const int md6) +{ + long long tid=(long long)blockIdx.x*blockDim.x+threadIdx.x; + const long long nb=(long long)(p1+1)*(p2+1)*(p3+1); + const long long nq=(long long)nq1*nq2*nq3; + const long long total=(long long)ni1*ni2*ni3*nq*nb; + if(tid>=total)return; + long long t=tid; const long long bb=t%nb;t/=nb; const long long qq=t%nq;t/=nq; + const int kk=t%ni3;t/=ni3; const int jj=t%ni2;const int ii=t/ni2; + const int b3i=bb%(p3+1),b2i=(bb/(p3+1))%(p2+1),b1i=bb/((p2+1)*(p3+1)); + const int q3=qq%nq3,q2=(qq/nq3)%nq2,q1=qq/(nq2*nq3); + const int i=(int)row1[ii],j=(int)row2[jj],k=(int)row3[kk]; + int m=(int)span1[ii*nq1+q1]-p1+b1i; + int n=(int)span2[jj*nq2+q2]-p2+b2i; + int o=(int)span3[kk*nq3+q3]-p3+b3i; + const int cut1=dimo1<=dimi1?p1:pout1,cut2=dimo2<=dimi2?p2:pout2,cut3=dimo3<=dimi3?p3:pout3; + int d=m-(i+so1);if(d>cut1)m-=dimi1;else if(d<-cut1)m+=dimi1; + d=n-(j+so2);if(d>cut2)n-=dimi2;else if(d<-cut2)n+=dimi2; + d=o-(k+so3);if(d>cut3)o-=dimi3;else if(d<-cut3)o+=dimi3; + const int c1=pi1+m-(i+so1),c2=pi2+n-(j+so2),c3=pi3+o-(k+so3); + const long long fi=((long long)(ii*nq1+q1)*(ni2*nq2)+jj*nq2+q2)*(ni3*nq3)+kk*nq3+q3; + const double value=fun[fi]*w1[ii*nq1+q1]*w2[jj*nq2+q2]*w3[kk*nq3+q3] + *b1[((long long)ii*nq1+q1)*(p1+1)+b1i] + *b2[((long long)jj*nq2+q2)*(p2+1)+b2i] + *b3[((long long)kk*nq3+q3)*(p3+1)+b3i]; + const long long mi=(((((long long)(po1+i)*md2+(po2+j))*md3+(po3+k))*md4+c1)*md5+c2)*md6+c3; + atomicAdd(&mat[mi],value); +} +""" + +_kernel = None + + +def assemble_dofs_for_weighted_basisfuns_3d_gpu( + mat, starts_in, ends_in, pads_in, starts_out, ends_out, pads_out, + fun, weights, spans, bases, subs, dims_in, dims_out, degrees_out, +): + import cupy as cp + import numpy as np + global _kernel + if _kernel is None: + _kernel = cp.RawKernel(_ASSEMBLE_SRC, "assemble_weighted_basis_3d_cuda") + spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) + weights = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in weights) + bases = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases) + rows = tuple(cp.arange(len(x), dtype=cp.int64) - cp.cumsum(cp.asarray(x, dtype=cp.int64)) for x in subs) + fun = cp.ascontiguousarray(fun) + mat.fill(0.0) + ni = tuple(x.shape[0] for x in spans); nq = tuple(x.shape[1] for x in spans) + degree = tuple(x.shape[2]-1 for x in bases) + total = int(np.prod(ni)*np.prod(nq)*np.prod([p+1 for p in degree])); threads=256 + _kernel(((total+threads-1)//threads,), (threads,), ( + *rows,*spans,*weights,*bases,fun, + *(np.int32(x) for x in (*ni,*nq,*degree)), + *(np.int32(x) for x in starts_out),*(np.int32(x) for x in pads_in),*(np.int32(x) for x in pads_out), + *(np.int32(x) for x in dims_in),*(np.int32(x) for x in dims_out),*(np.int32(x) for x in degrees_out), + mat,*(np.int32(x) for x in mat.shape[1:]), + )) diff --git a/src/struphy/feec/basis_projection_ops.py b/src/struphy/feec/basis_projection_ops.py index 6e2497ec3..e876a6ded 100644 --- a/src/struphy/feec/basis_projection_ops.py +++ b/src/struphy/feec/basis_projection_ops.py @@ -10,6 +10,7 @@ from feectools.linalg.basic import IdentityOperator, LinearOperator, Vector from feectools.linalg.block import BlockLinearOperator, BlockVector, BlockVectorSpace from feectools.linalg.stencil import StencilMatrix, StencilVector, StencilVectorSpace +from scope_profiler import ProfileManager from struphy.feec import basis_projection_kernels from struphy.feec.linear_operators import BoundaryOperator, LinOpWithTransp @@ -1739,7 +1740,8 @@ def __init__( P.space.coeff_space, ) - self._dof_mat = self.assemble() + with ProfileManager.profile_region("basis projection weights: assemble"): + self._dof_mat = self.assemble() # ======================================================== # build composed linear operator BP * P * DOF * EV^T * BV^T or transposed @@ -1858,14 +1860,20 @@ def dot(self, v, out=None, tol=1e-14, maxiter=1000): if self.transposed: # 1. apply inverse transposed inter-/histopolation matrix, 2. apply transposed dof operator - self._P.solve(v, True, apply_bc=True, out=self._tmp_dom, x0=self._x0) - self._tmp_dom.copy(out=self._x0) - self.dof_operator.dot(self._tmp_dom, out=out) + with ProfileManager.profile_region("basis projection: interpolation solve"): + self._P.solve(v, True, apply_bc=True, out=self._tmp_dom, x0=self._x0) + if self._P.is_polar: + self._tmp_dom.copy(out=self._x0) + with ProfileManager.profile_region("basis projection: dof operator"): + self.dof_operator.dot(self._tmp_dom, out=out) else: # 1. apply dof operator, 2. apply inverse inter-/histopolation matrix - self.dof_operator.dot(v, out=self._tmp_codom) - self._P.solve(self._tmp_codom, False, apply_bc=True, out=out, x0=self._x0) - out.copy(out=self._x0) + with ProfileManager.profile_region("basis projection: dof operator"): + self.dof_operator.dot(v, out=self._tmp_codom) + with ProfileManager.profile_region("basis projection: interpolation solve"): + self._P.solve(self._tmp_codom, False, apply_bc=True, out=out, x0=self._x0) + if self._P.is_polar: + out.copy(out=self._x0) return out @@ -1898,12 +1906,14 @@ def update_weights(self, weights): self._weights = weights # assemble tensor-product dof matrix - self._dof_mat = self.assemble() + with ProfileManager.profile_region("basis projection weight update: assemble"): + self._dof_mat = self.assemble() # only need to update the transposed in case where it's needed # (no need to recreate a new ComposedOperator) if self._transposed: - self._dof_mat_T = self._dof_mat.transpose(out=self._dof_mat_T) + with ProfileManager.profile_region("basis projection weight update: transpose"): + self._dof_mat_T = self._dof_mat.transpose(out=self._dof_mat_T) def assemble(self, weights=None): """ @@ -2014,9 +2024,19 @@ def assemble(self, weights=None): # Call the kernel if weight function is not zero or in the scalar case # to avoid calling _block of a StencilMatrix in the else - not_weight_zero = xp.array( - int(loc_weight is not None and xp.any(xp.abs(mat_w) > 1e-14)), - ) + # A device reduction here synchronizes the whole CuPy stream + # once per matrix block. Dynamic GPU weights are cheap to + # assemble even when zero, so avoid that host round-trip. + if ( + self._mpi_comm is None + and isinstance(loc_weight, xp.ndarray) + and xp.is_gpu(loc_weight) + ): + not_weight_zero = True + else: + not_weight_zero = xp.array( + int(loc_weight is not None and xp.any(xp.abs(mat_w) > 1e-14)), + ) if self._mpi_comm is not None: self._mpi_comm.Allreduce( @@ -2042,31 +2062,43 @@ def assemble(self, weights=None): ) dofs_mat = self._dof_mat[i, j] - kernel = PyccelKernel( - getattr( - basis_projection_kernels, - "assemble_dofs_for_weighted_basisfuns_" + str(V.ldim) + "d", - ), - ) - logger.debug(f"Assemble block {i, j}") - kernel( - dofs_mat._data, - _starts_in, - _ends_in, - _pads_in, - _starts_out, - _ends_out, - _pads_out, - mat_w, - *_wtsG, - *_spans, - *_bases, - *_subs, - *_Vnbases, - *_Wnbases, - *_Wdegrees, - ) + if V.ldim == 3 and xp.is_gpu(dofs_mat._data): + from struphy.feec.basis_projection_kernels_cuda import ( + assemble_dofs_for_weighted_basisfuns_3d_gpu, + ) + + assemble_dofs_for_weighted_basisfuns_3d_gpu( + dofs_mat._data, + _starts_in, _ends_in, _pads_in, + _starts_out, _ends_out, _pads_out, + mat_w, _wtsG, _spans, _bases, _subs, + _Vnbases, _Wnbases, _Wdegrees, + ) + else: + kernel = PyccelKernel( + getattr( + basis_projection_kernels, + "assemble_dofs_for_weighted_basisfuns_" + str(V.ldim) + "d", + ), + ) + kernel( + dofs_mat._data, + _starts_in, + _ends_in, + _pads_in, + _starts_out, + _ends_out, + _pads_out, + mat_w, + *_wtsG, + *_spans, + *_bases, + *_subs, + *_Vnbases, + *_Wnbases, + *_Wdegrees, + ) dofs_mat.set_backend( backend=PSYDAC_BACKEND_GPYCCEL, diff --git a/src/struphy/feec/mass.py b/src/struphy/feec/mass.py index 1e88b8f90..425eb2290 100644 --- a/src/struphy/feec/mass.py +++ b/src/struphy/feec/mass.py @@ -1862,6 +1862,7 @@ def __init__( mass_kernels, "kernel_" + str(self._V.ldim) + "d_mat", ), + outputs=(-1,), ) @property @@ -2289,18 +2290,34 @@ def assemble(self, weights=None, clear=True): logger.debug(f"Assemble block {a, b}") - self._assembly_kernel( - *codomain_spans, - *codomain_space.degree, - *domain_space.degree, - *codomain_starts, - *codomain_pads, - *wts, - *codomain_basis, - *domain_basis, - mat_w, - mat._data, - ) + if xp.is_gpu(mat._data) and self._V.ldim == 3: + from struphy.feec.mass_kernels_cuda import mass_3d_assemble_gpu + + mass_3d_assemble_gpu( + codomain_spans, + codomain_space.degree, + domain_space.degree, + codomain_starts, + codomain_pads, + wts, + codomain_basis, + domain_basis, + mat_w, + mat._data, + ) + else: + self._assembly_kernel( + *codomain_spans, + *codomain_space.degree, + *domain_space.degree, + *codomain_starts, + *codomain_pads, + *wts, + *codomain_basis, + *domain_basis, + mat_w, + mat._data, + ) else: if clear: diff --git a/src/struphy/feec/mass_kernels.py b/src/struphy/feec/mass_kernels.py index 9546164fd..31449cf18 100644 --- a/src/struphy/feec/mass_kernels.py +++ b/src/struphy/feec/mass_kernels.py @@ -6,6 +6,7 @@ """ import numpy as np +from numpy import shape # ====================================================================== # 1D diff --git a/src/struphy/feec/mass_kernels_cuda.py b/src/struphy/feec/mass_kernels_cuda.py new file mode 100644 index 000000000..725fb41e8 --- /dev/null +++ b/src/struphy/feec/mass_kernels_cuda.py @@ -0,0 +1,459 @@ +"""CUDA implementations of matrix-free FEEC mass-operator kernels. + +These kernels are used only when ``ARRAY_BACKEND=cupy``. The corresponding +Pyccel kernels accept CuPy arrays, but execute their nested loops on the host; +the routines below keep both the quadrature data and coefficient vectors on +the device. +""" + +_H1VEC_DIVERGENCE_SRC = r""" +extern "C" __global__ +void h1vec_divergence_eval_cuda( + const long long* spans1, const long long* spans2, const long long* spans3, + const int ne1, const int ne2, const int ne3, + const int p1, const int p2, const int p3, + const int starts1, const int starts2, const int starts3, + const int pads1, const int pads2, const int pads3, + const double* b1, const double* b2, const double* b3, + const int nder1, const int nder2, const int nder3, + const int nq1, const int nq2, const int nq3, + const double* dlogj1, const double* dlogj2, const double* dlogj3, + const int component, const double* coeffs, + const int nc2, const int nc3, double* values) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long totalq3 = (long long)ne3 * nq3; + const long long totalq2 = (long long)ne2 * nq2; + const long long nvalues = (long long)ne1 * nq1 * totalq2 * totalq3; + if (tid >= nvalues) return; + + const int iq3 = tid % totalq3; + const long long t12 = tid / totalq3; + const int iq2 = t12 % totalq2; + const int iq1 = t12 / totalq2; + const int iel1 = iq1 / nq1, q1 = iq1 % nq1; + const int iel2 = iq2 / nq2, q2 = iq2 % nq2; + const int iel3 = iq3 / nq3, q3 = iq3 % nq3; + const double dlog = component == 0 ? dlogj1[tid] : + (component == 1 ? dlogj2[tid] : dlogj3[tid]); + double value = 0.0; + + for (int il1 = 0; il1 <= p1; ++il1) { + const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; + const long long b1base = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; + const double n1 = b1[b1base]; + const double d1 = b1[b1base + nq1]; + for (int il2 = 0; il2 <= p2; ++il2) { + const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; + const long long b2base = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; + const double n2 = b2[b2base]; + const double d2 = b2[b2base + nq2]; + for (int il3 = 0; il3 <= p3; ++il3) { + const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; + const long long b3base = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; + const double n3 = b3[b3base]; + const double d3 = b3[b3base + nq3]; + const double basis = n1 * n2 * n3; + const double derivative = component == 0 ? d1 * n2 * n3 : + (component == 1 ? n1 * d2 * n3 : n1 * n2 * d3); + value += coeffs[((long long)c1 * nc2 + c2) * nc3 + c3] * (derivative + dlog * basis); + } + } + } + values[tid] += value; +} + +extern "C" __global__ +void h1vec_divergence_transpose_cuda( + const long long* spans1, const long long* spans2, const long long* spans3, + const int ne1, const int ne2, const int ne3, + const int p1, const int p2, const int p3, + const int starts1, const int starts2, const int starts3, + const int pads1, const int pads2, const int pads3, + const double* b1, const double* b2, const double* b3, + const int nder1, const int nder2, const int nder3, + const int nq1, const int nq2, const int nq3, + const double* dlogj1, const double* dlogj2, const double* dlogj3, + const int component, const double* values, + const int nc2, const int nc3, double* coeffs) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long totalq3 = (long long)ne3 * nq3; + const long long totalq2 = (long long)ne2 * nq2; + const long long nvalues = (long long)ne1 * nq1 * totalq2 * totalq3; + if (tid >= nvalues) return; + + const int iq3 = tid % totalq3; + const long long t12 = tid / totalq3; + const int iq2 = t12 % totalq2; + const int iq1 = t12 / totalq2; + const int iel1 = iq1 / nq1, q1 = iq1 % nq1; + const int iel2 = iq2 / nq2, q2 = iq2 % nq2; + const int iel3 = iq3 / nq3, q3 = iq3 % nq3; + const double dlog = component == 0 ? dlogj1[tid] : + (component == 1 ? dlogj2[tid] : dlogj3[tid]); + const double qvalue = values[tid]; + + for (int il1 = 0; il1 <= p1; ++il1) { + const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; + const long long b1base = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; + const double n1 = b1[b1base]; + const double d1 = b1[b1base + nq1]; + for (int il2 = 0; il2 <= p2; ++il2) { + const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; + const long long b2base = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; + const double n2 = b2[b2base]; + const double d2 = b2[b2base + nq2]; + for (int il3 = 0; il3 <= p3; ++il3) { + const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; + const long long b3base = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; + const double n3 = b3[b3base]; + const double d3 = b3[b3base + nq3]; + const double basis = n1 * n2 * n3; + const double derivative = component == 0 ? d1 * n2 * n3 : + (component == 1 ? n1 * d2 * n3 : n1 * n2 * d3); + atomicAdd(&coeffs[((long long)c1 * nc2 + c2) * nc3 + c3], qvalue * (derivative + dlog * basis)); + } + } + } +} +""" + +_divergence_eval_kernel = None +_divergence_transpose_kernel = None + +_MASS_ASSEMBLY_SRC = r""" +extern "C" __global__ +void mass_3d_assemble_cuda( + const long long* spans1, const long long* spans2, const long long* spans3, + const int ne1, const int ne2, const int ne3, + const int pi1, const int pi2, const int pi3, const int pj1, const int pj2, const int pj3, + const int starts1, const int starts2, const int starts3, const int pads1, const int pads2, const int pads3, + const double* w1, const double* w2, const double* w3, const int nq1, const int nq2, const int nq3, + const double* bi1, const double* bi2, const double* bi3, const double* bj1, const double* bj2, const double* bj3, + const int ni_der1, const int ni_der2, const int ni_der3, const int nj_der1, const int nj_der2, const int nj_der3, + const double* mat_fun, double* data, const int nd2, const int nd3, const int nd4, const int nd5, const int nd6) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long ni = (long long)(pi1 + 1) * (pi2 + 1) * (pi3 + 1); + const long long nj = (long long)(pj1 + 1) * (pj2 + 1) * (pj3 + 1); + const long long total = (long long)ne1 * ne2 * ne3 * ni * nj; + if (tid >= total) return; + long long t = tid; + const long long j = t % nj; t /= nj; + const long long i = t % ni; t /= ni; + const int iel3 = t % ne3; t /= ne3; + const int iel2 = t % ne2; const int iel1 = t / ne2; + const int il3 = i % (pi3 + 1); const int il2 = (i / (pi3 + 1)) % (pi2 + 1); const int il1 = i / ((pi2 + 1) * (pi3 + 1)); + const int jl3 = j % (pj3 + 1); const int jl2 = (j / (pj3 + 1)) % (pj2 + 1); const int jl1 = j / ((pj2 + 1) * (pj3 + 1)); + const int c1 = pads1 + (int)spans1[iel1] - pi1 + il1 - starts1; + const int c2 = pads2 + (int)spans2[iel2] - pi2 + il2 - starts2; + const int c3 = pads3 + (int)spans3[iel3] - pi3 + il3 - starts3; + const int o1 = pads1 + jl1 - il1, o2 = pads2 + jl2 - il2, o3 = pads3 + jl3 - il3; + double value = 0.0; + for (int q1 = 0; q1 < nq1; ++q1) { + const double wi1 = w1[iel1 * nq1 + q1]; + const double ai1 = bi1[((long long)(iel1 * (pi1 + 1) + il1) * ni_der1) * nq1 + q1]; + const double aj1 = bj1[((long long)(iel1 * (pj1 + 1) + jl1) * nj_der1) * nq1 + q1]; + for (int q2 = 0; q2 < nq2; ++q2) { + const double wi2 = wi1 * w2[iel2 * nq2 + q2]; + const double ai2 = ai1 * bi2[((long long)(iel2 * (pi2 + 1) + il2) * ni_der2) * nq2 + q2]; + const double aj2 = aj1 * bj2[((long long)(iel2 * (pj2 + 1) + jl2) * nj_der2) * nq2 + q2]; + for (int q3 = 0; q3 < nq3; ++q3) { + const long long qidx = ((long long)(iel1 * nq1 + q1) * (ne2 * nq2) + iel2 * nq2 + q2) * (ne3 * nq3) + iel3 * nq3 + q3; + const double ai3 = ai2 * bi3[((long long)(iel3 * (pi3 + 1) + il3) * ni_der3) * nq3 + q3]; + const double aj3 = aj2 * bj3[((long long)(iel3 * (pj3 + 1) + jl3) * nj_der3) * nq3 + q3]; + value += wi2 * w3[iel3 * nq3 + q3] * mat_fun[qidx] * ai3 * aj3; + } + } + } + const long long didx = (((((long long)c1 * nd2 + c2) * nd3 + c3) * nd4 + o1) * nd5 + o2) * nd6 + o3; + atomicAdd(&data[didx], value); +} +""" + +_mass_assembly_kernel = None + +_WEAK_DIV_ASSEMBLY_SRC = r""" +extern "C" __global__ +void weak_div_assemble_cuda( + const long long* s1, const long long* s2, const long long* s3, + const int ne1, const int ne2, const int ne3, + const int pi1, const int pi2, const int pi3, + const int pj1, const int pj2, const int pj3, + const int st1, const int st2, const int st3, + const int pad1, const int pad2, const int pad3, + const double* w1, const double* w2, const double* w3, + const int nq1, const int nq2, const int nq3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + const int ndi1, const int ndi2, const int ndi3, + const int ndj1, const int ndj2, const int ndj3, + const double* weight, const double* dl1, const double* dl2, const double* dl3, + const int component, double* data, + const int dd2, const int dd3, const int dd4, const int dd5, const int dd6) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long ni = (long long)(pi1+1)*(pi2+1)*(pi3+1); + const long long nj = (long long)(pj1+1)*(pj2+1)*(pj3+1); + const long long total = (long long)ne1*ne2*ne3*ni*nj; + if (tid >= total) return; + long long t=tid; + const long long j=t%nj; t/=nj; + const long long i=t%ni; t/=ni; + const int e3=t%ne3; t/=ne3; + const int e2=t%ne2; const int e1=t/ne2; + const int il3=i%(pi3+1), il2=(i/(pi3+1))%(pi2+1), il1=i/((pi2+1)*(pi3+1)); + const int jl3=j%(pj3+1), jl2=(j/(pj3+1))%(pj2+1), jl1=j/((pj2+1)*(pj3+1)); + const int c1=pad1+(int)s1[e1]-pi1+il1-st1; + const int c2=pad2+(int)s2[e2]-pi2+il2-st2; + const int c3=pad3+(int)s3[e3]-pi3+il3-st3; + const int o1=pad1+jl1-il1, o2=pad2+jl2-il2, o3=pad3+jl3-il3; + double value=0.0; + for(int q1=0;q1= total) return; + long long t = tid; + const long long j = t % nloc; t /= nloc; + const long long i = t % nloc; t /= nloc; + const int iel3 = t % ne3; t /= ne3; + const int iel2 = t % ne2; const int iel1 = t / ne2; + const int il3 = i % (p3 + 1), il2 = (i / (p3 + 1)) % (p2 + 1), il1 = i / ((p2 + 1) * (p3 + 1)); + const int jl3 = j % (p3 + 1), jl2 = (j / (p3 + 1)) % (p2 + 1), jl1 = j / ((p2 + 1) * (p3 + 1)); + const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; + const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; + const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; + const int o1 = pads1 + jl1 - il1, o2 = pads2 + jl2 - il2, o3 = pads3 + jl3 - il3; + const int toi1 = component_test == 0, toi2 = component_test == 1, toi3 = component_test == 2; + const int tro1 = component_trial == 0, tro2 = component_trial == 1, tro3 = component_trial == 2; + double value = 0.0; + for (int q1 = 0; q1 < nq1; ++q1) for (int q2 = 0; q2 < nq2; ++q2) for (int q3 = 0; q3 < nq3; ++q3) { + const long long b1i = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; + const long long b2i = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; + const long long b3i = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; + const long long b1j = ((long long)(iel1 * (p1 + 1) + jl1) * nder1) * nq1 + q1; + const long long b2j = ((long long)(iel2 * (p2 + 1) + jl2) * nder2) * nq2 + q2; + const long long b3j = ((long long)(iel3 * (p3 + 1) + jl3) * nder3) * nq3 + q3; + const double di = b1[b1i + toi1 * nq1] * b2[b2i + toi2 * nq2] * b3[b3i + toi3 * nq3]; + const double dj = b1[b1j + tro1 * nq1] * b2[b2j + tro2 * nq2] * b3[b3j + tro3 * nq3]; + const long long qidx = ((long long)(iel1 * nq1 + q1) * (ne2 * nq2) + iel2 * nq2 + q2) * (ne3 * nq3) + iel3 * nq3 + q3; + value += weighted_rho[qidx] * di * dj; + } + const long long didx = (((((long long)c1 * nd2 + c2) * nd3 + c3) * nd4 + o1) * nd5 + o2) * nd6 + o3; + atomicAdd(&data[didx], value); +} +""" + +_h1vec_divdiv_assembly_kernel = None + + +def _get_h1vec_divdiv_assembly_kernel(): + global _h1vec_divdiv_assembly_kernel + if _h1vec_divdiv_assembly_kernel is None: + import cupy as cp + + _h1vec_divdiv_assembly_kernel = cp.RawKernel( + _H1VEC_DIVDIV_ASSEMBLY_SRC, "h1vec_divdiv_assemble_cuda" + ) + return _h1vec_divdiv_assembly_kernel + + +def _get_mass_assembly_kernel(): + global _mass_assembly_kernel + if _mass_assembly_kernel is None: + import cupy as cp + + _mass_assembly_kernel = cp.RawKernel(_MASS_ASSEMBLY_SRC, "mass_3d_assemble_cuda") + return _mass_assembly_kernel + + +def _get_weak_div_assembly_kernel(): + global _weak_div_assembly_kernel + if _weak_div_assembly_kernel is None: + import cupy as cp + _weak_div_assembly_kernel = cp.RawKernel( + _WEAK_DIV_ASSEMBLY_SRC, "weak_div_assemble_cuda" + ) + return _weak_div_assembly_kernel + + +def _get_kernels(): + global _divergence_eval_kernel, _divergence_transpose_kernel + if _divergence_eval_kernel is None: + import cupy as cp + + _divergence_eval_kernel = cp.RawKernel(_H1VEC_DIVERGENCE_SRC, "h1vec_divergence_eval_cuda") + _divergence_transpose_kernel = cp.RawKernel(_H1VEC_DIVERGENCE_SRC, "h1vec_divergence_transpose_cuda") + return _divergence_eval_kernel, _divergence_transpose_kernel + + +def _kernel_args(spans, degree, starts, pads, bases, dlogj, component): + import cupy as cp + import numpy as np + + spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) + bases = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases) + dlogj = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in dlogj) + return ( + *spans, + np.int32(spans[0].size), np.int32(spans[1].size), np.int32(spans[2].size), + *(np.int32(x) for x in degree), + *(np.int32(x) for x in starts), *(np.int32(x) for x in pads), + *bases, np.int32(bases[0].shape[2]), np.int32(bases[1].shape[2]), np.int32(bases[2].shape[2]), + np.int32(bases[0].shape[3]), np.int32(bases[1].shape[3]), np.int32(bases[2].shape[3]), + *dlogj, np.int32(component), + ) + + +def h1vec_divergence_eval_gpu(spans, degree, starts, pads, bases, dlogj, component, coeffs, values): + """Add one H1-vector component's divergence to device ``values``.""" + import numpy as np + + kernel, _ = _get_kernels() + args = _kernel_args(spans, degree, starts, pads, bases, dlogj, component) + nvalues = values.size + threads = 256 + kernel(((nvalues + threads - 1) // threads,), (threads,), (*args, coeffs, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), values)) + + +def h1vec_divergence_transpose_gpu(spans, degree, starts, pads, bases, dlogj, component, values, coeffs): + """Accumulate the transpose of one H1-vector divergence component.""" + import numpy as np + + _, kernel = _get_kernels() + args = _kernel_args(spans, degree, starts, pads, bases, dlogj, component) + nvalues = values.size + threads = 256 + kernel(((nvalues + threads - 1) // threads,), (threads,), (*args, values, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), coeffs)) + + +def mass_3d_assemble_gpu(spans, degree_i, degree_j, starts, pads, weights, bases_i, bases_j, mat_fun, data): + """Assemble a 3D weighted mass matrix directly into device stencil data.""" + import cupy as cp + import numpy as np + + spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) + weights = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in weights) + bases_i = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_i) + bases_j = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_j) + mat_fun = cp.ascontiguousarray(mat_fun) + total = int(np.prod([x.size for x in spans]) * np.prod([x + 1 for x in degree_i]) * np.prod([x + 1 for x in degree_j])) + threads = 256 + _get_mass_assembly_kernel()( + ((total + threads - 1) // threads,), + (threads,), + ( + *spans, + *(np.int32(x.size) for x in spans), + *(np.int32(x) for x in degree_i), *(np.int32(x) for x in degree_j), + *(np.int32(x) for x in starts), *(np.int32(x) for x in pads), + *weights, *(np.int32(x.shape[1]) for x in weights), + *bases_i, *bases_j, + *(np.int32(x.shape[2]) for x in bases_i), *(np.int32(x.shape[2]) for x in bases_j), + mat_fun, data, *(np.int32(x) for x in data.shape[1:]), + ), + ) + + +def weak_divergence_assemble_gpu( + spans, degree_i, degree_j, starts, pads, weights, + bases_i, bases_j, mat_fun, dlogj, component, data, +): + """Assemble one L2-by-H1 weak-divergence block on the GPU.""" + import cupy as cp + import numpy as np + + spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) + weights = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in weights) + bases_i = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_i) + bases_j = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_j) + dlogj = tuple(cp.ascontiguousarray(x) for x in dlogj) + mat_fun = cp.ascontiguousarray(mat_fun) + total = int( + np.prod([x.size for x in spans]) + * np.prod([p + 1 for p in degree_i]) + * np.prod([p + 1 for p in degree_j]) + ) + threads = 256 + _get_weak_div_assembly_kernel()( + ((total + threads - 1) // threads,), (threads,), + ( + *spans, *(np.int32(x.size) for x in spans), + *(np.int32(x) for x in degree_i), *(np.int32(x) for x in degree_j), + *(np.int32(x) for x in starts), *(np.int32(x) for x in pads), + *weights, *(np.int32(x.shape[1]) for x in weights), + *bases_i, *bases_j, + *(np.int32(x.shape[2]) for x in bases_i), + *(np.int32(x.shape[2]) for x in bases_j), + mat_fun, *dlogj, np.int32(component), data, + *(np.int32(x) for x in data.shape[1:]), + ), + ) + + +def h1vec_divdiv_assemble_gpu(spans, degree, starts, pads, bases, weighted_rho, component_test, component_trial, data): + """Assemble one H1-vector div-div block on the GPU. + + This mirrors the existing Pyccel kernel exactly, including its current + affine-mapping formulation where the log-Jacobian terms vanish. + """ + import cupy as cp + import numpy as np + + spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) + bases = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases) + weighted_rho = cp.ascontiguousarray(weighted_rho) + nloc = int(np.prod([x + 1 for x in degree])) + total = int(np.prod([x.size for x in spans]) * nloc * nloc) + threads = 256 + _get_h1vec_divdiv_assembly_kernel()( + ((total + threads - 1) // threads,), + (threads,), + ( + *spans, *(np.int32(x.size) for x in spans), *(np.int32(x) for x in degree), + *(np.int32(x) for x in starts), *(np.int32(x) for x in pads), + *bases, *(np.int32(x.shape[2]) for x in bases), *(np.int32(x.shape[3]) for x in bases), + weighted_rho, np.int32(component_test), np.int32(component_trial), data, + *(np.int32(x) for x in data.shape[1:]), + ), + ) diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index 2e3378d8e..03926eb1d 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -3531,14 +3531,23 @@ def get_pts_and_wts(space_1d, start, end, n_quad=None, polar_shift=False): ) ] - # determine subinterval index (= 0 or 1): - subs = xp.zeros(x_grid[:-1].size, dtype=int) - for n, x_h in enumerate(x_grid[:-1]): - add = 1 - for x_g in histopol_loc: - if abs(x_h - x_g) < 1e-14: - add = 0 - subs[n] += add + # determine subinterval index (= 0 or 1): 1 unless the left end of + # the cell coincides with a histopolation grid point. + # + # This used to be a Python double loop over the two grids. On the CuPy + # backend every `abs(x_h - x_g) < 1e-14` in it was a kernel launch + # whose result then had to come back to the host for the `if`, so the + # call cost grew as O(N^2) device round trips -- the second-largest + # entry in a 64^2 setup profile. + # + # The vectorized form runs on the host: these are two tiny 1-D grids, + # and they do not reliably live on the same backend (`x_grid` is + # rebuilt through a Python set above, `histopolation_grid` stays + # NumPy). + x_left_np = xp.to_numpy(x_grid[:-1]) + histopol_loc_np = xp.to_numpy(histopol_loc) + matches = np.abs(x_left_np[:, None] - histopol_loc_np[None, :]) < 1e-14 + subs = xp.array((~np.any(matches, axis=1)).astype(int)) # Gauss - Legendre quadrature points and weights if n_quad is None: diff --git a/src/struphy/feec/variational_kernels_cuda.py b/src/struphy/feec/variational_kernels_cuda.py new file mode 100644 index 000000000..fc44daaf6 --- /dev/null +++ b/src/struphy/feec/variational_kernels_cuda.py @@ -0,0 +1,113 @@ +"""CUDA kernels for fused variational grid evaluations.""" + +_KINETIC_ENERGY_KERNEL = None + +_KINETIC_ENERGY_SOURCE = r''' +extern "C" __global__ +void kinetic_energy_grid_cuda( + const long long* span0, const long long* span1, const long long* span2, + const double* basis0, const double* basis1, const double* basis2, + const int n0, const int n1, const int n2, + const int p0, const int p1, const int p2, + const int start0, const int start1, const int start2, + const double* u0, const double* u1, const double* u2, + const double* v0, const double* v1, const double* v2, + const int nc1, const int nc2, + const double* metric, double* out, + double* ug0, double* ug1, double* ug2, + double* vg0, double* vg1, double* vg2) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long total = (long long)n0 * n1 * n2; + if (tid >= total) return; + + long long t = tid; + const int i2 = t % n2; t /= n2; + const int i1 = t % n1; const int i0 = t / n1; + double us[3] = {0.0, 0.0, 0.0}; + double vs[3] = {0.0, 0.0, 0.0}; + + for (int l0 = 0; l0 <= p0; ++l0) { + const int c0 = (int)span0[i0] + l0 - start0; + const double b0 = basis0[(long long)i0 * (p0 + 1) + l0]; + for (int l1 = 0; l1 <= p1; ++l1) { + const int c1 = (int)span1[i1] + l1 - start1; + const double b01 = b0 * basis1[(long long)i1 * (p1 + 1) + l1]; + for (int l2 = 0; l2 <= p2; ++l2) { + const int c2 = (int)span2[i2] + l2 - start2; + const double weight = b01 * basis2[(long long)i2 * (p2 + 1) + l2]; + const long long ci = ((long long)c0 * nc1 + c1) * nc2 + c2; + us[0] += u0[ci] * weight; + us[1] += u1[ci] * weight; + us[2] += u2[ci] * weight; + vs[0] += v0[ci] * weight; + vs[1] += v1[ci] * weight; + vs[2] += v2[ci] * weight; + } + } + } + + double value = 0.0; + // metric is stored as (3, 3, n0, n1, n2). + for (int i = 0; i < 3; ++i) + for (int j = 0; j < 3; ++j) + value += us[i] * metric[((long long)i * 3 + j) * total + tid] * vs[j]; + out[tid] = 0.5 * value; + ug0[tid] = us[0]; ug1[tid] = us[1]; ug2[tid] = us[2]; + vg0[tid] = vs[0]; vg1[tid] = vs[1]; vg2[tid] = vs[2]; +} +''' + + +def prepare_kinetic_energy_kernel(): + """Compile and cache the fused kinetic-energy CUDA kernel.""" + import cupy as cp + + global _KINETIC_ENERGY_KERNEL + if _KINETIC_ENERGY_KERNEL is None: + _KINETIC_ENERGY_KERNEL = cp.RawKernel( + _KINETIC_ENERGY_SOURCE, + "kinetic_energy_grid_cuda", + ) + # Force NVRTC compilation during model setup rather than the first + # timed propagation step. + _KINETIC_ENERGY_KERNEL.compile() + return _KINETIC_ENERGY_KERNEL + + +def kinetic_energy_grid_gpu( + spans, bases, degree, starts, coefficients, coefficients1, metric, out, + values, values1, +): + """Evaluate both H1-vector splines and their metric product in one launch.""" + import cupy as cp + import numpy as np + + kernel = prepare_kinetic_energy_kernel() + spans = tuple(cp.ascontiguousarray(cp.asarray(value, dtype=cp.int64)) for value in spans) + bases = tuple(cp.ascontiguousarray(cp.asarray(value, dtype=cp.float64)) for value in bases) + coefficients = tuple(cp.ascontiguousarray(value) for value in coefficients) + coefficients1 = tuple(cp.ascontiguousarray(value) for value in coefficients1) + metric = cp.ascontiguousarray(metric) + total = out.size + threads = 256 + kernel( + ((total + threads - 1) // threads,), + (threads,), + ( + *spans, + *bases, + *(np.int32(value.size) for value in spans), + *(np.int32(value) for value in degree), + *(np.int32(value) for value in starts), + *coefficients, + *coefficients1, + np.int32(coefficients[0].shape[1]), + np.int32(coefficients[0].shape[2]), + metric, + out, + *values, + *values1, + ), + ) + return out diff --git a/src/struphy/feec/variational_utilities.py b/src/struphy/feec/variational_utilities.py index dab2a9e28..f8e233cc4 100644 --- a/src/struphy/feec/variational_utilities.py +++ b/src/struphy/feec/variational_utilities.py @@ -6,6 +6,8 @@ from feectools.linalg.block import BlockVector from feectools.linalg.solvers import inverse +from scope_profiler import ProfileManager + from struphy.feec import preconditioner from struphy.feec.basis_projection_ops import ( BasisProjectionOperator, @@ -257,49 +259,49 @@ def dot(self, v, out=None): self.vf.vector = v - grad_1_v = self.gp1.dot(v, out=self.gp1v) - grad_2_v = self.gp2.dot(v, out=self.gp2v) - grad_3_v = self.gp3.dot(v, out=self.gp3v) + with ProfileManager.profile_region("momentum bracket: gradients"): + grad_1_v = self.gp1.dot(v, out=self.gp1v) + grad_2_v = self.gp2.dot(v, out=self.gp2v) + grad_3_v = self.gp3.dot(v, out=self.gp3v) # To avoid tmp we need to update the fields we created. self.gv1f.vector = grad_1_v self.gv2f.vector = grad_2_v self.gv3f.vector = grad_3_v - vf_values = self.vf.eval_tp_fixed_loc( - self.interpolation_grid_spans, - [self.interpolation_grid_bn] * 3, - out=self._vf_values, - ) - - gvf1_values = self.gv1f.eval_tp_fixed_loc( - self.interpolation_grid_spans, - self.interpolation_grid_gradient, - out=self._gvf1_values, - ) - - gvf2_values = self.gv2f.eval_tp_fixed_loc( - self.interpolation_grid_spans, - self.interpolation_grid_gradient, - out=self._gvf2_values, - ) - - gvf3_values = self.gv3f.eval_tp_fixed_loc( - self.interpolation_grid_spans, - self.interpolation_grid_gradient, - out=self._gvf3_values, - ) + with ProfileManager.profile_region("momentum bracket: spline evaluation"): + vf_values = self.vf.eval_tp_fixed_loc( + self.interpolation_grid_spans, + [self.interpolation_grid_bn] * 3, + out=self._vf_values, + ) + gvf1_values = self.gv1f.eval_tp_fixed_loc( + self.interpolation_grid_spans, + self.interpolation_grid_gradient, + out=self._gvf1_values, + ) + gvf2_values = self.gv2f.eval_tp_fixed_loc( + self.interpolation_grid_spans, + self.interpolation_grid_gradient, + out=self._gvf2_values, + ) + gvf3_values = self.gv3f.eval_tp_fixed_loc( + self.interpolation_grid_spans, + self.interpolation_grid_gradient, + out=self._gvf3_values, + ) - self.PiuT.update_weights([[vf_values[0], vf_values[1], vf_values[2]]]) + with ProfileManager.profile_region("momentum bracket: projector weights"): + self.PiuT.update_weights([[vf_values[0], vf_values[1], vf_values[2]]]) + self.PigvT_1.update_weights([[gvf1_values[0], gvf1_values[1], gvf1_values[2]]]) + self.PigvT_2.update_weights([[gvf2_values[0], gvf2_values[1], gvf2_values[2]]]) + self.PigvT_3.update_weights([[gvf3_values[0], gvf3_values[1], gvf3_values[2]]]) - self.PigvT_1.update_weights([[gvf1_values[0], gvf1_values[1], gvf1_values[2]]]) - self.PigvT_2.update_weights([[gvf2_values[0], gvf2_values[1], gvf2_values[2]]]) - self.PigvT_3.update_weights([[gvf3_values[0], gvf3_values[1], gvf3_values[2]]]) - - if out is not None: - self.mbrackvw.dot(self._u, out=out) - else: - out = self.mbrackvw.dot(self._u) + with ProfileManager.profile_region("momentum bracket: operator application"): + if out is not None: + self.mbrackvw.dot(self._u, out=out) + else: + out = self.mbrackvw.dot(self._u) return out @@ -371,6 +373,7 @@ def __init__(self, derham, transposed=False, weights=None): self._op = self.Proj @ self.div.T else: self._op = self.div @ self.Proj + self._dot_tmp = self._op.tmp_vectors[0] hist_grid = self._derham.V2splines.proj_grid_pts @@ -421,7 +424,25 @@ def transpose(self, conjugate=False): return L2_transport_operator(self._derham, not self._transposed, weights=self._weights) def dot(self, v, out=None): - out = self._op.dot(v, out=out) + direction = "transpose" if self._transposed else "forward" + if self._transposed: + with ProfileManager.profile_region( + f"L2 transport {direction}: divergence" + ): + self.div.T.dot(v, out=self._dot_tmp) + with ProfileManager.profile_region( + f"L2 transport {direction}: projection" + ): + out = self.Proj.dot(self._dot_tmp, out=out) + else: + with ProfileManager.profile_region( + f"L2 transport {direction}: projection" + ): + self.Proj.dot(v, out=self._dot_tmp) + with ProfileManager.profile_region( + f"L2 transport {direction}: divergence" + ): + out = self.div.dot(self._dot_tmp, out=out) return out def update_coeffs(self, coeff): @@ -1489,6 +1510,27 @@ def get_u2_grid(self, un, un1, out): self.uf.vector = un self.uf1.vector = un1 + tensor_u = self.uf.vector.tp if hasattr(self.uf.vector, "tp") else self.uf.vector + tensor_u1 = self.uf1.vector.tp if hasattr(self.uf1.vector, "tp") else self.uf1.vector + first_data = tensor_u.blocks[0]._data if hasattr(tensor_u, "blocks") else tensor_u._data + if xp.is_gpu(first_data): + from struphy.feec.variational_kernels_cuda import kinetic_energy_grid_gpu + + coefficients = tuple(block._data for block in tensor_u.blocks) + coefficients1 = tuple(block._data for block in tensor_u1.blocks) + return kinetic_energy_grid_gpu( + self.integration_grid_spans, + self.integration_grid_bn, + self._derham.degree, + self.uf.starts[0], + coefficients, + coefficients1, + self._proj_u2_metric_term, + out, + self._uf_values, + self._uf1_values, + ) + uf_values = self.uf.eval_tp_fixed_loc( self.integration_grid_spans, [ @@ -1522,7 +1564,7 @@ def assemble_M_un(self, un): """Update the weights of the matrix M_un with the vector fields given by the coeficient un""" self.uf.vector = un - uf_values = self.uf.eval_tp_fixed_loc( + self.uf.eval_tp_fixed_loc( self.integration_grid_spans, [ self.integration_grid_bn, @@ -1531,12 +1573,16 @@ def assemble_M_un(self, un): out=self._uf_values, ) + self.assemble_M_un_cached() + + def assemble_M_un_cached(self): + """Assemble ``M_un`` from velocity values cached by ``get_u2_grid``.""" for i in range(3): self._Guf_values[i] *= 0.0 for j in range(3): self._tmp_int_grid *= 0.0 self._tmp_int_grid += self._mass_u_metric_term[i, j] - self._tmp_int_grid *= uf_values[j] + self._tmp_int_grid *= self._uf_values[j] self._Guf_values[i] += self._tmp_int_grid self._M_un.assemble( @@ -1547,7 +1593,7 @@ def assemble_M_un1(self, un1): """Update the weights of the matrix M_un1 with the vector fields given by the coeficient un1""" self.uf1.vector = un1 - uf1_values = self.uf1.eval_tp_fixed_loc( + self.uf1.eval_tp_fixed_loc( self.integration_grid_spans, [ self.integration_grid_bn, @@ -1556,12 +1602,16 @@ def assemble_M_un1(self, un1): out=self._uf1_values, ) + self.assemble_M_un1_cached() + + def assemble_M_un1_cached(self): + """Assemble ``M_un1`` from velocity values cached by ``get_u2_grid``.""" for i in range(3): self._Guf_values[i] *= 0.0 for j in range(3): self._tmp_int_grid *= 0.0 self._tmp_int_grid += self._mass_u_metric_term[i, j] - self._tmp_int_grid *= uf1_values[j] + self._tmp_int_grid *= self._uf1_values[j] self._Guf_values[i] += self._tmp_int_grid self._M_un1.assemble( diff --git a/src/struphy/linear_algebra/schur_solver.py b/src/struphy/linear_algebra/schur_solver.py index 36c0c7956..560c68c76 100644 --- a/src/struphy/linear_algebra/schur_solver.py +++ b/src/struphy/linear_algebra/schur_solver.py @@ -1,4 +1,4 @@ -from feectools.linalg.basic import IdentityOperator, LinearOperator, Vector +from feectools.linalg.basic import IdentityOperator, LinearOperator, MatrixFreeLinearOperator, Vector from feectools.linalg.block import BlockLinearOperator, BlockVector from feectools.linalg.solvers import inverse from line_profiler import profile @@ -222,13 +222,35 @@ def __init__(self, M, solver_name, **solver_params): self._C = M[1, 0] assert isinstance(M[1, 1], IdentityOperator) - self._S = self._A - self._B @ self._C + # Avoid the generic composed/block operator for the Schur product. + # Its nested ``dot`` calls allocate intermediate vectors on every + # Krylov iteration. Reuse two work vectors instead. + self._schur_tmp_y = self._C.codomain.zeros() + self._schur_tmp_a = self._A.codomain.zeros() + self._S = MatrixFreeLinearOperator( + domain=self._A.domain, + codomain=self._A.codomain, + dot=lambda v, *, out=None: self._dot_schur(v, out=out), + ) self._solver = inverse(self._S, solver_name, **solver_params) # right-hand side vector (avoids temporary memory allocation!) self._rhs = self._A.codomain.zeros() + def _dot_schur(self, v, out=None): + """Apply ``A - B C`` using preallocated work vectors.""" + with ProfileManager.profile_region("density schur apply: C"): + self._C.dot(v, out=self._schur_tmp_y) + with ProfileManager.profile_region("density schur apply: B"): + self._B.dot(self._schur_tmp_y, out=out) + with ProfileManager.profile_region("density schur apply: A"): + self._A.dot(v, out=self._schur_tmp_a) + with ProfileManager.profile_region("density schur apply: combine"): + out *= -1.0 + out += self._schur_tmp_a + return out + @profile @ProfileManager.profile("solve: SchurSolverFull") def dot(self, v, out=None): @@ -263,15 +285,18 @@ def dot(self, v, out=None): by = v[1] # right-hand side vector rhs bx - B by - rhs = self._B.dot(by, out=self._rhs) - rhs *= -1 - rhs += bx + with ProfileManager.profile_region("density schur: rhs"): + rhs = self._B.dot(by, out=self._rhs) + rhs *= -1 + rhs += bx # solve linear system (in-place if out is not None) - x = self._solver.dot(rhs, out=out[0]) - y = self._C.dot(x, out=out[1]) - y *= -1 - y += by + with ProfileManager.profile_region("density schur: Krylov"): + x = self._solver.dot(rhs, out=out[0]) + with ProfileManager.profile_region("density schur: back substitution"): + y = self._C.dot(x, out=out[1]) + y *= -1 + y += by return out From 2cd55928a60a57881f772079e04b64d34f78ea19 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 31 Aug 2026 12:29:28 +0200 Subject: [PATCH 136/156] run gitlab ci on gpu runners --- .gitlab-ci.yml | 78 ++++++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 73 insertions(+), 5 deletions(-) diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 6d60c7e1f..aef299497 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -54,6 +54,35 @@ stages: .image_ubuntu_latest: image: gitlab-registry.mpcdf.mpg.de/struphy/struphy/ubuntu-latest +.image_gitlab_mpcdf_nvhpc: + image: gitlab-registry.mpcdf.mpg.de/mpcdf/ci-module-image/nvhpcsdk_24-openmpi_5_0:2025 + +# --- GPU runners --- + +# Tag for an MPCDF Nvidia runner with compute capability 8.0 (A40 / A100), per +# https://docs.mpcdf.mpg.de/doc/data/gitlab/gitlabrunners.html +# Only the hardware tag is requested: GitLab ANDs tags together, so adding +# `mpcdf-shared` here would make the job unschedulable if a GPU runner happens +# not to carry it. +.tags_gpu_nvidia: + tags: [gpu-nvidia-cc80] + +# The GPU image is a bare module image, not one of the struphy images, so there +# is no prebuilt /struphy_${LANGUAGE}_${OMP} venv to source (as +# .scripts.install_on_push does). These jobs therefore build their own venv and +# install struphy from the checkout. +.before_script_gpu: + before_script: + - module purge + - module load nvhpcsdk/24 openmpi/5.0 python-waterboa/2024.06 gcc/13 + - module list + - nvidia-smi + - python3 -m venv env_gpu_${CI_PIPELINE_ID} + - source env_gpu_${CI_PIPELINE_ID}/bin/activate + - pip install -U pip + - pip install cupy-cuda12x cunumpy + - python3 -c "import cupy; cupy.zeros(1); print('cupy ok', cupy.cuda.runtime.runtimeGetVersion())" + # --- job variables --- .variables_push: @@ -528,19 +557,20 @@ inspect_repo: # struphy params LinearVlasovAmpereOneSpecies --check-file $file # done +# Smoke test: does a GPU runner exist, and does cunumpy dispatch to it at all. +# Deliberately does not install struphy, so it stays fast and still reports +# something useful when the full GPU suite below is broken. test_cupy: - tags: [nvidia-cc80] - image: gitlab-registry.mpcdf.mpg.de/mpcdf/ci-module-image/nvhpcsdk_24-openmpi_5_0:2025 stage: test extends: - .rules_startup + - .image_gitlab_mpcdf_nvhpc + - .tags_gpu_nvidia before_script: - - module avail - - module list - module purge - module load nvhpcsdk/24 openmpi/5.0 python-waterboa/2024.06 gcc/13 + - module list script: - - ls - nvidia-smi # Install cupy - python3 -m pip install --user cupy-cuda12x @@ -552,6 +582,44 @@ test_cupy: - export ARRAY_BACKEND=cupy - python3 src/struphy/utils/cupy_vs_numpy.py +# The unit suite under ARRAY_BACKEND=cupy, i.e. the same tests as `unit_tests` +# but with every array on the device. +# +# allow_failure: the CuPy backend is still being ported (several code paths +# still fall back to the host), so this is here to report the state of that +# port, not yet to gate merges. Remove `allow_failure` once the suite is green. +unit_tests_gpu: + stage: test + timeout: 2h + allow_failure: true + extends: + - .rules_mr_to_devel + - .image_gitlab_mpcdf_nvhpc + - .tags_gpu_nvidia + - .before_script_gpu + variables: + ARRAY_BACKEND: cupy + script: + - pip install -e .[phys,mpi] + - !reference [.scripts, compile] + - !reference [.scripts, unit_tests] + +unit_tests_gpu_mpi: + stage: test + timeout: 2h + allow_failure: true + extends: + - .rules_mr_to_devel + - .image_gitlab_mpcdf_nvhpc + - .tags_gpu_nvidia + - .before_script_gpu + variables: + ARRAY_BACKEND: cupy + script: + - pip install -e .[phys,mpi] + - !reference [.scripts, compile] + - !reference [.scripts, unit_tests_mpi] + install_tests: stage: test extends: From d5eeb3b2f2c405c5b37650c6d78f700d866ef3c2 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 1 Sep 2026 11:06:29 +0200 Subject: [PATCH 137/156] small optimization --- .../params_VlasovAmpere_scaling.py | 3 +- src/struphy/pic/base.py | 83 +++++++++++-------- 2 files changed, 51 insertions(+), 35 deletions(-) diff --git a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py index e2e1cb2e4..bed902203 100644 --- a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py +++ b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py @@ -104,7 +104,6 @@ # Environment options env = EnvironmentOptions( sim_folder=f"sim_{args.id:02d}", - profiling_activated=True, save_restart=False, ) @@ -175,4 +174,4 @@ model.kinetic_ions.var.add_background(background) if __name__ == "__main__": - sim.run() + sim.run(profiling_activated=True) diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index fc5134fd9..a729017b5 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -2006,17 +2006,24 @@ def apply_kinetic_bc(self, newton=False): # ~9.5% faster on CuPy for the 3-axis periodic loop at Np_local=12.5M. periodic_not_hole_or_ghost = ~(self.holes | self.ghost_particles) + # Pass 1 -- locate the outside markers on every periodic axis, keeping only + # the (small) index arrays. The masks themselves cannot be held across axes: + # _find_outside_particles writes the shared _is_outside_left/_is_outside_right + # buffers, which the next axis overwrites. + # + # Reverted from a branchless/xp.where + unconditional-elementwise version: + # measured on a real 50M-marker GPU run, that version was a net loss -- on + # each call only a small fraction of markers are ever actually outside + # (holes/ghosts and in-range markers are the overwhelming majority), so the + # sparse, index-based writes below plus the early exit (skipping this axis + # entirely when nothing is outside) touch far less memory than an + # unconditional dense pass over all n_rows markers, even accounting for the + # nonzero sync the indices cost. Avoiding a device sync is not free if the + # alternative is doing O(n_rows) dense work every call instead of O(outside + # markers) sparse work most calls skip entirely. + shift_col_0 = self.first_pusher_idx + 3 + self.vdim + periodic_outside = {} for axis in self._periodic_axes: - # Reverted from a branchless/xp.where + unconditional-elementwise version: - # measured on a real 50M-marker GPU run, that version was a net loss -- on - # each call only a small fraction of markers are ever actually outside - # (holes/ghosts and in-range markers are the overwhelming majority), so the - # sparse, index-based writes below plus the early exit (skipping this axis - # entirely when nothing is outside) touch far less memory than an - # unconditional dense pass over all n_rows markers, even accounting for the - # nonzero sync the indices cost. Avoiding a device sync is not free if the - # alternative is doing O(n_rows) dense work every call instead of O(outside - # markers) sparse work most calls skip entirely. outside_inds = self._find_outside_particles( axis, eta=self._eta_bc_buf, @@ -2026,33 +2033,43 @@ def apply_kinetic_bc(self, newton=False): if len(outside_inds) == 0: continue + periodic_outside[axis] = ( + outside_inds, + xp.nonzero(self._is_outside_right)[0], + xp.nonzero(self._is_outside_left)[0], + ) + + # Zero the shift columns of exactly the axes that had markers outside -- the + # same set the per-axis loop below writes, so this is not a behaviour change + # (an axis with nothing outside keeps its column untouched, as before). The + # point is to do it in ONE dense pass over contiguous columns instead of one + # pass per axis: `markers[:, c] = 0.0` is a strided write over all n_rows and + # was the single most expensive operation in this function -- 1.32 ms per axis + # at Np_local=12.5M on an H100, against 0.03 ms for the sparse writes it + # exists to prepare. Hoisting it out of the loop measured 7.49 ms -> 4.93 ms + # per call for the 3-axis periodic case, with the no-markers-outside path + # unchanged (2.61 ms -> 2.69 ms, i.e. within noise) because it is still + # skipped entirely when `periodic_outside` is empty. + if periodic_outside and not newton: + active = sorted(periodic_outside) + if active[-1] - active[0] + 1 == len(active): + self.markers[:, shift_col_0 + active[0] : shift_col_0 + active[-1] + 1] = 0.0 + else: + # non-contiguous set of periodic axes: no single slice covers them + for axis in active: + self.markers[:, shift_col_0 + axis] = 0.0 + + # Pass 2 -- wrap the positions and set the shift for the alpha-weighted + # mid-point computation. + for axis, (outside_inds, outside_right_inds, outside_left_inds) in periodic_outside.items(): self.markers[outside_inds, axis] = self.markers[outside_inds, axis] % 1.0 - # set shift for alpha-weighted mid-point computation - outside_right_inds = np.nonzero(self._is_outside_right)[0] - outside_left_inds = np.nonzero(self._is_outside_left)[0] if newton: - self.markers[ - outside_right_inds, - self.first_pusher_idx + 3 + self.vdim + axis, - ] += 1.0 - self.markers[ - outside_left_inds, - self.first_pusher_idx + 3 + self.vdim + axis, - ] += -1.0 + self.markers[outside_right_inds, shift_col_0 + axis] += 1.0 + self.markers[outside_left_inds, shift_col_0 + axis] += -1.0 else: - self.markers[ - :, - self.first_pusher_idx + 3 + self.vdim + axis, - ] = 0.0 - self.markers[ - outside_right_inds, - self.first_pusher_idx + 3 + self.vdim + axis, - ] = 1.0 - self.markers[ - outside_left_inds, - self.first_pusher_idx + 3 + self.vdim + axis, - ] = -1.0 + self.markers[outside_right_inds, shift_col_0 + axis] = 1.0 + self.markers[outside_left_inds, shift_col_0 + axis] = -1.0 # put all coordinate inside the unit cube (avoid wrong Jacobian evaluations) outside_inds_per_axis = {} From c859853cc7cc63baba95465e39f8543995dc58f4 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 1 Sep 2026 11:07:42 +0200 Subject: [PATCH 138/156] formatting --- .../feec/basis_projection_kernels_cuda.py | 53 ++++++-- src/struphy/feec/basis_projection_ops.py | 24 ++-- src/struphy/feec/mass_kernels_cuda.py | 120 ++++++++++++------ src/struphy/feec/variational_kernels_cuda.py | 16 ++- src/struphy/feec/variational_utilities.py | 17 +-- 5 files changed, 156 insertions(+), 74 deletions(-) diff --git a/src/struphy/feec/basis_projection_kernels_cuda.py b/src/struphy/feec/basis_projection_kernels_cuda.py index ed32d4110..9aebfb12b 100644 --- a/src/struphy/feec/basis_projection_kernels_cuda.py +++ b/src/struphy/feec/basis_projection_kernels_cuda.py @@ -46,11 +46,25 @@ def assemble_dofs_for_weighted_basisfuns_3d_gpu( - mat, starts_in, ends_in, pads_in, starts_out, ends_out, pads_out, - fun, weights, spans, bases, subs, dims_in, dims_out, degrees_out, + mat, + starts_in, + ends_in, + pads_in, + starts_out, + ends_out, + pads_out, + fun, + weights, + spans, + bases, + subs, + dims_in, + dims_out, + degrees_out, ): import cupy as cp import numpy as np + global _kernel if _kernel is None: _kernel = cp.RawKernel(_ASSEMBLE_SRC, "assemble_weighted_basis_3d_cuda") @@ -60,13 +74,28 @@ def assemble_dofs_for_weighted_basisfuns_3d_gpu( rows = tuple(cp.arange(len(x), dtype=cp.int64) - cp.cumsum(cp.asarray(x, dtype=cp.int64)) for x in subs) fun = cp.ascontiguousarray(fun) mat.fill(0.0) - ni = tuple(x.shape[0] for x in spans); nq = tuple(x.shape[1] for x in spans) - degree = tuple(x.shape[2]-1 for x in bases) - total = int(np.prod(ni)*np.prod(nq)*np.prod([p+1 for p in degree])); threads=256 - _kernel(((total+threads-1)//threads,), (threads,), ( - *rows,*spans,*weights,*bases,fun, - *(np.int32(x) for x in (*ni,*nq,*degree)), - *(np.int32(x) for x in starts_out),*(np.int32(x) for x in pads_in),*(np.int32(x) for x in pads_out), - *(np.int32(x) for x in dims_in),*(np.int32(x) for x in dims_out),*(np.int32(x) for x in degrees_out), - mat,*(np.int32(x) for x in mat.shape[1:]), - )) + ni = tuple(x.shape[0] for x in spans) + nq = tuple(x.shape[1] for x in spans) + degree = tuple(x.shape[2] - 1 for x in bases) + total = int(np.prod(ni) * np.prod(nq) * np.prod([p + 1 for p in degree])) + threads = 256 + _kernel( + ((total + threads - 1) // threads,), + (threads,), + ( + *rows, + *spans, + *weights, + *bases, + fun, + *(np.int32(x) for x in (*ni, *nq, *degree)), + *(np.int32(x) for x in starts_out), + *(np.int32(x) for x in pads_in), + *(np.int32(x) for x in pads_out), + *(np.int32(x) for x in dims_in), + *(np.int32(x) for x in dims_out), + *(np.int32(x) for x in degrees_out), + mat, + *(np.int32(x) for x in mat.shape[1:]), + ), + ) diff --git a/src/struphy/feec/basis_projection_ops.py b/src/struphy/feec/basis_projection_ops.py index e876a6ded..626c1cefa 100644 --- a/src/struphy/feec/basis_projection_ops.py +++ b/src/struphy/feec/basis_projection_ops.py @@ -2027,11 +2027,7 @@ def assemble(self, weights=None): # A device reduction here synchronizes the whole CuPy stream # once per matrix block. Dynamic GPU weights are cheap to # assemble even when zero, so avoid that host round-trip. - if ( - self._mpi_comm is None - and isinstance(loc_weight, xp.ndarray) - and xp.is_gpu(loc_weight) - ): + if self._mpi_comm is None and isinstance(loc_weight, xp.ndarray) and xp.is_gpu(loc_weight): not_weight_zero = True else: not_weight_zero = xp.array( @@ -2070,10 +2066,20 @@ def assemble(self, weights=None): assemble_dofs_for_weighted_basisfuns_3d_gpu( dofs_mat._data, - _starts_in, _ends_in, _pads_in, - _starts_out, _ends_out, _pads_out, - mat_w, _wtsG, _spans, _bases, _subs, - _Vnbases, _Wnbases, _Wdegrees, + _starts_in, + _ends_in, + _pads_in, + _starts_out, + _ends_out, + _pads_out, + mat_w, + _wtsG, + _spans, + _bases, + _subs, + _Vnbases, + _Wnbases, + _Wdegrees, ) else: kernel = PyccelKernel( diff --git a/src/struphy/feec/mass_kernels_cuda.py b/src/struphy/feec/mass_kernels_cuda.py index 725fb41e8..f3a069320 100644 --- a/src/struphy/feec/mass_kernels_cuda.py +++ b/src/struphy/feec/mass_kernels_cuda.py @@ -292,9 +292,7 @@ def _get_h1vec_divdiv_assembly_kernel(): if _h1vec_divdiv_assembly_kernel is None: import cupy as cp - _h1vec_divdiv_assembly_kernel = cp.RawKernel( - _H1VEC_DIVDIV_ASSEMBLY_SRC, "h1vec_divdiv_assemble_cuda" - ) + _h1vec_divdiv_assembly_kernel = cp.RawKernel(_H1VEC_DIVDIV_ASSEMBLY_SRC, "h1vec_divdiv_assemble_cuda") return _h1vec_divdiv_assembly_kernel @@ -311,9 +309,8 @@ def _get_weak_div_assembly_kernel(): global _weak_div_assembly_kernel if _weak_div_assembly_kernel is None: import cupy as cp - _weak_div_assembly_kernel = cp.RawKernel( - _WEAK_DIV_ASSEMBLY_SRC, "weak_div_assemble_cuda" - ) + + _weak_div_assembly_kernel = cp.RawKernel(_WEAK_DIV_ASSEMBLY_SRC, "weak_div_assemble_cuda") return _weak_div_assembly_kernel @@ -336,12 +333,21 @@ def _kernel_args(spans, degree, starts, pads, bases, dlogj, component): dlogj = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in dlogj) return ( *spans, - np.int32(spans[0].size), np.int32(spans[1].size), np.int32(spans[2].size), + np.int32(spans[0].size), + np.int32(spans[1].size), + np.int32(spans[2].size), *(np.int32(x) for x in degree), - *(np.int32(x) for x in starts), *(np.int32(x) for x in pads), - *bases, np.int32(bases[0].shape[2]), np.int32(bases[1].shape[2]), np.int32(bases[2].shape[2]), - np.int32(bases[0].shape[3]), np.int32(bases[1].shape[3]), np.int32(bases[2].shape[3]), - *dlogj, np.int32(component), + *(np.int32(x) for x in starts), + *(np.int32(x) for x in pads), + *bases, + np.int32(bases[0].shape[2]), + np.int32(bases[1].shape[2]), + np.int32(bases[2].shape[2]), + np.int32(bases[0].shape[3]), + np.int32(bases[1].shape[3]), + np.int32(bases[2].shape[3]), + *dlogj, + np.int32(component), ) @@ -353,7 +359,11 @@ def h1vec_divergence_eval_gpu(spans, degree, starts, pads, bases, dlogj, compone args = _kernel_args(spans, degree, starts, pads, bases, dlogj, component) nvalues = values.size threads = 256 - kernel(((nvalues + threads - 1) // threads,), (threads,), (*args, coeffs, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), values)) + kernel( + ((nvalues + threads - 1) // threads,), + (threads,), + (*args, coeffs, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), values), + ) def h1vec_divergence_transpose_gpu(spans, degree, starts, pads, bases, dlogj, component, values, coeffs): @@ -364,7 +374,11 @@ def h1vec_divergence_transpose_gpu(spans, degree, starts, pads, bases, dlogj, co args = _kernel_args(spans, degree, starts, pads, bases, dlogj, component) nvalues = values.size threads = 256 - kernel(((nvalues + threads - 1) // threads,), (threads,), (*args, values, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), coeffs)) + kernel( + ((nvalues + threads - 1) // threads,), + (threads,), + (*args, values, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), coeffs), + ) def mass_3d_assemble_gpu(spans, degree_i, degree_j, starts, pads, weights, bases_i, bases_j, mat_fun, data): @@ -377,7 +391,9 @@ def mass_3d_assemble_gpu(spans, degree_i, degree_j, starts, pads, weights, bases bases_i = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_i) bases_j = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_j) mat_fun = cp.ascontiguousarray(mat_fun) - total = int(np.prod([x.size for x in spans]) * np.prod([x + 1 for x in degree_i]) * np.prod([x + 1 for x in degree_j])) + total = int( + np.prod([x.size for x in spans]) * np.prod([x + 1 for x in degree_i]) * np.prod([x + 1 for x in degree_j]) + ) threads = 256 _get_mass_assembly_kernel()( ((total + threads - 1) // threads,), @@ -385,19 +401,36 @@ def mass_3d_assemble_gpu(spans, degree_i, degree_j, starts, pads, weights, bases ( *spans, *(np.int32(x.size) for x in spans), - *(np.int32(x) for x in degree_i), *(np.int32(x) for x in degree_j), - *(np.int32(x) for x in starts), *(np.int32(x) for x in pads), - *weights, *(np.int32(x.shape[1]) for x in weights), - *bases_i, *bases_j, - *(np.int32(x.shape[2]) for x in bases_i), *(np.int32(x.shape[2]) for x in bases_j), - mat_fun, data, *(np.int32(x) for x in data.shape[1:]), + *(np.int32(x) for x in degree_i), + *(np.int32(x) for x in degree_j), + *(np.int32(x) for x in starts), + *(np.int32(x) for x in pads), + *weights, + *(np.int32(x.shape[1]) for x in weights), + *bases_i, + *bases_j, + *(np.int32(x.shape[2]) for x in bases_i), + *(np.int32(x.shape[2]) for x in bases_j), + mat_fun, + data, + *(np.int32(x) for x in data.shape[1:]), ), ) def weak_divergence_assemble_gpu( - spans, degree_i, degree_j, starts, pads, weights, - bases_i, bases_j, mat_fun, dlogj, component, data, + spans, + degree_i, + degree_j, + starts, + pads, + weights, + bases_i, + bases_j, + mat_fun, + dlogj, + component, + data, ): """Assemble one L2-by-H1 weak-divergence block on the GPU.""" import cupy as cp @@ -410,22 +443,29 @@ def weak_divergence_assemble_gpu( dlogj = tuple(cp.ascontiguousarray(x) for x in dlogj) mat_fun = cp.ascontiguousarray(mat_fun) total = int( - np.prod([x.size for x in spans]) - * np.prod([p + 1 for p in degree_i]) - * np.prod([p + 1 for p in degree_j]) + np.prod([x.size for x in spans]) * np.prod([p + 1 for p in degree_i]) * np.prod([p + 1 for p in degree_j]) ) threads = 256 _get_weak_div_assembly_kernel()( - ((total + threads - 1) // threads,), (threads,), + ((total + threads - 1) // threads,), + (threads,), ( - *spans, *(np.int32(x.size) for x in spans), - *(np.int32(x) for x in degree_i), *(np.int32(x) for x in degree_j), - *(np.int32(x) for x in starts), *(np.int32(x) for x in pads), - *weights, *(np.int32(x.shape[1]) for x in weights), - *bases_i, *bases_j, + *spans, + *(np.int32(x.size) for x in spans), + *(np.int32(x) for x in degree_i), + *(np.int32(x) for x in degree_j), + *(np.int32(x) for x in starts), + *(np.int32(x) for x in pads), + *weights, + *(np.int32(x.shape[1]) for x in weights), + *bases_i, + *bases_j, *(np.int32(x.shape[2]) for x in bases_i), *(np.int32(x.shape[2]) for x in bases_j), - mat_fun, *dlogj, np.int32(component), data, + mat_fun, + *dlogj, + np.int32(component), + data, *(np.int32(x) for x in data.shape[1:]), ), ) @@ -450,10 +490,18 @@ def h1vec_divdiv_assemble_gpu(spans, degree, starts, pads, bases, weighted_rho, ((total + threads - 1) // threads,), (threads,), ( - *spans, *(np.int32(x.size) for x in spans), *(np.int32(x) for x in degree), - *(np.int32(x) for x in starts), *(np.int32(x) for x in pads), - *bases, *(np.int32(x.shape[2]) for x in bases), *(np.int32(x.shape[3]) for x in bases), - weighted_rho, np.int32(component_test), np.int32(component_trial), data, + *spans, + *(np.int32(x.size) for x in spans), + *(np.int32(x) for x in degree), + *(np.int32(x) for x in starts), + *(np.int32(x) for x in pads), + *bases, + *(np.int32(x.shape[2]) for x in bases), + *(np.int32(x.shape[3]) for x in bases), + weighted_rho, + np.int32(component_test), + np.int32(component_trial), + data, *(np.int32(x) for x in data.shape[1:]), ), ) diff --git a/src/struphy/feec/variational_kernels_cuda.py b/src/struphy/feec/variational_kernels_cuda.py index fc44daaf6..9e0f9ae05 100644 --- a/src/struphy/feec/variational_kernels_cuda.py +++ b/src/struphy/feec/variational_kernels_cuda.py @@ -2,7 +2,7 @@ _KINETIC_ENERGY_KERNEL = None -_KINETIC_ENERGY_SOURCE = r''' +_KINETIC_ENERGY_SOURCE = r""" extern "C" __global__ void kinetic_energy_grid_cuda( const long long* span0, const long long* span1, const long long* span2, @@ -56,7 +56,7 @@ ug0[tid] = us[0]; ug1[tid] = us[1]; ug2[tid] = us[2]; vg0[tid] = vs[0]; vg1[tid] = vs[1]; vg2[tid] = vs[2]; } -''' +""" def prepare_kinetic_energy_kernel(): @@ -76,8 +76,16 @@ def prepare_kinetic_energy_kernel(): def kinetic_energy_grid_gpu( - spans, bases, degree, starts, coefficients, coefficients1, metric, out, - values, values1, + spans, + bases, + degree, + starts, + coefficients, + coefficients1, + metric, + out, + values, + values1, ): """Evaluate both H1-vector splines and their metric product in one launch.""" import cupy as cp diff --git a/src/struphy/feec/variational_utilities.py b/src/struphy/feec/variational_utilities.py index f8e233cc4..87e21334c 100644 --- a/src/struphy/feec/variational_utilities.py +++ b/src/struphy/feec/variational_utilities.py @@ -5,7 +5,6 @@ from feectools.linalg.basic import IdentityOperator, Vector from feectools.linalg.block import BlockVector from feectools.linalg.solvers import inverse - from scope_profiler import ProfileManager from struphy.feec import preconditioner @@ -426,22 +425,14 @@ def transpose(self, conjugate=False): def dot(self, v, out=None): direction = "transpose" if self._transposed else "forward" if self._transposed: - with ProfileManager.profile_region( - f"L2 transport {direction}: divergence" - ): + with ProfileManager.profile_region(f"L2 transport {direction}: divergence"): self.div.T.dot(v, out=self._dot_tmp) - with ProfileManager.profile_region( - f"L2 transport {direction}: projection" - ): + with ProfileManager.profile_region(f"L2 transport {direction}: projection"): out = self.Proj.dot(self._dot_tmp, out=out) else: - with ProfileManager.profile_region( - f"L2 transport {direction}: projection" - ): + with ProfileManager.profile_region(f"L2 transport {direction}: projection"): self.Proj.dot(v, out=self._dot_tmp) - with ProfileManager.profile_region( - f"L2 transport {direction}: divergence" - ): + with ProfileManager.profile_region(f"L2 transport {direction}: divergence"): out = self.div.dot(self._dot_tmp, out=out) return out From 3be59bafeee12ab039cf20307d9d3c1a7498289a Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 1 Sep 2026 11:08:23 +0200 Subject: [PATCH 139/156] Update feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 9191536b0..88cadbab0 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 9191536b0abbad584c4811f278a9f4722adbaa1d +Subproject commit 88cadbab0d784448834d98756e39561bc2d2eb5d From 6dd7d3f2d0d4d0f712b6871816299b4d0f7c5949 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 1 Sep 2026 14:38:31 +0200 Subject: [PATCH 140/156] gpu bug fixes --- src/struphy/feec/variational_utilities.py | 5 +++ src/struphy/ode/solvers.py | 14 +++++-- src/struphy/pic/base.py | 39 +++++++++++++++++-- src/struphy/pic/pushing/pusher.py | 5 ++- .../pic/pushing/pusher_kernels_cuda.py | 11 ++++-- .../propagators/push_random_diffusion.py | 11 ++++-- 6 files changed, 71 insertions(+), 14 deletions(-) diff --git a/src/struphy/feec/variational_utilities.py b/src/struphy/feec/variational_utilities.py index 87e21334c..c2af5de6e 100644 --- a/src/struphy/feec/variational_utilities.py +++ b/src/struphy/feec/variational_utilities.py @@ -1452,6 +1452,11 @@ class KineticEnergyEvaluator: """ def __init__(self, derham, domain, mass_ops): + # Kept for get_u2_grid's GPU branch, which needs derham.degree. The NumPy + # branch never touches it, so a missing assignment here failed only under + # ARRAY_BACKEND=cupy -- and there for every model that builds this evaluator. + self._derham = derham + integration_grid = [grid_1d.flatten() for grid_1d in derham.V0splines.quad_grid_pts[0]] self.integration_grid_spans, self.integration_grid_bn, self.integration_grid_bd = derham.prepare_eval_tp_fixed( diff --git a/src/struphy/ode/solvers.py b/src/struphy/ode/solvers.py index ed89bd098..0249ebe44 100644 --- a/src/struphy/ode/solvers.py +++ b/src/struphy/ode/solvers.py @@ -62,9 +62,17 @@ def __init__( @ProfileManager.profile("solve: ODEsolverFEEC") def __call__(self, tn, h): - a = self.butcher.a - b = self.butcher.b - c = self.butcher.c + # These coefficients scale FEEC vectors (StencilVector/BlockVector), which do + # not implement __array_ufunc__. Under CuPy `a[i, j]` is a 0-d *device* array, + # so `h * a[i, j] * vec` dispatches to CuPy's __mul__ and fails with + # NotImplemented; under NumPy the same expression yields a np.float64 scalar + # and falls back to the vector's __rmul__, which is why this only ever broke on + # the GPU. The tableau itself must stay on the active backend -- the particle + # pushers hand `a_stage`/`b`/`c` straight to CUDA kernels -- so only the scalars + # used here are brought to the host, once per call and s^2 values at most. + a = xp.to_numpy(self.butcher.a) + b = xp.to_numpy(self.butcher.b) + c = xp.to_numpy(self.butcher.c) # keep initial condition for v, vn in zip(self.y, self.yn): diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index a729017b5..02929d8c8 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -913,6 +913,20 @@ def ghost_particles(self): self._ghost_particles = self.markers[:, -1] == -2.0 return self._ghost_particles + @property + def has_ghost_particles(self): + """Whether any row of the markers array may currently be a ghost particle. + + Ghost particles only ever enter through :meth:`_communicate_boxes` (the SPH + ghost-box layer): that is the sole path that writes ``-2`` into the ID column, + both for the rows this rank marks and sends and for the rows it receives. + Every other model -- all the PIC ones -- never creates one, so this stays False + for the whole run and lets the hot marker-sorting path skip the full-array + passes that would only ever find nothing. See :meth:`_update_ghost_particles` + and :meth:`_remove_ghost_particles`. + """ + return getattr(self, "_has_ghost_particles", False) + @property def markers_wo_holes(self): """Array holding the marker information, excluding holes. The i-th row holds the i-th marker info.""" @@ -3400,15 +3414,26 @@ def _update_ghost_particles(self): as a ghost particle when its ID column (last column) equals -2, the marker set by :meth:`_prepare_ghost_particles`/:meth:`_sendrecv_markers_boxes` for SPH ghost-box particles received from a neighbouring process.""" - self._ghost_particles[:] = self.markers[:, -1] == -2.0 + # markers[:, -1] is a strided read over every row (~0.5 ms at Np_local=12.5M on + # an H100). When no ghost particle has been created since the last removal the + # answer is known to be all-False and the mask already holds it, so only the + # cheap valid_mks refresh is needed -- holes may still have changed. + if self.has_ghost_particles: + self._ghost_particles[:] = self.markers[:, -1] == -2.0 self._update_valid_mks() def _remove_ghost_particles(self): """Discard all current ghost particles: turn their marker-array rows into new holes (so the space can be reused before the next SPH ghost-box update).""" - self._update_ghost_particles() - new_holes = np.nonzero(self.ghost_particles) - self._markers[new_holes] = -1.0 + # Skip the ghost-specific work (a strided full-array compare plus a nonzero, + # which also forces a device sync) when no ghost exists to remove. update_holes + # still runs unconditionally: callers rely on it refreshing holes, which other + # operations do change. + if self.has_ghost_particles: + self._update_ghost_particles() + new_holes = np.nonzero(self.ghost_particles) + self._markers[new_holes] = -1.0 + self._has_ghost_particles = False self.update_holes() def _prepare_ghost_particles(self): @@ -4546,6 +4571,12 @@ def _communicate_boxes(self): # n_ghosts = xp.count_nonzero(self.ghost_particles) # logger.info(f"before communicate_boxes: {self.mpi_rank = }, {n_valid = } {n_holes = }, {n_ghosts = }") + # This is the one path that turns rows into ghost particles (it writes -2 into + # the ID column of the outgoing ghost markers, and receives rows already + # carrying it), so it is the one place that arms the flag. It must be set + # before the _update_ghost_particles call below, which is gated on it. + self._has_ghost_particles = True + self._prepare_ghost_particles() self._get_destinations_box() self._self_communication_boxes() diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 52067656a..90b1c7588 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -671,7 +671,10 @@ def __init__( gravity, kappa, ) = args_kernel - self._gpu_sph_gravity = cp.asarray(np.asarray(gravity, dtype=float), dtype=cp.float64) + # `gravity` arrives in the propagator's kernel args and so already lives on + # the active backend: under CuPy np.asarray on it raises rather than + # transferring. cp.asarray takes host and device input alike. + self._gpu_sph_gravity = cp.asarray(gravity, dtype=cp.float64) self._gpu_sph_kappa = float(kappa) self._gpu_sph_boxes = boxes self._gpu_sph_neighbours = neighbours diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index 6579f967e..d5a093cd0 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -3163,14 +3163,19 @@ def push_random_diffusion_stage_gpu(markers, n_cols, noise, diffusion_coeff: flo :func:`~struphy.pic.pushing.pusher_kernels.push_random_diffusion_stage`. Domain-independent (no geometry involved), so unlike the other ``*_general_gpu`` functions this one has no ``kind_map`` restriction. - ``noise`` is a plain host NumPy array (``struphy.propagators.push_random_diffusion.PushRandomDiffusion`` - fills it via ``numpy.random``, not ``xp.random``), transferred fresh each call.""" + ``noise`` may be a host or a device array: + :class:`~struphy.propagators.push_random_diffusion.PushRandomDiffusion` draws it + through ``xp.random``, so under CuPy it is already on the device and no transfer + happens here; a host array is accepted (and copied) so the signature stays + backend-agnostic.""" import cupy as cp import numpy as np n_markers = markers.shape[0] dev = markers - noise_dev = cp.asarray(np.ascontiguousarray(noise), dtype=cp.float64) + # cp.asarray takes host or device input; np.ascontiguousarray would raise on a + # device array rather than transferring it. + noise_dev = cp.ascontiguousarray(cp.asarray(noise, dtype=cp.float64)) scale = float(np.sqrt(2.0 * dt * diffusion_coeff)) threads = 256 blocks = (n_markers + threads - 1) // threads diff --git a/src/struphy/propagators/push_random_diffusion.py b/src/struphy/propagators/push_random_diffusion.py index 26afa4fb2..a078c8d9a 100644 --- a/src/struphy/propagators/push_random_diffusion.py +++ b/src/struphy/propagators/push_random_diffusion.py @@ -5,7 +5,7 @@ from cunumpy import PyccelKernel from line_profiler import profile -from numpy import array, random +import cunumpy as xp from struphy.io.options import OptionsBase from struphy.models.variables import PICVariable @@ -109,7 +109,9 @@ def allocate(self): particles = self.variables.var.particles - self._noise = array(particles.markers[:, :3]) + # Allocated on the active backend: this buffer is handed to the pusher kernel + # alongside the marker array, so under CuPy it has to be a device array. + self._noise = xp.array(particles.markers[:, :3]) self._butcher = self.options.butcher # temp fix due to refactoring of ButcherTableau: @@ -144,7 +146,10 @@ def __call__(self, dt): particles = self.variables.var.particles - self._noise[:] = random.multivariate_normal( + # Drawn through the backend's own RNG, so the samples are generated where the + # buffer lives instead of being copied host->device every step. On NumPy this + # is numpy.random, i.e. unchanged behaviour. + self._noise[:] = xp.random.multivariate_normal( self._mean, self._cov, len(particles.markers), From 82d92b0efc2234198d1edccae34bec28c949bde0 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 2 Sep 2026 09:57:38 +0200 Subject: [PATCH 141/156] formatting --- src/struphy/propagators/push_random_diffusion.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/struphy/propagators/push_random_diffusion.py b/src/struphy/propagators/push_random_diffusion.py index a078c8d9a..3f3b7fb8e 100644 --- a/src/struphy/propagators/push_random_diffusion.py +++ b/src/struphy/propagators/push_random_diffusion.py @@ -3,9 +3,9 @@ import logging from dataclasses import dataclass +import cunumpy as xp from cunumpy import PyccelKernel from line_profiler import profile -import cunumpy as xp from struphy.io.options import OptionsBase from struphy.models.variables import PICVariable From f1786c04f96c2b8f07f3631c9114e408cb497c63 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 2 Sep 2026 09:58:05 +0200 Subject: [PATCH 142/156] Formatting --- src/struphy/pic/accumulation/accum_kernels_gc_cuda.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index aca1cfac2..ad86aa85f 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -171,9 +171,7 @@ def gc_mag_density_0form_gpu( # duplicating the CUDA source, reuse the already-validated kernel. # --------------------------------------------------------------------------- -from struphy.pic.accumulation.accum_kernels_cuda import ( # noqa: E402 - charge_density_0form_gpu as _charge_density_0form_gpu, -) +from struphy.pic.accumulation.accum_kernels_cuda import charge_density_0form_gpu as _charge_density_0form_gpu # noqa: E402 def gc_density_0form_gpu(markers, weight_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, vec_dev): From 4445fe8d60eb5706da1e0cf5eb3d914e21c41100 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 2 Sep 2026 09:59:24 +0200 Subject: [PATCH 143/156] remove noqa --- src/struphy/pic/accumulation/accum_kernels_gc_cuda.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index ad86aa85f..030797962 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -171,7 +171,7 @@ def gc_mag_density_0form_gpu( # duplicating the CUDA source, reuse the already-validated kernel. # --------------------------------------------------------------------------- -from struphy.pic.accumulation.accum_kernels_cuda import charge_density_0form_gpu as _charge_density_0form_gpu # noqa: E402 +from struphy.pic.accumulation.accum_kernels_cuda import charge_density_0form_gpu as _charge_density_0form_gpu def gc_density_0form_gpu(markers, weight_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, vec_dev): From 03d431c31f58506f55dacef4cc11eaa281695120 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 2 Sep 2026 10:24:19 +0200 Subject: [PATCH 144/156] Moved the cuda code to .cu files --- pyproject.toml | 1 + src/struphy/cuda.py | 11 + .../feec/basis_projection_kernels_cuda.py | 44 +- .../_assemble_src.cu | 40 + .../_h1vec_divdiv_assembly_src.cu | 44 + .../_h1vec_divergence_src.cu | 111 ++ .../mass_kernels_cuda/_mass_assembly_src.cu | 48 + .../_weak_div_assembly_src.cu | 60 + .../kinetic_energy_grid.cu | 47 + src/struphy/feec/mass_kernels_cuda.py | 272 +-- src/struphy/feec/variational_kernels_cuda.py | 58 +- .../pic/accumulation/accum_kernels_cuda.py | 1034 +--------- .../pic/accumulation/accum_kernels_gc_cuda.py | 654 +------ .../_cc_lin_mhd_6d_1_src.cu | 118 ++ .../_cc_lin_mhd_6d_2_src.cu | 172 ++ .../_charge_density_0form_src.cu | 88 + .../_linear_vlasov_ampere_extra_src.cu | 187 ++ .../_pc_pressure_fillers_src.cu | 198 ++ .../_vlasov_maxwell_extra_src.cu | 97 + .../cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu | 83 + .../accum_kernels_cuda/pc_lin_mhd_6d_full.cu | 83 + .../_cc_lin_mhd_5d_curlb_src.cu | 144 ++ .../_cc_lin_mhd_5d_d_src.cu | 131 ++ .../_cc_lin_mhd_5d_gradb_dg_src.cu | 144 ++ .../_cc_lin_mhd_5d_gradb_src.cu | 111 ++ .../accum_kernels_gc_cuda/_fill_vec_src.cu | 23 + .../_gc_mag_density_0form_src.cu | 88 + .../cuda/sorting_kernels_cuda/_sort_src.cu | 84 + .../_sph_eval_flat_src.cu | 291 +++ .../_sph_eval_naive_src.cu | 86 + .../utilities_kernels_cuda/_gc_from_6d_src.cu | 75 + .../_gradb_ediff_src.cu | 58 + .../utilities_kernels_cuda/_utilities_src.cu | 303 +++ .../_dk_hamiltonian_src.cu | 124 ++ .../_gc_marker_column_src.cu | 212 ++ .../_sph_marker_column_src.cu | 111 ++ .../_general_geometry_src.cu | 1452 ++++++++++++++ .../_push_eta_cuboid_src.cu | 37 + .../_push_eta_rk_periodic_src.cu | 55 + .../_push_v_efield_cuboid_src.cu | 152 ++ .../_random_diffusion_src.cu | 19 + .../pusher_kernels_gc_cuda/_dg_1st_src.cu | 195 ++ .../_dg_2nd_order_src.cu | 227 +++ .../pusher_kernels_gc_cuda/_dg_newton_src.cu | 256 +++ .../_push_gc_bstar_src.cu | 109 ++ .../_push_gc_bxestar_src.cu | 96 + .../_push_gc_cc_j1_src.cu | 216 +++ .../_push_gc_cc_j2_dg_src.cu | 171 ++ .../_push_gc_cc_j2_stage_src.cu | 161 ++ .../_sph_pusher_src.cu | 194 ++ .../_reflect_src.cu | 36 + .../pic/pushing/eval_kernels_gc_cuda.py | 341 +--- .../pic/pushing/eval_kernels_sph_cuda.py | 114 +- .../pic/pushing/pusher_kernels_cuda.py | 1726 +---------------- .../pic/pushing/pusher_kernels_gc_cuda.py | 1448 +------------- .../pic/pushing/pusher_kernels_sph_cuda.py | 197 +- .../pushing/pusher_utilities_kernels_cuda.py | 39 +- src/struphy/pic/sorting_kernels_cuda.py | 87 +- src/struphy/pic/sph_eval_kernels_cuda.py | 382 +--- src/struphy/pic/utilities_kernels_cuda.py | 443 +---- 60 files changed, 6809 insertions(+), 6779 deletions(-) create mode 100644 src/struphy/cuda.py create mode 100644 src/struphy/feec/cuda/basis_projection_kernels_cuda/_assemble_src.cu create mode 100644 src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu create mode 100644 src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divergence_src.cu create mode 100644 src/struphy/feec/cuda/mass_kernels_cuda/_mass_assembly_src.cu create mode 100644 src/struphy/feec/cuda/mass_kernels_cuda/_weak_div_assembly_src.cu create mode 100644 src/struphy/feec/cuda/variational_kernels_cuda/kinetic_energy_grid.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_charge_density_0form_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_pc_pressure_fillers_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_vlasov_maxwell_extra_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d_full.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_fill_vec_src.cu create mode 100644 src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu create mode 100644 src/struphy/pic/cuda/sorting_kernels_cuda/_sort_src.cu create mode 100644 src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_flat_src.cu create mode 100644 src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_naive_src.cu create mode 100644 src/struphy/pic/cuda/utilities_kernels_cuda/_gc_from_6d_src.cu create mode 100644 src/struphy/pic/cuda/utilities_kernels_cuda/_gradb_ediff_src.cu create mode 100644 src/struphy/pic/cuda/utilities_kernels_cuda/_utilities_src.cu create mode 100644 src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_dk_hamiltonian_src.cu create mode 100644 src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_gc_marker_column_src.cu create mode 100644 src/struphy/pic/pushing/cuda/eval_kernels_sph_cuda/_sph_marker_column_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_general_geometry_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_cuboid_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_rk_periodic_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_v_efield_cuboid_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_random_diffusion_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_1st_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_2nd_order_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_newton_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bstar_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_kernels_sph_cuda/_sph_pusher_src.cu create mode 100644 src/struphy/pic/pushing/cuda/pusher_utilities_kernels_cuda/_reflect_src.cu diff --git a/pyproject.toml b/pyproject.toml index f3d5c8a64..cce558767 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -139,6 +139,7 @@ kinetic-diagnostics = "struphy.diagnostics.console_diagn:main" ] struphy = [ "compile_struphy.mk", + "**/*.cu", ] [tool.autopep8] diff --git a/src/struphy/cuda.py b/src/struphy/cuda.py new file mode 100644 index 000000000..7ee42c90f --- /dev/null +++ b/src/struphy/cuda.py @@ -0,0 +1,11 @@ +"""Helpers for loading CUDA C sources used by CuPy RawKernel wrappers.""" + +from functools import lru_cache +from pathlib import Path + + +@lru_cache(maxsize=None) +def load_cuda_source(module_file: str, source_name: str) -> str: + """Load a CUDA C source fragment stored alongside its Python wrapper.""" + path = Path(module_file).with_name("cuda") / source_name + return path.read_text(encoding="utf-8") diff --git a/src/struphy/feec/basis_projection_kernels_cuda.py b/src/struphy/feec/basis_projection_kernels_cuda.py index 9aebfb12b..a8970d894 100644 --- a/src/struphy/feec/basis_projection_kernels_cuda.py +++ b/src/struphy/feec/basis_projection_kernels_cuda.py @@ -1,46 +1,8 @@ """CUDA kernels for dynamic weighted basis-projection matrices.""" -_ASSEMBLE_SRC = r""" -extern "C" __global__ -void assemble_weighted_basis_3d_cuda( - const long long* row1,const long long* row2,const long long* row3, - const long long* span1,const long long* span2,const long long* span3, - const double* w1,const double* w2,const double* w3, - const double* b1,const double* b2,const double* b3,const double* fun, - const int ni1,const int ni2,const int ni3,const int nq1,const int nq2,const int nq3, - const int p1,const int p2,const int p3,const int so1,const int so2,const int so3, - const int pi1,const int pi2,const int pi3,const int po1,const int po2,const int po3, - const int dimi1,const int dimi2,const int dimi3,const int dimo1,const int dimo2,const int dimo3, - const int pout1,const int pout2,const int pout3,double* mat, - const int md2,const int md3,const int md4,const int md5,const int md6) -{ - long long tid=(long long)blockIdx.x*blockDim.x+threadIdx.x; - const long long nb=(long long)(p1+1)*(p2+1)*(p3+1); - const long long nq=(long long)nq1*nq2*nq3; - const long long total=(long long)ni1*ni2*ni3*nq*nb; - if(tid>=total)return; - long long t=tid; const long long bb=t%nb;t/=nb; const long long qq=t%nq;t/=nq; - const int kk=t%ni3;t/=ni3; const int jj=t%ni2;const int ii=t/ni2; - const int b3i=bb%(p3+1),b2i=(bb/(p3+1))%(p2+1),b1i=bb/((p2+1)*(p3+1)); - const int q3=qq%nq3,q2=(qq/nq3)%nq2,q1=qq/(nq2*nq3); - const int i=(int)row1[ii],j=(int)row2[jj],k=(int)row3[kk]; - int m=(int)span1[ii*nq1+q1]-p1+b1i; - int n=(int)span2[jj*nq2+q2]-p2+b2i; - int o=(int)span3[kk*nq3+q3]-p3+b3i; - const int cut1=dimo1<=dimi1?p1:pout1,cut2=dimo2<=dimi2?p2:pout2,cut3=dimo3<=dimi3?p3:pout3; - int d=m-(i+so1);if(d>cut1)m-=dimi1;else if(d<-cut1)m+=dimi1; - d=n-(j+so2);if(d>cut2)n-=dimi2;else if(d<-cut2)n+=dimi2; - d=o-(k+so3);if(d>cut3)o-=dimi3;else if(d<-cut3)o+=dimi3; - const int c1=pi1+m-(i+so1),c2=pi2+n-(j+so2),c3=pi3+o-(k+so3); - const long long fi=((long long)(ii*nq1+q1)*(ni2*nq2)+jj*nq2+q2)*(ni3*nq3)+kk*nq3+q3; - const double value=fun[fi]*w1[ii*nq1+q1]*w2[jj*nq2+q2]*w3[kk*nq3+q3] - *b1[((long long)ii*nq1+q1)*(p1+1)+b1i] - *b2[((long long)jj*nq2+q2)*(p2+1)+b2i] - *b3[((long long)kk*nq3+q3)*(p3+1)+b3i]; - const long long mi=(((((long long)(po1+i)*md2+(po2+j))*md3+(po3+k))*md4+c1)*md5+c2)*md6+c3; - atomicAdd(&mat[mi],value); -} -""" +from struphy.cuda import load_cuda_source + +_ASSEMBLE_SRC = load_cuda_source(__file__, "basis_projection_kernels_cuda/_assemble_src.cu") _kernel = None diff --git a/src/struphy/feec/cuda/basis_projection_kernels_cuda/_assemble_src.cu b/src/struphy/feec/cuda/basis_projection_kernels_cuda/_assemble_src.cu new file mode 100644 index 000000000..29ce05734 --- /dev/null +++ b/src/struphy/feec/cuda/basis_projection_kernels_cuda/_assemble_src.cu @@ -0,0 +1,40 @@ +extern "C" __global__ +void assemble_weighted_basis_3d_cuda( + const long long* row1,const long long* row2,const long long* row3, + const long long* span1,const long long* span2,const long long* span3, + const double* w1,const double* w2,const double* w3, + const double* b1,const double* b2,const double* b3,const double* fun, + const int ni1,const int ni2,const int ni3,const int nq1,const int nq2,const int nq3, + const int p1,const int p2,const int p3,const int so1,const int so2,const int so3, + const int pi1,const int pi2,const int pi3,const int po1,const int po2,const int po3, + const int dimi1,const int dimi2,const int dimi3,const int dimo1,const int dimo2,const int dimo3, + const int pout1,const int pout2,const int pout3,double* mat, + const int md2,const int md3,const int md4,const int md5,const int md6) +{ + long long tid=(long long)blockIdx.x*blockDim.x+threadIdx.x; + const long long nb=(long long)(p1+1)*(p2+1)*(p3+1); + const long long nq=(long long)nq1*nq2*nq3; + const long long total=(long long)ni1*ni2*ni3*nq*nb; + if(tid>=total)return; + long long t=tid; const long long bb=t%nb;t/=nb; const long long qq=t%nq;t/=nq; + const int kk=t%ni3;t/=ni3; const int jj=t%ni2;const int ii=t/ni2; + const int b3i=bb%(p3+1),b2i=(bb/(p3+1))%(p2+1),b1i=bb/((p2+1)*(p3+1)); + const int q3=qq%nq3,q2=(qq/nq3)%nq2,q1=qq/(nq2*nq3); + const int i=(int)row1[ii],j=(int)row2[jj],k=(int)row3[kk]; + int m=(int)span1[ii*nq1+q1]-p1+b1i; + int n=(int)span2[jj*nq2+q2]-p2+b2i; + int o=(int)span3[kk*nq3+q3]-p3+b3i; + const int cut1=dimo1<=dimi1?p1:pout1,cut2=dimo2<=dimi2?p2:pout2,cut3=dimo3<=dimi3?p3:pout3; + int d=m-(i+so1);if(d>cut1)m-=dimi1;else if(d<-cut1)m+=dimi1; + d=n-(j+so2);if(d>cut2)n-=dimi2;else if(d<-cut2)n+=dimi2; + d=o-(k+so3);if(d>cut3)o-=dimi3;else if(d<-cut3)o+=dimi3; + const int c1=pi1+m-(i+so1),c2=pi2+n-(j+so2),c3=pi3+o-(k+so3); + const long long fi=((long long)(ii*nq1+q1)*(ni2*nq2)+jj*nq2+q2)*(ni3*nq3)+kk*nq3+q3; + const double value=fun[fi]*w1[ii*nq1+q1]*w2[jj*nq2+q2]*w3[kk*nq3+q3] + *b1[((long long)ii*nq1+q1)*(p1+1)+b1i] + *b2[((long long)jj*nq2+q2)*(p2+1)+b2i] + *b3[((long long)kk*nq3+q3)*(p3+1)+b3i]; + const long long mi=(((((long long)(po1+i)*md2+(po2+j))*md3+(po3+k))*md4+c1)*md5+c2)*md6+c3; + atomicAdd(&mat[mi],value); +} + diff --git a/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu b/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu new file mode 100644 index 000000000..4e16c9502 --- /dev/null +++ b/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu @@ -0,0 +1,44 @@ +extern "C" __global__ +void h1vec_divdiv_assemble_cuda( + const long long* spans1, const long long* spans2, const long long* spans3, + const int ne1, const int ne2, const int ne3, const int p1, const int p2, const int p3, + const int starts1, const int starts2, const int starts3, const int pads1, const int pads2, const int pads3, + const double* b1, const double* b2, const double* b3, const int nder1, const int nder2, const int nder3, + const int nq1, const int nq2, const int nq3, const double* weighted_rho, + const int component_test, const int component_trial, double* data, + const int nd2, const int nd3, const int nd4, const int nd5, const int nd6) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long nloc = (long long)(p1 + 1) * (p2 + 1) * (p3 + 1); + const long long total = (long long)ne1 * ne2 * ne3 * nloc * nloc; + if (tid >= total) return; + long long t = tid; + const long long j = t % nloc; t /= nloc; + const long long i = t % nloc; t /= nloc; + const int iel3 = t % ne3; t /= ne3; + const int iel2 = t % ne2; const int iel1 = t / ne2; + const int il3 = i % (p3 + 1), il2 = (i / (p3 + 1)) % (p2 + 1), il1 = i / ((p2 + 1) * (p3 + 1)); + const int jl3 = j % (p3 + 1), jl2 = (j / (p3 + 1)) % (p2 + 1), jl1 = j / ((p2 + 1) * (p3 + 1)); + const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; + const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; + const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; + const int o1 = pads1 + jl1 - il1, o2 = pads2 + jl2 - il2, o3 = pads3 + jl3 - il3; + const int toi1 = component_test == 0, toi2 = component_test == 1, toi3 = component_test == 2; + const int tro1 = component_trial == 0, tro2 = component_trial == 1, tro3 = component_trial == 2; + double value = 0.0; + for (int q1 = 0; q1 < nq1; ++q1) for (int q2 = 0; q2 < nq2; ++q2) for (int q3 = 0; q3 < nq3; ++q3) { + const long long b1i = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; + const long long b2i = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; + const long long b3i = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; + const long long b1j = ((long long)(iel1 * (p1 + 1) + jl1) * nder1) * nq1 + q1; + const long long b2j = ((long long)(iel2 * (p2 + 1) + jl2) * nder2) * nq2 + q2; + const long long b3j = ((long long)(iel3 * (p3 + 1) + jl3) * nder3) * nq3 + q3; + const double di = b1[b1i + toi1 * nq1] * b2[b2i + toi2 * nq2] * b3[b3i + toi3 * nq3]; + const double dj = b1[b1j + tro1 * nq1] * b2[b2j + tro2 * nq2] * b3[b3j + tro3 * nq3]; + const long long qidx = ((long long)(iel1 * nq1 + q1) * (ne2 * nq2) + iel2 * nq2 + q2) * (ne3 * nq3) + iel3 * nq3 + q3; + value += weighted_rho[qidx] * di * dj; + } + const long long didx = (((((long long)c1 * nd2 + c2) * nd3 + c3) * nd4 + o1) * nd5 + o2) * nd6 + o3; + atomicAdd(&data[didx], value); +} + diff --git a/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divergence_src.cu b/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divergence_src.cu new file mode 100644 index 000000000..e3fc07078 --- /dev/null +++ b/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divergence_src.cu @@ -0,0 +1,111 @@ +extern "C" __global__ +void h1vec_divergence_eval_cuda( + const long long* spans1, const long long* spans2, const long long* spans3, + const int ne1, const int ne2, const int ne3, + const int p1, const int p2, const int p3, + const int starts1, const int starts2, const int starts3, + const int pads1, const int pads2, const int pads3, + const double* b1, const double* b2, const double* b3, + const int nder1, const int nder2, const int nder3, + const int nq1, const int nq2, const int nq3, + const double* dlogj1, const double* dlogj2, const double* dlogj3, + const int component, const double* coeffs, + const int nc2, const int nc3, double* values) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long totalq3 = (long long)ne3 * nq3; + const long long totalq2 = (long long)ne2 * nq2; + const long long nvalues = (long long)ne1 * nq1 * totalq2 * totalq3; + if (tid >= nvalues) return; + + const int iq3 = tid % totalq3; + const long long t12 = tid / totalq3; + const int iq2 = t12 % totalq2; + const int iq1 = t12 / totalq2; + const int iel1 = iq1 / nq1, q1 = iq1 % nq1; + const int iel2 = iq2 / nq2, q2 = iq2 % nq2; + const int iel3 = iq3 / nq3, q3 = iq3 % nq3; + const double dlog = component == 0 ? dlogj1[tid] : + (component == 1 ? dlogj2[tid] : dlogj3[tid]); + double value = 0.0; + + for (int il1 = 0; il1 <= p1; ++il1) { + const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; + const long long b1base = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; + const double n1 = b1[b1base]; + const double d1 = b1[b1base + nq1]; + for (int il2 = 0; il2 <= p2; ++il2) { + const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; + const long long b2base = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; + const double n2 = b2[b2base]; + const double d2 = b2[b2base + nq2]; + for (int il3 = 0; il3 <= p3; ++il3) { + const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; + const long long b3base = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; + const double n3 = b3[b3base]; + const double d3 = b3[b3base + nq3]; + const double basis = n1 * n2 * n3; + const double derivative = component == 0 ? d1 * n2 * n3 : + (component == 1 ? n1 * d2 * n3 : n1 * n2 * d3); + value += coeffs[((long long)c1 * nc2 + c2) * nc3 + c3] * (derivative + dlog * basis); + } + } + } + values[tid] += value; +} + +extern "C" __global__ +void h1vec_divergence_transpose_cuda( + const long long* spans1, const long long* spans2, const long long* spans3, + const int ne1, const int ne2, const int ne3, + const int p1, const int p2, const int p3, + const int starts1, const int starts2, const int starts3, + const int pads1, const int pads2, const int pads3, + const double* b1, const double* b2, const double* b3, + const int nder1, const int nder2, const int nder3, + const int nq1, const int nq2, const int nq3, + const double* dlogj1, const double* dlogj2, const double* dlogj3, + const int component, const double* values, + const int nc2, const int nc3, double* coeffs) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long totalq3 = (long long)ne3 * nq3; + const long long totalq2 = (long long)ne2 * nq2; + const long long nvalues = (long long)ne1 * nq1 * totalq2 * totalq3; + if (tid >= nvalues) return; + + const int iq3 = tid % totalq3; + const long long t12 = tid / totalq3; + const int iq2 = t12 % totalq2; + const int iq1 = t12 / totalq2; + const int iel1 = iq1 / nq1, q1 = iq1 % nq1; + const int iel2 = iq2 / nq2, q2 = iq2 % nq2; + const int iel3 = iq3 / nq3, q3 = iq3 % nq3; + const double dlog = component == 0 ? dlogj1[tid] : + (component == 1 ? dlogj2[tid] : dlogj3[tid]); + const double qvalue = values[tid]; + + for (int il1 = 0; il1 <= p1; ++il1) { + const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; + const long long b1base = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; + const double n1 = b1[b1base]; + const double d1 = b1[b1base + nq1]; + for (int il2 = 0; il2 <= p2; ++il2) { + const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; + const long long b2base = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; + const double n2 = b2[b2base]; + const double d2 = b2[b2base + nq2]; + for (int il3 = 0; il3 <= p3; ++il3) { + const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; + const long long b3base = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; + const double n3 = b3[b3base]; + const double d3 = b3[b3base + nq3]; + const double basis = n1 * n2 * n3; + const double derivative = component == 0 ? d1 * n2 * n3 : + (component == 1 ? n1 * d2 * n3 : n1 * n2 * d3); + atomicAdd(&coeffs[((long long)c1 * nc2 + c2) * nc3 + c3], qvalue * (derivative + dlog * basis)); + } + } + } +} + diff --git a/src/struphy/feec/cuda/mass_kernels_cuda/_mass_assembly_src.cu b/src/struphy/feec/cuda/mass_kernels_cuda/_mass_assembly_src.cu new file mode 100644 index 000000000..87a00fad9 --- /dev/null +++ b/src/struphy/feec/cuda/mass_kernels_cuda/_mass_assembly_src.cu @@ -0,0 +1,48 @@ +extern "C" __global__ +void mass_3d_assemble_cuda( + const long long* spans1, const long long* spans2, const long long* spans3, + const int ne1, const int ne2, const int ne3, + const int pi1, const int pi2, const int pi3, const int pj1, const int pj2, const int pj3, + const int starts1, const int starts2, const int starts3, const int pads1, const int pads2, const int pads3, + const double* w1, const double* w2, const double* w3, const int nq1, const int nq2, const int nq3, + const double* bi1, const double* bi2, const double* bi3, const double* bj1, const double* bj2, const double* bj3, + const int ni_der1, const int ni_der2, const int ni_der3, const int nj_der1, const int nj_der2, const int nj_der3, + const double* mat_fun, double* data, const int nd2, const int nd3, const int nd4, const int nd5, const int nd6) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long ni = (long long)(pi1 + 1) * (pi2 + 1) * (pi3 + 1); + const long long nj = (long long)(pj1 + 1) * (pj2 + 1) * (pj3 + 1); + const long long total = (long long)ne1 * ne2 * ne3 * ni * nj; + if (tid >= total) return; + long long t = tid; + const long long j = t % nj; t /= nj; + const long long i = t % ni; t /= ni; + const int iel3 = t % ne3; t /= ne3; + const int iel2 = t % ne2; const int iel1 = t / ne2; + const int il3 = i % (pi3 + 1); const int il2 = (i / (pi3 + 1)) % (pi2 + 1); const int il1 = i / ((pi2 + 1) * (pi3 + 1)); + const int jl3 = j % (pj3 + 1); const int jl2 = (j / (pj3 + 1)) % (pj2 + 1); const int jl1 = j / ((pj2 + 1) * (pj3 + 1)); + const int c1 = pads1 + (int)spans1[iel1] - pi1 + il1 - starts1; + const int c2 = pads2 + (int)spans2[iel2] - pi2 + il2 - starts2; + const int c3 = pads3 + (int)spans3[iel3] - pi3 + il3 - starts3; + const int o1 = pads1 + jl1 - il1, o2 = pads2 + jl2 - il2, o3 = pads3 + jl3 - il3; + double value = 0.0; + for (int q1 = 0; q1 < nq1; ++q1) { + const double wi1 = w1[iel1 * nq1 + q1]; + const double ai1 = bi1[((long long)(iel1 * (pi1 + 1) + il1) * ni_der1) * nq1 + q1]; + const double aj1 = bj1[((long long)(iel1 * (pj1 + 1) + jl1) * nj_der1) * nq1 + q1]; + for (int q2 = 0; q2 < nq2; ++q2) { + const double wi2 = wi1 * w2[iel2 * nq2 + q2]; + const double ai2 = ai1 * bi2[((long long)(iel2 * (pi2 + 1) + il2) * ni_der2) * nq2 + q2]; + const double aj2 = aj1 * bj2[((long long)(iel2 * (pj2 + 1) + jl2) * nj_der2) * nq2 + q2]; + for (int q3 = 0; q3 < nq3; ++q3) { + const long long qidx = ((long long)(iel1 * nq1 + q1) * (ne2 * nq2) + iel2 * nq2 + q2) * (ne3 * nq3) + iel3 * nq3 + q3; + const double ai3 = ai2 * bi3[((long long)(iel3 * (pi3 + 1) + il3) * ni_der3) * nq3 + q3]; + const double aj3 = aj2 * bj3[((long long)(iel3 * (pj3 + 1) + jl3) * nj_der3) * nq3 + q3]; + value += wi2 * w3[iel3 * nq3 + q3] * mat_fun[qidx] * ai3 * aj3; + } + } + } + const long long didx = (((((long long)c1 * nd2 + c2) * nd3 + c3) * nd4 + o1) * nd5 + o2) * nd6 + o3; + atomicAdd(&data[didx], value); +} + diff --git a/src/struphy/feec/cuda/mass_kernels_cuda/_weak_div_assembly_src.cu b/src/struphy/feec/cuda/mass_kernels_cuda/_weak_div_assembly_src.cu new file mode 100644 index 000000000..511f7e26b --- /dev/null +++ b/src/struphy/feec/cuda/mass_kernels_cuda/_weak_div_assembly_src.cu @@ -0,0 +1,60 @@ +extern "C" __global__ +void weak_div_assemble_cuda( + const long long* s1, const long long* s2, const long long* s3, + const int ne1, const int ne2, const int ne3, + const int pi1, const int pi2, const int pi3, + const int pj1, const int pj2, const int pj3, + const int st1, const int st2, const int st3, + const int pad1, const int pad2, const int pad3, + const double* w1, const double* w2, const double* w3, + const int nq1, const int nq2, const int nq3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + const int ndi1, const int ndi2, const int ndi3, + const int ndj1, const int ndj2, const int ndj3, + const double* weight, const double* dl1, const double* dl2, const double* dl3, + const int component, double* data, + const int dd2, const int dd3, const int dd4, const int dd5, const int dd6) +{ + const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; + const long long ni = (long long)(pi1+1)*(pi2+1)*(pi3+1); + const long long nj = (long long)(pj1+1)*(pj2+1)*(pj3+1); + const long long total = (long long)ne1*ne2*ne3*ni*nj; + if (tid >= total) return; + long long t=tid; + const long long j=t%nj; t/=nj; + const long long i=t%ni; t/=ni; + const int e3=t%ne3; t/=ne3; + const int e2=t%ne2; const int e1=t/ne2; + const int il3=i%(pi3+1), il2=(i/(pi3+1))%(pi2+1), il1=i/((pi2+1)*(pi3+1)); + const int jl3=j%(pj3+1), jl2=(j/(pj3+1))%(pj2+1), jl1=j/((pj2+1)*(pj3+1)); + const int c1=pad1+(int)s1[e1]-pi1+il1-st1; + const int c2=pad2+(int)s2[e2]-pi2+il2-st2; + const int c3=pad3+(int)s3[e3]-pi3+il3-st3; + const int o1=pad1+jl1-il1, o2=pad2+jl2-il2, o3=pad3+jl3-il3; + double value=0.0; + for(int q1=0;q1= total) return; + + long long t = tid; + const int i2 = t % n2; t /= n2; + const int i1 = t % n1; const int i0 = t / n1; + double us[3] = {0.0, 0.0, 0.0}; + double vs[3] = {0.0, 0.0, 0.0}; + + for (int l0 = 0; l0 <= p0; ++l0) { + const int c0 = (int)span0[i0] + l0 - start0; + const double b0 = basis0[(long long)i0 * (p0 + 1) + l0]; + for (int l1 = 0; l1 <= p1; ++l1) { + const int c1 = (int)span1[i1] + l1 - start1; + const double b01 = b0 * basis1[(long long)i1 * (p1 + 1) + l1]; + for (int l2 = 0; l2 <= p2; ++l2) { + const int c2 = (int)span2[i2] + l2 - start2; + const double weight = b01 * basis2[(long long)i2 * (p2 + 1) + l2]; + const long long ci = ((long long)c0 * nc1 + c1) * nc2 + c2; + us[0] += u0[ci] * weight; us[1] += u1[ci] * weight; us[2] += u2[ci] * weight; + vs[0] += v0[ci] * weight; vs[1] += v1[ci] * weight; vs[2] += v2[ci] * weight; + } + } + } + double value = 0.0; + for (int i = 0; i < 3; ++i) + for (int j = 0; j < 3; ++j) + value += us[i] * metric[((long long)i * 3 + j) * total + tid] * vs[j]; + out[tid] = 0.5 * value; + ug0[tid] = us[0]; ug1[tid] = us[1]; ug2[tid] = us[2]; + vg0[tid] = vs[0]; vg1[tid] = vs[1]; vg2[tid] = vs[2]; +} diff --git a/src/struphy/feec/mass_kernels_cuda.py b/src/struphy/feec/mass_kernels_cuda.py index f3a069320..f572c683d 100644 --- a/src/struphy/feec/mass_kernels_cuda.py +++ b/src/struphy/feec/mass_kernels_cuda.py @@ -5,284 +5,22 @@ the routines below keep both the quadrature data and coefficient vectors on the device. """ +from struphy.cuda import load_cuda_source -_H1VEC_DIVERGENCE_SRC = r""" -extern "C" __global__ -void h1vec_divergence_eval_cuda( - const long long* spans1, const long long* spans2, const long long* spans3, - const int ne1, const int ne2, const int ne3, - const int p1, const int p2, const int p3, - const int starts1, const int starts2, const int starts3, - const int pads1, const int pads2, const int pads3, - const double* b1, const double* b2, const double* b3, - const int nder1, const int nder2, const int nder3, - const int nq1, const int nq2, const int nq3, - const double* dlogj1, const double* dlogj2, const double* dlogj3, - const int component, const double* coeffs, - const int nc2, const int nc3, double* values) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long totalq3 = (long long)ne3 * nq3; - const long long totalq2 = (long long)ne2 * nq2; - const long long nvalues = (long long)ne1 * nq1 * totalq2 * totalq3; - if (tid >= nvalues) return; - - const int iq3 = tid % totalq3; - const long long t12 = tid / totalq3; - const int iq2 = t12 % totalq2; - const int iq1 = t12 / totalq2; - const int iel1 = iq1 / nq1, q1 = iq1 % nq1; - const int iel2 = iq2 / nq2, q2 = iq2 % nq2; - const int iel3 = iq3 / nq3, q3 = iq3 % nq3; - const double dlog = component == 0 ? dlogj1[tid] : - (component == 1 ? dlogj2[tid] : dlogj3[tid]); - double value = 0.0; - - for (int il1 = 0; il1 <= p1; ++il1) { - const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; - const long long b1base = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; - const double n1 = b1[b1base]; - const double d1 = b1[b1base + nq1]; - for (int il2 = 0; il2 <= p2; ++il2) { - const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; - const long long b2base = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; - const double n2 = b2[b2base]; - const double d2 = b2[b2base + nq2]; - for (int il3 = 0; il3 <= p3; ++il3) { - const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; - const long long b3base = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; - const double n3 = b3[b3base]; - const double d3 = b3[b3base + nq3]; - const double basis = n1 * n2 * n3; - const double derivative = component == 0 ? d1 * n2 * n3 : - (component == 1 ? n1 * d2 * n3 : n1 * n2 * d3); - value += coeffs[((long long)c1 * nc2 + c2) * nc3 + c3] * (derivative + dlog * basis); - } - } - } - values[tid] += value; -} - -extern "C" __global__ -void h1vec_divergence_transpose_cuda( - const long long* spans1, const long long* spans2, const long long* spans3, - const int ne1, const int ne2, const int ne3, - const int p1, const int p2, const int p3, - const int starts1, const int starts2, const int starts3, - const int pads1, const int pads2, const int pads3, - const double* b1, const double* b2, const double* b3, - const int nder1, const int nder2, const int nder3, - const int nq1, const int nq2, const int nq3, - const double* dlogj1, const double* dlogj2, const double* dlogj3, - const int component, const double* values, - const int nc2, const int nc3, double* coeffs) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long totalq3 = (long long)ne3 * nq3; - const long long totalq2 = (long long)ne2 * nq2; - const long long nvalues = (long long)ne1 * nq1 * totalq2 * totalq3; - if (tid >= nvalues) return; - - const int iq3 = tid % totalq3; - const long long t12 = tid / totalq3; - const int iq2 = t12 % totalq2; - const int iq1 = t12 / totalq2; - const int iel1 = iq1 / nq1, q1 = iq1 % nq1; - const int iel2 = iq2 / nq2, q2 = iq2 % nq2; - const int iel3 = iq3 / nq3, q3 = iq3 % nq3; - const double dlog = component == 0 ? dlogj1[tid] : - (component == 1 ? dlogj2[tid] : dlogj3[tid]); - const double qvalue = values[tid]; - - for (int il1 = 0; il1 <= p1; ++il1) { - const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; - const long long b1base = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; - const double n1 = b1[b1base]; - const double d1 = b1[b1base + nq1]; - for (int il2 = 0; il2 <= p2; ++il2) { - const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; - const long long b2base = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; - const double n2 = b2[b2base]; - const double d2 = b2[b2base + nq2]; - for (int il3 = 0; il3 <= p3; ++il3) { - const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; - const long long b3base = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; - const double n3 = b3[b3base]; - const double d3 = b3[b3base + nq3]; - const double basis = n1 * n2 * n3; - const double derivative = component == 0 ? d1 * n2 * n3 : - (component == 1 ? n1 * d2 * n3 : n1 * n2 * d3); - atomicAdd(&coeffs[((long long)c1 * nc2 + c2) * nc3 + c3], qvalue * (derivative + dlog * basis)); - } - } - } -} -""" +_H1VEC_DIVERGENCE_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_h1vec_divergence_src.cu") _divergence_eval_kernel = None _divergence_transpose_kernel = None -_MASS_ASSEMBLY_SRC = r""" -extern "C" __global__ -void mass_3d_assemble_cuda( - const long long* spans1, const long long* spans2, const long long* spans3, - const int ne1, const int ne2, const int ne3, - const int pi1, const int pi2, const int pi3, const int pj1, const int pj2, const int pj3, - const int starts1, const int starts2, const int starts3, const int pads1, const int pads2, const int pads3, - const double* w1, const double* w2, const double* w3, const int nq1, const int nq2, const int nq3, - const double* bi1, const double* bi2, const double* bi3, const double* bj1, const double* bj2, const double* bj3, - const int ni_der1, const int ni_der2, const int ni_der3, const int nj_der1, const int nj_der2, const int nj_der3, - const double* mat_fun, double* data, const int nd2, const int nd3, const int nd4, const int nd5, const int nd6) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long ni = (long long)(pi1 + 1) * (pi2 + 1) * (pi3 + 1); - const long long nj = (long long)(pj1 + 1) * (pj2 + 1) * (pj3 + 1); - const long long total = (long long)ne1 * ne2 * ne3 * ni * nj; - if (tid >= total) return; - long long t = tid; - const long long j = t % nj; t /= nj; - const long long i = t % ni; t /= ni; - const int iel3 = t % ne3; t /= ne3; - const int iel2 = t % ne2; const int iel1 = t / ne2; - const int il3 = i % (pi3 + 1); const int il2 = (i / (pi3 + 1)) % (pi2 + 1); const int il1 = i / ((pi2 + 1) * (pi3 + 1)); - const int jl3 = j % (pj3 + 1); const int jl2 = (j / (pj3 + 1)) % (pj2 + 1); const int jl1 = j / ((pj2 + 1) * (pj3 + 1)); - const int c1 = pads1 + (int)spans1[iel1] - pi1 + il1 - starts1; - const int c2 = pads2 + (int)spans2[iel2] - pi2 + il2 - starts2; - const int c3 = pads3 + (int)spans3[iel3] - pi3 + il3 - starts3; - const int o1 = pads1 + jl1 - il1, o2 = pads2 + jl2 - il2, o3 = pads3 + jl3 - il3; - double value = 0.0; - for (int q1 = 0; q1 < nq1; ++q1) { - const double wi1 = w1[iel1 * nq1 + q1]; - const double ai1 = bi1[((long long)(iel1 * (pi1 + 1) + il1) * ni_der1) * nq1 + q1]; - const double aj1 = bj1[((long long)(iel1 * (pj1 + 1) + jl1) * nj_der1) * nq1 + q1]; - for (int q2 = 0; q2 < nq2; ++q2) { - const double wi2 = wi1 * w2[iel2 * nq2 + q2]; - const double ai2 = ai1 * bi2[((long long)(iel2 * (pi2 + 1) + il2) * ni_der2) * nq2 + q2]; - const double aj2 = aj1 * bj2[((long long)(iel2 * (pj2 + 1) + jl2) * nj_der2) * nq2 + q2]; - for (int q3 = 0; q3 < nq3; ++q3) { - const long long qidx = ((long long)(iel1 * nq1 + q1) * (ne2 * nq2) + iel2 * nq2 + q2) * (ne3 * nq3) + iel3 * nq3 + q3; - const double ai3 = ai2 * bi3[((long long)(iel3 * (pi3 + 1) + il3) * ni_der3) * nq3 + q3]; - const double aj3 = aj2 * bj3[((long long)(iel3 * (pj3 + 1) + jl3) * nj_der3) * nq3 + q3]; - value += wi2 * w3[iel3 * nq3 + q3] * mat_fun[qidx] * ai3 * aj3; - } - } - } - const long long didx = (((((long long)c1 * nd2 + c2) * nd3 + c3) * nd4 + o1) * nd5 + o2) * nd6 + o3; - atomicAdd(&data[didx], value); -} -""" +_MASS_ASSEMBLY_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_mass_assembly_src.cu") _mass_assembly_kernel = None -_WEAK_DIV_ASSEMBLY_SRC = r""" -extern "C" __global__ -void weak_div_assemble_cuda( - const long long* s1, const long long* s2, const long long* s3, - const int ne1, const int ne2, const int ne3, - const int pi1, const int pi2, const int pi3, - const int pj1, const int pj2, const int pj3, - const int st1, const int st2, const int st3, - const int pad1, const int pad2, const int pad3, - const double* w1, const double* w2, const double* w3, - const int nq1, const int nq2, const int nq3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - const int ndi1, const int ndi2, const int ndi3, - const int ndj1, const int ndj2, const int ndj3, - const double* weight, const double* dl1, const double* dl2, const double* dl3, - const int component, double* data, - const int dd2, const int dd3, const int dd4, const int dd5, const int dd6) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long ni = (long long)(pi1+1)*(pi2+1)*(pi3+1); - const long long nj = (long long)(pj1+1)*(pj2+1)*(pj3+1); - const long long total = (long long)ne1*ne2*ne3*ni*nj; - if (tid >= total) return; - long long t=tid; - const long long j=t%nj; t/=nj; - const long long i=t%ni; t/=ni; - const int e3=t%ne3; t/=ne3; - const int e2=t%ne2; const int e1=t/ne2; - const int il3=i%(pi3+1), il2=(i/(pi3+1))%(pi2+1), il1=i/((pi2+1)*(pi3+1)); - const int jl3=j%(pj3+1), jl2=(j/(pj3+1))%(pj2+1), jl1=j/((pj2+1)*(pj3+1)); - const int c1=pad1+(int)s1[e1]-pi1+il1-st1; - const int c2=pad2+(int)s2[e2]-pi2+il2-st2; - const int c3=pad3+(int)s3[e3]-pi3+il3-st3; - const int o1=pad1+jl1-il1, o2=pad2+jl2-il2, o3=pad3+jl3-il3; - double value=0.0; - for(int q1=0;q1= total) return; - long long t = tid; - const long long j = t % nloc; t /= nloc; - const long long i = t % nloc; t /= nloc; - const int iel3 = t % ne3; t /= ne3; - const int iel2 = t % ne2; const int iel1 = t / ne2; - const int il3 = i % (p3 + 1), il2 = (i / (p3 + 1)) % (p2 + 1), il1 = i / ((p2 + 1) * (p3 + 1)); - const int jl3 = j % (p3 + 1), jl2 = (j / (p3 + 1)) % (p2 + 1), jl1 = j / ((p2 + 1) * (p3 + 1)); - const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; - const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; - const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; - const int o1 = pads1 + jl1 - il1, o2 = pads2 + jl2 - il2, o3 = pads3 + jl3 - il3; - const int toi1 = component_test == 0, toi2 = component_test == 1, toi3 = component_test == 2; - const int tro1 = component_trial == 0, tro2 = component_trial == 1, tro3 = component_trial == 2; - double value = 0.0; - for (int q1 = 0; q1 < nq1; ++q1) for (int q2 = 0; q2 < nq2; ++q2) for (int q3 = 0; q3 < nq3; ++q3) { - const long long b1i = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; - const long long b2i = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; - const long long b3i = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; - const long long b1j = ((long long)(iel1 * (p1 + 1) + jl1) * nder1) * nq1 + q1; - const long long b2j = ((long long)(iel2 * (p2 + 1) + jl2) * nder2) * nq2 + q2; - const long long b3j = ((long long)(iel3 * (p3 + 1) + jl3) * nder3) * nq3 + q3; - const double di = b1[b1i + toi1 * nq1] * b2[b2i + toi2 * nq2] * b3[b3i + toi3 * nq3]; - const double dj = b1[b1j + tro1 * nq1] * b2[b2j + tro2 * nq2] * b3[b3j + tro3 * nq3]; - const long long qidx = ((long long)(iel1 * nq1 + q1) * (ne2 * nq2) + iel2 * nq2 + q2) * (ne3 * nq3) + iel3 * nq3 + q3; - value += weighted_rho[qidx] * di * dj; - } - const long long didx = (((((long long)c1 * nd2 + c2) * nd3 + c3) * nd4 + o1) * nd5 + o2) * nd6 + o3; - atomicAdd(&data[didx], value); -} -""" +_H1VEC_DIVDIV_ASSEMBLY_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu") _h1vec_divdiv_assembly_kernel = None diff --git a/src/struphy/feec/variational_kernels_cuda.py b/src/struphy/feec/variational_kernels_cuda.py index 9e0f9ae05..41b018638 100644 --- a/src/struphy/feec/variational_kernels_cuda.py +++ b/src/struphy/feec/variational_kernels_cuda.py @@ -1,62 +1,10 @@ """CUDA kernels for fused variational grid evaluations.""" -_KINETIC_ENERGY_KERNEL = None - -_KINETIC_ENERGY_SOURCE = r""" -extern "C" __global__ -void kinetic_energy_grid_cuda( - const long long* span0, const long long* span1, const long long* span2, - const double* basis0, const double* basis1, const double* basis2, - const int n0, const int n1, const int n2, - const int p0, const int p1, const int p2, - const int start0, const int start1, const int start2, - const double* u0, const double* u1, const double* u2, - const double* v0, const double* v1, const double* v2, - const int nc1, const int nc2, - const double* metric, double* out, - double* ug0, double* ug1, double* ug2, - double* vg0, double* vg1, double* vg2) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long total = (long long)n0 * n1 * n2; - if (tid >= total) return; +from struphy.cuda import load_cuda_source - long long t = tid; - const int i2 = t % n2; t /= n2; - const int i1 = t % n1; const int i0 = t / n1; - double us[3] = {0.0, 0.0, 0.0}; - double vs[3] = {0.0, 0.0, 0.0}; - - for (int l0 = 0; l0 <= p0; ++l0) { - const int c0 = (int)span0[i0] + l0 - start0; - const double b0 = basis0[(long long)i0 * (p0 + 1) + l0]; - for (int l1 = 0; l1 <= p1; ++l1) { - const int c1 = (int)span1[i1] + l1 - start1; - const double b01 = b0 * basis1[(long long)i1 * (p1 + 1) + l1]; - for (int l2 = 0; l2 <= p2; ++l2) { - const int c2 = (int)span2[i2] + l2 - start2; - const double weight = b01 * basis2[(long long)i2 * (p2 + 1) + l2]; - const long long ci = ((long long)c0 * nc1 + c1) * nc2 + c2; - us[0] += u0[ci] * weight; - us[1] += u1[ci] * weight; - us[2] += u2[ci] * weight; - vs[0] += v0[ci] * weight; - vs[1] += v1[ci] * weight; - vs[2] += v2[ci] * weight; - } - } - } +_KINETIC_ENERGY_KERNEL = None - double value = 0.0; - // metric is stored as (3, 3, n0, n1, n2). - for (int i = 0; i < 3; ++i) - for (int j = 0; j < 3; ++j) - value += us[i] * metric[((long long)i * 3 + j) * total + tid] * vs[j]; - out[tid] = 0.5 * value; - ug0[tid] = us[0]; ug1[tid] = us[1]; ug2[tid] = us[2]; - vg0[tid] = vs[0]; vg1[tid] = vs[1]; vg2[tid] = vs[2]; -} -""" +_KINETIC_ENERGY_SOURCE = load_cuda_source(__file__, "variational_kernels_cuda/kinetic_energy_grid.cu") def prepare_kinetic_energy_kernel(): diff --git a/src/struphy/pic/accumulation/accum_kernels_cuda.py b/src/struphy/pic/accumulation/accum_kernels_cuda.py index 638ec6d99..fa45593ce 100644 --- a/src/struphy/pic/accumulation/accum_kernels_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_cuda.py @@ -23,96 +23,9 @@ just the marker weight), so it reuses only the B-spline evaluation device functions, not the geometry-mapping ones. """ +from struphy.cuda import load_cuda_source -_CHARGE_DENSITY_0FORM_SRC = r""" -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -// Only the N-spline values (bn) are needed for an H^1/0-form fill; D-spline -// values are computed alongside (same recursion as -// pusher_kernels_cuda.py's b_d_splines_dev) and simply unused. -__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) -{ - double left[MAXP]; - double right[MAXP]; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -extern "C" __global__ -void charge_density_0form_cuda( - const double* markers, - const int n_cols, - const int n_markers, - const int weight_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - double* vec, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double filling = row[weight_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bn1[il1] * filling; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bn2[il2]; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bn3[il3]; - atomicAdd(&vec[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3], b3); - } - } - } -} -""" +_CHARGE_DENSITY_0FORM_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_charge_density_0form_src.cu") _charge_density_0form_kernel = None @@ -197,194 +110,7 @@ def charge_density_0form_gpu( # fill_mat_vec_dev/fill_mat_dev below are direct ports of those two. # --------------------------------------------------------------------------- -_LINEAR_VLASOV_AMPERE_EXTRA_SRC = r""" -__device__ void outer_dev(const double* a, const double* b, double* c) -{ - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) - c[3*i+j] = a[i] * b[j]; -} - -// Port of filler_kernels.fill_mat_vec: fills one matrix block (banded -// storage, j = pad + jl - il) and, along the shared (i1,i2,i3) row loop, -// also fills the corresponding vector block. -__device__ void fill_mat_vec_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat, int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double* vec, int vn2, int vn3, - double filling_vec) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - - atomicAdd(&vec[(size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3], b3 * filling_vec); - - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat[idx], b6); - } - } - } - } - } - } -} - -// Port of filler_kernels.fill_mat: matrix-only block fill (off-diagonal -// blocks, no associated vector). -__device__ void fill_mat_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat, int d2, int d3, int d4, int d5, int d6, - double filling_mat) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1] * filling_mat; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1]; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat[idx], b6); - } - } - } - } - } - } -} - -extern "C" __global__ -void linear_vlasov_ampere_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const double* f0_values, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - double* mat11, double* mat12, double* mat13, - double* mat22, double* mat23, double* mat33, - double* vec1, double* vec2, double* vec3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, - const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, - const int v2_n2, const int v2_n3, - const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - const double weight = row[6]; - const double s0 = row[7]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - double df_inv[9], df_inv_v[3]; - matrix_inv_dev(dfm, df_inv); - matvec_dev(df_inv, v, df_inv_v); - - double filling_m[9]; - outer_dev(df_inv_v, df_inv_v, filling_m); - const double fm_scale = f0_values[ip] / s0; - for (int k = 0; k < 9; k++) filling_m[k] *= fm_scale; - - double filling_v[3]; - filling_v[0] = weight * df_inv_v[0]; - filling_v[1] = weight * df_inv_v[1]; - filling_v[2] = weight * df_inv_v[2]; - - const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; - const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, - vec1, v1_n2,v1_n3, filling_v[0]); - - fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, - vec2, v2_n2,v2_n3, filling_v[1]); - - fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, - vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); -} -""" +_LINEAR_VLASOV_AMPERE_EXTRA_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu") def _linear_vlasov_ampere_source(): @@ -404,104 +130,7 @@ def _linear_vlasov_ampere_source(): # markers[ip,-1]==-2.0 check), unlike linear_vlasov_ampere -- ported as-is. # --------------------------------------------------------------------------- -_VLASOV_MAXWELL_EXTRA_SRC = r""" -extern "C" __global__ -void vlasov_maxwell_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - double* mat11, double* mat12, double* mat13, - double* mat22, double* mat23, double* mat33, - double* vec1, double* vec2, double* vec3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, - const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, - const int v2_n2, const int v2_n3, - const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - const double weight = row[6]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - double df_inv[9], df_inv_v[3]; - matrix_inv_dev(dfm, df_inv); - matvec_dev(df_inv, v, df_inv_v); - - // g_inv = DF^-1 @ DF^-T ; g_inv[i,j] = sum_k df_inv[i,k]*df_inv[j,k] - double filling_m[9]; - for (int i = 0; i < 3; i++) { - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; - filling_m[3*i+j] = weight * s; - } - } - - double filling_v[3]; - filling_v[0] = weight * df_inv_v[0]; - filling_v[1] = weight * df_inv_v[1]; - filling_v[2] = weight * df_inv_v[2]; - - const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; - const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, - vec1, v1_n2,v1_n3, filling_v[0]); - - fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, - vec2, v2_n2,v2_n3, filling_v[1]); - - fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, - vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); -} -""" +_VLASOV_MAXWELL_EXTRA_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_vlasov_maxwell_extra_src.cu") def _vlasov_maxwell_source(): @@ -735,125 +364,7 @@ def dims(a): # df_dispatch_dev is only called inside the basis_u==1/2 branches here. # --------------------------------------------------------------------------- -_CC_LIN_MHD_6D_1_SRC = r""" -extern "C" __global__ -void cc_lin_mhd_6d_1_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int b2_1_n2, const int b2_1_n3, - const double* b2_2, const int b2_2_n2, const int b2_2_n3, - const double* b2_3, const int b2_3_n2, const int b2_3_n3, - const int basis_u, const double scale_mat, const double boundary_cut, - double* mat12, double* mat13, double* mat23, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double weight = row[6]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - b2_1, b2_1_n2, b2_1_n3, b2_2, b2_2_n2, b2_2_n3, b2_3, b2_3_n2, b2_3_n3, b); - - // b_prod = bx() as a row-major 3x3 matrix - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - double fill12, fill13, fill23; - - if (basis_u == 0) { - fill12 = -weight * b_prod[1] * scale_mat; - fill13 = -weight * b_prod[2] * scale_mat; - fill23 = -weight * b_prod[5] * scale_mat; - - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - - } else if (basis_u == 1) { - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - double df_inv[9], g_inv[9]; - matrix_inv_dev(dfm, df_inv); - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; - g_inv[3*i+j] = s; - } - double tmp1[9], tmp2[9]; - matmat_dev(g_inv, b_prod, tmp1); - matmat_dev(tmp1, g_inv, tmp2); - - fill12 = -weight * tmp2[1] * scale_mat; - fill13 = -weight * tmp2[2] * scale_mat; - fill23 = -weight * tmp2[5] * scale_mat; - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - - } else if (basis_u == 2) { - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - const double det2 = det_df * det_df; - - fill12 = -weight * b_prod[1] * scale_mat / det2; - fill13 = -weight * b_prod[2] * scale_mat / det2; - fill23 = -weight * b_prod[5] * scale_mat / det2; - - // Hdiv component shapes: comp1 = N-D-D, comp2 = D-N-D, comp3 = D-D-N - fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - } -} -""" +_CC_LIN_MHD_6D_1_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu") def _cc_lin_mhd_6d_1_source(): @@ -976,179 +487,7 @@ def dims(a): # not replicated here); only basis_u=1 needs the full g_inv = DF^-1 DF^-T. # --------------------------------------------------------------------------- -_CC_LIN_MHD_6D_2_SRC = r""" -extern "C" __global__ -void cc_lin_mhd_6d_2_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int b2_1_n2, const int b2_1_n3, - const double* b2_2, const int b2_2_n2, const int b2_2_n3, - const double* b2_3, const int b2_3_n2, const int b2_3_n3, - const int basis_u, const double scale_mat, const double scale_vec, const double boundary_cut, - double* mat11, double* mat12, double* mat13, - double* mat22, double* mat23, double* mat33, - double* vec1, double* vec2, double* vec3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, - const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, - const int v2_n2, const int v2_n3, - const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - const double weight = row[6]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - b2_1, b2_1_n2, b2_1_n3, b2_2, b2_2_n2, b2_2_n3, b2_3, b2_3_n2, b2_3_n3, b); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - double df_inv[9]; - matrix_inv_dev(dfm, df_inv); - - double tmp1[9], tmp_m[9], tmp_v[3]; - - if (basis_u == 1) { - double g_inv[9]; - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; - g_inv[3*i+j] = s; - } - double tmp0[9]; - matmat_dev(g_inv, b_prod, tmp0); - matmat_dev(tmp0, df_inv, tmp1); - } else { - // basis_u == 0 or 2: tmp1 = b_prod @ df_inv (g_inv computed but - // unused in the CPU reference for these two branches) - matmat_dev(b_prod, df_inv, tmp1); - } - - // tmp_m = tmp1 @ tmp1^T ; tmp_v = tmp1 @ v - for (int i = 0; i < 3; i++) { - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += tmp1[3*i+k] * tmp1[3*j+k]; - tmp_m[3*i+j] = s; - } - } - matvec_dev(tmp1, v, tmp_v); - - double mat_scale = weight * scale_mat; - double vec_scale = weight * scale_vec; - if (basis_u == 2) { - mat_scale /= det_df * det_df; - vec_scale /= det_df; - } - - double filling_m[9], filling_v[3]; - for (int k = 0; k < 9; k++) filling_m[k] = tmp_m[k] * mat_scale; - for (int k = 0; k < 3; k++) filling_v[k] = tmp_v[k] * vec_scale; - - const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; - const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - if (basis_u == 0) { - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - - } else if (basis_u == 1) { - fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); - fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); - fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - - } else if (basis_u == 2) { - // Hdiv component shapes: comp1 = N-D-D, comp2 = D-N-D, comp3 = D-D-N - fill_mat_vec_dev(p1,pd2,pd3, p1,pd2,pd3, bn1,bd2,bd3, bn1,bd2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); - fill_mat_vec_dev(pd1,p2,pd3, pd1,p2,pd3, bd1,bn2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); - fill_mat_vec_dev(pd1,pd2,p3, pd1,pd2,p3, bd1,bd2,bn3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - } -} -""" +_CC_LIN_MHD_6D_2_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu") def _cc_lin_mhd_6d_2_source(): @@ -1311,364 +650,9 @@ def dims(a): # --------------------------------------------------------------------------- _SPATIAL_BLOCKS = ("11", "12", "13", "22", "23", "33") -# (row_degrees_index, col_degrees_index) as 0/1/2 picking from (p, pd) per -# axis -- i.e. which of bn/bd (and p/pd) each spatial block's row/col use in -# each of the 3 axes. 0 = N-spline/degree p, 1 = D-spline/degree p-1. -_SPATIAL_BASIS = { - "11": ((1, 0, 0), (1, 0, 0)), - "22": ((0, 1, 0), (0, 1, 0)), - "33": ((0, 0, 1), (0, 0, 1)), - "12": ((1, 0, 0), (0, 1, 0)), - "13": ((1, 0, 0), (0, 0, 1)), - "23": ((0, 1, 0), (0, 0, 1)), -} -_DIAG_SPATIAL_BLOCKS = ("11", "22", "33") - -_PC_PRESSURE_FILLERS_SRC = r""" -// Port of filler_kernels.fill_mat_vec_pressure_full: like fill_mat_vec_dev -// but scatters into 6 matrix blocks (scaled by vx*vx, vx*vy, vx*vz, vy*vy, -// vy*vz, vz*vz) and 3 vector blocks (scaled by vx, vy, vz) in one pass. -__device__ void fill_mat_vec_pressure_full_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat_11, double* mat_12, double* mat_13, double* mat_22, double* mat_23, double* mat_33, - int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double* vec_1, double* vec_2, double* vec_3, int vn2, int vn3, - double filling_vec, - double vx, double vy, double vz) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - size_t vidx = (size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3; - double bv = b3 * filling_vec; - atomicAdd(&vec_1[vidx], bv * vx); - atomicAdd(&vec_2[vidx], bv * vy); - atomicAdd(&vec_3[vidx], bv * vz); - - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat_11[idx], b6 * vx * vx); - atomicAdd(&mat_12[idx], b6 * vx * vy); - atomicAdd(&mat_13[idx], b6 * vx * vz); - atomicAdd(&mat_22[idx], b6 * vy * vy); - atomicAdd(&mat_23[idx], b6 * vy * vz); - atomicAdd(&mat_33[idx], b6 * vz * vz); - } - } - } - } - } - } -} - -// Port of filler_kernels.fill_mat_pressure_full: same as above minus the -// vector part (off-diagonal spatial blocks have no associated vector). -__device__ void fill_mat_pressure_full_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat_11, double* mat_12, double* mat_13, double* mat_22, double* mat_23, double* mat_33, - int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double vx, double vy, double vz) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat_11[idx], b6 * vx * vx); - atomicAdd(&mat_12[idx], b6 * vx * vy); - atomicAdd(&mat_13[idx], b6 * vx * vz); - atomicAdd(&mat_22[idx], b6 * vy * vy); - atomicAdd(&mat_23[idx], b6 * vy * vz); - atomicAdd(&mat_33[idx], b6 * vz * vz); - } - } - } - } - } - } -} - -// Port of filler_kernels.fill_mat_vec_pressure: the "perp" (xy-plane only) -// variant -- 3 matrix blocks (vx*vx, vx*vy, vy*vy) and 2 vector blocks -// (vx, vy). -__device__ void fill_mat_vec_pressure_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat_11, double* mat_12, double* mat_22, - int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double* vec_1, double* vec_2, int vn2, int vn3, - double filling_vec, - double vx, double vy) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - size_t vidx = (size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3; - double bv = b3 * filling_vec; - atomicAdd(&vec_1[vidx], bv * vx); - atomicAdd(&vec_2[vidx], bv * vy); - - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat_11[idx], b6 * vx * vx); - atomicAdd(&mat_12[idx], b6 * vx * vy); - atomicAdd(&mat_22[idx], b6 * vy * vy); - } - } - } - } - } - } -} - -// Port of filler_kernels.fill_mat_pressure: "perp" matrix-only variant. -__device__ void fill_mat_pressure_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat_11, double* mat_12, double* mat_22, - int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double vx, double vy) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat_11[idx], b6 * vx * vx); - atomicAdd(&mat_12[idx], b6 * vx * vy); - atomicAdd(&mat_22[idx], b6 * vy * vy); - } - } - } - } - } - } -} -""" - - -def _basis_args(row_or_col): - """row_or_col is a 3-tuple of 0/1 (0=N-spline/degree p, 1=D-spline/degree p-1) - for (axis1, axis2, axis3). Returns (degree_expr_list, basis_expr_list).""" - deg = [ - "p1" if row_or_col[0] == 0 else "pd1", - "p2" if row_or_col[1] == 0 else "pd2", - "p3" if row_or_col[2] == 0 else "pd3", - ] - bas = [("bn1", "bd1")[row_or_col[0]], ("bn2", "bd2")[row_or_col[1]], ("bn3", "bd3")[row_or_col[2]]] - return deg, bas - - -def _build_pc_lin_mhd_6d_kernel_src(full: bool) -> str: - vel_pairs = _SPATIAL_BLOCKS if full else ("11", "12", "22") - vec_is = ("1", "2", "3") if full else ("1", "2") - kernel_name = "pc_lin_mhd_6d_full_cuda" if full else "pc_lin_mhd_6d_cuda" - weight_col = 8 if full else 6 - - mat_params = ", ".join(f"double* mat{sp}_{vel}" for vel in vel_pairs for sp in _SPATIAL_BLOCKS) - vec_params = ", ".join(f"double* vec{mu}_{i}" for i in vec_is for mu in ("1", "2", "3")) - mat_dim_params = ", ".join( - f"const int m{sp}_d2, const int m{sp}_d3, const int m{sp}_d4, const int m{sp}_d5, const int m{sp}_d6" - for sp in _SPATIAL_BLOCKS - ) - vec_dim_params = ", ".join(f"const int v{mu}_n2, const int v{mu}_n3" for mu in ("1", "2", "3")) - - lines = [] - lines.append(f'extern "C" __global__\nvoid {kernel_name}(') - lines.append(" const double* markers, const int n_cols, const int n_markers,") - lines.append(" const int kind_map, const double* params,") - lines.append(" const int p1, const int p2, const int p3,") - lines.append(" const double* tn1, const int len_tn1,") - lines.append(" const double* tn2, const int len_tn2,") - lines.append(" const double* tn3, const int len_tn3,") - lines.append(" const int start0, const int start1, const int start2,") - lines.append(" const double ep_scale,") - lines.append(f" {mat_params},") - lines.append(f" {vec_params},") - lines.append(f" {mat_dim_params},") - lines.append(f" {vec_dim_params})") - lines.append("{") - lines.append(" int ip = blockIdx.x * blockDim.x + threadIdx.x;") - lines.append(" if (ip >= n_markers) return;") - lines.append("") - lines.append(" const double* row = markers + (size_t)ip * n_cols;") - lines.append(" if (row[0] == -1.0) return;") - lines.append("") - lines.append(" const double eta1 = row[0], eta2 = row[1], eta3 = row[2];") - lines.append(" const double v[3] = {row[3], row[4], row[5]};") - lines.append(f" const double weight = row[{weight_col}];") - lines.append("") - lines.append(" double dfm[9];") - lines.append(" if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return;") - lines.append(" double df_inv[9];") - lines.append(" matrix_inv_dev(dfm, df_inv);") - lines.append(" double g_inv[9];") - lines.append(" for (int i = 0; i < 3; i++)") - lines.append(" for (int j = 0; j < 3; j++) {") - lines.append(" double s = 0.0;") - lines.append(" for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k];") - lines.append(" g_inv[3*i+j] = s;") - lines.append(" }") - lines.append(" double tmp_v[3];") - lines.append(" matvec_dev(df_inv, v, tmp_v);") - lines.append("") - # fill11..fill33 are per-SPATIAL-block (mu,nu) scalars (weight*g_inv[mu,nu]* - # ep_scale) -- needed by every spatial block's filler call regardless of - # ``full``, since even the "perp" (non-full) variant fills all 6 spatial - # blocks, just with fewer velocity-pairs per block. Only fill3/vz (the - # z-velocity-component scalar, used solely by the vector fill) is - # full-only. - lines.append(" const double fill11 = weight * g_inv[0] * ep_scale;") - lines.append(" const double fill12 = weight * g_inv[1] * ep_scale;") - lines.append(" const double fill13 = weight * g_inv[2] * ep_scale;") - lines.append(" const double fill22 = weight * g_inv[4] * ep_scale;") - lines.append(" const double fill23 = weight * g_inv[5] * ep_scale;") - lines.append(" const double fill33 = weight * g_inv[8] * ep_scale;") - # fill1/fill2/fill3 are per-DIAG-SPATIAL-BLOCK (mu=1/2/3) vector filling - # scalars (weight*tmp_v[mu-1]*ep_scale, tmp_v = DF^-1 @ v) -- needed by - # spatial block 33's diagonal fill regardless of ``full`` too, just like - # fill11..fill33 above. Only ``vz`` (the raw marker velocity's own - # z-component, used as a multiplier for the 3rd velocity-pair/-component - # outputs) is full-only. - lines.append(" const double fill1 = weight * tmp_v[0] * ep_scale;") - lines.append(" const double fill2 = weight * tmp_v[1] * ep_scale;") - lines.append(" const double fill3 = weight * tmp_v[2] * ep_scale;") - lines.append(" const double vx = v[0], vy = v[1]" + (", vz = v[2];" if full else ";")) - lines.append("") - lines.append(" const int span1 = find_span_dev(tn1, p1, len_tn1, eta1);") - lines.append(" const int span2 = find_span_dev(tn2, p2, len_tn2, eta2);") - lines.append(" const int span3 = find_span_dev(tn3, p3, len_tn3, eta3);") - lines.append(" double bn1[MAXP+1], bd1[MAXP];") - lines.append(" double bn2[MAXP+1], bd2[MAXP];") - lines.append(" double bn3[MAXP+1], bd3[MAXP];") - lines.append(" b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1);") - lines.append(" b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2);") - lines.append(" b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3);") - lines.append(" const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1;") - lines.append("") - - for sp in _SPATIAL_BLOCKS: - row, col = _SPATIAL_BASIS[sp] - row_deg, row_bas = _basis_args(row) - col_deg, col_bas = _basis_args(col) - mat_out = ", ".join(f"mat{sp}_{vel}" for vel in vel_pairs) - dims = f"m{sp}_d2,m{sp}_d3,m{sp}_d4,m{sp}_d5,m{sp}_d6" - fillmat = {"11": "fill11", "22": "fill22", "33": "fill33", "12": "fill12", "13": "fill13", "23": "fill23"}[sp] - common = ( - f"{row_deg[0]},{row_deg[1]},{row_deg[2]}, {col_deg[0]},{col_deg[1]},{col_deg[2]}, " - f"{row_bas[0]},{row_bas[1]},{row_bas[2]}, {col_bas[0]},{col_bas[1]},{col_bas[2]}, " - f"span1,span2,span3, start0,start1,start2, p1,p2,p3" - ) - if sp in _DIAG_SPATIAL_BLOCKS: - mu = sp[0] - vec_out = ", ".join(f"vec{mu}_{i}" for i in vec_is) - vdims = f"v{mu}_n2,v{mu}_n3" - fillvec = {"11": "fill1", "22": "fill2", "33": "fill3"}[sp] - if full: - lines.append( - f" fill_mat_vec_pressure_full_dev({common},\n" - f" {mat_out}, {dims}, {fillmat},\n" - f" {vec_out}, {vdims}, {fillvec}, vx,vy,vz);" - ) - else: - lines.append( - f" fill_mat_vec_pressure_dev({common},\n" - f" {mat_out}, {dims}, {fillmat},\n" - f" {vec_out}, {vdims}, {fillvec}, vx,vy);" - ) - else: - if full: - lines.append( - f" fill_mat_pressure_full_dev({common},\n {mat_out}, {dims}, {fillmat}, vx,vy,vz);" - ) - else: - lines.append(f" fill_mat_pressure_dev({common},\n {mat_out}, {dims}, {fillmat}, vx,vy);") - lines.append("") - - lines.append("}") - return "\n".join(lines) - - -_PC_LIN_MHD_6D_FULL_SRC = _build_pc_lin_mhd_6d_kernel_src(full=True) -_PC_LIN_MHD_6D_SRC = _build_pc_lin_mhd_6d_kernel_src(full=False) +_PC_PRESSURE_FILLERS_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_pc_pressure_fillers_src.cu") +_PC_LIN_MHD_6D_FULL_SRC = load_cuda_source(__file__, "accum_kernels_cuda/pc_lin_mhd_6d_full.cu") +_PC_LIN_MHD_6D_SRC = load_cuda_source(__file__, "accum_kernels_cuda/pc_lin_mhd_6d.cu") def _pc_lin_mhd_6d_full_source(): diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index 030797962..d814ea506 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -11,96 +11,9 @@ just with a ``mu * weight * scale`` filling instead of a plain weight, and ``mu`` read from the marker's ``mu_idx`` column instead of a fixed offset. """ +from struphy.cuda import load_cuda_source -_GC_MAG_DENSITY_0FORM_SRC = r""" -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) -{ - double left[MAXP]; - double right[MAXP]; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -extern "C" __global__ -void gc_mag_density_0form_cuda( - const double* markers, - const int n_cols, - const int n_markers, - const int mu_idx, - const double scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - double* vec, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double weight = row[5]; - const double mu = row[mu_idx]; - const double filling = mu * weight * scale; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bn1[il1] * filling; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bn2[il2]; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bn3[il3]; - atomicAdd(&vec[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3], b3); - } - } - } -} -""" +_GC_MAG_DENSITY_0FORM_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu") _gc_mag_density_0form_kernel = None @@ -199,138 +112,7 @@ def gc_density_0form_gpu(markers, weight_idx, pn, tn1_dev, tn2_dev, tn3_dev, sta # _LINEAR_VLASOV_AMPERE_EXTRA_SRC unchanged. # --------------------------------------------------------------------------- -_CC_LIN_MHD_5D_D_SRC = r""" -extern "C" __global__ -void cc_lin_mhd_5d_D_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const double epsilon, const double ep_scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int bb1_n2, const int bb1_n3, - const double* b2_2, const int bb2_n2, const int bb2_n3, - const double* b2_3, const int bb3_n2, const int bb3_n3, - const double* nb11, const int nb1_n2, const int nb1_n3, - const double* nb12, const int nb2_n2, const int nb2_n3, - const double* nb13, const int nb3_n2, const int nb3_n3, - const double* cnb1, const int cb1_n2, const int cb1_n3, - const double* cnb2, const int cb2_n2, const int cb2_n3, - const double* cnb3, const int cb3_n2, const int cb3_n3, - const int basis_u, - double* mat12, double* mat13, double* mat23, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - const double weight = row[5]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b2_1,bb1_n2,bb1_n3, b2_2,bb2_n2,bb2_n3, b2_3,bb3_n2,bb3_n3, b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb11,nb1_n2,nb1_n3, nb12,nb2_n2,nb2_n3, nb13,nb3_n2,nb3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,cb1_n2,cb1_n3, cnb2,cb2_n2,cb2_n3, cnb3,cb3_n2,cb3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = b[k] + epsilon * v * curl_norm_b[k]; - - const double b_para = dot3_dev(norm_b1, b); - const double b_star_para = dot3_dev(norm_b1, b_star); - const double density_const = 1.0 - b_para / b_star_para; - - const double pref = -weight * density_const * ep_scale / epsilon; - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - double f12, f13, f23; - - if (basis_u == 0) { - f12 = pref * b_prod[1]; - f13 = pref * b_prod[2]; - f23 = pref * b_prod[5]; - - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - - } else if (basis_u == 1) { - double df_inv[9], g_inv[9]; - matrix_inv_dev(dfm, df_inv); - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) { - double sacc = 0.0; - for (int k = 0; k < 3; k++) sacc += df_inv[3*i+k] * df_inv[3*j+k]; - g_inv[3*i+j] = sacc; - } - double tmp1[9], tmp2[9]; - matmat_dev(g_inv, b_prod, tmp1); - matmat_dev(tmp1, g_inv, tmp2); - - f12 = pref * tmp2[1]; - f13 = pref * tmp2[2]; - f23 = pref * tmp2[5]; - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - - } else if (basis_u == 2) { - const double det2 = det_df * det_df; - f12 = pref * b_prod[1] / det2; - f13 = pref * b_prod[2] / det2; - f23 = pref * b_prod[5] / det2; - - fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - } -} -""" +_CC_LIN_MHD_5D_D_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu") _cc_lin_mhd_5d_D_kernel = None @@ -448,143 +230,9 @@ def dims(a): # Port of filler_kernels.fill_vec; shared by all vector-filling accumulators # in this module. -_FILL_VEC_SRC = r""" -__device__ void fill_vec_dev( - int pi1, int pi2, int pi3, - const double* bi1, const double* bi2, const double* bi3, - int span1, int span2, int span3, - int start0, int start1, int start2, - double* vec, int vn2, int vn3, - double filling) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1] * filling; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - atomicAdd(&vec[(size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3], b3); - } - } - } -} -""" +_FILL_VEC_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_fill_vec_src.cu") -_CC_LIN_MHD_5D_GRADB_SRC = r""" -extern "C" __global__ -void cc_lin_mhd_5d_gradB_cuda( - const double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, const double ep_scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* gpb1, const int g1_n2, const int g1_n3, - const double* gpb2, const int g2_n2, const int g2_n3, - const double* gpb3, const int g3_n2, const int g3_n3, - const double* gpq1, const int q1_n2, const int q1_n3, - const double* gpq2, const int q2_n2, const int q2_n3, - const double* gpq3, const int q3_n2, const int q3_n3, - const int basis_u, - double* vec1, const int v1_n2, const int v1_n3, - double* vec2, const int v2_n2, const int v2_n3, - double* vec3, const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double weight = row[5]; - const double v = row[3]; - const double mu = row[mu_idx]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], norm_b1[3], curl_norm_b[3], grad_PB[3], grad_PBeq[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gpb1,g1_n2,g1_n3, gpb2,g2_n2,g2_n3, gpb3,g3_n2,g3_n3, grad_PB); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gpq1,q1_n2,q1_n3, gpq2,q2_n2,q2_n3, gpq3,q3_n2,q3_n3, grad_PBeq); - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - double tmp[9], tmp_v[3], fv[3]; - - if (basis_u == 0) { - matmat_dev(b_prod, norm_b_prod, tmp); - matvec_dev(tmp, grad_PB, tmp_v); - for (int k = 0; k < 3; k++) fv[k] = weight * tmp_v[k] * mu / abs_b_star_para * ep_scale; - - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec1, v1_n2,v1_n3, fv[0]); - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec2, v2_n2,v2_n3, fv[1]); - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec3, v3_n2,v3_n3, fv[2]); - - } else if (basis_u == 2) { - for (int k = 0; k < 3; k++) grad_PB[k] += grad_PBeq[k]; - matmat_dev(b_prod, norm_b_prod, tmp); - matvec_dev(tmp, grad_PB, tmp_v); - for (int k = 0; k < 3; k++) - fv[k] = weight * tmp_v[k] * mu / abs_b_star_para / det_df * ep_scale; - - // Hdiv components: N-D-D, D-N-D, D-D-N - fill_vec_dev(p1,pd2,pd3, bn1,bd2,bd3, span1,span2,span3, start0,start1,start2, - vec1, v1_n2,v1_n3, fv[0]); - fill_vec_dev(pd1,p2,pd3, bd1,bn2,bd3, span1,span2,span3, start0,start1,start2, - vec2, v2_n2,v2_n3, fv[1]); - fill_vec_dev(pd1,pd2,p3, bd1,bd2,bn3, span1,span2,span3, start0,start1,start2, - vec3, v3_n2,v3_n3, fv[2]); - } -} -""" +_CC_LIN_MHD_5D_GRADB_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu") _cc_gradB_kernel = None @@ -709,151 +357,7 @@ def d(a): # fixed (per-marker) semantics. # --------------------------------------------------------------------------- -_CC_LIN_MHD_5D_CURLB_SRC = r""" -extern "C" __global__ -void cc_lin_mhd_5d_curlb_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const double epsilon, const double ep_scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const int basis_u, - double* mat11, double* mat12, double* mat13, - double* mat22, double* mat23, double* mat33, - double* vec1, double* vec2, double* vec3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, - const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, - const int v2_n2, const int v2_n3, - const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double weight = row[5]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double bfull_star[3]; - for (int k = 0; k < 3; k++) bfull_star[k] = b[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, bfull_star); - - // tmp = curl_norm_b (x) curl_norm_b - double tmp[9]; - outer_dev(curl_norm_b, curl_norm_b, tmp); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - double b_prod_neg[9]; - for (int k = 0; k < 9; k++) b_prod_neg[k] = -b_prod[k]; - - double tmp1[9], tmp_m[9], tmp_v[3]; - matmat_dev(b_prod, tmp, tmp1); - matmat_dev(tmp1, b_prod_neg, tmp_m); - matvec_dev(b_prod, curl_norm_b, tmp_v); - - double fm[9], fv[3]; - if (basis_u == 0) { - const double sm = weight * v * v / (abs_b_star_para * abs_b_star_para) * ep_scale; - const double sv = weight * v * v / abs_b_star_para * ep_scale; - for (int k = 0; k < 9; k++) fm[k] = tmp_m[k] * sm; - for (int k = 0; k < 3; k++) fv[k] = tmp_v[k] * sv; - } else { - const double sm = weight * v * v / (abs_b_star_para * abs_b_star_para) - / (det_df * det_df) * ep_scale; - const double sv = weight * v * v / abs_b_star_para / det_df * ep_scale; - for (int k = 0; k < 9; k++) fm[k] = tmp_m[k] * sm; - for (int k = 0; k < 3; k++) fv[k] = tmp_v[k] * sv; - } - - const double f11 = fm[0], f12 = fm[1], f13 = fm[2]; - const double f22 = fm[4], f23 = fm[5], f33 = fm[8]; - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - if (basis_u == 0) { - // V0vec: every block N-N-N - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, f11, vec1, v1_n2,v1_n3, fv[0]); - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, f22, vec2, v2_n2,v2_n3, fv[1]); - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, f33, vec3, v3_n2,v3_n3, fv[2]); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - - } else if (basis_u == 2) { - // V2 (Hdiv): comp1 N-D-D, comp2 D-N-D, comp3 D-D-N - fill_mat_vec_dev(p1,pd2,pd3, p1,pd2,pd3, bn1,bd2,bd3, bn1,bd2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, f11, vec1, v1_n2,v1_n3, fv[0]); - fill_mat_vec_dev(pd1,p2,pd3, pd1,p2,pd3, bd1,bn2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, f22, vec2, v2_n2,v2_n3, fv[1]); - fill_mat_vec_dev(pd1,pd2,p3, pd1,pd2,p3, bd1,bd2,bn3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, f33, vec3, v3_n2,v3_n3, fv[2]); - fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - } -} -""" +_CC_LIN_MHD_5D_CURLB_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu") _cc_curlb_kernel = None @@ -997,151 +501,7 @@ def dims(a): # (times 1/det for basis_u=2). Only basis_u 0 and 2 exist here. # --------------------------------------------------------------------------- -_CC_LIN_MHD_5D_GRADB_DG_SRC = r""" -__device__ double dg_mod1_dev(double x) -{ - double r = fmod(x, 1.0); - if (r < 0.0) r += 1.0; - return r; -} - -extern "C" __global__ -void cc_lin_mhd_5d_gradB_dg_cuda( - const double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, const double ep_scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* beq_1, const int e1_n2, const int e1_n3, - const double* beq_2, const int e2_n2, const int e2_n3, - const double* beq_3, const int e3_n2, const int e3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* gpb1, const int g1_n2, const int g1_n3, - const double* gpb2, const int g2_n2, const int g2_n3, - const double* gpb3, const int g3_n2, const int g3_n3, - const double* gpq1, const int q1_n2, const int q1_n3, - const double* gpq2, const int q2_n2, const int q2_n3, - const double* gpq3, const int q3_n2, const int q3_n3, - const int basis_u, const double konst, const int is_dg, - double* vec1, const int v1_n2, const int v1_n3, - double* vec2, const int v2_n2, const int v2_n3, - double* vec3, const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3], eta_diff[3] = {0.0, 0.0, 0.0}; - if (is_dg) { - for (int k = 0; k < 3; k++) { - eta[k] = dg_mod1_dev((row[k] + row[first_init_idx + k]) / 2.0); - eta_diff[k] = row[k] - row[first_init_idx + k]; - } - } else { - for (int k = 0; k < 3; k++) eta[k] = row[k]; - } - - const double weight = row[5]; - const double v = row[3]; - const double mu = row[mu_idx]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta[0], eta[1], eta[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], beq[3], norm_b1[3], curl_norm_b[3], grad_PB[3], grad_PBeq[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - beq_1,e1_n2,e1_n3, beq_2,e2_n2,e2_n3, beq_3,e3_n2,e3_n3, beq); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gpb1,g1_n2,g1_n3, gpb2,g2_n2,g2_n3, gpb3,g3_n2,g3_n3, grad_PB); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gpq1,q1_n2,q1_n3, gpq2,q2_n2,q2_n3, gpq3,q3_n2,q3_n3, grad_PBeq); - - // NOTE: unlike cc_lin_mhd_5d_gradB, B* here includes the equilibrium field. - double bfull_star[3]; - for (int k = 0; k < 3; k++) bfull_star[k] = b[k] + beq[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, bfull_star); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - double beq_prod[9] = {0.0, -beq[2], beq[1], beq[2], 0.0, -beq[0], -beq[1], beq[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - // basis_u == 0 has no 1/det; basis_u == 2 carries one. - const double inv_det = (basis_u == 2) ? (1.0 / det_df) : 1.0; - const double w_fac = weight * mu / abs_b_star_para * inv_det * ep_scale; - const double d_fac = konst / abs_b_star_para * inv_det; - - double tmp[9], tmp_v[3], fv[3] = {0.0, 0.0, 0.0}; - - // the two field blocks, Beq first then B, each contributing - // grad_PBeq, grad_PB and (for `dg`) the eta_diff correction - for (int blk = 0; blk < 2; blk++) { - matmat_dev(blk == 0 ? beq_prod : b_prod, norm_b_prod, tmp); - - matvec_dev(tmp, grad_PBeq, tmp_v); - for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * w_fac; - - matvec_dev(tmp, grad_PB, tmp_v); - for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * w_fac; - - if (is_dg) { - matvec_dev(tmp, eta_diff, tmp_v); - for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * d_fac; - } - } - - if (basis_u == 0) { - // H1vec: N-N-N in all three components - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec1, v1_n2,v1_n3, fv[0]); - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec2, v2_n2,v2_n3, fv[1]); - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec3, v3_n2,v3_n3, fv[2]); - } else if (basis_u == 2) { - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - fill_vec_dev(p1,pd2,pd3, bn1,bd2,bd3, span1,span2,span3, start0,start1,start2, - vec1, v1_n2,v1_n3, fv[0]); - fill_vec_dev(pd1,p2,pd3, bd1,bn2,bd3, span1,span2,span3, start0,start1,start2, - vec2, v2_n2,v2_n3, fv[1]); - fill_vec_dev(pd1,pd2,p3, bd1,bd2,bn3, span1,span2,span3, start0,start1,start2, - vec3, v3_n2,v3_n3, fv[2]); - } -} -""" +_CC_LIN_MHD_5D_GRADB_DG_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu") _cc_gradB_dg_kernel = None diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu new file mode 100644 index 000000000..c84809bd1 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu @@ -0,0 +1,118 @@ +extern "C" __global__ +void cc_lin_mhd_6d_1_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int b2_1_n2, const int b2_1_n3, + const double* b2_2, const int b2_2_n2, const int b2_2_n3, + const double* b2_3, const int b2_3_n2, const int b2_3_n3, + const int basis_u, const double scale_mat, const double boundary_cut, + double* mat12, double* mat13, double* mat23, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double weight = row[6]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + b2_1, b2_1_n2, b2_1_n3, b2_2, b2_2_n2, b2_2_n3, b2_3, b2_3_n2, b2_3_n3, b); + + // b_prod = bx() as a row-major 3x3 matrix + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + double fill12, fill13, fill23; + + if (basis_u == 0) { + fill12 = -weight * b_prod[1] * scale_mat; + fill13 = -weight * b_prod[2] * scale_mat; + fill23 = -weight * b_prod[5] * scale_mat; + + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + + } else if (basis_u == 1) { + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + double df_inv[9], g_inv[9]; + matrix_inv_dev(dfm, df_inv); + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; + g_inv[3*i+j] = s; + } + double tmp1[9], tmp2[9]; + matmat_dev(g_inv, b_prod, tmp1); + matmat_dev(tmp1, g_inv, tmp2); + + fill12 = -weight * tmp2[1] * scale_mat; + fill13 = -weight * tmp2[2] * scale_mat; + fill23 = -weight * tmp2[5] * scale_mat; + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + + } else if (basis_u == 2) { + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + const double det2 = det_df * det_df; + + fill12 = -weight * b_prod[1] * scale_mat / det2; + fill13 = -weight * b_prod[2] * scale_mat / det2; + fill23 = -weight * b_prod[5] * scale_mat / det2; + + // Hdiv component shapes: comp1 = N-D-D, comp2 = D-N-D, comp3 = D-D-N + fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu new file mode 100644 index 000000000..65593c8ce --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu @@ -0,0 +1,172 @@ +extern "C" __global__ +void cc_lin_mhd_6d_2_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int b2_1_n2, const int b2_1_n3, + const double* b2_2, const int b2_2_n2, const int b2_2_n3, + const double* b2_3, const int b2_3_n2, const int b2_3_n3, + const int basis_u, const double scale_mat, const double scale_vec, const double boundary_cut, + double* mat11, double* mat12, double* mat13, + double* mat22, double* mat23, double* mat33, + double* vec1, double* vec2, double* vec3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, + const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, + const int v2_n2, const int v2_n3, + const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + const double weight = row[6]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + b2_1, b2_1_n2, b2_1_n3, b2_2, b2_2_n2, b2_2_n3, b2_3, b2_3_n2, b2_3_n3, b); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + double df_inv[9]; + matrix_inv_dev(dfm, df_inv); + + double tmp1[9], tmp_m[9], tmp_v[3]; + + if (basis_u == 1) { + double g_inv[9]; + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; + g_inv[3*i+j] = s; + } + double tmp0[9]; + matmat_dev(g_inv, b_prod, tmp0); + matmat_dev(tmp0, df_inv, tmp1); + } else { + // basis_u == 0 or 2: tmp1 = b_prod @ df_inv (g_inv computed but + // unused in the CPU reference for these two branches) + matmat_dev(b_prod, df_inv, tmp1); + } + + // tmp_m = tmp1 @ tmp1^T ; tmp_v = tmp1 @ v + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += tmp1[3*i+k] * tmp1[3*j+k]; + tmp_m[3*i+j] = s; + } + } + matvec_dev(tmp1, v, tmp_v); + + double mat_scale = weight * scale_mat; + double vec_scale = weight * scale_vec; + if (basis_u == 2) { + mat_scale /= det_df * det_df; + vec_scale /= det_df; + } + + double filling_m[9], filling_v[3]; + for (int k = 0; k < 9; k++) filling_m[k] = tmp_m[k] * mat_scale; + for (int k = 0; k < 3; k++) filling_v[k] = tmp_v[k] * vec_scale; + + const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; + const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + if (basis_u == 0) { + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + + } else if (basis_u == 1) { + fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); + fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); + fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + + } else if (basis_u == 2) { + // Hdiv component shapes: comp1 = N-D-D, comp2 = D-N-D, comp3 = D-D-N + fill_mat_vec_dev(p1,pd2,pd3, p1,pd2,pd3, bn1,bd2,bd3, bn1,bd2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); + fill_mat_vec_dev(pd1,p2,pd3, pd1,p2,pd3, bd1,bn2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); + fill_mat_vec_dev(pd1,pd2,p3, pd1,pd2,p3, bd1,bd2,bn3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_charge_density_0form_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_charge_density_0form_src.cu new file mode 100644 index 000000000..0a578638c --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_charge_density_0form_src.cu @@ -0,0 +1,88 @@ +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +// Only the N-spline values (bn) are needed for an H^1/0-form fill; D-spline +// values are computed alongside (same recursion as +// pusher_kernels_cuda.py's b_d_splines_dev) and simply unused. +__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) +{ + double left[MAXP]; + double right[MAXP]; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +extern "C" __global__ +void charge_density_0form_cuda( + const double* markers, + const int n_cols, + const int n_markers, + const int weight_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + double* vec, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double filling = row[weight_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bn1[il1] * filling; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bn2[il2]; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bn3[il3]; + atomicAdd(&vec[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3], b3); + } + } + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu new file mode 100644 index 000000000..fb3c5133f --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu @@ -0,0 +1,187 @@ +__device__ void outer_dev(const double* a, const double* b, double* c) +{ + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) + c[3*i+j] = a[i] * b[j]; +} + +// Port of filler_kernels.fill_mat_vec: fills one matrix block (banded +// storage, j = pad + jl - il) and, along the shared (i1,i2,i3) row loop, +// also fills the corresponding vector block. +__device__ void fill_mat_vec_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat, int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double* vec, int vn2, int vn3, + double filling_vec) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + + atomicAdd(&vec[(size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3], b3 * filling_vec); + + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat[idx], b6); + } + } + } + } + } + } +} + +// Port of filler_kernels.fill_mat: matrix-only block fill (off-diagonal +// blocks, no associated vector). +__device__ void fill_mat_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat, int d2, int d3, int d4, int d5, int d6, + double filling_mat) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1] * filling_mat; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1]; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat[idx], b6); + } + } + } + } + } + } +} + +extern "C" __global__ +void linear_vlasov_ampere_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const double* f0_values, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + double* mat11, double* mat12, double* mat13, + double* mat22, double* mat23, double* mat33, + double* vec1, double* vec2, double* vec3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, + const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, + const int v2_n2, const int v2_n3, + const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + const double weight = row[6]; + const double s0 = row[7]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + double df_inv[9], df_inv_v[3]; + matrix_inv_dev(dfm, df_inv); + matvec_dev(df_inv, v, df_inv_v); + + double filling_m[9]; + outer_dev(df_inv_v, df_inv_v, filling_m); + const double fm_scale = f0_values[ip] / s0; + for (int k = 0; k < 9; k++) filling_m[k] *= fm_scale; + + double filling_v[3]; + filling_v[0] = weight * df_inv_v[0]; + filling_v[1] = weight * df_inv_v[1]; + filling_v[2] = weight * df_inv_v[2]; + + const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; + const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, + vec1, v1_n2,v1_n3, filling_v[0]); + + fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, + vec2, v2_n2,v2_n3, filling_v[1]); + + fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, + vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_pc_pressure_fillers_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_pc_pressure_fillers_src.cu new file mode 100644 index 000000000..2943db3d8 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_pc_pressure_fillers_src.cu @@ -0,0 +1,198 @@ +// Port of filler_kernels.fill_mat_vec_pressure_full: like fill_mat_vec_dev +// but scatters into 6 matrix blocks (scaled by vx*vx, vx*vy, vx*vz, vy*vy, +// vy*vz, vz*vz) and 3 vector blocks (scaled by vx, vy, vz) in one pass. +__device__ void fill_mat_vec_pressure_full_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat_11, double* mat_12, double* mat_13, double* mat_22, double* mat_23, double* mat_33, + int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double* vec_1, double* vec_2, double* vec_3, int vn2, int vn3, + double filling_vec, + double vx, double vy, double vz) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + size_t vidx = (size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3; + double bv = b3 * filling_vec; + atomicAdd(&vec_1[vidx], bv * vx); + atomicAdd(&vec_2[vidx], bv * vy); + atomicAdd(&vec_3[vidx], bv * vz); + + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat_11[idx], b6 * vx * vx); + atomicAdd(&mat_12[idx], b6 * vx * vy); + atomicAdd(&mat_13[idx], b6 * vx * vz); + atomicAdd(&mat_22[idx], b6 * vy * vy); + atomicAdd(&mat_23[idx], b6 * vy * vz); + atomicAdd(&mat_33[idx], b6 * vz * vz); + } + } + } + } + } + } +} + +// Port of filler_kernels.fill_mat_pressure_full: same as above minus the +// vector part (off-diagonal spatial blocks have no associated vector). +__device__ void fill_mat_pressure_full_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat_11, double* mat_12, double* mat_13, double* mat_22, double* mat_23, double* mat_33, + int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double vx, double vy, double vz) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat_11[idx], b6 * vx * vx); + atomicAdd(&mat_12[idx], b6 * vx * vy); + atomicAdd(&mat_13[idx], b6 * vx * vz); + atomicAdd(&mat_22[idx], b6 * vy * vy); + atomicAdd(&mat_23[idx], b6 * vy * vz); + atomicAdd(&mat_33[idx], b6 * vz * vz); + } + } + } + } + } + } +} + +// Port of filler_kernels.fill_mat_vec_pressure: the "perp" (xy-plane only) +// variant -- 3 matrix blocks (vx*vx, vx*vy, vy*vy) and 2 vector blocks +// (vx, vy). +__device__ void fill_mat_vec_pressure_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat_11, double* mat_12, double* mat_22, + int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double* vec_1, double* vec_2, int vn2, int vn3, + double filling_vec, + double vx, double vy) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + size_t vidx = (size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3; + double bv = b3 * filling_vec; + atomicAdd(&vec_1[vidx], bv * vx); + atomicAdd(&vec_2[vidx], bv * vy); + + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat_11[idx], b6 * vx * vx); + atomicAdd(&mat_12[idx], b6 * vx * vy); + atomicAdd(&mat_22[idx], b6 * vy * vy); + } + } + } + } + } + } +} + +// Port of filler_kernels.fill_mat_pressure: "perp" matrix-only variant. +__device__ void fill_mat_pressure_dev( + int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, + const double* bi1, const double* bi2, const double* bi3, + const double* bj1, const double* bj2, const double* bj3, + int span1, int span2, int span3, + int start0, int start1, int start2, + int pad0, int pad1, int pad2, + double* mat_11, double* mat_12, double* mat_22, + int d2, int d3, int d4, int d5, int d6, + double filling_mat, + double vx, double vy) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1]; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + for (int jl1 = 0; jl1 <= pj1; jl1++) { + int j1 = pad0 + jl1 - il1; + double b4 = b3 * bj1[jl1] * filling_mat; + for (int jl2 = 0; jl2 <= pj2; jl2++) { + int j2 = pad1 + jl2 - il2; + double b5 = b4 * bj2[jl2]; + for (int jl3 = 0; jl3 <= pj3; jl3++) { + int j3 = pad2 + jl3 - il3; + double b6 = b5 * bj3[jl3]; + size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; + atomicAdd(&mat_11[idx], b6 * vx * vx); + atomicAdd(&mat_12[idx], b6 * vx * vy); + atomicAdd(&mat_22[idx], b6 * vy * vy); + } + } + } + } + } + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_vlasov_maxwell_extra_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_vlasov_maxwell_extra_src.cu new file mode 100644 index 000000000..41fc2f777 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_vlasov_maxwell_extra_src.cu @@ -0,0 +1,97 @@ +extern "C" __global__ +void vlasov_maxwell_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + double* mat11, double* mat12, double* mat13, + double* mat22, double* mat23, double* mat33, + double* vec1, double* vec2, double* vec3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, + const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, + const int v2_n2, const int v2_n3, + const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + const double weight = row[6]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + double df_inv[9], df_inv_v[3]; + matrix_inv_dev(dfm, df_inv); + matvec_dev(df_inv, v, df_inv_v); + + // g_inv = DF^-1 @ DF^-T ; g_inv[i,j] = sum_k df_inv[i,k]*df_inv[j,k] + double filling_m[9]; + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; + filling_m[3*i+j] = weight * s; + } + } + + double filling_v[3]; + filling_v[0] = weight * df_inv_v[0]; + filling_v[1] = weight * df_inv_v[1]; + filling_v[2] = weight * df_inv_v[2]; + + const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; + const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, + vec1, v1_n2,v1_n3, filling_v[0]); + + fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, + vec2, v2_n2,v2_n3, filling_v[1]); + + fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, + vec3, v3_n2,v3_n3, filling_v[2]); + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); + + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); + + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu new file mode 100644 index 000000000..f245f4ba5 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu @@ -0,0 +1,83 @@ +extern "C" __global__ +void pc_lin_mhd_6d_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double ep_scale, + double* mat11_11, double* mat12_11, double* mat13_11, double* mat22_11, double* mat23_11, double* mat33_11, double* mat11_12, double* mat12_12, double* mat13_12, double* mat22_12, double* mat23_12, double* mat33_12, double* mat11_22, double* mat12_22, double* mat13_22, double* mat22_22, double* mat23_22, double* mat33_22, + double* vec1_1, double* vec2_1, double* vec3_1, double* vec1_2, double* vec2_2, double* vec3_2, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, const int v2_n2, const int v2_n3, const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v[3] = {row[3], row[4], row[5]}; + const double weight = row[6]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + double df_inv[9]; + matrix_inv_dev(dfm, df_inv); + double g_inv[9]; + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; + g_inv[3*i+j] = s; + } + double tmp_v[3]; + matvec_dev(df_inv, v, tmp_v); + + const double fill11 = weight * g_inv[0] * ep_scale; + const double fill12 = weight * g_inv[1] * ep_scale; + const double fill13 = weight * g_inv[2] * ep_scale; + const double fill22 = weight * g_inv[4] * ep_scale; + const double fill23 = weight * g_inv[5] * ep_scale; + const double fill33 = weight * g_inv[8] * ep_scale; + const double fill1 = weight * tmp_v[0] * ep_scale; + const double fill2 = weight * tmp_v[1] * ep_scale; + const double fill3 = weight * tmp_v[2] * ep_scale; + const double vx = v[0], vy = v[1]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + fill_mat_vec_pressure_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11_11, mat11_12, mat11_22, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, + vec1_1, vec1_2, v1_n2,v1_n3, fill1, vx,vy); + + fill_mat_pressure_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12_11, mat12_12, mat12_22, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12, vx,vy); + + fill_mat_pressure_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13_11, mat13_12, mat13_22, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13, vx,vy); + + fill_mat_vec_pressure_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22_11, mat22_12, mat22_22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, + vec2_1, vec2_2, v2_n2,v2_n3, fill2, vx,vy); + + fill_mat_pressure_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23_11, mat23_12, mat23_22, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23, vx,vy); + + fill_mat_vec_pressure_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33_11, mat33_12, mat33_22, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, + vec3_1, vec3_2, v3_n2,v3_n3, fill3, vx,vy); + +} diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d_full.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d_full.cu new file mode 100644 index 000000000..373752396 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d_full.cu @@ -0,0 +1,83 @@ +extern "C" __global__ +void pc_lin_mhd_6d_full_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double ep_scale, + double* mat11_11, double* mat12_11, double* mat13_11, double* mat22_11, double* mat23_11, double* mat33_11, double* mat11_12, double* mat12_12, double* mat13_12, double* mat22_12, double* mat23_12, double* mat33_12, double* mat11_13, double* mat12_13, double* mat13_13, double* mat22_13, double* mat23_13, double* mat33_13, double* mat11_22, double* mat12_22, double* mat13_22, double* mat22_22, double* mat23_22, double* mat33_22, double* mat11_23, double* mat12_23, double* mat13_23, double* mat22_23, double* mat23_23, double* mat33_23, double* mat11_33, double* mat12_33, double* mat13_33, double* mat22_33, double* mat23_33, double* mat33_33, + double* vec1_1, double* vec2_1, double* vec3_1, double* vec1_2, double* vec2_2, double* vec3_2, double* vec1_3, double* vec2_3, double* vec3_3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, const int v2_n2, const int v2_n3, const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v[3] = {row[3], row[4], row[5]}; + const double weight = row[8]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + double df_inv[9]; + matrix_inv_dev(dfm, df_inv); + double g_inv[9]; + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) { + double s = 0.0; + for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; + g_inv[3*i+j] = s; + } + double tmp_v[3]; + matvec_dev(df_inv, v, tmp_v); + + const double fill11 = weight * g_inv[0] * ep_scale; + const double fill12 = weight * g_inv[1] * ep_scale; + const double fill13 = weight * g_inv[2] * ep_scale; + const double fill22 = weight * g_inv[4] * ep_scale; + const double fill23 = weight * g_inv[5] * ep_scale; + const double fill33 = weight * g_inv[8] * ep_scale; + const double fill1 = weight * tmp_v[0] * ep_scale; + const double fill2 = weight * tmp_v[1] * ep_scale; + const double fill3 = weight * tmp_v[2] * ep_scale; + const double vx = v[0], vy = v[1], vz = v[2]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + fill_mat_vec_pressure_full_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11_11, mat11_12, mat11_13, mat11_22, mat11_23, mat11_33, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, + vec1_1, vec1_2, vec1_3, v1_n2,v1_n3, fill1, vx,vy,vz); + + fill_mat_pressure_full_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12_11, mat12_12, mat12_13, mat12_22, mat12_23, mat12_33, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12, vx,vy,vz); + + fill_mat_pressure_full_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13_11, mat13_12, mat13_13, mat13_22, mat13_23, mat13_33, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13, vx,vy,vz); + + fill_mat_vec_pressure_full_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22_11, mat22_12, mat22_13, mat22_22, mat22_23, mat22_33, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, + vec2_1, vec2_2, vec2_3, v2_n2,v2_n3, fill2, vx,vy,vz); + + fill_mat_pressure_full_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23_11, mat23_12, mat23_13, mat23_22, mat23_23, mat23_33, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23, vx,vy,vz); + + fill_mat_vec_pressure_full_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33_11, mat33_12, mat33_13, mat33_22, mat33_23, mat33_33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, + vec3_1, vec3_2, vec3_3, v3_n2,v3_n3, fill3, vx,vy,vz); + +} diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu new file mode 100644 index 000000000..1f8aabf22 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu @@ -0,0 +1,144 @@ +extern "C" __global__ +void cc_lin_mhd_5d_curlb_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const double epsilon, const double ep_scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const int basis_u, + double* mat11, double* mat12, double* mat13, + double* mat22, double* mat23, double* mat33, + double* vec1, double* vec2, double* vec3, + const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, + const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, + const int v1_n2, const int v1_n3, + const int v2_n2, const int v2_n3, + const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double weight = row[5]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double bfull_star[3]; + for (int k = 0; k < 3; k++) bfull_star[k] = b[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, bfull_star); + + // tmp = curl_norm_b (x) curl_norm_b + double tmp[9]; + outer_dev(curl_norm_b, curl_norm_b, tmp); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + double b_prod_neg[9]; + for (int k = 0; k < 9; k++) b_prod_neg[k] = -b_prod[k]; + + double tmp1[9], tmp_m[9], tmp_v[3]; + matmat_dev(b_prod, tmp, tmp1); + matmat_dev(tmp1, b_prod_neg, tmp_m); + matvec_dev(b_prod, curl_norm_b, tmp_v); + + double fm[9], fv[3]; + if (basis_u == 0) { + const double sm = weight * v * v / (abs_b_star_para * abs_b_star_para) * ep_scale; + const double sv = weight * v * v / abs_b_star_para * ep_scale; + for (int k = 0; k < 9; k++) fm[k] = tmp_m[k] * sm; + for (int k = 0; k < 3; k++) fv[k] = tmp_v[k] * sv; + } else { + const double sm = weight * v * v / (abs_b_star_para * abs_b_star_para) + / (det_df * det_df) * ep_scale; + const double sv = weight * v * v / abs_b_star_para / det_df * ep_scale; + for (int k = 0; k < 9; k++) fm[k] = tmp_m[k] * sm; + for (int k = 0; k < 3; k++) fv[k] = tmp_v[k] * sv; + } + + const double f11 = fm[0], f12 = fm[1], f13 = fm[2]; + const double f22 = fm[4], f23 = fm[5], f33 = fm[8]; + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + if (basis_u == 0) { + // V0vec: every block N-N-N + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, f11, vec1, v1_n2,v1_n3, fv[0]); + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, f22, vec2, v2_n2,v2_n3, fv[1]); + fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, f33, vec3, v3_n2,v3_n3, fv[2]); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + + } else if (basis_u == 2) { + // V2 (Hdiv): comp1 N-D-D, comp2 D-N-D, comp3 D-D-N + fill_mat_vec_dev(p1,pd2,pd3, p1,pd2,pd3, bn1,bd2,bd3, bn1,bd2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, f11, vec1, v1_n2,v1_n3, fv[0]); + fill_mat_vec_dev(pd1,p2,pd3, pd1,p2,pd3, bd1,bn2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, f22, vec2, v2_n2,v2_n3, fv[1]); + fill_mat_vec_dev(pd1,pd2,p3, pd1,pd2,p3, bd1,bd2,bn3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, f33, vec3, v3_n2,v3_n3, fv[2]); + fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu new file mode 100644 index 000000000..ddea85479 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu @@ -0,0 +1,131 @@ +extern "C" __global__ +void cc_lin_mhd_5d_D_cuda( + const double* markers, const int n_cols, const int n_markers, + const int kind_map, const double* params, + const double epsilon, const double ep_scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int bb1_n2, const int bb1_n3, + const double* b2_2, const int bb2_n2, const int bb2_n3, + const double* b2_3, const int bb3_n2, const int bb3_n3, + const double* nb11, const int nb1_n2, const int nb1_n3, + const double* nb12, const int nb2_n2, const int nb2_n3, + const double* nb13, const int nb3_n2, const int nb3_n3, + const double* cnb1, const int cb1_n2, const int cb1_n3, + const double* cnb2, const int cb2_n2, const int cb2_n3, + const double* cnb3, const int cb3_n2, const int cb3_n3, + const int basis_u, + double* mat12, double* mat13, double* mat23, + const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, + const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, + const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + const double weight = row[5]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b2_1,bb1_n2,bb1_n3, b2_2,bb2_n2,bb2_n3, b2_3,bb3_n2,bb3_n3, b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb11,nb1_n2,nb1_n3, nb12,nb2_n2,nb2_n3, nb13,nb3_n2,nb3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,cb1_n2,cb1_n3, cnb2,cb2_n2,cb2_n3, cnb3,cb3_n2,cb3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = b[k] + epsilon * v * curl_norm_b[k]; + + const double b_para = dot3_dev(norm_b1, b); + const double b_star_para = dot3_dev(norm_b1, b_star); + const double density_const = 1.0 - b_para / b_star_para; + + const double pref = -weight * density_const * ep_scale / epsilon; + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + + double f12, f13, f23; + + if (basis_u == 0) { + f12 = pref * b_prod[1]; + f13 = pref * b_prod[2]; + f23 = pref * b_prod[5]; + + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + + } else if (basis_u == 1) { + double df_inv[9], g_inv[9]; + matrix_inv_dev(dfm, df_inv); + for (int i = 0; i < 3; i++) + for (int j = 0; j < 3; j++) { + double sacc = 0.0; + for (int k = 0; k < 3; k++) sacc += df_inv[3*i+k] * df_inv[3*j+k]; + g_inv[3*i+j] = sacc; + } + double tmp1[9], tmp2[9]; + matmat_dev(g_inv, b_prod, tmp1); + matmat_dev(tmp1, g_inv, tmp2); + + f12 = pref * tmp2[1]; + f13 = pref * tmp2[2]; + f23 = pref * tmp2[5]; + + fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + + } else if (basis_u == 2) { + const double det2 = det_df * det_df; + f12 = pref * b_prod[1] / det2; + f13 = pref * b_prod[2] / det2; + f23 = pref * b_prod[5] / det2; + + fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); + fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); + fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, + span1,span2,span3, start0,start1,start2, p1,p2,p3, + mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu new file mode 100644 index 000000000..64aa28596 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu @@ -0,0 +1,144 @@ +__device__ double dg_mod1_dev(double x) +{ + double r = fmod(x, 1.0); + if (r < 0.0) r += 1.0; + return r; +} + +extern "C" __global__ +void cc_lin_mhd_5d_gradB_dg_cuda( + const double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, const double ep_scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* beq_1, const int e1_n2, const int e1_n3, + const double* beq_2, const int e2_n2, const int e2_n3, + const double* beq_3, const int e3_n2, const int e3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* gpb1, const int g1_n2, const int g1_n3, + const double* gpb2, const int g2_n2, const int g2_n3, + const double* gpb3, const int g3_n2, const int g3_n3, + const double* gpq1, const int q1_n2, const int q1_n3, + const double* gpq2, const int q2_n2, const int q2_n3, + const double* gpq3, const int q3_n2, const int q3_n3, + const int basis_u, const double konst, const int is_dg, + double* vec1, const int v1_n2, const int v1_n3, + double* vec2, const int v2_n2, const int v2_n3, + double* vec3, const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3], eta_diff[3] = {0.0, 0.0, 0.0}; + if (is_dg) { + for (int k = 0; k < 3; k++) { + eta[k] = dg_mod1_dev((row[k] + row[first_init_idx + k]) / 2.0); + eta_diff[k] = row[k] - row[first_init_idx + k]; + } + } else { + for (int k = 0; k < 3; k++) eta[k] = row[k]; + } + + const double weight = row[5]; + const double v = row[3]; + const double mu = row[mu_idx]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta[0], eta[1], eta[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], beq[3], norm_b1[3], curl_norm_b[3], grad_PB[3], grad_PBeq[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + beq_1,e1_n2,e1_n3, beq_2,e2_n2,e2_n3, beq_3,e3_n2,e3_n3, beq); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gpb1,g1_n2,g1_n3, gpb2,g2_n2,g2_n3, gpb3,g3_n2,g3_n3, grad_PB); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gpq1,q1_n2,q1_n3, gpq2,q2_n2,q2_n3, gpq3,q3_n2,q3_n3, grad_PBeq); + + // NOTE: unlike cc_lin_mhd_5d_gradB, B* here includes the equilibrium field. + double bfull_star[3]; + for (int k = 0; k < 3; k++) bfull_star[k] = b[k] + beq[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, bfull_star); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + double beq_prod[9] = {0.0, -beq[2], beq[1], beq[2], 0.0, -beq[0], -beq[1], beq[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + // basis_u == 0 has no 1/det; basis_u == 2 carries one. + const double inv_det = (basis_u == 2) ? (1.0 / det_df) : 1.0; + const double w_fac = weight * mu / abs_b_star_para * inv_det * ep_scale; + const double d_fac = konst / abs_b_star_para * inv_det; + + double tmp[9], tmp_v[3], fv[3] = {0.0, 0.0, 0.0}; + + // the two field blocks, Beq first then B, each contributing + // grad_PBeq, grad_PB and (for `dg`) the eta_diff correction + for (int blk = 0; blk < 2; blk++) { + matmat_dev(blk == 0 ? beq_prod : b_prod, norm_b_prod, tmp); + + matvec_dev(tmp, grad_PBeq, tmp_v); + for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * w_fac; + + matvec_dev(tmp, grad_PB, tmp_v); + for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * w_fac; + + if (is_dg) { + matvec_dev(tmp, eta_diff, tmp_v); + for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * d_fac; + } + } + + if (basis_u == 0) { + // H1vec: N-N-N in all three components + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec1, v1_n2,v1_n3, fv[0]); + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec2, v2_n2,v2_n3, fv[1]); + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec3, v3_n2,v3_n3, fv[2]); + } else if (basis_u == 2) { + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + fill_vec_dev(p1,pd2,pd3, bn1,bd2,bd3, span1,span2,span3, start0,start1,start2, + vec1, v1_n2,v1_n3, fv[0]); + fill_vec_dev(pd1,p2,pd3, bd1,bn2,bd3, span1,span2,span3, start0,start1,start2, + vec2, v2_n2,v2_n3, fv[1]); + fill_vec_dev(pd1,pd2,p3, bd1,bd2,bn3, span1,span2,span3, start0,start1,start2, + vec3, v3_n2,v3_n3, fv[2]); + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu new file mode 100644 index 000000000..324680361 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu @@ -0,0 +1,111 @@ +extern "C" __global__ +void cc_lin_mhd_5d_gradB_cuda( + const double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, const double ep_scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* gpb1, const int g1_n2, const int g1_n3, + const double* gpb2, const int g2_n2, const int g2_n3, + const double* gpb3, const int g3_n2, const int g3_n3, + const double* gpq1, const int q1_n2, const int q1_n3, + const double* gpq2, const int q2_n2, const int q2_n3, + const double* gpq3, const int q3_n2, const int q3_n3, + const int basis_u, + double* vec1, const int v1_n2, const int v1_n3, + double* vec2, const int v2_n2, const int v2_n3, + double* vec3, const int v3_n2, const int v3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double weight = row[5]; + const double v = row[3]; + const double mu = row[mu_idx]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], norm_b1[3], curl_norm_b[3], grad_PB[3], grad_PBeq[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gpb1,g1_n2,g1_n3, gpb2,g2_n2,g2_n3, gpb3,g3_n2,g3_n3, grad_PB); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gpq1,q1_n2,q1_n3, gpq2,q2_n2,q2_n3, gpq3,q3_n2,q3_n3, grad_PBeq); + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; + double tmp[9], tmp_v[3], fv[3]; + + if (basis_u == 0) { + matmat_dev(b_prod, norm_b_prod, tmp); + matvec_dev(tmp, grad_PB, tmp_v); + for (int k = 0; k < 3; k++) fv[k] = weight * tmp_v[k] * mu / abs_b_star_para * ep_scale; + + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec1, v1_n2,v1_n3, fv[0]); + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec2, v2_n2,v2_n3, fv[1]); + fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + vec3, v3_n2,v3_n3, fv[2]); + + } else if (basis_u == 2) { + for (int k = 0; k < 3; k++) grad_PB[k] += grad_PBeq[k]; + matmat_dev(b_prod, norm_b_prod, tmp); + matvec_dev(tmp, grad_PB, tmp_v); + for (int k = 0; k < 3; k++) + fv[k] = weight * tmp_v[k] * mu / abs_b_star_para / det_df * ep_scale; + + // Hdiv components: N-D-D, D-N-D, D-D-N + fill_vec_dev(p1,pd2,pd3, bn1,bd2,bd3, span1,span2,span3, start0,start1,start2, + vec1, v1_n2,v1_n3, fv[0]); + fill_vec_dev(pd1,p2,pd3, bd1,bn2,bd3, span1,span2,span3, start0,start1,start2, + vec2, v2_n2,v2_n3, fv[1]); + fill_vec_dev(pd1,pd2,p3, bd1,bd2,bn3, span1,span2,span3, start0,start1,start2, + vec3, v3_n2,v3_n3, fv[2]); + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_fill_vec_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_fill_vec_src.cu new file mode 100644 index 000000000..d00c9a0a3 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_fill_vec_src.cu @@ -0,0 +1,23 @@ +__device__ void fill_vec_dev( + int pi1, int pi2, int pi3, + const double* bi1, const double* bi2, const double* bi3, + int span1, int span2, int span3, + int start0, int start1, int start2, + double* vec, int vn2, int vn3, + double filling) +{ + for (int il1 = 0; il1 <= pi1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bi1[il1] * filling; + for (int il2 = 0; il2 <= pi2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bi2[il2]; + for (int il3 = 0; il3 <= pi3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bi3[il3]; + atomicAdd(&vec[(size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3], b3); + } + } + } +} + diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu new file mode 100644 index 000000000..dd9e0a142 --- /dev/null +++ b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu @@ -0,0 +1,88 @@ +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) +{ + double left[MAXP]; + double right[MAXP]; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +extern "C" __global__ +void gc_mag_density_0form_cuda( + const double* markers, + const int n_cols, + const int n_markers, + const int mu_idx, + const double scale, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + double* vec, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + const double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double weight = row[5]; + const double mu = row[mu_idx]; + const double filling = mu * weight * scale; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + double b1 = bn1[il1] * filling; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + double b2 = b1 * bn2[il2]; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + double b3 = b2 * bn3[il3]; + atomicAdd(&vec[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3], b3); + } + } + } +} + diff --git a/src/struphy/pic/cuda/sorting_kernels_cuda/_sort_src.cu b/src/struphy/pic/cuda/sorting_kernels_cuda/_sort_src.cu new file mode 100644 index 000000000..97ca0070a --- /dev/null +++ b/src/struphy/pic/cuda/sorting_kernels_cuda/_sort_src.cu @@ -0,0 +1,84 @@ +extern "C" __device__ long long flatten_index_dev( + long long n1, long long n2, long long n3, + long long nx, long long ny, long long nz) +{ + // fortran_ordering (the struphy default) + return n1 + n2 * (nx + 2) + n3 * (nx + 2) * (ny + 2); +} + +extern "C" __device__ long long find_box_dev( + double eta1, double eta2, double eta3, + long long nx, long long ny, long long nz, + const double* domain_array) +{ + if (eta1 == domain_array[0]) eta1 += 1e-8; + if (eta2 == domain_array[3]) eta2 += 1e-8; + if (eta3 == domain_array[6]) eta3 += 1e-8; + if (eta1 == domain_array[1]) eta1 -= 1e-8; + if (eta2 == domain_array[4]) eta2 -= 1e-8; + if (eta3 == domain_array[7]) eta3 -= 1e-8; + + double x_l = domain_array[0] - (domain_array[1] - domain_array[0]) / nx; + double x_r = domain_array[1] + (domain_array[1] - domain_array[0]) / nx; + double y_l = domain_array[3] - (domain_array[4] - domain_array[3]) / ny; + double y_r = domain_array[4] + (domain_array[4] - domain_array[3]) / ny; + double z_l = domain_array[6] - (domain_array[7] - domain_array[6]) / nz; + double z_r = domain_array[7] + (domain_array[7] - domain_array[6]) / nz; + + if (eta1 < x_l || eta1 > x_r || eta2 < y_l || eta2 > y_r || eta3 < z_l || eta3 > z_r) + return -1; + + long long n1 = (long long)floor((eta1 - x_l) / (x_r - x_l) * (nx + 2)); + long long n2 = (long long)floor((eta2 - y_l) / (y_r - y_l) * (ny + 2)); + long long n3 = (long long)floor((eta3 - z_l) / (z_r - z_l) * (nz + 2)); + + return flatten_index_dev(n1, n2, n3, nx, ny, nz); +} + +extern "C" __global__ +void assign_box_to_each_particle_cuda( + const double* eta, // AoS, row p at eta[3*p : 3*p+3] + const int* holes, + const long long n_mks, + const long long nx, + const long long ny, + const long long nz, + const double* domain_array, + double* box_out) +{ + long long p = (long long)blockIdx.x * blockDim.x + threadIdx.x; + if (p >= n_mks) return; + + long long n_boxes_total = (nx + 2) * (ny + 2) * (nz + 2); + long long n_box; + + if (holes[p]) { + n_box = n_boxes_total; + } else { + long long a = find_box_dev(eta[3 * p], eta[3 * p + 1], eta[3 * p + 2], nx, ny, nz, domain_array); + n_box = (a >= n_boxes_total || a < 0) ? n_boxes_total : a; + } + + box_out[p] = (double) n_box; +} + +extern "C" __global__ +void assign_particles_to_boxes_cuda( + const double* box_id, + const int* holes, + const long long n_mks, + int* boxes, + int* next_index, + const long long box_cols) +{ + long long p = (long long)blockIdx.x * blockDim.x + threadIdx.x; + if (p >= n_mks) return; + if (holes[p]) return; + + int a = (int) box_id[p]; + int slot = atomicAdd(&next_index[a], 1); + if (slot < box_cols) { + boxes[(long long) a * box_cols + slot] = (int) p; + } +} + diff --git a/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_flat_src.cu b/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_flat_src.cu new file mode 100644 index 000000000..90e836ad4 --- /dev/null +++ b/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_flat_src.cu @@ -0,0 +1,291 @@ +#define PI 3.14159265358979323846 + +__device__ double distance_dev(double x, double y, bool periodic) +{ + double d = x - y; + if (periodic) { + while (d > 0.5) d -= 1.0; + while (d < -0.5) d += 1.0; + } + return d; +} + +// --- uni-variate kernels (struphy.pic.sph_smoothing_kernels) --- + +__device__ double trigonometric_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return 0.785398163397448 / h * cos(x / h * PI / 2.0); + return 0.0; +} + +__device__ double grad_trigonometric_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return -(1.2337005501361697 / (h * h)) * sin(x / h * PI / 2.0); + return 0.0; +} + +__device__ double gaussian_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return 1.0 / (sqrt(PI) * h / 3.0) * exp(-(x * x) / ((h / 3.0) * (h / 3.0))); + return 0.0; +} + +__device__ double grad_gaussian_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return -54.0 * x / (h * h * h * sqrt(PI)) * exp(-(x * x) / ((h / 3.0) * (h / 3.0))); + return 0.0; +} + +__device__ double linear_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return (1.0 - fabs(x / h)) / h; + return 0.0; +} + +__device__ double grad_linear_uni(double x, double h) +{ + if (fabs(x / h) <= 1.0) return (x > 0.0) ? -(1.0 / (h * h)) : (1.0 / (h * h)); + return 0.0; +} + +// --- kernel_type dispatch (struphy.pic.sph_smoothing_kernels.smoothing_kernel) --- + +__device__ double smoothing_kernel_dev( + int kernel_type, + double r1, double r2, double r3, + double h1, double h2, double h3) +{ + switch (kernel_type) { + // 1d + case 100: return trigonometric_uni(r1, h1); + case 101: return grad_trigonometric_uni(r1, h1); + case 110: return gaussian_uni(r1, h1); + case 111: return grad_gaussian_uni(r1, h1); + case 120: return linear_uni(r1, h1); + case 121: return grad_linear_uni(r1, h1); + + // 2d (tensor products) + case 340: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2); + case 341: return grad_trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2); + case 342: return trigonometric_uni(r1, h1) * grad_trigonometric_uni(r2, h2); + case 350: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2); + case 351: return grad_gaussian_uni(r1, h1) * gaussian_uni(r2, h2); + case 352: return gaussian_uni(r1, h1) * grad_gaussian_uni(r2, h2); + case 360: return linear_uni(r1, h1) * linear_uni(r2, h2); + case 361: return grad_linear_uni(r1, h1) * linear_uni(r2, h2); + case 362: return linear_uni(r1, h1) * grad_linear_uni(r2, h2); + + // 3d (tensor products) + case 670: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); + case 671: return grad_trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); + case 672: return trigonometric_uni(r1, h1) * grad_trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); + case 673: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * grad_trigonometric_uni(r3, h3); + case 680: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * gaussian_uni(r3, h3); + case 681: return grad_gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * gaussian_uni(r3, h3); + case 682: return gaussian_uni(r1, h1) * grad_gaussian_uni(r2, h2) * gaussian_uni(r3, h3); + case 683: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * grad_gaussian_uni(r3, h3); + case 700: return linear_uni(r1, h1) * linear_uni(r2, h2) * linear_uni(r3, h3); + case 701: return grad_linear_uni(r1, h1) * linear_uni(r2, h2) * linear_uni(r3, h3); + case 702: return linear_uni(r1, h1) * grad_linear_uni(r2, h2) * linear_uni(r3, h3); + case 703: return linear_uni(r1, h1) * linear_uni(r2, h2) * grad_linear_uni(r3, h3); + + // 3d, radially symmetric (linear_isotropic_3d and its gradient) + case 690: { + double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); + double h = h1; + if (r / h > 1.0) return 0.0; + return (1.0 - r / h) / (1.0471975512 * h * h * h); + } + case 691: { + double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); + double h = h1; + if (r / h > 1.0) return 0.0; + if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); + return -r1 / (r * h) / (1.0471975512 * h * h * h); + } + case 692: { + double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); + double h = h1; + if (r / h > 1.0) return 0.0; + if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); + return -r2 / (r * h) / (1.0471975512 * h * h * h); + } + case 693: { + double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); + double h = h1; + if (r / h > 1.0) return 0.0; + if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); + return -r3 / (r * h) / (1.0471975512 * h * h * h); + } + } + return 0.0; +} + +// --- box lookup (struphy.pic.sorting_kernels.find_box / flatten_index) --- + +__device__ int find_box_dev( + double eta1, double eta2, double eta3, + int nx, int ny, int nz, + const double* domain_array) +{ + if (eta1 == domain_array[0]) eta1 += 1e-8; + if (eta2 == domain_array[3]) eta2 += 1e-8; + if (eta3 == domain_array[6]) eta3 += 1e-8; + if (eta1 == domain_array[1]) eta1 -= 1e-8; + if (eta2 == domain_array[4]) eta2 -= 1e-8; + if (eta3 == domain_array[7]) eta3 -= 1e-8; + + double x_l = domain_array[0] - (domain_array[1] - domain_array[0]) / nx; + double x_r = domain_array[1] + (domain_array[1] - domain_array[0]) / nx; + double y_l = domain_array[3] - (domain_array[4] - domain_array[3]) / ny; + double y_r = domain_array[4] + (domain_array[4] - domain_array[3]) / ny; + double z_l = domain_array[6] - (domain_array[7] - domain_array[6]) / nz; + double z_r = domain_array[7] + (domain_array[7] - domain_array[6]) / nz; + + if (eta1 < x_l || eta1 > x_r || eta2 < y_l || eta2 > y_r || eta3 < z_l || eta3 > z_r) + return -1; + + int n1 = (int)floor((eta1 - x_l) / (x_r - x_l) * (nx + 2)); + int n2 = (int)floor((eta2 - y_l) / (y_r - y_l) * (ny + 2)); + int n3 = (int)floor((eta3 - z_l) / (z_r - z_l) * (nz + 2)); + + // flatten_index, fortran_ordering (the struphy default) + return n1 + n2 * (nx + 2) + n3 * (nx + 2) * (ny + 2); +} + +// --- entry point (struphy.pic.sph_eval_kernels.box_based_evaluation_flat) --- + +extern "C" __global__ +void box_based_evaluation_flat_cuda( + const double* markers, + const int n_cols, + const double* eta1, + const double* eta2, + const double* eta3, + const int n_eval, + const int nx, + const int ny, + const int nz, + const double* domain_array, + const int* boxes, + const int n_box_cols, + const int* neighbours, + const int* holes, + const int periodic1, + const int periodic2, + const int periodic3, + const int index, + const int kernel_type, + const double h1, + const double h2, + const double h3, + double* out) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n_eval) return; + + double e1 = eta1[i], e2 = eta2[i], e3 = eta3[i]; + + int loc_box = find_box_dev(e1, e2, e3, nx, ny, nz, domain_array); + if (loc_box == -1) { + out[i] = 0.0; + return; + } + + double acc = 0.0; + for (int neigh = 0; neigh < 27; neigh++) { + int box_to_search = neighbours[loc_box * 27 + neigh]; + int c = 0; + while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { + int p = boxes[(size_t)box_to_search * n_box_cols + c]; + c++; + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + } + out[i] = acc; +} + +// --- entry point (struphy.pic.sph_eval_kernels.box_based_evaluation_meshgrid) --- +// +// eta1/eta2/eta3 are the 3 distinct 1-D axis vectors of the meshgrid (the +// Pyccel kernel this ports only ever reads eta1[i,0,0]/eta2[0,j,0]/eta3[0,0,k], +// never the broadcast values, so the Python wrapper passes just the axes -- +// no reason to transfer the O(n1*n2*n3) redundant meshgrid). One CUDA thread +// per (i, j, k) evaluation point, flattened to match out's C-order layout. + +extern "C" __global__ +void box_based_evaluation_meshgrid_cuda( + const double* markers, + const int n_cols, + const double* eta1, + const double* eta2, + const double* eta3, + const int n1_eval, + const int n2_eval, + const int n3_eval, + const int nx, + const int ny, + const int nz, + const double* domain_array, + const int* boxes, + const int n_box_cols, + const int* neighbours, + const int* holes, + const int periodic1, + const int periodic2, + const int periodic3, + const int index, + const int kernel_type, + const double h1, + const double h2, + const double h3, + double* out) +{ + size_t idx = (size_t)blockIdx.x * blockDim.x + threadIdx.x; + size_t n_total = (size_t)n1_eval * n2_eval * n3_eval; + if (idx >= n_total) return; + + int i = idx / ((size_t)n2_eval * n3_eval); + int rem = idx % ((size_t)n2_eval * n3_eval); + int j = rem / n3_eval; + int k = rem % n3_eval; + + out[idx] = 0.0; + + double e1 = eta1[i]; + if (e1 < domain_array[0] || (e1 >= domain_array[1] && e1 != 1.0)) return; + + double e2 = eta2[j]; + if (e2 < domain_array[3] || (e2 >= domain_array[4] && e2 != 1.0)) return; + + double e3 = eta3[k]; + if (e3 < domain_array[6] || (e3 >= domain_array[7] && e3 != 1.0)) return; + + int loc_box = find_box_dev(e1, e2, e3, nx, ny, nz, domain_array); + if (loc_box == -1) return; + + double acc = 0.0; + for (int neigh = 0; neigh < 27; neigh++) { + int box_to_search = neighbours[loc_box * 27 + neigh]; + int c = 0; + while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { + int p = boxes[(size_t)box_to_search * n_box_cols + c]; + c++; + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + } + out[idx] = acc; +} + diff --git a/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_naive_src.cu b/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_naive_src.cu new file mode 100644 index 000000000..db6e99e06 --- /dev/null +++ b/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_naive_src.cu @@ -0,0 +1,86 @@ +extern "C" __global__ +void naive_evaluation_flat_cuda( + const double* markers, + const int n_cols, + const int n_markers, + const double Np, + const double* eta1, + const double* eta2, + const double* eta3, + const int n_eval, + const int* holes, + const int periodic1, + const int periodic2, + const int periodic3, + const int index, + const int kernel_type, + const double h1, + const double h2, + const double h3, + double* out) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n_eval) return; + + double e1 = eta1[i], e2 = eta2[i], e3 = eta3[i]; + + double acc = 0.0; + for (int p = 0; p < n_markers; p++) { + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + out[i] = acc / Np; +} + +extern "C" __global__ +void naive_evaluation_meshgrid_cuda( + const double* markers, + const int n_cols, + const int n_markers, + const double Np, + const double* eta1, + const double* eta2, + const double* eta3, + const int n1_eval, + const int n2_eval, + const int n3_eval, + const int* holes, + const int periodic1, + const int periodic2, + const int periodic3, + const int index, + const int kernel_type, + const double h1, + const double h2, + const double h3, + double* out) +{ + size_t idx = (size_t)blockIdx.x * blockDim.x + threadIdx.x; + size_t n_total = (size_t)n1_eval * n2_eval * n3_eval; + if (idx >= n_total) return; + + int i = idx / ((size_t)n2_eval * n3_eval); + int rem = idx % ((size_t)n2_eval * n3_eval); + int j = rem / n3_eval; + int k = rem % n3_eval; + + double e1 = eta1[i], e2 = eta2[j], e3 = eta3[k]; + + double acc = 0.0; + for (int p = 0; p < n_markers; p++) { + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + out[idx] = acc / Np; +} + diff --git a/src/struphy/pic/cuda/utilities_kernels_cuda/_gc_from_6d_src.cu b/src/struphy/pic/cuda/utilities_kernels_cuda/_gc_from_6d_src.cu new file mode 100644 index 000000000..8075806a4 --- /dev/null +++ b/src/struphy/pic/cuda/utilities_kernels_cuda/_gc_from_6d_src.cu @@ -0,0 +1,75 @@ +extern "C" __global__ +void eval_guiding_center_from_6d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b21, const int b1_n2, const int b1_n3, + const double* b22, const int b2_n2, const int b2_n3, + const double* b23, const int b3_n2, const int b3_n3, + const double* absB, const int a_n2, const int a_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double x = row[first_diagnostics_idx]; + const double y = row[first_diagnostics_idx + 1]; + const double z = row[first_diagnostics_idx + 2]; + double v[3] = {row[3], row[4], row[5]}; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b2[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + b21, b1_n2, b1_n3, b22, b2_n2, b2_n3, b23, b3_n2, b3_n3, b2); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, a_n2, a_n3); + + // normalized magnetic field, cartesian + b2[0] /= abs_B; b2[1] /= abs_B; b2[2] /= abs_B; + double norm_b_cart[3]; + matvec_dev(dfm, b2, norm_b_cart); + norm_b_cart[0] /= det_df; norm_b_cart[1] /= det_df; norm_b_cart[2] /= det_df; + + const double v_parallel = dot3_dev(norm_b_cart, v); + + double temp[3], v_perp[3]; + cross_dev(v, norm_b_cart, temp); + cross_dev(norm_b_cart, temp, v_perp); + const double v_perp_square = v_perp[0]*v_perp[0] + v_perp[1]*v_perp[1] + v_perp[2]*v_perp[2]; + + row[first_diagnostics_idx + 6] = v_parallel; + row[first_diagnostics_idx + 4] = 0.5 * v_perp_square / abs_B; + + double Larmor_r[3]; + cross_dev(norm_b_cart, v_perp, Larmor_r); + for (int k = 0; k < 3; k++) Larmor_r[k] = Larmor_r[k] / abs_B * epsilon; + + row[first_diagnostics_idx + 0] = x - Larmor_r[0]; + row[first_diagnostics_idx + 1] = y - Larmor_r[1]; + row[first_diagnostics_idx + 2] = z - Larmor_r[2]; +} + diff --git a/src/struphy/pic/cuda/utilities_kernels_cuda/_gradb_ediff_src.cu b/src/struphy/pic/cuda/utilities_kernels_cuda/_gradb_ediff_src.cu new file mode 100644 index 000000000..47509643e --- /dev/null +++ b/src/struphy/pic/cuda/utilities_kernels_cuda/_gradb_ediff_src.cu @@ -0,0 +1,58 @@ +__device__ double gradb_ediff_mod1_dev(double x) +{ + double r = fmod(x, 1.0); + if (r < 0.0) r += 1.0; + return r; +} + +extern "C" __global__ +void eval_gradB_ediff_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int mu_idx, const int idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* pb1, const int p1_n2, const int p1_n3, + const double* pb2, const int p2_n2, const int p2_n3, + const double* pb3, const int p3_n2, const int p3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta_mid[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + eta_mid[k] = gradb_ediff_mod1_dev((row[k] + row[first_init_idx + k]) / 2.0); + eta_diff[k] = row[k] - row[first_init_idx + k]; + } + const double mu = row[mu_idx]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double gradB[3], grad_PB_b[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, gradB); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + pb1,p1_n2,p1_n3, pb2,p2_n2,p2_n3, pb3,p3_n2,p3_n3, grad_PB_b); + + double tmp[3]; + for (int k = 0; k < 3; k++) tmp[k] = gradB[k] + grad_PB_b[k]; + + row[idx] = mu * dot3_dev(eta_diff, tmp); +} + diff --git a/src/struphy/pic/cuda/utilities_kernels_cuda/_utilities_src.cu b/src/struphy/pic/cuda/utilities_kernels_cuda/_utilities_src.cu new file mode 100644 index 000000000..18be443fe --- /dev/null +++ b/src/struphy/pic/cuda/utilities_kernels_cuda/_utilities_src.cu @@ -0,0 +1,303 @@ +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) +{ + double left[MAXP]; + double right[MAXP]; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +__device__ double eval_0form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bn2, const double* bn3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c, int n2x, int n3x) +{ + double out = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] * bn1[il1] * bn2[il2] * bn3[il3]; + } + } + } + return out; +} + +// markers[ip, first_diagnostics_idx] = mu_p * |B_0(eta_p)| +extern "C" __global__ +void eval_magnetic_background_energy_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, const int mu_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* abs_B0, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double mu = row[mu_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, abs_B0, n2x, n3x); + + row[first_diagnostics_idx] = mu * abs_B; +} + +// markers[ip, first_diagnostics_idx] = v_par^2 / 2 + mu_p * |B(eta_p)| +extern "C" __global__ +void eval_energy_5d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, const int mu_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* absB, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v_parallel = row[3]; + const double mu = row[mu_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, n2x, n3x); + + row[first_diagnostics_idx] = 0.5 * v_parallel * v_parallel + mu * abs_B; +} + +// markers[ip, idx_can_momentum] = shifted canonical toroidal momentum (5D) +extern "C" __global__ +void eval_canonical_toroidal_moment_5d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, const int mu_idx, const int idx_can_momentum, + const double epsilon, const double B0, const double R0, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* absB, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v_para = row[3]; + const double mu = row[mu_idx]; + const double energy = row[first_diagnostics_idx]; + const double psi = row[idx_can_momentum]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, n2x, n3x); + + double out = psi - epsilon * B0 * R0 / abs_B * v_para; + if (energy - mu * B0 > 0.0) { + // sign(v_para) matches numpy.sign: 0 for exactly 0 + const double sgn = (v_para > 0.0) ? 1.0 : ((v_para < 0.0) ? -1.0 : 0.0); + out += epsilon * sgn * sqrt(2.0 * (energy - mu * B0)) * R0; + } + row[idx_can_momentum] = out; +} + +// markers[ip, first_diagnostics_idx + 5] = shifted canonical toroidal momentum (6D) +extern "C" __global__ +void eval_canonical_toroidal_moment_6d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, + const double epsilon, const double B0, const double R0, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* absB, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double energy = row[first_diagnostics_idx + 3]; + const double mu = row[first_diagnostics_idx + 4]; + const double psi = row[first_diagnostics_idx + 5]; + const double v_para = row[first_diagnostics_idx + 6]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, n2x, n3x); + + double out = psi - epsilon * B0 * R0 / abs_B * v_para; + if (energy - mu * B0 > 0.0) { + const double sgn = (v_para > 0.0) ? 1.0 : ((v_para < 0.0) ? -1.0 : 0.0); + out += epsilon * sgn * sqrt(2.0 * (energy - mu * B0)) * R0; + } + row[first_diagnostics_idx + 5] = out; +} + +// markers[ip, first_diagnostics_idx + 1] = v_perp^2 / (2 |B(eta_p)|) +extern "C" __global__ +void eval_magnetic_moment_5d_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* absB, const int n2x, const int n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v_perp = row[4]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_splines_dev(tn1, p1, eta1, span1, bn1); + b_splines_dev(tn2, p2, eta2, span2, bn2); + b_splines_dev(tn3, p3, eta3, span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, absB, n2x, n3x); + + row[first_diagnostics_idx + 1] = 0.5 * v_perp * v_perp / abs_B; +} + +// markers[ip, first_diagnostics_idx] = mu_p * (|B_0| + PBb)(eta_p) +// NOTE: the CPU reference also evaluates the Jacobian DF(eta) here, but never +// uses the result, so it is not replicated. +extern "C" __global__ +void eval_magnetic_energy_PBb_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_diagnostics_idx, const int mu_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* abs_B0, const int a_n2x, const int a_n3x, + const double* PBb, const int b_n2x, const int b_n3x) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + // eta = mod(markers[0:3], 1.0); fmod can return negative, match numpy mod + double eta[3]; + for (int k = 0; k < 3; k++) { + double e = fmod(row[k], 1.0); + if (e < 0.0) e += 1.0; + eta[k] = e; + } + + const double mu = row[mu_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + b_splines_dev(tn1, p1, eta[0], span1, bn1); + b_splines_dev(tn2, p2, eta[1], span2, bn2); + b_splines_dev(tn3, p3, eta[2], span3, bn3); + + const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, abs_B0, a_n2x, a_n3x); + const double PB_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, PBb, b_n2x, b_n3x); + + row[first_diagnostics_idx] = mu * (abs_B + PB_b); +} + diff --git a/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_dk_hamiltonian_src.cu b/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_dk_hamiltonian_src.cu new file mode 100644 index 000000000..4c800ad94 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_dk_hamiltonian_src.cu @@ -0,0 +1,124 @@ +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) +{ + double left[MAXP]; + double right[MAXP]; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +__device__ double eval_0form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bn2, const double* bn3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c, int n2x, int n3x) +{ + double out = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] + * bn1[il1] * bn2[il2] * bn3[il3]; + } + } + } + return out; +} + +__device__ double mod1_dev(double x) +{ + double r = fmod(x, 1.0); + if (r < 0.0) r += 1.0; + return r; +} + +extern "C" __global__ +void driftkinetic_hamiltonian_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, + const int first_init_idx, const int first_shift_idx, const int mu_idx, + const double a0, const double a1, const double a2, const double a3, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* B_dot_b, const int b_n2, const int b_n3, + const double* phi_c, const int p_n2, const int p_n3, + const int evaluate_e_field) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double alpha[3] = {a0, a1, a2}; + double eta[3]; + for (int i = 0; i < 3; i++) { + const double eta_k = row[i] + row[first_shift_idx + i]; + const double eta_n = row[first_init_idx + i]; + eta[i] = mod1_dev(alpha[i] * eta_k + (1.0 - alpha[i]) * eta_n); + } + + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + const double v = a3 * v_k + (1.0 - a3) * v_n; + const double mu = row[mu_idx]; + + double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + b_splines_dev(tn1, p1, eta[0], span1, bn1); + b_splines_dev(tn2, p2, eta[1], span2, bn2); + b_splines_dev(tn3, p3, eta[2], span3, bn3); + + double phi = 0.0; + if (evaluate_e_field) { + phi = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, phi_c, p_n2, p_n3); + } + + const double bdb = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, B_dot_b, b_n2, b_n3); + + row[column_nr] = epsilon * v * v / 2.0 + epsilon * mu * bdb + phi; +} + diff --git a/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_gc_marker_column_src.cu b/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_gc_marker_column_src.cu new file mode 100644 index 000000000..7d739ed5f --- /dev/null +++ b/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_gc_marker_column_src.cu @@ -0,0 +1,212 @@ +__device__ void weighted_eta_v_dev( + const double* row, int first_init_idx, int first_shift_idx, + const double* alpha, double* eta, double* v_out) +{ + for (int k = 0; k < 3; k++) { + const double eta_k = row[k] + row[first_shift_idx + k]; + const double eta_n = row[first_init_idx + k]; + double e = alpha[k] * eta_k + (1.0 - alpha[k]) * eta_n; + double r = fmod(e, 1.0); + if (r < 0.0) r += 1.0; + eta[k] = r; + } + if (v_out) { + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + *v_out = alpha[3] * v_k + (1.0 - alpha[3]) * v_n; + } +} + +extern "C" __global__ +void grad_driftkinetic_hamiltonian_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int n_comps, const int* comps, + const int first_init_idx, const int first_shift_idx, const int mu_idx, + const double* alpha, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const int evaluate_e_field) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3]; + weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, 0); + const double mu = row[mu_idx]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + for (int j = 0; j < n_comps; j++) row[column_nr + j] = grad_H[comps[j]]; +} + +extern "C" __global__ +void bstar_parallel_3form_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, + const int first_init_idx, const int first_shift_idx, + const double* alpha, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* cub, const int cub_n2, const int cub_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3], v; + weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, &v); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta[0], eta[1], eta[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bn2[MAXP+1], bn3[MAXP+1]; + double bd1[MAXP], bd2[MAXP], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, cub, cub_n2, cub_n3); + + b_star_parallel = (b_star_parallel * epsilon * v + B_dot_b) * det_df; + + row[column_nr] = b_star_parallel; +} + +extern "C" __global__ +void bstar_2form_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int n_comps, const int* comps, + const int first_init_idx, const int first_shift_idx, + const double* alpha, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b1, const int b1_n2, const int b1_n3, + const double* b2, const int b2_n2, const int b2_n3, + const double* b3, const int b3_n2, const int b3_n3, + const double* cb1, const int c1_n2, const int c1_n3, + const double* cb2, const int c2_n2, const int c2_n3, + const double* cb3, const int c3_n2, const int c3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3], v; + weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, &v); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + double bb[3], b_star[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b1,b1_n2,b1_n3, b2,b2_n2,b2_n3, b3,b3_n2,b3_n3, bb); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cb1,c1_n2,c1_n3, cb2,c2_n2,c2_n3, cb3,c3_n2,c3_n3, b_star); + + for (int k = 0; k < 3; k++) b_star[k] = b_star[k] * epsilon * v + bb[k]; + + for (int j = 0; j < n_comps; j++) row[column_nr + j] = b_star[comps[j]]; +} + +extern "C" __global__ +void unit_b_1form_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int n_comps, const int* comps, + const int first_init_idx, const int first_shift_idx, + const double* alpha, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* ub1, const int u1_n2, const int u1_n3, + const double* ub2, const int u2_n2, const int u2_n3, + const double* ub3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + double eta[3]; + weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, 0); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); + + double unit_b1[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ub1,u1_n2,u1_n3, ub2,u2_n2,u2_n3, ub3,u3_n2,u3_n3, unit_b1); + + for (int j = 0; j < n_comps; j++) row[column_nr + j] = unit_b1[comps[j]]; +} + diff --git a/src/struphy/pic/pushing/cuda/eval_kernels_sph_cuda/_sph_marker_column_src.cu b/src/struphy/pic/pushing/cuda/eval_kernels_sph_cuda/_sph_marker_column_src.cu new file mode 100644 index 000000000..23e19ef76 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/eval_kernels_sph_cuda/_sph_marker_column_src.cu @@ -0,0 +1,111 @@ +extern "C" __global__ +void sph_pressure_coeffs_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int weight_idx, + const int* valid_mks, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const int loc_box = (int)row[n_cols - 2]; + + const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); + + const double weight = row[weight_idx]; + const double gamma = 5.0 / 3.0; + + row[column_nr] = n_at_eta; + row[column_nr + 1] = weight / n_at_eta; + row[column_nr + 2] = weight * pow(n_at_eta, gamma - 2.0); +} + +extern "C" __global__ +void sph_mean_velocity_coeffs_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int weight_idx, + const int* valid_mks, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const int loc_box = (int)row[n_cols - 2]; + + const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); + + const double weight = row[weight_idx]; + const double scale = weight / n_at_eta; + + row[column_nr + 0] = scale * row[3]; + row[column_nr + 1] = scale * row[4]; + row[column_nr + 2] = scale * row[5]; +} + +extern "C" __global__ +void sph_viscosity_tensor_cuda( + double* markers, const int n_cols, const int n_markers, + const int column_nr, const int weight_idx, const int first_free_idx, + const int* valid_mks, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3, + const double mu) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const int loc_box = (int)row[n_cols - 2]; + + const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); + const double weight = row[weight_idx]; + + double grad_v[3][3]; + for (int j = 0; j < 3; j++) { + for (int k = 0; k < 3; k++) { + grad_v[j][k] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, + first_free_idx + j, kernel_type + 1 + k, h1, h2, h3); + } + } + + double d_dev[3][3]; + for (int j = 0; j < 3; j++) + for (int k = 0; k < 3; k++) + d_dev[j][k] = 0.5 * (grad_v[j][k] + grad_v[k][j]); + + const double mean_trace = (d_dev[0][0] + d_dev[1][1] + d_dev[2][2]) / 3.0; + d_dev[0][0] -= mean_trace; + d_dev[1][1] -= mean_trace; + d_dev[2][2] -= mean_trace; + + const double scale = -2.0 * mu * (weight / n_at_eta); + for (int j = 0; j < 3; j++) { + for (int k = 0; k < 3; k++) { + row[column_nr + 3 * j + k] = d_dev[j][k] * scale; + } + } +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_general_geometry_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_general_geometry_src.cu new file mode 100644 index 000000000..0d77a1311 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_general_geometry_src.cu @@ -0,0 +1,1452 @@ +#define MAXP 8 + +__device__ void matrix_inv_dev(const double* a, double* b) +{ + double det_a = a[0]*(a[4]*a[8] - a[5]*a[7]) + - a[1]*(a[3]*a[8] - a[5]*a[6]) + + a[2]*(a[3]*a[7] - a[4]*a[6]); + + b[0] = (a[4]*a[8] - a[7]*a[5]) / det_a; + b[1] = (a[7]*a[2] - a[1]*a[8]) / det_a; + b[2] = (a[1]*a[5] - a[4]*a[2]) / det_a; + b[3] = (a[5]*a[6] - a[8]*a[3]) / det_a; + b[4] = (a[8]*a[0] - a[2]*a[6]) / det_a; + b[5] = (a[2]*a[3] - a[5]*a[0]) / det_a; + b[6] = (a[3]*a[7] - a[6]*a[4]) / det_a; + b[7] = (a[6]*a[1] - a[0]*a[7]) / det_a; + b[8] = (a[0]*a[4] - a[3]*a[1]) / det_a; +} + +// c = a^T @ b (used for both DF^-1 @ v and DF^-T @ e_form: pass dfinv or +// its transpose accordingly -- here we need dfinv @ v (not transposed) for +// push_eta_stage, and dfinvT @ e_form for push_v_with_efield, so both a +// plain and a transposed matvec are provided). +__device__ void matvec_dev(const double* a, const double* v, double* out) +{ + out[0] = a[0]*v[0] + a[1]*v[1] + a[2]*v[2]; + out[1] = a[3]*v[0] + a[4]*v[1] + a[5]*v[2]; + out[2] = a[6]*v[0] + a[7]*v[1] + a[8]*v[2]; +} + +// c = a @ b, 3x3 row-major matrices. +__device__ void matmat_dev(const double* a, const double* b, double* c) +{ + for (int i = 0; i < 3; i++) { + for (int j = 0; j < 3; j++) { + c[3*i+j] = a[3*i+0]*b[0*3+j] + a[3*i+1]*b[1*3+j] + a[3*i+2]*b[2*3+j]; + } + } +} + +__device__ void matvecT_dev(const double* a, const double* v, double* out) +{ + out[0] = a[0]*v[0] + a[3]*v[1] + a[6]*v[2]; + out[1] = a[1]*v[0] + a[4]*v[1] + a[7]*v[2]; + out[2] = a[2]*v[0] + a[5]*v[1] + a[8]*v[2]; +} + +// df_out is row-major 3x3 (df_out[3*i+j] = dF_i/deta_j), matching +// struphy.geometry.mappings_kernels.cuboid_df / colella_df exactly. +__device__ void cuboid_df_dev(const double* params, double* df_out) +{ + // params = (l1, r1, l2, r2, l3, r3) + for (int k = 0; k < 9; k++) df_out[k] = 0.0; + df_out[0] = params[1] - params[0]; + df_out[4] = params[3] - params[2]; + df_out[8] = params[5] - params[4]; +} + +__device__ void colella_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (Lx, Ly, alpha, Lz) + const double lx = params[0], ly = params[1], alpha = params[2], lz = params[3]; + const double twopi = 6.283185307179586; + const double s1 = sin(twopi * eta1), c1 = cos(twopi * eta1); + const double s2 = sin(twopi * eta2), c2 = cos(twopi * eta2); + + df_out[0] = lx * (1.0 + alpha * c1 * s2 * twopi); + df_out[1] = lx * alpha * s1 * c2 * twopi; + df_out[2] = 0.0; + df_out[3] = ly * alpha * c1 * s2 * twopi; + df_out[4] = ly * (1.0 + alpha * s1 * c2 * twopi); + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void orthogonal_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (Lx, Ly, alpha, Lz) + const double lx = params[0], ly = params[1], alpha = params[2], lz = params[3]; + const double twopi = 6.283185307179586; + + for (int k = 0; k < 9; k++) df_out[k] = 0.0; + df_out[0] = lx * (1.0 + alpha * cos(twopi * eta1) * twopi); + df_out[4] = ly * (1.0 + alpha * cos(twopi * eta2) * twopi); + df_out[8] = lz; +} + +__device__ void hollow_cyl_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (a1, a2, Lz, poc); faithful port of + // struphy.geometry.mappings_kernels.hollow_cyl_df, including its + // existing df_out[0,0]/df_out[1,0] not dividing eta2's argument by poc + // (unlike f_out and every other entry here) -- not "fixed" here, since + // this is a port, not a bugfix. + const double a1 = params[0], a2 = params[1], lz = params[2], poc = params[3]; + const double twopi = 6.283185307179586; + const double da = a2 - a1; + const double r = a1 + eta1 * da; + + df_out[0] = da * cos(twopi * eta2); + df_out[1] = -twopi / poc * r * sin(twopi * eta2 / poc); + df_out[2] = 0.0; + df_out[3] = da * sin(twopi * eta2); + df_out[4] = twopi / poc * r * cos(twopi * eta2 / poc); + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void powered_ellipse_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (rx, ry, Lz, s) + const double rx = params[0], ry = params[1], lz = params[2], s = params[3]; + const double twopi = 6.283185307179586; + const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); + const double e_sm1 = pow(eta1, s - 1.0); + const double e_s = pow(eta1, s); + + df_out[0] = e_sm1 * rx * c2; + df_out[1] = -twopi * e_s * rx * s2; + df_out[2] = 0.0; + df_out[3] = e_sm1 * ry * s2; + df_out[4] = twopi * e_s * ry * c2; + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void hollow_torus_df_dev(double eta1, double eta2, double eta3, const double* params, double* df_out) +{ + // params = (a1, a2, R0, sfl, pol_period, tor_period) + const double a1 = params[0], a2 = params[1], r0 = params[2]; + const double sfl = params[3], pol_period = params[4], tor_period = params[5]; + const double pi = 3.14159265358979323846; + const double twopi = 6.283185307179586; + const double da = a2 - a1; + + if (sfl == 1.0) { + const double r = a1 + da * eta1; + const double eps = r / r0; + const double eps_p = da / r0; + const double tpe = tan(pi * eta2); + const double cpe = cos(pi * eta2); + const double tpe_p = pi / (cpe * cpe); + const double g = sqrt((1.0 + eps) / (1.0 - eps)); + const double g_p = 1.0 / (2.0 * g) * (eps_p * (1.0 - eps) + (1.0 + eps) * eps_p) / ((1.0 - eps) * (1.0 - eps)); + const double theta = 2.0 * atan(g * tpe); + const double denom = 1.0 + (g * tpe) * (g * tpe); + const double dtheta_deta1 = 2.0 / denom * g_p * tpe; + const double dtheta_deta2 = 2.0 / denom * g * tpe_p; + const double ct = cos(theta), st = sin(theta); + const double cf = cos(twopi * eta3 / tor_period), sf = sin(twopi * eta3 / tor_period); + + df_out[0] = (da * ct - r * st * dtheta_deta1) * cf; + df_out[1] = -r * st * dtheta_deta2 * cf; + df_out[2] = -twopi / tor_period * (r * ct + r0) * sf; + + df_out[3] = (da * ct - r * st * dtheta_deta1) * (-1.0) * sf; + df_out[4] = -r * st * dtheta_deta2 * (-1.0) * sf; + df_out[5] = twopi / tor_period * (r * ct + r0) * (-1.0) * cf; + + df_out[6] = da * st + r * ct * dtheta_deta1; + df_out[7] = r * ct * dtheta_deta2; + df_out[8] = 0.0; + } else { + const double r = a1 + eta1 * da; + const double cp = cos(twopi * eta2 / pol_period), sp = sin(twopi * eta2 / pol_period); + const double cf = cos(twopi * eta3 / tor_period), sf = sin(twopi * eta3 / tor_period); + + df_out[0] = da * cp * cf; + df_out[1] = -twopi / pol_period * r * sp * cf; + df_out[2] = -twopi / tor_period * (r * cp + r0) * sf; + + df_out[3] = da * cp * (-1.0) * sf; + df_out[4] = -twopi / pol_period * r * sp * (-1.0) * sf; + df_out[5] = (r * cp + r0) * (-1.0) * cf * twopi / tor_period; + + df_out[6] = da * sp; + df_out[7] = r * cp * twopi / pol_period; + df_out[8] = 0.0; + } +} + +__device__ void shafranov_shift_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (rx, ry, Lz, delta) + const double rx = params[0], ry = params[1], lz = params[2], de = params[3]; + const double twopi = 6.283185307179586; + const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); + + df_out[0] = rx * c2 - 2.0 * eta1 * rx * de; + df_out[1] = -twopi * (eta1 * rx) * s2; + df_out[2] = 0.0; + df_out[3] = ry * s2; + df_out[4] = twopi * (eta1 * ry) * c2; + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void shafranov_sqrt_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (rx, ry, Lz, delta) + const double rx = params[0], ry = params[1], lz = params[2], de = params[3]; + const double twopi = 6.283185307179586; + const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); + + df_out[0] = rx * c2 - 0.5 / sqrt(eta1) * rx * de; + df_out[1] = -twopi * (eta1 * rx) * s2; + df_out[2] = 0.0; + df_out[3] = ry * s2; + df_out[4] = twopi * (eta1 * ry) * c2; + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +__device__ void shafranov_dshaped_df_dev(double eta1, double eta2, const double* params, double* df_out) +{ + // params = (R0, Lz, delta_x, delta_y, delta_gs, epsilon_gs, kappa_gs) + const double r0 = params[0], lz = params[1], dx = params[2], dy = params[3]; + const double dg = params[4], eg = params[5], kg = params[6]; + const double pi = 3.14159265358979323846; + const double twopi = 6.283185307179586; + const double asin_dg = asin(dg); + const double s2 = sin(twopi * eta2), c2 = cos(twopi * eta2); + const double phase = eta1 * s2 * asin_dg + twopi * eta2; + + df_out[0] = r0 * ( + -2.0 * dx * eta1 + - eg * eta1 * s2 * asin_dg * sin(phase) + + eg * cos(phase) + ); + df_out[1] = -r0 * eg * eta1 * (twopi * eta1 * c2 * asin_dg + twopi) * sin(phase); + df_out[2] = 0.0; + df_out[3] = r0 * (-2.0 * dy * eta1 + eg * kg * s2); + df_out[4] = twopi * r0 * eg * eta1 * kg * c2; + df_out[5] = 0.0; + df_out[6] = 0.0; + df_out[7] = 0.0; + df_out[8] = lz; +} + +// Returns 1 if kind_map is supported and df_out was filled, 0 otherwise. +__device__ int df_dispatch_dev(int kind_map, double eta1, double eta2, double eta3, + const double* params, double* df_out) +{ + if (kind_map == 10) { cuboid_df_dev(params, df_out); return 1; } + if (kind_map == 11) { orthogonal_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 12) { colella_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 20) { hollow_cyl_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 21) { powered_ellipse_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 22) { hollow_torus_df_dev(eta1, eta2, eta3, params, df_out); return 1; } + if (kind_map == 30) { shafranov_shift_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 31) { shafranov_sqrt_df_dev(eta1, eta2, params, df_out); return 1; } + if (kind_map == 32) { shafranov_dshaped_df_dev(eta1, eta2, params, df_out); return 1; } + return 0; +} + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +// Same as pusher_kernels_cuda.py's push_v_with_efield_cuboid's b_d_splines_dev, +// duplicated here because each cp.RawKernel source string is compiled +// independently (no cross-source linking). +__device__ void b_d_splines_dev(const double* t, int p, double eta, int span, double* bn, double* bd) +{ + double left[MAXP]; + double right[MAXP]; + int pd = p - 1; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + for (int i = 0; i < p; i++) bd[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + + if (j == p - 1) { + for (int il = 0; il <= pd; il++) { + bd[pd - il] = (double)p / (t[span - il + p] - t[span - il]) * bn[pd - il]; + } + } + + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +__device__ double det3_dev(const double* a) +{ + return a[0]*(a[4]*a[8] - a[5]*a[7]) + - a[1]*(a[3]*a[8] - a[5]*a[6]) + + a[2]*(a[3]*a[7] - a[4]*a[6]); +} + +__device__ void cross_dev(const double* a, const double* b, double* out) +{ + out[0] = a[1]*b[2] - a[2]*b[1]; + out[1] = a[2]*b[0] - a[0]*b[2]; + out[2] = a[0]*b[1] - a[1]*b[0]; +} + +__device__ double dot3_dev(const double* a, const double* b) +{ + return a[0]*b[0] + a[1]*b[1] + a[2]*b[2]; +} + +// Single-point evaluation of a Derham 2-form spline, matching +// struphy.bsplines.evaluation_kernels_3d.eval_2form_spline_mpi (N-D-D / +// D-N-D / D-D-N tensor-product sums, the dual basis combination of the +// 1-form evaluation in push_v_with_efield_general above). +__device__ void eval_2form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bd1, + const double* bn2, const double* bd2, + const double* bn3, const double* bd3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c1, int n2x1, int n3x1, + const double* c2, int n2x2, int n3x2, + const double* c3, int n2x3, int n3x3, + double* out) +{ + out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; + + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bn1[il1] * bd2[il2] * bd3[il3]; + } + } + } + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bd1[il1] * bn2[il2] * bd3[il3]; + } + } + } + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bd1[il1] * bd2[il2] * bn3[il3]; + } + } + } +} + +extern "C" __global__ +void push_eta_stage_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; + + double dfm[9], dfinv[9], v[3], k[3]; + v[0] = row[3]; v[1] = row[4]; v[2] = row[5]; + + df_dispatch_dev(kind_map, row[0], row[1], row[2], params, dfm); + matrix_inv_dev(dfm, dfinv); + matvec_dev(dfinv, v, k); + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_v_with_efield_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* e1_1, const int n2x1, const int n3x1, + const double* e1_2, const int n2x2, const int n3x2, + const double* e1_3, const int n2x3, const int n3x3, + const int kind_map, + const double* params, + const double dt_const) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + + double bn1[MAXP + 1], bd1[MAXP]; + double bn2[MAXP + 1], bd2[MAXP]; + double bn3[MAXP + 1], bd3[MAXP]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double e_form[3] = {0.0, 0.0, 0.0}; + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + e_form[0] += e1_1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; + } + } + } + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + e_form[1] += e1_2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; + } + } + } + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + e_form[2] += e1_3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; + } + } + } + + double dfm[9], dfinv[9], dfinvT_e[3]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + matvecT_dev(dfinv, e_form, dfinvT_e); + + row[3] += dt_const * dfinvT_e[0]; + row[4] += dt_const * dfinvT_e[1]; + row[5] += dt_const * dfinvT_e[2]; +} + +// Shared setup for push_vxb_analytic_general / push_vxb_implicit_general: +// evaluate DF(eta), its determinant, and the Cartesian B-field at the +// marker's position. Returns 0 (and leaves b_cart untouched) if the marker +// is a hole/ghost, matching both CPU kernels' skip check. +__device__ int eval_b_cart_dev( + const double* row, const int n_cols, const int first_init_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const int kind_map, const double* params, + double* b_cart) +{ + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + + double bn1[MAXP + 1], bd1[MAXP]; + double bn2[MAXP + 1], bd2[MAXP]; + double bn3[MAXP + 1], bd3[MAXP]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b_form[3]; + eval_2form_dev( + p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, + span1, span2, span3, start0, start1, start2, + b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + b_form + ); + + double dfm[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + const double det_df = det3_dev(dfm); + + matvec_dev(dfm, b_form, b_cart); + b_cart[0] /= det_df; + b_cart[1] /= det_df; + b_cart[2] /= det_df; + return 1; +} + +extern "C" __global__ +void push_vxb_analytic_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; + + double b_cart[3]; + eval_b_cart_dev( + row, n_cols, first_init_idx, p1, p2, p3, + tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, + b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, b_cart + ); + + const double b_abs = sqrt(b_cart[0]*b_cart[0] + b_cart[1]*b_cart[1] + b_cart[2]*b_cart[2]); + if (b_abs == 0.0) return; + + double b_norm[3] = {b_cart[0]/b_abs, b_cart[1]/b_abs, b_cart[2]/b_abs}; + double v[3] = {row[3], row[4], row[5]}; + + const double vpar = dot3_dev(v, b_norm); + + double vxb_norm[3], vperp[3], b_normxvperp[3]; + cross_dev(v, b_norm, vxb_norm); + cross_dev(b_norm, vxb_norm, vperp); + cross_dev(b_norm, vperp, b_normxvperp); + + const double cbt = cos(b_abs * dt), sbt = sin(b_abs * dt); + row[3] = vpar * b_norm[0] + cbt * vperp[0] - sbt * b_normxvperp[0]; + row[4] = vpar * b_norm[1] + cbt * vperp[1] - sbt * b_normxvperp[1]; + row[5] = vpar * b_norm[2] + cbt * vperp[2] - sbt * b_normxvperp[2]; +} + +extern "C" __global__ +void push_vxb_implicit_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + // NOTE: the CPU push_vxb_implicit only checks the hole flag, not the + // ghost flag (unlike push_vxb_analytic) -- faithfully reproduced here. + if (row[first_init_idx] == -1.0) return; + + double b_cart[3]; + eval_b_cart_dev( + row, n_cols, first_init_idx, p1, p2, p3, + tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, + b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, b_cart + ); + + // b_prod = [[0, bz, -by], [-bz, 0, bx], [by, -bx, 0]] (row-major), such + // that b_prod @ v == v x b_cart (matches the CPU kernel's b_prod, which + // solves v x B via a matrix product rather than a cross product). + double b_prod[9] = { + 0.0, b_cart[2], -b_cart[1], + -b_cart[2], 0.0, b_cart[0], + b_cart[1], -b_cart[0], 0.0, + }; + + double rhs[9], lhs[9]; + for (int k = 0; k < 9; k++) { + const double id = (k == 0 || k == 4 || k == 8) ? 1.0 : 0.0; + rhs[k] = id + 0.5 * dt * b_prod[k]; + lhs[k] = id - 0.5 * dt * b_prod[k]; + } + + double lhs_inv[9]; + matrix_inv_dev(lhs, lhs_inv); + + double v[3] = {row[3], row[4], row[5]}; + double vec[3], res[3]; + matvec_dev(rhs, v, vec); + matvec_dev(lhs_inv, vec, res); + + row[3] = res[0]; + row[4] = res[1]; + row[5] = res[2]; +} + +// Single-point evaluation of a Derham 1-form spline (D-N-N / N-D-N / N-N-D), +// matching struphy.bsplines.evaluation_kernels_3d.eval_1form_spline_mpi. +// A standalone copy of the same math already inlined in +// push_v_with_efield_general above -- kept separate (not factored out and +// reused there) to avoid touching that already-validated kernel. +__device__ void eval_1form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bd1, + const double* bn2, const double* bd2, + const double* bn3, const double* bd3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c1, int n2x1, int n3x1, + const double* c2, int n2x2, int n3x2, + const double* c3, int n2x3, int n3x3, + double* out) +{ + out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; + + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; + } + } + } + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; + } + } + } + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; + } + } + } +} + +// Single-point evaluation of a vector-field spline (H^1)^3 (N-N-N for every +// component), matching +// struphy.bsplines.evaluation_kernels_3d.eval_vectorfield_spline_mpi. +__device__ void eval_vectorfield_dev( + int p1, int p2, int p3, + const double* bn1, const double* bn2, const double* bn3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c1, int n2x1, int n3x1, + const double* c2, int n2x2, int n3x2, + const double* c3, int n2x3, int n3x3, + double* out) +{ + out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + double b123 = bn1[il1] * bn2[il2] * bn3[il3]; + out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * b123; + out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * b123; + out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * b123; + } + } + } +} + +// Shared setup for push_bxu_{Hdiv,Hcurl,H1vec}_general: evaluate DF(eta), +// its determinant, and the Cartesian B-field (always a 2-form) at the +// marker's position. Also computes and caches the local N-/D-spline basis +// values and span indices, reused by the caller for its own U-field +// evaluation (which differs per FEEC space). +__device__ void eval_b_cart_and_basis_dev( + double eta1, double eta2, double eta3, + int p1, int p2, int p3, + const double* tn1, int len_tn1, + const double* tn2, int len_tn2, + const double* tn3, int len_tn3, + int start0, int start1, int start2, + const double* b2_1, int n2x1, int n3x1, + const double* b2_2, int n2x2, int n3x2, + const double* b2_3, int n2x3, int n3x3, + int kind_map, const double* params, + double* bn1, double* bd1, double* bn2, double* bd2, double* bn3, double* bd3, + int* span1_out, int* span2_out, int* span3_out, + double* dfm, double* b_cart) +{ + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + *span1_out = span1; *span2_out = span2; *span3_out = span3; + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double b_form[3]; + eval_2form_dev( + p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, + span1, span2, span3, start0, start1, start2, + b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + b_form + ); + + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + const double det_df = det3_dev(dfm); + matvec_dev(dfm, b_form, b_cart); + b_cart[0] /= det_df; + b_cart[1] /= det_df; + b_cart[2] /= det_df; +} + +extern "C" __global__ +void push_bxu_Hdiv_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const double* u2_1, const int m2x1, const int m3x1, + const double* u2_2, const int m2x2, const int m3x2, + const double* u2_3, const int m2x3, const int m3x3, + const int kind_map, + const double* params, + const double boundary_cut, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfm[9], b_cart[3]; + eval_b_cart_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart + ); + + double u_form[3]; + eval_2form_dev( + p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, + span1, span2, span3, start0, start1, start2, + u2_1, m2x1, m3x1, u2_2, m2x2, m3x2, u2_3, m2x3, m3x3, + u_form + ); + const double det_df = det3_dev(dfm); + double u_cart[3]; + matvec_dev(dfm, u_form, u_cart); + u_cart[0] /= det_df; u_cart[1] /= det_df; u_cart[2] /= det_df; + + double e_cart[3]; + cross_dev(b_cart, u_cart, e_cart); + row[3] += dt * e_cart[0]; + row[4] += dt * e_cart[1]; + row[5] += dt * e_cart[2]; +} + +extern "C" __global__ +void push_bxu_Hcurl_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const double* u1_1, const int m2x1, const int m3x1, + const double* u1_2, const int m2x2, const int m3x2, + const double* u1_3, const int m2x3, const int m3x3, + const int kind_map, + const double* params, + const double boundary_cut, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfm[9], b_cart[3]; + eval_b_cart_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart + ); + + double u_form[3]; + eval_1form_dev( + p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, + span1, span2, span3, start0, start1, start2, + u1_1, m2x1, m3x1, u1_2, m2x2, m3x2, u1_3, m2x3, m3x3, + u_form + ); + double dfinv[9], dfinvT[9], u_cart[3]; + matrix_inv_dev(dfm, dfinv); + matvecT_dev(dfinv, u_form, u_cart); + + double e_cart[3]; + cross_dev(b_cart, u_cart, e_cart); + row[3] += dt * e_cart[0]; + row[4] += dt * e_cart[1]; + row[5] += dt * e_cart[2]; +} + +extern "C" __global__ +void push_bxu_H1vec_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b2_1, const int n2x1, const int n3x1, + const double* b2_2, const int n2x2, const int n3x2, + const double* b2_3, const int n2x3, const int n3x3, + const double* uv_1, const int m2x1, const int m3x1, + const double* uv_2, const int m2x2, const int m3x2, + const double* uv_3, const int m2x3, const int m3x3, + const int kind_map, + const double* params, + const double boundary_cut, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfm[9], b_cart[3]; + eval_b_cart_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart + ); + + double u_form[3]; + eval_vectorfield_dev( + p1, p2, p3, bn1, bn2, bn3, + span1, span2, span3, start0, start1, start2, + uv_1, m2x1, m3x1, uv_2, m2x2, m3x2, uv_3, m2x3, m3x3, + u_form + ); + double u_cart[3]; + matvec_dev(dfm, u_form, u_cart); + + double e_cart[3]; + cross_dev(b_cart, u_cart, e_cart); + row[3] += dt * e_cart[0]; + row[4] += dt * e_cart[1]; + row[5] += dt * e_cart[2]; +} + +// Shared setup for push_pc_GXu{_full,}_general: DF(eta)/dfinv/dfinvT plus +// span/basis values, reused by the caller to evaluate the 3 (or 2) rows of +// the GXu matrix via eval_1form_dev. +__device__ void eval_dfinvt_and_basis_dev( + double eta1, double eta2, double eta3, + int p1, int p2, int p3, + const double* tn1, int len_tn1, + const double* tn2, int len_tn2, + const double* tn3, int len_tn3, + int kind_map, const double* params, + double* bn1, double* bd1, double* bn2, double* bd2, double* bn3, double* bd3, + int* span1_out, int* span2_out, int* span3_out, + double* dfinvt) +{ + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + *span1_out = span1; *span2_out = span2; *span3_out = span3; + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + // dfinvt = dfinv^T, stored explicitly (row-major) since the caller needs + // it as a plain matrix for matvec_dev, not just for a single matvecT_dev + // application. + dfinvt[0] = dfinv[0]; dfinvt[1] = dfinv[3]; dfinvt[2] = dfinv[6]; + dfinvt[3] = dfinv[1]; dfinvt[4] = dfinv[4]; dfinvt[5] = dfinv[7]; + dfinvt[6] = dfinv[2]; dfinvt[7] = dfinv[5]; dfinvt[8] = dfinv[8]; +} + +extern "C" __global__ +void push_pc_GXu_full_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* g11, const double* g12, const double* g13, + const double* g21, const double* g22, const double* g23, + const double* g31, const double* g32, const double* g33, + const int n2xc1, const int n3xc1, + const int n2xc2, const int n3xc2, + const int n2xc3, const int n3xc3, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfinvt[9]; + eval_dfinvt_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfinvt + ); + + // components 1/2/3 of a 1-form generally have different shapes + // (D-N-N / N-D-N / N-N-D), but the shape only depends on the component + // index, not on which "row" of GXu is being evaluated -- so the same + // (n2xc1,n3xc1)/(n2xc2,n3xc2)/(n2xc3,n3xc3) apply to all three rows. + double gxu_row0[3], gxu_row1[3], gxu_row2[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g11, n2xc1, n3xc1, g12, n2xc2, n3xc2, g13, n2xc3, n3xc3, gxu_row0); + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g21, n2xc1, n3xc1, g22, n2xc2, n3xc2, g23, n2xc3, n3xc3, gxu_row1); + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g31, n2xc1, n3xc1, g32, n2xc2, n3xc2, g33, n2xc3, n3xc3, gxu_row2); + + // GXu[i][j] = gxu_row_i[j]; e[j] = sum_i GXu[i][j] * v[i] + double v[3] = {row[3], row[4], row[5]}; + double e[3]; + e[0] = gxu_row0[0]*v[0] + gxu_row1[0]*v[1] + gxu_row2[0]*v[2]; + e[1] = gxu_row0[1]*v[0] + gxu_row1[1]*v[1] + gxu_row2[1]*v[2]; + e[2] = gxu_row0[2]*v[0] + gxu_row1[2]*v[1] + gxu_row2[2]*v[2]; + + double e_cart[3]; + matvec_dev(dfinvt, e, e_cart); + + row[3] -= dt * e_cart[0] / 2.0; + row[4] -= dt * e_cart[1] / 2.0; + row[5] -= dt * e_cart[2] / 2.0; +} + +extern "C" __global__ +void push_pc_GXu_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* g11, const double* g12, const double* g13, + const double* g21, const double* g22, const double* g23, + const int n2xc1, const int n3xc1, + const int n2xc2, const int n3xc2, + const int n2xc3, const int n3xc3, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + int span1, span2, span3; + double dfinvt[9]; + eval_dfinvt_and_basis_dev( + eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, + kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfinvt + ); + + double gxu_row0[3], gxu_row1[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g11, n2xc1, n3xc1, g12, n2xc2, n3xc2, g13, n2xc3, n3xc3, gxu_row0); + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + g21, n2xc1, n3xc1, g22, n2xc2, n3xc2, g23, n2xc3, n3xc3, gxu_row1); + + double v[3] = {row[3], row[4], row[5]}; + double e[3]; + e[0] = gxu_row0[0]*v[0] + gxu_row1[0]*v[1]; + e[1] = gxu_row0[1]*v[0] + gxu_row1[1]*v[1]; + e[2] = gxu_row0[2]*v[0] + gxu_row1[2]*v[1]; + + double e_cart[3]; + matvec_dev(dfinvt, e, e_cart); + + row[3] -= dt * e_cart[0] / 2.0; + row[4] -= dt * e_cart[1] / 2.0; + row[5] -= dt * e_cart[2] / 2.0; +} + +extern "C" __global__ +void push_pc_eta_stage_Hcurl_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* u_1, const int n2x1, const int n3x1, + const double* u_2, const int n2x2, const int n3x2, + const double* u_3, const int n2x3, const int n3x3, + const int use_perp_model, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9], dfinvt[9], ginv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + dfinvt[0]=dfinv[0]; dfinvt[1]=dfinv[3]; dfinvt[2]=dfinv[6]; + dfinvt[3]=dfinv[1]; dfinvt[4]=dfinv[4]; dfinvt[5]=dfinv[7]; + dfinvt[6]=dfinv[2]; dfinvt[7]=dfinv[5]; dfinvt[8]=dfinv[8]; + matmat_dev(dfinv, dfinvt, ginv); + + double k_v[3]; + matvec_dev(dfinv, v, k_v); + + double u[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); + if (use_perp_model) u[2] = 0.0; + + double k_u[3]; + matvec_dev(ginv, u, k_u); + + double k[3] = {k_v[0]+k_u[0], k_v[1]+k_u[1], k_v[2]+k_u[2]}; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_pc_eta_stage_Hdiv_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* u_1, const int n2x1, const int n3x1, + const double* u_2, const int n2x2, const int n3x2, + const double* u_3, const int n2x3, const int n3x3, + const int use_perp_model, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + const double det_df = det3_dev(dfm); + matrix_inv_dev(dfm, dfinv); + + double k_v[3]; + matvec_dev(dfinv, v, k_v); + + double u[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); + if (use_perp_model) u[2] = 0.0; + + double k_u[3] = {u[0]/det_df, u[1]/det_df, u[2]/det_df}; + double k[3] = {k_v[0]+k_u[0], k_v[1]+k_u[1], k_v[2]+k_u[2]}; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_pc_eta_stage_H1vec_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* u_1, const int n2x1, const int n3x1, + const double* u_2, const int n2x2, const int n3x2, + const double* u_3, const int n2x3, const int n3x3, + const int use_perp_model, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + + double k_v[3]; + matvec_dev(dfinv, v, k_v); + + double u[3]; + eval_vectorfield_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, start0, start1, start2, + u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); + if (use_perp_model) u[2] = 0.0; + + double k[3] = {k_v[0]+u[0], k_v[1]+u[1], k_v[2]+u[2]}; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_weights_with_efield_lin_va_general( + double* markers, + const int n_cols, + const int n_markers, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* e1_1, const int n2x1, const int n3x1, + const double* e1_2, const int n2x2, const int n3x2, + const double* e1_3, const int n2x3, const int n3x3, + const double* f0_values, + const double kappa, + const double vth, + const int kind_map, + const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9], dfinv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + + double dfinv_v[3]; + matvec_dev(dfinv, v, dfinv_v); + + double e_vec[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + e1_1, n2x1, n3x1, e1_2, n2x2, n3x2, e1_3, n2x3, n3x3, e_vec); + + const double update = (dfinv_v[0]*e_vec[0] + dfinv_v[1]*e_vec[1] + dfinv_v[2]*e_vec[2]) + * f0_values[ip] * kappa * dt / (2.0 * row[7] * vth * vth); + row[6] += update; +} + +// Single-point evaluation of a Derham 0-form spline (N-N-N), matching +// struphy.bsplines.evaluation_kernels_3d.eval_0form_spline_mpi. +__device__ double eval_0form_dev( + int p1, int p2, int p3, + const double* bn1, const double* bn2, const double* bn3, + int span1, int span2, int span3, + int start0, int start1, int start2, + const double* c, int n2x, int n3x) +{ + double out = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] * bn1[il1] * bn2[il2] * bn3[il3]; + } + } + } + return out; +} + +extern "C" __global__ +void push_deterministic_diffusion_stage_general( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* pi_u, const int n2xu, const int n3xu, + const double* pi_grad_u1, const int n2x1, const int n3x1, + const double* pi_grad_u2, const int n2x2, const int n3x2, + const double* pi_grad_u3, const int n2x3, const int n3x3, + const double diffusion_coeff, + const int kind_map, + const double* params, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + + double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + const double pi_u_value = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, pi_u, n2xu, n3xu); + + double pi_du_value[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, + pi_grad_u1, n2x1, n3x1, pi_grad_u2, n2x2, n3x2, pi_grad_u3, n2x3, n3x3, pi_du_value); + + // ginv = G^-1 = DF^-1 @ DF^-T, matching struphy.geometry.evaluation_kernels.g_inv + // (computed there as (DF^T @ DF)^-1 instead -- same result, different + // intermediate path, reusing the dfinv this file already needs elsewhere). + double dfm[9], dfinv[9], dfinvt[9], ginv[9]; + df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); + matrix_inv_dev(dfm, dfinv); + dfinvt[0]=dfinv[0]; dfinvt[1]=dfinv[3]; dfinvt[2]=dfinv[6]; + dfinvt[3]=dfinv[1]; dfinvt[4]=dfinv[4]; dfinvt[5]=dfinv[7]; + dfinvt[6]=dfinv[2]; dfinvt[7]=dfinv[5]; dfinvt[8]=dfinv[8]; + matmat_dev(dfinv, dfinvt, ginv); + + double tmp[3] = { + -diffusion_coeff * pi_du_value[0] / pi_u_value, + -diffusion_coeff * pi_du_value[1] / pi_u_value, + -diffusion_coeff * pi_du_value[2] / pi_u_value, + }; + double k[3]; + matvec_dev(ginv, tmp, k); + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_cuboid_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_cuboid_src.cu new file mode 100644 index 000000000..25c3adb1b --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_cuboid_src.cu @@ -0,0 +1,37 @@ +extern "C" __global__ +void push_eta_stage_cuboid( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const double sx, + const double sy, + const double sz, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + + // skip holes and ghost/boundary particles, matching push_eta_stage + if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double kx = sx * row[3]; + const double ky = sy * row[4]; + const double kz = sz * row[5]; + + // accumulate for the last stage (must happen before the position update, + // which reads the just-updated accumulator) + row[first_free_idx + 0] += dt_b * kx; + row[first_free_idx + 1] += dt_b * ky; + row[first_free_idx + 2] += dt_b * kz; + + row[0] = row[first_init_idx + 0] + dt_a * kx + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * ky + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * kz + last * row[first_free_idx + 2]; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_rk_periodic_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_rk_periodic_src.cu new file mode 100644 index 000000000..5eb0454ef --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_rk_periodic_src.cu @@ -0,0 +1,55 @@ +extern "C" __global__ +void push_eta_rk_periodic( + double* markers, + const int n_cols, + const int n_markers, + const int first_init_idx, + const int first_free_idx, + const int first_shift_idx, + const double sx, + const double sy, + const double sz, + const double dt_a, + const double dt_b, + const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + + if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double kx = sx * row[3]; + const double ky = sy * row[4]; + const double kz = sz * row[5]; + + row[first_free_idx + 0] += dt_b * kx; + row[first_free_idx + 1] += dt_b * ky; + row[first_free_idx + 2] += dt_b * kz; + + double e0 = row[first_init_idx + 0] + dt_a * kx + last * row[first_free_idx + 0]; + double e1 = row[first_init_idx + 1] + dt_a * ky + last * row[first_free_idx + 1]; + double e2 = row[first_init_idx + 2] + dt_a * kz + last * row[first_free_idx + 2]; + + // periodic wrap + shift bookkeeping, matching the periodic branch of + // Particles.apply_kinetic_bc (Python's a % 1.0 is always in [0, 1)) + double shift0 = 0.0, shift1 = 0.0, shift2 = 0.0; + + if (e0 > 1.0) { e0 = fmod(e0, 1.0); shift0 = 1.0; } + else if (e0 < 0.0) { e0 = fmod(e0, 1.0); if (e0 < 0.0) e0 += 1.0; shift0 = -1.0; } + + if (e1 > 1.0) { e1 = fmod(e1, 1.0); shift1 = 1.0; } + else if (e1 < 0.0) { e1 = fmod(e1, 1.0); if (e1 < 0.0) e1 += 1.0; shift1 = -1.0; } + + if (e2 > 1.0) { e2 = fmod(e2, 1.0); shift2 = 1.0; } + else if (e2 < 0.0) { e2 = fmod(e2, 1.0); if (e2 < 0.0) e2 += 1.0; shift2 = -1.0; } + + row[0] = e0; + row[1] = e1; + row[2] = e2; + row[first_shift_idx + 0] = shift0; + row[first_shift_idx + 1] = shift1; + row[first_shift_idx + 2] = shift2; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_v_efield_cuboid_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_v_efield_cuboid_src.cu new file mode 100644 index 000000000..33b40e175 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_v_efield_cuboid_src.cu @@ -0,0 +1,152 @@ +#define MAXP 8 + +__device__ int find_span_dev(const double* t, int p, int len_t, double eta) +{ + int low = p; + int high = len_t - 1 - p; + + if (eta <= t[low]) return low; + if (eta >= t[high]) return high - 1; + + int span = (low + high) / 2; + while (eta < t[span] || eta >= t[span + 1]) { + if (eta < t[span]) high = span; + else low = span; + span = (low + high) / 2; + } + return span; +} + +// Combined N-spline (bn, p+1 values) and D-spline (bd, p values) evaluation, +// matching struphy.bsplines.bsplines_kernels.b_d_splines_slim exactly. +__device__ void b_d_splines_dev(const double* t, int p, double eta, int span, double* bn, double* bd) +{ + double left[MAXP]; + double right[MAXP]; + int pd = p - 1; + + for (int i = 0; i <= p; i++) bn[i] = 0.0; + for (int i = 0; i < p; i++) bd[i] = 0.0; + bn[0] = 1.0; + + for (int j = 0; j < p; j++) { + left[j] = eta - t[span - j]; + right[j] = t[span + 1 + j] - eta; + double saved = 0.0; + + if (j == p - 1) { + for (int il = 0; il <= pd; il++) { + bd[pd - il] = (double)p / (t[span - il + p] - t[span - il]) * bn[pd - il]; + } + } + + for (int r = 0; r <= j; r++) { + double temp = bn[r] / (right[r] + left[j - r]); + bn[r] = saved + right[r] * temp; + saved = left[j - r] * temp; + } + bn[j + 1] = saved; + } +} + +extern "C" __global__ +void push_v_with_efield_cuboid( + double* markers, + const int n_cols, + const int n_markers, + const int p1, + const int p2, + const int p3, + const double* tn1, + const int len_tn1, + const double* tn2, + const int len_tn2, + const double* tn3, + const int len_tn3, + const int start0, + const int start1, + const int start2, + const double* e1_1, + const int n2x1, + const int n3x1, + const double* e1_2, + const int n2x2, + const int n3x2, + const double* e1_3, + const int n2x3, + const int n3x3, + const double sx, + const double sy, + const double sz, + const double dt_const) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + + // skip holes and ghost/boundary particles, matching Particles.valid_mks + if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; + + const double eta1 = row[0]; + const double eta2 = row[1]; + const double eta3 = row[2]; + + double bn1[MAXP + 1], bd1[MAXP]; + double bn2[MAXP + 1], bd2[MAXP]; + double bn3[MAXP + 1], bd3[MAXP]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + // e_form[0]: D-spline in direction 1, N-splines in directions 2, 3 + double e_form0 = 0.0; + for (int il1 = 0; il1 < p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + e_form0 += e1_1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; + } + } + } + + // e_form[1]: N-spline in direction 1, D-spline in direction 2, N-spline in direction 3 + double e_form1 = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 < p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 <= p3; il3++) { + int i3 = span3 + il3 - start2; + e_form1 += e1_2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; + } + } + } + + // e_form[2]: N-splines in directions 1, 2, D-spline in direction 3 + double e_form2 = 0.0; + for (int il1 = 0; il1 <= p1; il1++) { + int i1 = span1 + il1 - start0; + for (int il2 = 0; il2 <= p2; il2++) { + int i2 = span2 + il2 - start1; + for (int il3 = 0; il3 < p3; il3++) { + int i3 = span3 + il3 - start2; + e_form2 += e1_3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; + } + } + } + + // Cartesian E-field is DF^-T @ e_form; for Cuboid, DF is diag(sx^-1, sy^-1, sz^-1) + // so DF^-T is diag(sx, sy, sz) -- same convention as push_eta_stage_cuboid's scale. + row[3] += dt_const * sx * e_form0; + row[4] += dt_const * sy * e_form1; + row[5] += dt_const * sz * e_form2; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_random_diffusion_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_random_diffusion_src.cu new file mode 100644 index 000000000..da406865f --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_random_diffusion_src.cu @@ -0,0 +1,19 @@ +extern "C" __global__ +void push_random_diffusion_stage( + double* markers, + const int n_cols, + const int n_markers, + const double* noise, + const double scale) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + row[0] += scale * noise[3*ip + 0]; + row[1] += scale * noise[3*ip + 1]; + row[2] += scale * noise[3*ip + 2]; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_1st_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_1st_src.cu new file mode 100644 index 000000000..59e6df71c --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_1st_src.cu @@ -0,0 +1,195 @@ +// mod(x, 1.0) matching numpy (result in [0, 1)) +__device__ double mod1_dev(double x) +{ + double r = fmod(x, 1.0); + if (r < 0.0) r += 1.0; + return r; +} + +extern "C" __global__ +void push_gc_bxEstar_discrete_gradient_1st_order_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int gb1_n2, const int gb1_n3, + const double* gb2, const int gb2_n2, const int gb2_n3, + const double* gb3, const int gb3_n2, const int gb3_n3, + const double* e1c, const int e1_n2, const int e1_n3, + const double* e2c, const int e2_n2, const int e2_n3, + const double* e3c, const int e3_n2, const int e3_n3, + const int evaluate_e_field, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; + for (int i = 0; i < 3; i++) { + eta_k[i] = row[i] + row[first_shift_idx + i]; + eta_n[i] = row[first_init_idx + i]; + eta_mid[i] = mod1_dev((eta_k[i] + eta_n[i]) / 2.0); + eta_diff[i] = eta_k[i] - eta_n[i]; + } + + const double mu = row[mu_idx]; + const double H_n = row[first_free_idx]; + const double b_star_parallel = row[first_free_idx + 1]; + double unit_b1[3] = { + row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; + const double H_k = row[first_free_idx + 5]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,gb1_n2,gb1_n3, gb2,gb2_n2,gb2_n3, gb3,gb3_n2,gb3_n3, grad_H); + for (int i = 0; i < 3; i++) grad_H[i] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + e1c,e1_n2,e1_n3, e2c,e2_n2,e2_n3, e3c,e3_n2,e3_n3, e_field); + for (int i = 0; i < 3; i++) grad_H[i] += -e_field[i]; + } + + const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H); + const double dZ_squared = dot3_dev(eta_diff, eta_diff); + + double grad_I[3]; + if (dZ_squared == 0.0) { + for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i]; + } else { + const double c = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; + for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i] + eta_diff[i] * c; + } + + double Exb[3]; + cross_dev(unit_b1, grad_I, Exb); + + double k[3]; + for (int i = 0; i < 3; i++) k[i] = Exb[i] / b_star_parallel; + + for (int i = 0; i < 3; i++) row[i] = eta_n[i] + dt * k[i]; + + row[residual_idx] = sqrt( + (row[0] - eta_k[0]) * (row[0] - eta_k[0]) + + (row[1] - eta_k[1]) * (row[1] - eta_k[1]) + + (row[2] - eta_k[2]) * (row[2] - eta_k[2])); +} + +extern "C" __global__ +void push_gc_Bstar_discrete_gradient_1st_order_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int gb1_n2, const int gb1_n3, + const double* gb2, const int gb2_n2, const int gb2_n3, + const double* gb3, const int gb3_n2, const int gb3_n3, + const double* e1c, const int e1_n2, const int e1_n3, + const double* e2c, const int e2_n2, const int e2_n3, + const double* e3c, const int e3_n2, const int e3_n3, + const int evaluate_e_field, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; + for (int i = 0; i < 3; i++) { + eta_k[i] = row[i] + row[first_shift_idx + i]; + eta_n[i] = row[first_init_idx + i]; + eta_mid[i] = mod1_dev((eta_k[i] + eta_n[i]) / 2.0); + eta_diff[i] = eta_k[i] - eta_n[i]; + } + + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + const double v_mid = (v_k + v_n) / 2.0; + const double v_diff = v_k - v_n; + + const double mu = row[mu_idx]; + const double H_n = row[first_free_idx]; + const double b_star_parallel = epsilon * row[first_free_idx + 1]; + double b_star[3] = { + row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; + const double H_k = row[first_free_idx + 5]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,gb1_n2,gb1_n3, gb2,gb2_n2,gb2_n3, gb3,gb3_n2,gb3_n3, grad_H); + for (int i = 0; i < 3; i++) grad_H[i] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + e1c,e1_n2,e1_n3, e2c,e2_n2,e2_n3, e3c,e3_n2,e3_n3, e_field); + for (int i = 0; i < 3; i++) grad_H[i] += -e_field[i]; + } + + const double grad_H_v = epsilon * v_mid; + const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H) + v_diff * grad_H_v; + const double dZ_squared = dot3_dev(eta_diff, eta_diff) + v_diff * v_diff; + + double grad_I[3]; + double grad_I_v; + if (dZ_squared == 0.0) { + for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i]; + grad_I_v = grad_H_v; + } else { + const double c = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; + for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i] + eta_diff[i] * c; + grad_I_v = grad_H_v + v_diff * c; + } + + double k[3]; + for (int i = 0; i < 3; i++) k[i] = b_star[i] / b_star_parallel * grad_I_v; + + double k_v = dot3_dev(b_star, grad_I); + k_v /= -b_star_parallel; + + for (int i = 0; i < 3; i++) row[i] = eta_n[i] + dt * k[i]; + row[3] = v_n + dt * k_v; + + row[residual_idx] = sqrt( + (row[0] - eta_k[0]) * (row[0] - eta_k[0]) + + (row[1] - eta_k[1]) * (row[1] - eta_k[1]) + + (row[2] - eta_k[2]) * (row[2] - eta_k[2]) + + ((row[3] - v_k) / v_k) * ((row[3] - v_k) / v_k)); +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_2nd_order_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_2nd_order_src.cu new file mode 100644 index 000000000..9a6210f42 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_2nd_order_src.cu @@ -0,0 +1,227 @@ +extern "C" __global__ +void push_gc_bxEstar_discrete_gradient_2nd_order_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* ub1, const int u1_n2, const int u1_n3, + const double* ub2, const int u2_n2, const int u2_n3, + const double* ub3, const int u3_n2, const int u3_n3, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* cub, const int cub_n2, const int cub_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const int evaluate_e_field, const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + eta_k[k] = row[k] + row[first_shift_idx + k]; + eta_n[k] = row[first_init_idx + k]; + double m = fmod((eta_k[k] + eta_n[k]) / 2.0, 1.0); + if (m < 0.0) m += 1.0; + eta_mid[k] = m; + eta_diff[k] = eta_k[k] - eta_n[k]; + } + const double v = row[3]; + const double mu = row[mu_idx]; + + const double H_n = row[first_free_idx]; + const double H_k = row[first_free_idx + 1]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double unit_b1[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ub1,u1_n2,u1_n3, ub2,u2_n2,u2_n3, ub3,u3_n2,u3_n3, unit_b1); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H); + const double dZ_squared = dot3_dev(eta_diff, eta_diff); + + double grad_I[3]; + if (dZ_squared == 0.0) { + for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k]; + } else { + const double s = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; + for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k] + eta_diff[k] * s; + } + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, cub, cub_n2, cub_n3); + b_star_parallel = (b_star_parallel * epsilon * v + B_dot_b) * det_df; + + double Exb[3]; + cross_dev(unit_b1, grad_I, Exb); + + double k_vec[3]; + for (int k = 0; k < 3; k++) k_vec[k] = Exb[k] / b_star_parallel; + + row[0] = eta_n[0] + dt * k_vec[0]; + row[1] = eta_n[1] + dt * k_vec[1]; + row[2] = eta_n[2] + dt * k_vec[2]; + + const double r0 = row[0] - eta_k[0], r1 = row[1] - eta_k[1], r2 = row[2] - eta_k[2]; + row[residual_idx] = sqrt(r0*r0 + r1*r1 + r2*r2); +} + +extern "C" __global__ +void push_gc_Bstar_discrete_gradient_2nd_order_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* b2_1, const int b1_n2, const int b1_n3, + const double* b2_2, const int b2_n2, const int b2_n3, + const double* b2_3, const int b3_n2, const int b3_n3, + const double* cb1, const int c1_n2, const int c1_n3, + const double* cb2, const int c2_n2, const int c2_n3, + const double* cb3, const int c3_n2, const int c3_n3, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* cub, const int cub_n2, const int cub_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const int evaluate_e_field, const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + eta_k[k] = row[k] + row[first_shift_idx + k]; + eta_n[k] = row[first_init_idx + k]; + double m = fmod((eta_k[k] + eta_n[k]) / 2.0, 1.0); + if (m < 0.0) m += 1.0; + eta_mid[k] = m; + eta_diff[k] = eta_k[k] - eta_n[k]; + } + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + const double v_mid = (v_k + v_n) / 2.0; + const double v_diff = v_k - v_n; + const double mu = row[mu_idx]; + + const double H_n = row[first_free_idx]; + const double H_k = row[first_free_idx + 1]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + const double grad_H_v = epsilon * v_mid; + const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H) + v_diff * grad_H_v; + const double dZ_squared = dot3_dev(eta_diff, eta_diff) + v_diff * v_diff; + + double grad_I[3]; + double grad_I_v; + if (dZ_squared == 0.0) { + for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k]; + grad_I_v = grad_H_v; + } else { + const double s = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; + for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k] + eta_diff[k] * s; + grad_I_v = grad_H_v + v_diff * s; + } + + double b2[3], b_star[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b2_1,b1_n2,b1_n3, b2_2,b2_n2,b2_n3, b2_3,b3_n2,b3_n3, b2); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cb1,c1_n2,c1_n3, cb2,c2_n2,c2_n3, cb3,c3_n2,c3_n3, b_star); + for (int k = 0; k < 3; k++) b_star[k] = b_star[k] * epsilon * v_mid + b2[k]; + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, cub, cub_n2, cub_n3); + b_star_parallel = (b_star_parallel * epsilon * v_mid + B_dot_b) * epsilon * det_df; + + double k_vec[3]; + for (int k = 0; k < 3; k++) k_vec[k] = b_star[k] / b_star_parallel * grad_I_v; + const double k_v = -dot3_dev(b_star, grad_I) / b_star_parallel; + + row[0] = eta_n[0] + dt * k_vec[0]; + row[1] = eta_n[1] + dt * k_vec[1]; + row[2] = eta_n[2] + dt * k_vec[2]; + row[3] = v_n + dt * k_v; + + const double r0 = row[0] - eta_k[0], r1 = row[1] - eta_k[1], r2 = row[2] - eta_k[2]; + const double rv = (row[3] - v_k) / v_k; + row[residual_idx] = sqrt(r0*r0 + r1*r1 + r2*r2 + rv*rv); +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_newton_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_newton_src.cu new file mode 100644 index 000000000..29bcb624a --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_newton_src.cu @@ -0,0 +1,256 @@ +extern "C" __global__ +void push_gc_bxEstar_discrete_gradient_1st_order_newton_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const double* phi, const int p_n2, const int p_n3, + const int evaluate_e_field, const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + const double eta_k_shifted = row[k] + row[first_shift_idx + k]; + eta_k[k] = row[k]; + eta_diff[k] = eta_k_shifted - row[first_init_idx + k]; + } + const double v = row[3]; + const double mu = row[mu_idx]; + + const double H_n = row[first_free_idx]; + const double b_star_parallel = row[first_free_idx + 1]; + const double unit_b1[3] = {row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; + const double H_k1 = row[first_free_idx + 5]; + const double H_k12 = row[first_free_idx + 6]; + const double grad_H_1 = row[first_free_idx + 7]; + const double grad_H_12[2] = {row[first_free_idx + 8], row[first_free_idx + 9]}; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_k[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_k[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_k[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_k[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_k[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_k[2], span3, bn3, bd3); + + double phi_val = 0.0; + if (evaluate_e_field) { + phi_val = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, phi, p_n2, p_n3); + } + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + const double H_k = epsilon * v * v / 2.0 + epsilon * mu * B_dot_b + phi_val; + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + double grad_I[3]; + grad_I[0] = (eta_diff[0] == 0.0) ? grad_H[0] : (H_k1 - H_n) / eta_diff[0]; + grad_I[1] = (eta_diff[1] == 0.0) ? grad_H[1] : (H_k12 - H_k1) / eta_diff[1]; + grad_I[2] = (eta_diff[2] == 0.0) ? grad_H[2] : (H_k - H_k12) / eta_diff[2]; + + double bcross_mat[9] = { + 0.0, -unit_b1[2], unit_b1[1], + unit_b1[2], 0.0, -unit_b1[0], + -unit_b1[1], unit_b1[0], 0.0}; + for (int k = 0; k < 9; k++) bcross_mat[k] /= b_star_parallel; + + double func[3]; + matvec_dev(bcross_mat, grad_I, func); + for (int k = 0; k < 3; k++) func[k] = eta_diff[k] - dt * func[k]; + + double Ddg[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; + if (eta_diff[0] != 0.0) Ddg[0] = (grad_H_1 * eta_diff[0] - (H_k1 - H_n)) / (eta_diff[0] * eta_diff[0]); + if (eta_diff[1] != 0.0) { + Ddg[4] = (grad_H_12[1] * eta_diff[1] - (H_k12 - H_k1)) / (eta_diff[1] * eta_diff[1]); + Ddg[3] = (grad_H_12[0] - grad_H_1) / eta_diff[1]; + } + if (eta_diff[2] != 0.0) { + Ddg[8] = (grad_H[2] * eta_diff[2] - (H_k - H_k12)) / (eta_diff[2] * eta_diff[2]); + Ddg[6] = (grad_H[0] - grad_H_12[0]) / eta_diff[2]; + Ddg[7] = (grad_H[1] - grad_H_12[1]) / eta_diff[2]; + } + + double Dfunc[9]; + matmat_dev(bcross_mat, Ddg, Dfunc); + for (int k = 0; k < 9; k++) Dfunc[k] *= -dt; + Dfunc[0] += 1.0; Dfunc[4] += 1.0; Dfunc[8] += 1.0; + + double Dfunc_inv[9], k_vec[3]; + matrix_inv_dev(Dfunc, Dfunc_inv); + matvec_dev(Dfunc_inv, func, k_vec); + + row[0] -= k_vec[0]; + row[1] -= k_vec[1]; + row[2] -= k_vec[2]; + + row[residual_idx] = sqrt(k_vec[0]*k_vec[0] + k_vec[1]*k_vec[1] + k_vec[2]*k_vec[2]); +} + +extern "C" __global__ +void push_gc_Bstar_discrete_gradient_1st_order_newton_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_shift_idx, + const int residual_idx, const int first_free_idx, const int mu_idx, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* gb1, const int g1_n2, const int g1_n3, + const double* gb2, const int g2_n2, const int g2_n3, + const double* gb3, const int g3_n2, const int g3_n3, + const double* bdb, const int bdb_n2, const int bdb_n3, + const double* ef1, const int e1_n2, const int e1_n3, + const double* ef2, const int e2_n2, const int e2_n3, + const double* ef3, const int e3_n2, const int e3_n3, + const double* phi, const int p_n2, const int p_n3, + const int evaluate_e_field, const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + double eta_k[3], eta_diff[3]; + for (int k = 0; k < 3; k++) { + const double eta_k_shifted = row[k] + row[first_shift_idx + k]; + eta_k[k] = row[k]; + eta_diff[k] = eta_k_shifted - row[first_init_idx + k]; + } + const double v_k = row[3]; + const double v_n = row[first_init_idx + 3]; + const double v_diff = v_k - v_n; + const double mu = row[mu_idx]; + + const double H_n = row[first_free_idx]; + const double b_star_parallel = epsilon * row[first_free_idx + 1]; + const double b_star[3] = {row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; + const double H_k1 = row[first_free_idx + 5]; + const double H_k12 = row[first_free_idx + 6]; + const double grad_H_1 = row[first_free_idx + 7]; + const double grad_H_12[2] = {row[first_free_idx + 8], row[first_free_idx + 9]}; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_k[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_k[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_k[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_k[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_k[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_k[2], span3, bn3, bd3); + + double phi_val = 0.0; + if (evaluate_e_field) { + phi_val = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, phi, p_n2, p_n3); + } + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, bdb, bdb_n2, bdb_n3); + const double H_k = epsilon * v_k * v_k / 2.0 + epsilon * mu * B_dot_b + phi_val; + const double H_k123 = epsilon * v_n * v_n / 2.0 + epsilon * mu * B_dot_b + phi_val; + + double grad_H[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); + for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); + for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; + } + + const double grad_H_v = epsilon * v_k; + + double grad_I[3]; + grad_I[0] = (eta_diff[0] == 0.0) ? grad_H[0] : (H_k1 - H_n) / eta_diff[0]; + grad_I[1] = (eta_diff[1] == 0.0) ? grad_H[1] : (H_k12 - H_k1) / eta_diff[1]; + grad_I[2] = (eta_diff[2] == 0.0) ? grad_H[2] : (H_k123 - H_k12) / eta_diff[2]; + const double grad_I_v = (v_diff == 0.0) ? grad_H_v : (H_k - H_k123) / v_diff; + + double J_vec[3]; + for (int k = 0; k < 3; k++) J_vec[k] = b_star[k] / b_star_parallel; + + double func[3]; + for (int k = 0; k < 3; k++) func[k] = eta_diff[k] - dt * (J_vec[k] * grad_I_v); + double func_v = v_diff + dt * dot3_dev(J_vec, grad_I); + + double Ddg[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; + if (eta_diff[0] != 0.0) Ddg[0] = (grad_H_1 * eta_diff[0] - (H_k1 - H_n)) / (eta_diff[0] * eta_diff[0]); + if (eta_diff[1] != 0.0) { + Ddg[4] = (grad_H_12[1] * eta_diff[1] - (H_k12 - H_k1)) / (eta_diff[1] * eta_diff[1]); + Ddg[3] = (grad_H_12[0] - grad_H_1) / eta_diff[1]; + } + if (eta_diff[2] != 0.0) { + Ddg[8] = (grad_H[2] * eta_diff[2] - (H_k123 - H_k12)) / (eta_diff[2] * eta_diff[2]); + Ddg[6] = (grad_H[0] - grad_H_12[0]) / eta_diff[2]; + Ddg[7] = (grad_H[1] - grad_H_12[1]) / eta_diff[2]; + } + const double Ddg_v = (v_diff == 0.0) ? 0.0 : (grad_H_v * v_diff - (H_k - H_k123)) / (v_diff * v_diff); + + // DF = [[I, B], [C^T, 1]], B = -dt*Ddg_v*J_vec, C = dt*Ddg^T @ J_vec + double Bv[3], Cv[3]; + for (int k = 0; k < 3; k++) Bv[k] = -dt * Ddg_v * J_vec[k]; + double DdgT[9] = {Ddg[0], Ddg[3], Ddg[6], Ddg[1], Ddg[4], Ddg[7], Ddg[2], Ddg[5], Ddg[8]}; + matvec_dev(DdgT, J_vec, Cv); + for (int k = 0; k < 3; k++) Cv[k] *= dt; + + const double schur = 1.0 - dot3_dev(Cv, Bv); + + double A_inv[9]; + A_inv[0] = Bv[0]*Cv[0]; A_inv[1] = Bv[0]*Cv[1]; A_inv[2] = Bv[0]*Cv[2]; + A_inv[3] = Bv[1]*Cv[0]; A_inv[4] = Bv[1]*Cv[1]; A_inv[5] = Bv[1]*Cv[2]; + A_inv[6] = Bv[2]*Cv[0]; A_inv[7] = Bv[2]*Cv[1]; A_inv[8] = Bv[2]*Cv[2]; + for (int k = 0; k < 9; k++) A_inv[k] /= schur; + A_inv[0] += 1.0; A_inv[4] += 1.0; A_inv[8] += 1.0; + + double Binv[3], Cinv[3]; + for (int k = 0; k < 3; k++) { Binv[k] = -Bv[k] / schur; Cinv[k] = -Cv[k] / schur; } + + double k_vec[3]; + matvec_dev(A_inv, func, k_vec); + for (int k = 0; k < 3; k++) k_vec[k] += Binv[k] * func_v; + double k_v = dot3_dev(Cinv, func) + func_v / schur; + + row[0] -= k_vec[0]; + row[1] -= k_vec[1]; + row[2] -= k_vec[2]; + row[3] -= k_v; + + row[residual_idx] = sqrt(k_vec[0]*k_vec[0] + k_vec[1]*k_vec[1] + k_vec[2]*k_vec[2] + (k_v/v_k)*(k_v/v_k)); +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bstar_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bstar_src.cu new file mode 100644 index 000000000..4a79eb487 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bstar_src.cu @@ -0,0 +1,109 @@ +extern "C" __global__ +void push_gc_Bstar_explicit_multistage_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_free_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* grad_b_full_1, const int gb1_n2, const int gb1_n3, + const double* grad_b_full_2, const int gb2_n2, const int gb2_n3, + const double* grad_b_full_3, const int gb3_n2, const int gb3_n3, + const double* b2_1, const int b1_n2, const int b1_n3, + const double* b2_2, const int b2n2, const int b2n3, + const double* b2_3, const int b3_n2, const int b3_n3, + const double* curl_unit_b2_1, const int cb1_n2, const int cb1_n3, + const double* curl_unit_b2_2, const int cb2_n2, const int cb2_n3, + const double* curl_unit_b2_3, const int cb3_n2, const int cb3_n3, + const double* B_dot_b_coeffs, const int bdb_n2, const int bdb_n3, + const double* curl_unit_b_dot_b0, const int cub_n2, const int cub_n3, + const double* e_field_1, const int e1_n2, const int e1_n3, + const double* e_field_2, const int e2_n2, const int e2_n3, + const double* e_field_3, const int e3_n2, const int e3_n3, + const int evaluate_e_field, + const double dt_a, const double dt_b, const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + const double mu = row[mu_idx]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double e_star[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + grad_b_full_1, gb1_n2, gb1_n3, grad_b_full_2, gb2_n2, gb2_n3, grad_b_full_3, gb3_n2, gb3_n3, e_star); + e_star[0] *= -epsilon * mu; + e_star[1] *= -epsilon * mu; + e_star[2] *= -epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + e_field_1, e1_n2, e1_n3, e_field_2, e2_n2, e2_n3, e_field_3, e3_n2, e3_n3, e_field); + e_star[0] += e_field[0]; + e_star[1] += e_field[1]; + e_star[2] += e_field[2]; + } + + double b2[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + b2_1, b1_n2, b1_n3, b2_2, b2n2, b2n3, b2_3, b3_n2, b3_n3, b2); + + double b_star[3]; + eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + curl_unit_b2_1, cb1_n2, cb1_n3, curl_unit_b2_2, cb2_n2, cb2_n3, curl_unit_b2_3, cb3_n2, cb3_n3, b_star); + b_star[0] = b_star[0] * epsilon * v + b2[0]; + b_star[1] = b_star[1] * epsilon * v + b2[1]; + b_star[2] = b_star[2] * epsilon * v + b2[2]; + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, B_dot_b_coeffs, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, curl_unit_b_dot_b0, cub_n2, cub_n3); + b_star_parallel = b_star_parallel * epsilon * v + B_dot_b; + b_star_parallel *= det_df; + + double k[3]; + k[0] = b_star[0] / b_star_parallel * v; + k[1] = b_star[1] / b_star_parallel * v; + k[2] = b_star[2] / b_star_parallel * v; + + double k_v = dot3_dev(b_star, e_star); + k_v /= b_star_parallel * epsilon; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + row[first_free_idx + 3] += dt_b * k_v; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; + row[3] = row[first_init_idx + 3] + dt_a * k_v + last * row[first_free_idx + 3]; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu new file mode 100644 index 000000000..f731e1268 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu @@ -0,0 +1,96 @@ +extern "C" __global__ +void push_gc_bxEstar_explicit_multistage_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_free_idx, const int mu_idx, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* unit_b1_1, const int ub1_n2, const int ub1_n3, + const double* unit_b1_2, const int ub2_n2, const int ub2_n3, + const double* unit_b1_3, const int ub3_n2, const int ub3_n3, + const double* grad_b_full_1, const int gb1_n2, const int gb1_n3, + const double* grad_b_full_2, const int gb2_n2, const int gb2_n3, + const double* grad_b_full_3, const int gb3_n2, const int gb3_n3, + const double* B_dot_b_coeffs, const int bdb_n2, const int bdb_n3, + const double* curl_unit_b_dot_b0, const int cub_n2, const int cub_n3, + const double* e_field_1, const int e1_n2, const int e1_n3, + const double* e_field_2, const int e2_n2, const int e2_n3, + const double* e_field_3, const int e3_n2, const int e3_n3, + const int evaluate_e_field, + const double dt_a, const double dt_b, const double last) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + const double mu = row[mu_idx]; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double unit_b1[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + unit_b1_1, ub1_n2, ub1_n3, unit_b1_2, ub2_n2, ub2_n3, unit_b1_3, ub3_n2, ub3_n3, unit_b1); + + double e_star[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + grad_b_full_1, gb1_n2, gb1_n3, grad_b_full_2, gb2_n2, gb2_n3, grad_b_full_3, gb3_n2, gb3_n3, e_star); + e_star[0] *= -epsilon * mu; + e_star[1] *= -epsilon * mu; + e_star[2] *= -epsilon * mu; + + if (evaluate_e_field) { + double e_field[3]; + eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, + start0, start1, start2, + e_field_1, e1_n2, e1_n3, e_field_2, e2_n2, e2_n3, e_field_3, e3_n2, e3_n3, e_field); + e_star[0] += e_field[0]; + e_star[1] += e_field[1]; + e_star[2] += e_field[2]; + } + + const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, B_dot_b_coeffs, bdb_n2, bdb_n3); + double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, + start0, start1, start2, curl_unit_b_dot_b0, cub_n2, cub_n3); + b_star_parallel = b_star_parallel * epsilon * v + B_dot_b; + b_star_parallel *= det_df; + + double Exb[3]; + cross_dev(e_star, unit_b1, Exb); + + double k[3]; + k[0] = Exb[0] / b_star_parallel; + k[1] = Exb[1] / b_star_parallel; + k[2] = Exb[2] / b_star_parallel; + + row[first_free_idx + 0] += dt_b * k[0]; + row[first_free_idx + 1] += dt_b * k[1]; + row[first_free_idx + 2] += dt_b * k[2]; + + row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu new file mode 100644 index 000000000..bb74b9e50 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu @@ -0,0 +1,216 @@ +extern "C" __global__ +void push_gc_cc_J1_H1vec_cuda( + double* markers, const int n_cols, const int n_markers, + const double dt, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + + double b[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_vectorfield_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double e[3]; + cross_dev(b, u, e); + const double temp = dot3_dev(e, curl_norm_b); + + row[3] += temp / abs_b_star_para * v * dt; +} + +extern "C" __global__ +void push_gc_cc_J1_Hcurl_cuda( + double* markers, const int n_cols, const int n_markers, + const double dt, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], u_form[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u_form); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + // g_inv = (DF^T DF)^-1, transforms the 1-form u into H1vec components + double df_t[9] = { + dfm[0], dfm[3], dfm[6], + dfm[1], dfm[4], dfm[7], + dfm[2], dfm[5], dfm[8], + }; + double g[9], g_inv[9], u0[3]; + matmat_dev(df_t, dfm, g); + matrix_inv_dev(g, g_inv); + matvec_dev(g_inv, u_form, u0); + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = (b[k] + curl_norm_b[k] * v * epsilon) / det_df; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double e[3]; + cross_dev(b, u0, e); + const double temp = dot3_dev(e, curl_norm_b) / det_df; + + row[3] += temp / abs_b_star_para * v * dt; +} + +extern "C" __global__ +void push_gc_cc_J1_Hdiv_cuda( + double* markers, const int n_cols, const int n_markers, + const double dt, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double b[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + for (int k = 0; k < 3; k++) u[k] /= det_df; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double e[3]; + cross_dev(b, u, e); + const double temp = dot3_dev(e, curl_norm_b); + + row[3] += temp / abs_b_star_para * v * dt; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu new file mode 100644 index 000000000..b84496689 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu @@ -0,0 +1,171 @@ +extern "C" __global__ +void push_gc_cc_J2_dg_init_Hdiv_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, + const double dt, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double bb[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double tmp[9], e[3]; + matmat_dev(norm_b_prod, b_prod, tmp); + matvec_dev(tmp, u, e); + for (int k = 0; k < 3; k++) e[k] /= (abs_b_star_para * det_df); + + row[0] -= dt * e[0]; + row[1] -= dt * e[1]; + row[2] -= dt * e[2]; +} + +extern "C" __global__ +void push_gc_cc_J2_dg_Hdiv_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, + const double dt, const double const_, const double alpha, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3, + const double* ud_1, const int ud1_n2, const int ud1_n3, + const double* ud_2, const int ud2_n2, const int ud2_n3, + const double* ud_3, const int ud3_n2, const int ud3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + + const double eta_old0 = row[0], eta_old1 = row[1], eta_old2 = row[2]; + double eta_mid[3]; + eta_mid[0] = mod1_dev((row[0] + row[first_init_idx + 0]) / 2.0); + eta_mid[1] = mod1_dev((row[1] + row[first_init_idx + 1]) / 2.0); + eta_mid[2] = mod1_dev((row[2] + row[first_init_idx + 2]) / 2.0); + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; + const double det_df = det3_dev(dfm); + + double bb[3], u[3], ud[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + ud_1,ud1_n2,ud1_n3, ud_2,ud2_n2,ud2_n3, ud_3,ud3_n2,ud3_n3, ud); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double tmp[9], e[3], e2[3]; + matmat_dev(norm_b_prod, b_prod, tmp); + matvec_dev(tmp, u, e); + matvec_dev(tmp, ud, e2); + for (int k = 0; k < 3; k++) e[k] = (e[k] + const_ * e2[k]) / (abs_b_star_para * det_df); + + double eta_new[3]; + eta_new[0] = row[first_init_idx + 0] - dt * e[0]; + eta_new[1] = row[first_init_idx + 1] - dt * e[1]; + eta_new[2] = row[first_init_idx + 2] - dt * e[2]; + + row[0] = alpha * eta_new[0] + (1.0 - alpha) * eta_old0; + row[1] = alpha * eta_new[1] + (1.0 - alpha) * eta_old1; + row[2] = alpha * eta_new[2] + (1.0 - alpha) * eta_old2; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu new file mode 100644 index 000000000..327e3f647 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu @@ -0,0 +1,161 @@ +extern "C" __global__ +void push_gc_cc_J2_stage_H1vec_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_free_idx, + const double dt_a, const double dt_b, const double last, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double bb[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); + eval_vectorfield_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double tmp[9], e[3]; + matmat_dev(norm_b_prod, b_prod, tmp); + matvec_dev(tmp, u, e); + for (int k = 0; k < 3; k++) e[k] /= abs_b_star_para; + + row[first_free_idx + 0] -= dt_b * e[0]; + row[first_free_idx + 1] -= dt_b * e[1]; + row[first_free_idx + 2] -= dt_b * e[2]; + + row[0] = row[first_init_idx + 0] - dt_a * e[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] - dt_a * e[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] - dt_a * e[2] + last * row[first_free_idx + 2]; +} + +extern "C" __global__ +void push_gc_cc_J2_stage_Hdiv_cuda( + double* markers, const int n_cols, const int n_markers, + const int first_init_idx, const int first_free_idx, + const double dt_a, const double dt_b, const double last, + const int kind_map, const double* params, + const double epsilon, + const int p1, const int p2, const int p3, + const double* tn1, const int len_tn1, + const double* tn2, const int len_tn2, + const double* tn3, const int len_tn3, + const int start0, const int start1, const int start2, + const double* b_1, const int b1_n2, const int b1_n3, + const double* b_2, const int b2_n2, const int b2_n3, + const double* b_3, const int b3_n2, const int b3_n3, + const double* nb1, const int n1_n2, const int n1_n3, + const double* nb2, const int n2_n2, const int n2_n3, + const double* nb3, const int n3_n2, const int n3_n3, + const double* cnb1, const int c1_n2, const int c1_n3, + const double* cnb2, const int c2_n2, const int c2_n3, + const double* cnb3, const int c3_n2, const int c3_n3, + const double* u_1, const int u1_n2, const int u1_n3, + const double* u_2, const int u2_n2, const int u2_n3, + const double* u_3, const int u3_n2, const int u3_n3) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + + double* row = markers + (size_t)ip * n_cols; + if (row[0] == -1.0) return; + if (row[first_init_idx] == -1.0) return; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + const double v = row[3]; + + const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); + const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); + const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); + double bn1[MAXP+1], bd1[MAXP]; + double bn2[MAXP+1], bd2[MAXP]; + double bn3[MAXP+1], bd3[MAXP]; + b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); + b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); + b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + const double det_df = det3_dev(dfm); + + double bb[3], u[3], norm_b1[3], curl_norm_b[3]; + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); + eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); + eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, + cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); + + double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; + double norm_b_prod[9] = { + 0.0, -norm_b1[2], norm_b1[1], + norm_b1[2], 0.0, -norm_b1[0], + -norm_b1[1], norm_b1[0], 0.0}; + + double b_star[3]; + for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; + const double abs_b_star_para = dot3_dev(norm_b1, b_star); + + double tmp[9], e[3]; + matmat_dev(norm_b_prod, b_prod, tmp); + matvec_dev(tmp, u, e); + for (int k = 0; k < 3; k++) e[k] /= (abs_b_star_para * det_df); + + row[first_free_idx + 0] -= dt_b * e[0]; + row[first_free_idx + 1] -= dt_b * e[1]; + row[first_free_idx + 2] -= dt_b * e[2]; + + row[0] = row[first_init_idx + 0] - dt_a * e[0] + last * row[first_free_idx + 0]; + row[1] = row[first_init_idx + 1] - dt_a * e[1] + last * row[first_free_idx + 1]; + row[2] = row[first_init_idx + 2] - dt_a * e[2] + last * row[first_free_idx + 2]; +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_sph_cuda/_sph_pusher_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_sph_cuda/_sph_pusher_src.cu new file mode 100644 index 000000000..8ae9c8ef7 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_sph_cuda/_sph_pusher_src.cu @@ -0,0 +1,194 @@ +// Port of struphy.pic.sph_eval_kernels.box_based_kernel: SPH sum over the 27 +// neighbouring boxes of the marker's own box. +__device__ double box_based_kernel_dev( + const double* markers, const int n_cols, + double e1, double e2, double e3, + int loc_box, + const int* boxes, const int n_box_cols, + const int* neighbours, + const int* holes, + int periodic1, int periodic2, int periodic3, + int index, int kernel_type, + double h1, double h2, double h3) +{ + if (loc_box == -1) return 0.0; + + double acc = 0.0; + for (int neigh = 0; neigh < 27; neigh++) { + int box_to_search = neighbours[loc_box * 27 + neigh]; + int c = 0; + while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { + int p = boxes[(size_t)box_to_search * n_box_cols + c]; + c++; + if (!holes[p]) { + double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); + double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); + double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); + acc += markers[(size_t)p * n_cols + index] + * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); + } + } + } + return acc; +} + +// Shared tail of all three pushers: pull the logical-space force back to +// Cartesian with DF^-T and apply it to the marker velocity. +__device__ void apply_force_dev( + double* row, double e1, double e2, double e3, + int kind_map, const double* params, + const double* force_logical, const double* gravity, + double dt) +{ + double dfm[9], dfinv[9], force_cart[3]; + if (!df_dispatch_dev(kind_map, e1, e2, e3, params, dfm)) return; + matrix_inv_dev(dfm, dfinv); + // dfinvT @ force_logical == matvecT(dfinv, force_logical) + matvecT_dev(dfinv, force_logical, force_cart); + + row[3] -= dt * (force_cart[0] - gravity[0]); + row[4] -= dt * (force_cart[1] - gravity[1]); + row[5] -= dt * (force_cart[2] - gravity[2]); +} + +// --- push_v_sph_pressure (isothermal closure) --- +extern "C" __global__ +void push_v_sph_pressure_cuda( + double* markers, const int n_cols, const int n_markers, + const int* valid_mks, + const int weight_idx, const int first_free_idx, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3, + const double* gravity, const double kappa, + const int kind_map, const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const double n_at_eta = row[first_free_idx]; + const int loc_box = (int)row[n_cols - 2]; + + double grad_u[3] = {0.0, 0.0, 0.0}; + + grad_u[0] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 1, h1, h2, h3); + grad_u[0] *= kappa / n_at_eta; + grad_u[0] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 1, h1, h2, h3); + + if (kernel_type >= 340) { + grad_u[1] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 2, h1, h2, h3); + grad_u[1] *= kappa / n_at_eta; + grad_u[1] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 2, h1, h2, h3); + } + + if (kernel_type >= 670) { + grad_u[2] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 3, h1, h2, h3); + grad_u[2] *= kappa / n_at_eta; + grad_u[2] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 3, h1, h2, h3); + } + + apply_force_dev(row, e1, e2, e3, kind_map, params, grad_u, gravity, dt); +} + +// --- push_v_sph_pressure_ideal_gas (polytropic closure, gamma = 5/3) --- +extern "C" __global__ +void push_v_sph_pressure_ideal_gas_cuda( + double* markers, const int n_cols, const int n_markers, + const int* valid_mks, + const int weight_idx, const int first_free_idx, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3, + const double* gravity, const double kappa, + const int kind_map, const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + const double gamma = 5.0 / 3.0; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const double n_at_eta = row[first_free_idx]; + const int loc_box = (int)row[n_cols - 2]; + + const double pref = kappa * pow(n_at_eta, gamma - 2.0); + double grad_u[3] = {0.0, 0.0, 0.0}; + + grad_u[0] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 1, h1, h2, h3); + grad_u[0] *= pref; + grad_u[0] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 1, h1, h2, h3); + + if (kernel_type >= 340) { + grad_u[1] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 2, h1, h2, h3); + grad_u[1] *= pref; + grad_u[1] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 2, h1, h2, h3); + } + + if (kernel_type >= 670) { + grad_u[2] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 3, h1, h2, h3); + grad_u[2] *= pref; + grad_u[2] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 3, h1, h2, h3); + } + + apply_force_dev(row, e1, e2, e3, kind_map, params, grad_u, gravity, dt); +} + +// --- push_v_viscosity (deviatoric strain-rate tensor) --- +extern "C" __global__ +void push_v_viscosity_cuda( + double* markers, const int n_cols, const int n_markers, + const int* valid_mks, + const int first_free_idx, + const int* boxes, const int n_box_cols, + const int* neighbours, const int* holes, + const int periodic1, const int periodic2, const int periodic3, + const int kernel_type, + const double h1, const double h2, const double h3, + const int kind_map, const double* params, + const double dt) +{ + int ip = blockIdx.x * blockDim.x + threadIdx.x; + if (ip >= n_markers) return; + if (!valid_mks[ip]) return; + + double* row = markers + (size_t)ip * n_cols; + const double e1 = row[0], e2 = row[1], e3 = row[2]; + const int loc_box = (int)row[n_cols - 2]; + + double f_visc[3] = {0.0, 0.0, 0.0}; + for (int j = 0; j < 3; j++) { + for (int k = 0; k < 3; k++) { + const int coeff_idx = first_free_idx + 3 * (j + 1) + k; + f_visc[j] += box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, + neighbours, holes, periodic1, periodic2, periodic3, + coeff_idx, kernel_type + 1 + k, h1, h2, h3); + } + } + + const double no_gravity[3] = {0.0, 0.0, 0.0}; + apply_force_dev(row, e1, e2, e3, kind_map, params, f_visc, no_gravity, dt); +} + diff --git a/src/struphy/pic/pushing/cuda/pusher_utilities_kernels_cuda/_reflect_src.cu b/src/struphy/pic/pushing/cuda/pusher_utilities_kernels_cuda/_reflect_src.cu new file mode 100644 index 000000000..692e42b60 --- /dev/null +++ b/src/struphy/pic/pushing/cuda/pusher_utilities_kernels_cuda/_reflect_src.cu @@ -0,0 +1,36 @@ +extern "C" __global__ +void reflect_cuda( + double* markers, const int n_cols, + const long long* outside_inds, const int n_outside, + const int axis, + const int kind_map, const double* params) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + if (i >= n_outside) return; + + const long long ip = outside_inds[i]; + double* row = markers + (size_t)ip * n_cols; + + const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; + double v[3] = {row[3], row[4], row[5]}; + + double dfm[9]; + if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; + + double dfinv[9], v_logical[3]; + matrix_inv_dev(dfm, dfinv); + + // pull back of the velocity + matvec_dev(dfinv, v, v_logical); + + // reverse the velocity component along `axis` + v_logical[axis] *= -1.0; + + // push forward of the velocity + matvec_dev(dfm, v_logical, v); + + row[3] = v[0]; + row[4] = v[1]; + row[5] = v[2]; +} + diff --git a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py index 1fbb93de7..d6bdc25be 100644 --- a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py @@ -12,132 +12,9 @@ It is a plain per-marker 0-form spline evaluation, so it reuses the shared ``find_span_dev``/``b_splines_dev``/``eval_0form_dev`` device functions. """ +from struphy.cuda import load_cuda_source -_DK_HAMILTONIAN_SRC = r""" -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) -{ - double left[MAXP]; - double right[MAXP]; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -__device__ double eval_0form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bn2, const double* bn3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c, int n2x, int n3x) -{ - double out = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] - * bn1[il1] * bn2[il2] * bn3[il3]; - } - } - } - return out; -} - -__device__ double mod1_dev(double x) -{ - double r = fmod(x, 1.0); - if (r < 0.0) r += 1.0; - return r; -} - -extern "C" __global__ -void driftkinetic_hamiltonian_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, - const int first_init_idx, const int first_shift_idx, const int mu_idx, - const double a0, const double a1, const double a2, const double a3, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* B_dot_b, const int b_n2, const int b_n3, - const double* phi_c, const int p_n2, const int p_n3, - const int evaluate_e_field) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double alpha[3] = {a0, a1, a2}; - double eta[3]; - for (int i = 0; i < 3; i++) { - const double eta_k = row[i] + row[first_shift_idx + i]; - const double eta_n = row[first_init_idx + i]; - eta[i] = mod1_dev(alpha[i] * eta_k + (1.0 - alpha[i]) * eta_n); - } - - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - const double v = a3 * v_k + (1.0 - a3) * v_n; - const double mu = row[mu_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - b_splines_dev(tn1, p1, eta[0], span1, bn1); - b_splines_dev(tn2, p2, eta[1], span2, bn2); - b_splines_dev(tn3, p3, eta[2], span3, bn3); - - double phi = 0.0; - if (evaluate_e_field) { - phi = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, phi_c, p_n2, p_n3); - } - - const double bdb = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, B_dot_b, b_n2, b_n3); - - row[column_nr] = epsilon * v * v / 2.0 + epsilon * mu * bdb + phi; -} -""" +_DK_HAMILTONIAN_SRC = load_cuda_source(__file__, "eval_kernels_gc_cuda/_dk_hamiltonian_src.cu") _dk_kernel = None @@ -234,219 +111,7 @@ def driftkinetic_hamiltonian_gpu( # into one device helper. # --------------------------------------------------------------------------- -_GC_MARKER_COLUMN_SRC = r""" -__device__ void weighted_eta_v_dev( - const double* row, int first_init_idx, int first_shift_idx, - const double* alpha, double* eta, double* v_out) -{ - for (int k = 0; k < 3; k++) { - const double eta_k = row[k] + row[first_shift_idx + k]; - const double eta_n = row[first_init_idx + k]; - double e = alpha[k] * eta_k + (1.0 - alpha[k]) * eta_n; - double r = fmod(e, 1.0); - if (r < 0.0) r += 1.0; - eta[k] = r; - } - if (v_out) { - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - *v_out = alpha[3] * v_k + (1.0 - alpha[3]) * v_n; - } -} - -extern "C" __global__ -void grad_driftkinetic_hamiltonian_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int n_comps, const int* comps, - const int first_init_idx, const int first_shift_idx, const int mu_idx, - const double* alpha, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const int evaluate_e_field) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3]; - weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, 0); - const double mu = row[mu_idx]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - for (int j = 0; j < n_comps; j++) row[column_nr + j] = grad_H[comps[j]]; -} - -extern "C" __global__ -void bstar_parallel_3form_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, - const int first_init_idx, const int first_shift_idx, - const double* alpha, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* cub, const int cub_n2, const int cub_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3], v; - weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, &v); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta[0], eta[1], eta[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bn2[MAXP+1], bn3[MAXP+1]; - double bd1[MAXP], bd2[MAXP], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, cub, cub_n2, cub_n3); - - b_star_parallel = (b_star_parallel * epsilon * v + B_dot_b) * det_df; - - row[column_nr] = b_star_parallel; -} - -extern "C" __global__ -void bstar_2form_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int n_comps, const int* comps, - const int first_init_idx, const int first_shift_idx, - const double* alpha, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b1, const int b1_n2, const int b1_n3, - const double* b2, const int b2_n2, const int b2_n3, - const double* b3, const int b3_n2, const int b3_n3, - const double* cb1, const int c1_n2, const int c1_n3, - const double* cb2, const int c2_n2, const int c2_n3, - const double* cb3, const int c3_n2, const int c3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3], v; - weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, &v); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - double bb[3], b_star[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b1,b1_n2,b1_n3, b2,b2_n2,b2_n3, b3,b3_n2,b3_n3, bb); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cb1,c1_n2,c1_n3, cb2,c2_n2,c2_n3, cb3,c3_n2,c3_n3, b_star); - - for (int k = 0; k < 3; k++) b_star[k] = b_star[k] * epsilon * v + bb[k]; - - for (int j = 0; j < n_comps; j++) row[column_nr + j] = b_star[comps[j]]; -} - -extern "C" __global__ -void unit_b_1form_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int n_comps, const int* comps, - const int first_init_idx, const int first_shift_idx, - const double* alpha, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* ub1, const int u1_n2, const int u1_n3, - const double* ub2, const int u2_n2, const int u2_n3, - const double* ub3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3]; - weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, 0); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - double unit_b1[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ub1,u1_n2,u1_n3, ub2,u2_n2,u2_n3, ub3,u3_n2,u3_n3, unit_b1); - - for (int j = 0; j < n_comps; j++) row[column_nr + j] = unit_b1[comps[j]]; -} -""" +_GC_MARKER_COLUMN_SRC = load_cuda_source(__file__, "eval_kernels_gc_cuda/_gc_marker_column_src.cu") _gc_marker_column_kernels = {} diff --git a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py index ef9d77627..472d6212a 100644 --- a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py +++ b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py @@ -14,119 +14,9 @@ though the two geometry helpers are unused here since none of these three kernels touch the domain Jacobian). """ +from struphy.cuda import load_cuda_source -_SPH_MARKER_COLUMN_SRC = r""" -extern "C" __global__ -void sph_pressure_coeffs_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int weight_idx, - const int* valid_mks, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const int loc_box = (int)row[n_cols - 2]; - - const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); - - const double weight = row[weight_idx]; - const double gamma = 5.0 / 3.0; - - row[column_nr] = n_at_eta; - row[column_nr + 1] = weight / n_at_eta; - row[column_nr + 2] = weight * pow(n_at_eta, gamma - 2.0); -} - -extern "C" __global__ -void sph_mean_velocity_coeffs_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int weight_idx, - const int* valid_mks, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const int loc_box = (int)row[n_cols - 2]; - - const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); - - const double weight = row[weight_idx]; - const double scale = weight / n_at_eta; - - row[column_nr + 0] = scale * row[3]; - row[column_nr + 1] = scale * row[4]; - row[column_nr + 2] = scale * row[5]; -} - -extern "C" __global__ -void sph_viscosity_tensor_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int weight_idx, const int first_free_idx, - const int* valid_mks, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3, - const double mu) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const int loc_box = (int)row[n_cols - 2]; - - const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); - const double weight = row[weight_idx]; - - double grad_v[3][3]; - for (int j = 0; j < 3; j++) { - for (int k = 0; k < 3; k++) { - grad_v[j][k] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, - first_free_idx + j, kernel_type + 1 + k, h1, h2, h3); - } - } - - double d_dev[3][3]; - for (int j = 0; j < 3; j++) - for (int k = 0; k < 3; k++) - d_dev[j][k] = 0.5 * (grad_v[j][k] + grad_v[k][j]); - - const double mean_trace = (d_dev[0][0] + d_dev[1][1] + d_dev[2][2]) / 3.0; - d_dev[0][0] -= mean_trace; - d_dev[1][1] -= mean_trace; - d_dev[2][2] -= mean_trace; - - const double scale = -2.0 * mu * (weight / n_at_eta); - for (int j = 0; j < 3; j++) { - for (int k = 0; k < 3; k++) { - row[column_nr + 3 * j + k] = d_dev[j][k] * scale; - } - } -} -""" +_SPH_MARKER_COLUMN_SRC = load_cuda_source(__file__, "eval_kernels_sph_cuda/_sph_marker_column_src.cu") _sph_marker_column_kernels = {} diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index d5a093cd0..179c41066 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -49,45 +49,9 @@ they are passed straight through with no transfer at all -- only the marker array round-trips through the device, exactly once per call. """ +from struphy.cuda import load_cuda_source -_PUSH_ETA_CUBOID_SRC = r""" -extern "C" __global__ -void push_eta_stage_cuboid( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const double sx, - const double sy, - const double sz, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - - // skip holes and ghost/boundary particles, matching push_eta_stage - if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double kx = sx * row[3]; - const double ky = sy * row[4]; - const double kz = sz * row[5]; - - // accumulate for the last stage (must happen before the position update, - // which reads the just-updated accumulator) - row[first_free_idx + 0] += dt_b * kx; - row[first_free_idx + 1] += dt_b * ky; - row[first_free_idx + 2] += dt_b * kz; - - row[0] = row[first_init_idx + 0] + dt_a * kx + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * ky + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * kz + last * row[first_free_idx + 2]; -} -""" +_PUSH_ETA_CUBOID_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_eta_cuboid_src.cu") _push_eta_cuboid_kernel = None @@ -147,62 +111,7 @@ def push_eta_stage_cuboid_gpu( ) -_PUSH_ETA_RK_PERIODIC_SRC = r""" -extern "C" __global__ -void push_eta_rk_periodic( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int first_shift_idx, - const double sx, - const double sy, - const double sz, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - - if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double kx = sx * row[3]; - const double ky = sy * row[4]; - const double kz = sz * row[5]; - - row[first_free_idx + 0] += dt_b * kx; - row[first_free_idx + 1] += dt_b * ky; - row[first_free_idx + 2] += dt_b * kz; - - double e0 = row[first_init_idx + 0] + dt_a * kx + last * row[first_free_idx + 0]; - double e1 = row[first_init_idx + 1] + dt_a * ky + last * row[first_free_idx + 1]; - double e2 = row[first_init_idx + 2] + dt_a * kz + last * row[first_free_idx + 2]; - - // periodic wrap + shift bookkeeping, matching the periodic branch of - // Particles.apply_kinetic_bc (Python's a % 1.0 is always in [0, 1)) - double shift0 = 0.0, shift1 = 0.0, shift2 = 0.0; - - if (e0 > 1.0) { e0 = fmod(e0, 1.0); shift0 = 1.0; } - else if (e0 < 0.0) { e0 = fmod(e0, 1.0); if (e0 < 0.0) e0 += 1.0; shift0 = -1.0; } - - if (e1 > 1.0) { e1 = fmod(e1, 1.0); shift1 = 1.0; } - else if (e1 < 0.0) { e1 = fmod(e1, 1.0); if (e1 < 0.0) e1 += 1.0; shift1 = -1.0; } - - if (e2 > 1.0) { e2 = fmod(e2, 1.0); shift2 = 1.0; } - else if (e2 < 0.0) { e2 = fmod(e2, 1.0); if (e2 < 0.0) e2 += 1.0; shift2 = -1.0; } - - row[0] = e0; - row[1] = e1; - row[2] = e2; - row[first_shift_idx + 0] = shift0; - row[first_shift_idx + 1] = shift1; - row[first_shift_idx + 2] = shift2; -} -""" +_PUSH_ETA_RK_PERIODIC_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_eta_rk_periodic_src.cu") _push_eta_rk_periodic_kernel = None @@ -279,159 +188,7 @@ def push_eta_rk_periodic_gpu( ) -_PUSH_V_EFIELD_CUBOID_SRC = r""" -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -// Combined N-spline (bn, p+1 values) and D-spline (bd, p values) evaluation, -// matching struphy.bsplines.bsplines_kernels.b_d_splines_slim exactly. -__device__ void b_d_splines_dev(const double* t, int p, double eta, int span, double* bn, double* bd) -{ - double left[MAXP]; - double right[MAXP]; - int pd = p - 1; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - for (int i = 0; i < p; i++) bd[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - - if (j == p - 1) { - for (int il = 0; il <= pd; il++) { - bd[pd - il] = (double)p / (t[span - il + p] - t[span - il]) * bn[pd - il]; - } - } - - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -extern "C" __global__ -void push_v_with_efield_cuboid( - double* markers, - const int n_cols, - const int n_markers, - const int p1, - const int p2, - const int p3, - const double* tn1, - const int len_tn1, - const double* tn2, - const int len_tn2, - const double* tn3, - const int len_tn3, - const int start0, - const int start1, - const int start2, - const double* e1_1, - const int n2x1, - const int n3x1, - const double* e1_2, - const int n2x2, - const int n3x2, - const double* e1_3, - const int n2x3, - const int n3x3, - const double sx, - const double sy, - const double sz, - const double dt_const) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - - // skip holes and ghost/boundary particles, matching Particles.valid_mks - if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double eta1 = row[0]; - const double eta2 = row[1]; - const double eta3 = row[2]; - - double bn1[MAXP + 1], bd1[MAXP]; - double bn2[MAXP + 1], bd2[MAXP]; - double bn3[MAXP + 1], bd3[MAXP]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - // e_form[0]: D-spline in direction 1, N-splines in directions 2, 3 - double e_form0 = 0.0; - for (int il1 = 0; il1 < p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - e_form0 += e1_1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; - } - } - } - - // e_form[1]: N-spline in direction 1, D-spline in direction 2, N-spline in direction 3 - double e_form1 = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 < p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - e_form1 += e1_2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; - } - } - } - - // e_form[2]: N-splines in directions 1, 2, D-spline in direction 3 - double e_form2 = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 < p3; il3++) { - int i3 = span3 + il3 - start2; - e_form2 += e1_3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; - } - } - } - - // Cartesian E-field is DF^-T @ e_form; for Cuboid, DF is diag(sx^-1, sy^-1, sz^-1) - // so DF^-T is diag(sx, sy, sz) -- same convention as push_eta_stage_cuboid's scale. - row[3] += dt_const * sx * e_form0; - row[4] += dt_const * sy * e_form1; - row[5] += dt_const * sz * e_form2; -} -""" +_PUSH_V_EFIELD_CUBOID_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_v_efield_cuboid_src.cu") _push_v_efield_cuboid_kernel = None @@ -555,1459 +312,7 @@ def push_v_with_efield_cuboid_gpu( # kind_map, they are simply # not wired up for one. -_GENERAL_GEOMETRY_SRC = r""" -#define MAXP 8 - -__device__ void matrix_inv_dev(const double* a, double* b) -{ - double det_a = a[0]*(a[4]*a[8] - a[5]*a[7]) - - a[1]*(a[3]*a[8] - a[5]*a[6]) - + a[2]*(a[3]*a[7] - a[4]*a[6]); - - b[0] = (a[4]*a[8] - a[7]*a[5]) / det_a; - b[1] = (a[7]*a[2] - a[1]*a[8]) / det_a; - b[2] = (a[1]*a[5] - a[4]*a[2]) / det_a; - b[3] = (a[5]*a[6] - a[8]*a[3]) / det_a; - b[4] = (a[8]*a[0] - a[2]*a[6]) / det_a; - b[5] = (a[2]*a[3] - a[5]*a[0]) / det_a; - b[6] = (a[3]*a[7] - a[6]*a[4]) / det_a; - b[7] = (a[6]*a[1] - a[0]*a[7]) / det_a; - b[8] = (a[0]*a[4] - a[3]*a[1]) / det_a; -} - -// c = a^T @ b (used for both DF^-1 @ v and DF^-T @ e_form: pass dfinv or -// its transpose accordingly -- here we need dfinv @ v (not transposed) for -// push_eta_stage, and dfinvT @ e_form for push_v_with_efield, so both a -// plain and a transposed matvec are provided). -__device__ void matvec_dev(const double* a, const double* v, double* out) -{ - out[0] = a[0]*v[0] + a[1]*v[1] + a[2]*v[2]; - out[1] = a[3]*v[0] + a[4]*v[1] + a[5]*v[2]; - out[2] = a[6]*v[0] + a[7]*v[1] + a[8]*v[2]; -} - -// c = a @ b, 3x3 row-major matrices. -__device__ void matmat_dev(const double* a, const double* b, double* c) -{ - for (int i = 0; i < 3; i++) { - for (int j = 0; j < 3; j++) { - c[3*i+j] = a[3*i+0]*b[0*3+j] + a[3*i+1]*b[1*3+j] + a[3*i+2]*b[2*3+j]; - } - } -} - -__device__ void matvecT_dev(const double* a, const double* v, double* out) -{ - out[0] = a[0]*v[0] + a[3]*v[1] + a[6]*v[2]; - out[1] = a[1]*v[0] + a[4]*v[1] + a[7]*v[2]; - out[2] = a[2]*v[0] + a[5]*v[1] + a[8]*v[2]; -} - -// df_out is row-major 3x3 (df_out[3*i+j] = dF_i/deta_j), matching -// struphy.geometry.mappings_kernels.cuboid_df / colella_df exactly. -__device__ void cuboid_df_dev(const double* params, double* df_out) -{ - // params = (l1, r1, l2, r2, l3, r3) - for (int k = 0; k < 9; k++) df_out[k] = 0.0; - df_out[0] = params[1] - params[0]; - df_out[4] = params[3] - params[2]; - df_out[8] = params[5] - params[4]; -} - -__device__ void colella_df_dev(double eta1, double eta2, const double* params, double* df_out) -{ - // params = (Lx, Ly, alpha, Lz) - const double lx = params[0], ly = params[1], alpha = params[2], lz = params[3]; - const double twopi = 6.283185307179586; - const double s1 = sin(twopi * eta1), c1 = cos(twopi * eta1); - const double s2 = sin(twopi * eta2), c2 = cos(twopi * eta2); - - df_out[0] = lx * (1.0 + alpha * c1 * s2 * twopi); - df_out[1] = lx * alpha * s1 * c2 * twopi; - df_out[2] = 0.0; - df_out[3] = ly * alpha * c1 * s2 * twopi; - df_out[4] = ly * (1.0 + alpha * s1 * c2 * twopi); - df_out[5] = 0.0; - df_out[6] = 0.0; - df_out[7] = 0.0; - df_out[8] = lz; -} - -__device__ void orthogonal_df_dev(double eta1, double eta2, const double* params, double* df_out) -{ - // params = (Lx, Ly, alpha, Lz) - const double lx = params[0], ly = params[1], alpha = params[2], lz = params[3]; - const double twopi = 6.283185307179586; - - for (int k = 0; k < 9; k++) df_out[k] = 0.0; - df_out[0] = lx * (1.0 + alpha * cos(twopi * eta1) * twopi); - df_out[4] = ly * (1.0 + alpha * cos(twopi * eta2) * twopi); - df_out[8] = lz; -} - -__device__ void hollow_cyl_df_dev(double eta1, double eta2, const double* params, double* df_out) -{ - // params = (a1, a2, Lz, poc); faithful port of - // struphy.geometry.mappings_kernels.hollow_cyl_df, including its - // existing df_out[0,0]/df_out[1,0] not dividing eta2's argument by poc - // (unlike f_out and every other entry here) -- not "fixed" here, since - // this is a port, not a bugfix. - const double a1 = params[0], a2 = params[1], lz = params[2], poc = params[3]; - const double twopi = 6.283185307179586; - const double da = a2 - a1; - const double r = a1 + eta1 * da; - - df_out[0] = da * cos(twopi * eta2); - df_out[1] = -twopi / poc * r * sin(twopi * eta2 / poc); - df_out[2] = 0.0; - df_out[3] = da * sin(twopi * eta2); - df_out[4] = twopi / poc * r * cos(twopi * eta2 / poc); - df_out[5] = 0.0; - df_out[6] = 0.0; - df_out[7] = 0.0; - df_out[8] = lz; -} - -__device__ void powered_ellipse_df_dev(double eta1, double eta2, const double* params, double* df_out) -{ - // params = (rx, ry, Lz, s) - const double rx = params[0], ry = params[1], lz = params[2], s = params[3]; - const double twopi = 6.283185307179586; - const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); - const double e_sm1 = pow(eta1, s - 1.0); - const double e_s = pow(eta1, s); - - df_out[0] = e_sm1 * rx * c2; - df_out[1] = -twopi * e_s * rx * s2; - df_out[2] = 0.0; - df_out[3] = e_sm1 * ry * s2; - df_out[4] = twopi * e_s * ry * c2; - df_out[5] = 0.0; - df_out[6] = 0.0; - df_out[7] = 0.0; - df_out[8] = lz; -} - -__device__ void hollow_torus_df_dev(double eta1, double eta2, double eta3, const double* params, double* df_out) -{ - // params = (a1, a2, R0, sfl, pol_period, tor_period) - const double a1 = params[0], a2 = params[1], r0 = params[2]; - const double sfl = params[3], pol_period = params[4], tor_period = params[5]; - const double pi = 3.14159265358979323846; - const double twopi = 6.283185307179586; - const double da = a2 - a1; - - if (sfl == 1.0) { - const double r = a1 + da * eta1; - const double eps = r / r0; - const double eps_p = da / r0; - const double tpe = tan(pi * eta2); - const double cpe = cos(pi * eta2); - const double tpe_p = pi / (cpe * cpe); - const double g = sqrt((1.0 + eps) / (1.0 - eps)); - const double g_p = 1.0 / (2.0 * g) * (eps_p * (1.0 - eps) + (1.0 + eps) * eps_p) / ((1.0 - eps) * (1.0 - eps)); - const double theta = 2.0 * atan(g * tpe); - const double denom = 1.0 + (g * tpe) * (g * tpe); - const double dtheta_deta1 = 2.0 / denom * g_p * tpe; - const double dtheta_deta2 = 2.0 / denom * g * tpe_p; - const double ct = cos(theta), st = sin(theta); - const double cf = cos(twopi * eta3 / tor_period), sf = sin(twopi * eta3 / tor_period); - - df_out[0] = (da * ct - r * st * dtheta_deta1) * cf; - df_out[1] = -r * st * dtheta_deta2 * cf; - df_out[2] = -twopi / tor_period * (r * ct + r0) * sf; - - df_out[3] = (da * ct - r * st * dtheta_deta1) * (-1.0) * sf; - df_out[4] = -r * st * dtheta_deta2 * (-1.0) * sf; - df_out[5] = twopi / tor_period * (r * ct + r0) * (-1.0) * cf; - - df_out[6] = da * st + r * ct * dtheta_deta1; - df_out[7] = r * ct * dtheta_deta2; - df_out[8] = 0.0; - } else { - const double r = a1 + eta1 * da; - const double cp = cos(twopi * eta2 / pol_period), sp = sin(twopi * eta2 / pol_period); - const double cf = cos(twopi * eta3 / tor_period), sf = sin(twopi * eta3 / tor_period); - - df_out[0] = da * cp * cf; - df_out[1] = -twopi / pol_period * r * sp * cf; - df_out[2] = -twopi / tor_period * (r * cp + r0) * sf; - - df_out[3] = da * cp * (-1.0) * sf; - df_out[4] = -twopi / pol_period * r * sp * (-1.0) * sf; - df_out[5] = (r * cp + r0) * (-1.0) * cf * twopi / tor_period; - - df_out[6] = da * sp; - df_out[7] = r * cp * twopi / pol_period; - df_out[8] = 0.0; - } -} - -__device__ void shafranov_shift_df_dev(double eta1, double eta2, const double* params, double* df_out) -{ - // params = (rx, ry, Lz, delta) - const double rx = params[0], ry = params[1], lz = params[2], de = params[3]; - const double twopi = 6.283185307179586; - const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); - - df_out[0] = rx * c2 - 2.0 * eta1 * rx * de; - df_out[1] = -twopi * (eta1 * rx) * s2; - df_out[2] = 0.0; - df_out[3] = ry * s2; - df_out[4] = twopi * (eta1 * ry) * c2; - df_out[5] = 0.0; - df_out[6] = 0.0; - df_out[7] = 0.0; - df_out[8] = lz; -} - -__device__ void shafranov_sqrt_df_dev(double eta1, double eta2, const double* params, double* df_out) -{ - // params = (rx, ry, Lz, delta) - const double rx = params[0], ry = params[1], lz = params[2], de = params[3]; - const double twopi = 6.283185307179586; - const double c2 = cos(twopi * eta2), s2 = sin(twopi * eta2); - - df_out[0] = rx * c2 - 0.5 / sqrt(eta1) * rx * de; - df_out[1] = -twopi * (eta1 * rx) * s2; - df_out[2] = 0.0; - df_out[3] = ry * s2; - df_out[4] = twopi * (eta1 * ry) * c2; - df_out[5] = 0.0; - df_out[6] = 0.0; - df_out[7] = 0.0; - df_out[8] = lz; -} - -__device__ void shafranov_dshaped_df_dev(double eta1, double eta2, const double* params, double* df_out) -{ - // params = (R0, Lz, delta_x, delta_y, delta_gs, epsilon_gs, kappa_gs) - const double r0 = params[0], lz = params[1], dx = params[2], dy = params[3]; - const double dg = params[4], eg = params[5], kg = params[6]; - const double pi = 3.14159265358979323846; - const double twopi = 6.283185307179586; - const double asin_dg = asin(dg); - const double s2 = sin(twopi * eta2), c2 = cos(twopi * eta2); - const double phase = eta1 * s2 * asin_dg + twopi * eta2; - - df_out[0] = r0 * ( - -2.0 * dx * eta1 - - eg * eta1 * s2 * asin_dg * sin(phase) - + eg * cos(phase) - ); - df_out[1] = -r0 * eg * eta1 * (twopi * eta1 * c2 * asin_dg + twopi) * sin(phase); - df_out[2] = 0.0; - df_out[3] = r0 * (-2.0 * dy * eta1 + eg * kg * s2); - df_out[4] = twopi * r0 * eg * eta1 * kg * c2; - df_out[5] = 0.0; - df_out[6] = 0.0; - df_out[7] = 0.0; - df_out[8] = lz; -} - -// Returns 1 if kind_map is supported and df_out was filled, 0 otherwise. -__device__ int df_dispatch_dev(int kind_map, double eta1, double eta2, double eta3, - const double* params, double* df_out) -{ - if (kind_map == 10) { cuboid_df_dev(params, df_out); return 1; } - if (kind_map == 11) { orthogonal_df_dev(eta1, eta2, params, df_out); return 1; } - if (kind_map == 12) { colella_df_dev(eta1, eta2, params, df_out); return 1; } - if (kind_map == 20) { hollow_cyl_df_dev(eta1, eta2, params, df_out); return 1; } - if (kind_map == 21) { powered_ellipse_df_dev(eta1, eta2, params, df_out); return 1; } - if (kind_map == 22) { hollow_torus_df_dev(eta1, eta2, eta3, params, df_out); return 1; } - if (kind_map == 30) { shafranov_shift_df_dev(eta1, eta2, params, df_out); return 1; } - if (kind_map == 31) { shafranov_sqrt_df_dev(eta1, eta2, params, df_out); return 1; } - if (kind_map == 32) { shafranov_dshaped_df_dev(eta1, eta2, params, df_out); return 1; } - return 0; -} - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -// Same as pusher_kernels_cuda.py's push_v_with_efield_cuboid's b_d_splines_dev, -// duplicated here because each cp.RawKernel source string is compiled -// independently (no cross-source linking). -__device__ void b_d_splines_dev(const double* t, int p, double eta, int span, double* bn, double* bd) -{ - double left[MAXP]; - double right[MAXP]; - int pd = p - 1; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - for (int i = 0; i < p; i++) bd[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - - if (j == p - 1) { - for (int il = 0; il <= pd; il++) { - bd[pd - il] = (double)p / (t[span - il + p] - t[span - il]) * bn[pd - il]; - } - } - - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -__device__ double det3_dev(const double* a) -{ - return a[0]*(a[4]*a[8] - a[5]*a[7]) - - a[1]*(a[3]*a[8] - a[5]*a[6]) - + a[2]*(a[3]*a[7] - a[4]*a[6]); -} - -__device__ void cross_dev(const double* a, const double* b, double* out) -{ - out[0] = a[1]*b[2] - a[2]*b[1]; - out[1] = a[2]*b[0] - a[0]*b[2]; - out[2] = a[0]*b[1] - a[1]*b[0]; -} - -__device__ double dot3_dev(const double* a, const double* b) -{ - return a[0]*b[0] + a[1]*b[1] + a[2]*b[2]; -} - -// Single-point evaluation of a Derham 2-form spline, matching -// struphy.bsplines.evaluation_kernels_3d.eval_2form_spline_mpi (N-D-D / -// D-N-D / D-D-N tensor-product sums, the dual basis combination of the -// 1-form evaluation in push_v_with_efield_general above). -__device__ void eval_2form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bd1, - const double* bn2, const double* bd2, - const double* bn3, const double* bd3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c1, int n2x1, int n3x1, - const double* c2, int n2x2, int n3x2, - const double* c3, int n2x3, int n3x3, - double* out) -{ - out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; - - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 < p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 < p3; il3++) { - int i3 = span3 + il3 - start2; - out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bn1[il1] * bd2[il2] * bd3[il3]; - } - } - } - for (int il1 = 0; il1 < p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 < p3; il3++) { - int i3 = span3 + il3 - start2; - out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bd1[il1] * bn2[il2] * bd3[il3]; - } - } - } - for (int il1 = 0; il1 < p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 < p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bd1[il1] * bd2[il2] * bn3[il3]; - } - } - } -} - -extern "C" __global__ -void push_eta_stage_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; - - double dfm[9], dfinv[9], v[3], k[3]; - v[0] = row[3]; v[1] = row[4]; v[2] = row[5]; - - df_dispatch_dev(kind_map, row[0], row[1], row[2], params, dfm); - matrix_inv_dev(dfm, dfinv); - matvec_dev(dfinv, v, k); - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_v_with_efield_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* e1_1, const int n2x1, const int n3x1, - const double* e1_2, const int n2x2, const int n3x2, - const double* e1_3, const int n2x3, const int n3x3, - const int kind_map, - const double* params, - const double dt_const) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - - double bn1[MAXP + 1], bd1[MAXP]; - double bn2[MAXP + 1], bd2[MAXP]; - double bn3[MAXP + 1], bd3[MAXP]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double e_form[3] = {0.0, 0.0, 0.0}; - for (int il1 = 0; il1 < p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - e_form[0] += e1_1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; - } - } - } - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 < p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - e_form[1] += e1_2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; - } - } - } - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 < p3; il3++) { - int i3 = span3 + il3 - start2; - e_form[2] += e1_3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; - } - } - } - - double dfm[9], dfinv[9], dfinvT_e[3]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - matvecT_dev(dfinv, e_form, dfinvT_e); - - row[3] += dt_const * dfinvT_e[0]; - row[4] += dt_const * dfinvT_e[1]; - row[5] += dt_const * dfinvT_e[2]; -} - -// Shared setup for push_vxb_analytic_general / push_vxb_implicit_general: -// evaluate DF(eta), its determinant, and the Cartesian B-field at the -// marker's position. Returns 0 (and leaves b_cart untouched) if the marker -// is a hole/ghost, matching both CPU kernels' skip check. -__device__ int eval_b_cart_dev( - const double* row, const int n_cols, const int first_init_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const int kind_map, const double* params, - double* b_cart) -{ - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - - double bn1[MAXP + 1], bd1[MAXP]; - double bn2[MAXP + 1], bd2[MAXP]; - double bn3[MAXP + 1], bd3[MAXP]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b_form[3]; - eval_2form_dev( - p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, - span1, span2, span3, start0, start1, start2, - b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - b_form - ); - - double dfm[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - const double det_df = det3_dev(dfm); - - matvec_dev(dfm, b_form, b_cart); - b_cart[0] /= det_df; - b_cart[1] /= det_df; - b_cart[2] /= det_df; - return 1; -} - -extern "C" __global__ -void push_vxb_analytic_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; - - double b_cart[3]; - eval_b_cart_dev( - row, n_cols, first_init_idx, p1, p2, p3, - tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, - b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, b_cart - ); - - const double b_abs = sqrt(b_cart[0]*b_cart[0] + b_cart[1]*b_cart[1] + b_cart[2]*b_cart[2]); - if (b_abs == 0.0) return; - - double b_norm[3] = {b_cart[0]/b_abs, b_cart[1]/b_abs, b_cart[2]/b_abs}; - double v[3] = {row[3], row[4], row[5]}; - - const double vpar = dot3_dev(v, b_norm); - - double vxb_norm[3], vperp[3], b_normxvperp[3]; - cross_dev(v, b_norm, vxb_norm); - cross_dev(b_norm, vxb_norm, vperp); - cross_dev(b_norm, vperp, b_normxvperp); - - const double cbt = cos(b_abs * dt), sbt = sin(b_abs * dt); - row[3] = vpar * b_norm[0] + cbt * vperp[0] - sbt * b_normxvperp[0]; - row[4] = vpar * b_norm[1] + cbt * vperp[1] - sbt * b_normxvperp[1]; - row[5] = vpar * b_norm[2] + cbt * vperp[2] - sbt * b_normxvperp[2]; -} - -extern "C" __global__ -void push_vxb_implicit_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - // NOTE: the CPU push_vxb_implicit only checks the hole flag, not the - // ghost flag (unlike push_vxb_analytic) -- faithfully reproduced here. - if (row[first_init_idx] == -1.0) return; - - double b_cart[3]; - eval_b_cart_dev( - row, n_cols, first_init_idx, p1, p2, p3, - tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, - b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, b_cart - ); - - // b_prod = [[0, bz, -by], [-bz, 0, bx], [by, -bx, 0]] (row-major), such - // that b_prod @ v == v x b_cart (matches the CPU kernel's b_prod, which - // solves v x B via a matrix product rather than a cross product). - double b_prod[9] = { - 0.0, b_cart[2], -b_cart[1], - -b_cart[2], 0.0, b_cart[0], - b_cart[1], -b_cart[0], 0.0, - }; - - double rhs[9], lhs[9]; - for (int k = 0; k < 9; k++) { - const double id = (k == 0 || k == 4 || k == 8) ? 1.0 : 0.0; - rhs[k] = id + 0.5 * dt * b_prod[k]; - lhs[k] = id - 0.5 * dt * b_prod[k]; - } - - double lhs_inv[9]; - matrix_inv_dev(lhs, lhs_inv); - - double v[3] = {row[3], row[4], row[5]}; - double vec[3], res[3]; - matvec_dev(rhs, v, vec); - matvec_dev(lhs_inv, vec, res); - - row[3] = res[0]; - row[4] = res[1]; - row[5] = res[2]; -} - -// Single-point evaluation of a Derham 1-form spline (D-N-N / N-D-N / N-N-D), -// matching struphy.bsplines.evaluation_kernels_3d.eval_1form_spline_mpi. -// A standalone copy of the same math already inlined in -// push_v_with_efield_general above -- kept separate (not factored out and -// reused there) to avoid touching that already-validated kernel. -__device__ void eval_1form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bd1, - const double* bn2, const double* bd2, - const double* bn3, const double* bd3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c1, int n2x1, int n3x1, - const double* c2, int n2x2, int n3x2, - const double* c3, int n2x3, int n3x3, - double* out) -{ - out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; - - for (int il1 = 0; il1 < p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; - } - } - } - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 < p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; - } - } - } - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 < p3; il3++) { - int i3 = span3 + il3 - start2; - out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; - } - } - } -} - -// Single-point evaluation of a vector-field spline (H^1)^3 (N-N-N for every -// component), matching -// struphy.bsplines.evaluation_kernels_3d.eval_vectorfield_spline_mpi. -__device__ void eval_vectorfield_dev( - int p1, int p2, int p3, - const double* bn1, const double* bn2, const double* bn3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c1, int n2x1, int n3x1, - const double* c2, int n2x2, int n3x2, - const double* c3, int n2x3, int n3x3, - double* out) -{ - out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - double b123 = bn1[il1] * bn2[il2] * bn3[il3]; - out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * b123; - out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * b123; - out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * b123; - } - } - } -} - -// Shared setup for push_bxu_{Hdiv,Hcurl,H1vec}_general: evaluate DF(eta), -// its determinant, and the Cartesian B-field (always a 2-form) at the -// marker's position. Also computes and caches the local N-/D-spline basis -// values and span indices, reused by the caller for its own U-field -// evaluation (which differs per FEEC space). -__device__ void eval_b_cart_and_basis_dev( - double eta1, double eta2, double eta3, - int p1, int p2, int p3, - const double* tn1, int len_tn1, - const double* tn2, int len_tn2, - const double* tn3, int len_tn3, - int start0, int start1, int start2, - const double* b2_1, int n2x1, int n3x1, - const double* b2_2, int n2x2, int n3x2, - const double* b2_3, int n2x3, int n3x3, - int kind_map, const double* params, - double* bn1, double* bd1, double* bn2, double* bd2, double* bn3, double* bd3, - int* span1_out, int* span2_out, int* span3_out, - double* dfm, double* b_cart) -{ - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - *span1_out = span1; *span2_out = span2; *span3_out = span3; - - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b_form[3]; - eval_2form_dev( - p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, - span1, span2, span3, start0, start1, start2, - b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - b_form - ); - - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - const double det_df = det3_dev(dfm); - matvec_dev(dfm, b_form, b_cart); - b_cart[0] /= det_df; - b_cart[1] /= det_df; - b_cart[2] /= det_df; -} - -extern "C" __global__ -void push_bxu_Hdiv_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const double* u2_1, const int m2x1, const int m3x1, - const double* u2_2, const int m2x2, const int m3x2, - const double* u2_3, const int m2x3, const int m3x3, - const int kind_map, - const double* params, - const double boundary_cut, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfm[9], b_cart[3]; - eval_b_cart_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart - ); - - double u_form[3]; - eval_2form_dev( - p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, - span1, span2, span3, start0, start1, start2, - u2_1, m2x1, m3x1, u2_2, m2x2, m3x2, u2_3, m2x3, m3x3, - u_form - ); - const double det_df = det3_dev(dfm); - double u_cart[3]; - matvec_dev(dfm, u_form, u_cart); - u_cart[0] /= det_df; u_cart[1] /= det_df; u_cart[2] /= det_df; - - double e_cart[3]; - cross_dev(b_cart, u_cart, e_cart); - row[3] += dt * e_cart[0]; - row[4] += dt * e_cart[1]; - row[5] += dt * e_cart[2]; -} - -extern "C" __global__ -void push_bxu_Hcurl_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const double* u1_1, const int m2x1, const int m3x1, - const double* u1_2, const int m2x2, const int m3x2, - const double* u1_3, const int m2x3, const int m3x3, - const int kind_map, - const double* params, - const double boundary_cut, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfm[9], b_cart[3]; - eval_b_cart_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart - ); - - double u_form[3]; - eval_1form_dev( - p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, - span1, span2, span3, start0, start1, start2, - u1_1, m2x1, m3x1, u1_2, m2x2, m3x2, u1_3, m2x3, m3x3, - u_form - ); - double dfinv[9], dfinvT[9], u_cart[3]; - matrix_inv_dev(dfm, dfinv); - matvecT_dev(dfinv, u_form, u_cart); - - double e_cart[3]; - cross_dev(b_cart, u_cart, e_cart); - row[3] += dt * e_cart[0]; - row[4] += dt * e_cart[1]; - row[5] += dt * e_cart[2]; -} - -extern "C" __global__ -void push_bxu_H1vec_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const double* uv_1, const int m2x1, const int m3x1, - const double* uv_2, const int m2x2, const int m3x2, - const double* uv_3, const int m2x3, const int m3x3, - const int kind_map, - const double* params, - const double boundary_cut, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfm[9], b_cart[3]; - eval_b_cart_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart - ); - - double u_form[3]; - eval_vectorfield_dev( - p1, p2, p3, bn1, bn2, bn3, - span1, span2, span3, start0, start1, start2, - uv_1, m2x1, m3x1, uv_2, m2x2, m3x2, uv_3, m2x3, m3x3, - u_form - ); - double u_cart[3]; - matvec_dev(dfm, u_form, u_cart); - - double e_cart[3]; - cross_dev(b_cart, u_cart, e_cart); - row[3] += dt * e_cart[0]; - row[4] += dt * e_cart[1]; - row[5] += dt * e_cart[2]; -} - -// Shared setup for push_pc_GXu{_full,}_general: DF(eta)/dfinv/dfinvT plus -// span/basis values, reused by the caller to evaluate the 3 (or 2) rows of -// the GXu matrix via eval_1form_dev. -__device__ void eval_dfinvt_and_basis_dev( - double eta1, double eta2, double eta3, - int p1, int p2, int p3, - const double* tn1, int len_tn1, - const double* tn2, int len_tn2, - const double* tn3, int len_tn3, - int kind_map, const double* params, - double* bn1, double* bd1, double* bn2, double* bd2, double* bn3, double* bd3, - int* span1_out, int* span2_out, int* span3_out, - double* dfinvt) -{ - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - *span1_out = span1; *span2_out = span2; *span3_out = span3; - - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - // dfinvt = dfinv^T, stored explicitly (row-major) since the caller needs - // it as a plain matrix for matvec_dev, not just for a single matvecT_dev - // application. - dfinvt[0] = dfinv[0]; dfinvt[1] = dfinv[3]; dfinvt[2] = dfinv[6]; - dfinvt[3] = dfinv[1]; dfinvt[4] = dfinv[4]; dfinvt[5] = dfinv[7]; - dfinvt[6] = dfinv[2]; dfinvt[7] = dfinv[5]; dfinvt[8] = dfinv[8]; -} - -extern "C" __global__ -void push_pc_GXu_full_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* g11, const double* g12, const double* g13, - const double* g21, const double* g22, const double* g23, - const double* g31, const double* g32, const double* g33, - const int n2xc1, const int n3xc1, - const int n2xc2, const int n3xc2, - const int n2xc3, const int n3xc3, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfinvt[9]; - eval_dfinvt_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfinvt - ); - - // components 1/2/3 of a 1-form generally have different shapes - // (D-N-N / N-D-N / N-N-D), but the shape only depends on the component - // index, not on which "row" of GXu is being evaluated -- so the same - // (n2xc1,n3xc1)/(n2xc2,n3xc2)/(n2xc3,n3xc3) apply to all three rows. - double gxu_row0[3], gxu_row1[3], gxu_row2[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g11, n2xc1, n3xc1, g12, n2xc2, n3xc2, g13, n2xc3, n3xc3, gxu_row0); - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g21, n2xc1, n3xc1, g22, n2xc2, n3xc2, g23, n2xc3, n3xc3, gxu_row1); - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g31, n2xc1, n3xc1, g32, n2xc2, n3xc2, g33, n2xc3, n3xc3, gxu_row2); - - // GXu[i][j] = gxu_row_i[j]; e[j] = sum_i GXu[i][j] * v[i] - double v[3] = {row[3], row[4], row[5]}; - double e[3]; - e[0] = gxu_row0[0]*v[0] + gxu_row1[0]*v[1] + gxu_row2[0]*v[2]; - e[1] = gxu_row0[1]*v[0] + gxu_row1[1]*v[1] + gxu_row2[1]*v[2]; - e[2] = gxu_row0[2]*v[0] + gxu_row1[2]*v[1] + gxu_row2[2]*v[2]; - - double e_cart[3]; - matvec_dev(dfinvt, e, e_cart); - - row[3] -= dt * e_cart[0] / 2.0; - row[4] -= dt * e_cart[1] / 2.0; - row[5] -= dt * e_cart[2] / 2.0; -} - -extern "C" __global__ -void push_pc_GXu_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* g11, const double* g12, const double* g13, - const double* g21, const double* g22, const double* g23, - const int n2xc1, const int n3xc1, - const int n2xc2, const int n3xc2, - const int n2xc3, const int n3xc3, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfinvt[9]; - eval_dfinvt_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfinvt - ); - - double gxu_row0[3], gxu_row1[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g11, n2xc1, n3xc1, g12, n2xc2, n3xc2, g13, n2xc3, n3xc3, gxu_row0); - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g21, n2xc1, n3xc1, g22, n2xc2, n3xc2, g23, n2xc3, n3xc3, gxu_row1); - - double v[3] = {row[3], row[4], row[5]}; - double e[3]; - e[0] = gxu_row0[0]*v[0] + gxu_row1[0]*v[1]; - e[1] = gxu_row0[1]*v[0] + gxu_row1[1]*v[1]; - e[2] = gxu_row0[2]*v[0] + gxu_row1[2]*v[1]; - - double e_cart[3]; - matvec_dev(dfinvt, e, e_cart); - - row[3] -= dt * e_cart[0] / 2.0; - row[4] -= dt * e_cart[1] / 2.0; - row[5] -= dt * e_cart[2] / 2.0; -} - -extern "C" __global__ -void push_pc_eta_stage_Hcurl_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* u_1, const int n2x1, const int n3x1, - const double* u_2, const int n2x2, const int n3x2, - const double* u_3, const int n2x3, const int n3x3, - const int use_perp_model, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9], dfinvt[9], ginv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - dfinvt[0]=dfinv[0]; dfinvt[1]=dfinv[3]; dfinvt[2]=dfinv[6]; - dfinvt[3]=dfinv[1]; dfinvt[4]=dfinv[4]; dfinvt[5]=dfinv[7]; - dfinvt[6]=dfinv[2]; dfinvt[7]=dfinv[5]; dfinvt[8]=dfinv[8]; - matmat_dev(dfinv, dfinvt, ginv); - - double k_v[3]; - matvec_dev(dfinv, v, k_v); - - double u[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); - if (use_perp_model) u[2] = 0.0; - - double k_u[3]; - matvec_dev(ginv, u, k_u); - - double k[3] = {k_v[0]+k_u[0], k_v[1]+k_u[1], k_v[2]+k_u[2]}; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_pc_eta_stage_Hdiv_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* u_1, const int n2x1, const int n3x1, - const double* u_2, const int n2x2, const int n3x2, - const double* u_3, const int n2x3, const int n3x3, - const int use_perp_model, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - const double det_df = det3_dev(dfm); - matrix_inv_dev(dfm, dfinv); - - double k_v[3]; - matvec_dev(dfinv, v, k_v); - - double u[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); - if (use_perp_model) u[2] = 0.0; - - double k_u[3] = {u[0]/det_df, u[1]/det_df, u[2]/det_df}; - double k[3] = {k_v[0]+k_u[0], k_v[1]+k_u[1], k_v[2]+k_u[2]}; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_pc_eta_stage_H1vec_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* u_1, const int n2x1, const int n3x1, - const double* u_2, const int n2x2, const int n3x2, - const double* u_3, const int n2x3, const int n3x3, - const int use_perp_model, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - - double k_v[3]; - matvec_dev(dfinv, v, k_v); - - double u[3]; - eval_vectorfield_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, start0, start1, start2, - u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); - if (use_perp_model) u[2] = 0.0; - - double k[3] = {k_v[0]+u[0], k_v[1]+u[1], k_v[2]+u[2]}; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_weights_with_efield_lin_va_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* e1_1, const int n2x1, const int n3x1, - const double* e1_2, const int n2x2, const int n3x2, - const double* e1_3, const int n2x3, const int n3x3, - const double* f0_values, - const double kappa, - const double vth, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - - double dfinv_v[3]; - matvec_dev(dfinv, v, dfinv_v); - - double e_vec[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - e1_1, n2x1, n3x1, e1_2, n2x2, n3x2, e1_3, n2x3, n3x3, e_vec); - - const double update = (dfinv_v[0]*e_vec[0] + dfinv_v[1]*e_vec[1] + dfinv_v[2]*e_vec[2]) - * f0_values[ip] * kappa * dt / (2.0 * row[7] * vth * vth); - row[6] += update; -} - -// Single-point evaluation of a Derham 0-form spline (N-N-N), matching -// struphy.bsplines.evaluation_kernels_3d.eval_0form_spline_mpi. -__device__ double eval_0form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bn2, const double* bn3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c, int n2x, int n3x) -{ - double out = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] * bn1[il1] * bn2[il2] * bn3[il3]; - } - } - } - return out; -} - -extern "C" __global__ -void push_deterministic_diffusion_stage_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* pi_u, const int n2xu, const int n3xu, - const double* pi_grad_u1, const int n2x1, const int n3x1, - const double* pi_grad_u2, const int n2x2, const int n3x2, - const double* pi_grad_u3, const int n2x3, const int n3x3, - const double diffusion_coeff, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - const double pi_u_value = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, pi_u, n2xu, n3xu); - - double pi_du_value[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - pi_grad_u1, n2x1, n3x1, pi_grad_u2, n2x2, n3x2, pi_grad_u3, n2x3, n3x3, pi_du_value); - - // ginv = G^-1 = DF^-1 @ DF^-T, matching struphy.geometry.evaluation_kernels.g_inv - // (computed there as (DF^T @ DF)^-1 instead -- same result, different - // intermediate path, reusing the dfinv this file already needs elsewhere). - double dfm[9], dfinv[9], dfinvt[9], ginv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - dfinvt[0]=dfinv[0]; dfinvt[1]=dfinv[3]; dfinvt[2]=dfinv[6]; - dfinvt[3]=dfinv[1]; dfinvt[4]=dfinv[4]; dfinvt[5]=dfinv[7]; - dfinvt[6]=dfinv[2]; dfinvt[7]=dfinv[5]; dfinvt[8]=dfinv[8]; - matmat_dev(dfinv, dfinvt, ginv); - - double tmp[3] = { - -diffusion_coeff * pi_du_value[0] / pi_u_value, - -diffusion_coeff * pi_du_value[1] / pi_u_value, - -diffusion_coeff * pi_du_value[2] / pi_u_value, - }; - double k[3]; - matvec_dev(ginv, tmp, k); - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} -""" +_GENERAL_GEOMETRY_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_general_geometry_src.cu") _push_eta_general_kernel = None _push_v_efield_general_kernel = None @@ -3125,26 +1430,7 @@ def push_deterministic_diffusion_stage_general_gpu( # domain-independent RawKernel source instead of living in # _GENERAL_GEOMETRY_SRC -- it applies to every domain, not just # SUPPORTED_GENERAL_KIND_MAPS. -_RANDOM_DIFFUSION_SRC = r""" -extern "C" __global__ -void push_random_diffusion_stage( - double* markers, - const int n_cols, - const int n_markers, - const double* noise, - const double scale) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - row[0] += scale * noise[3*ip + 0]; - row[1] += scale * noise[3*ip + 1]; - row[2] += scale * noise[3*ip + 2]; -} -""" +_RANDOM_DIFFUSION_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_random_diffusion_src.cu") _push_random_diffusion_kernel = None diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index e1eacf013..237c45507 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -23,215 +23,11 @@ :mod:`~struphy.pic.accumulation.accum_kernels_gc_cuda` (same ``atomicAdd``-scatter approach as ``charge_density_0form``). """ +from struphy.cuda import load_cuda_source -_PUSH_GC_BXESTAR_SRC = r""" -extern "C" __global__ -void push_gc_bxEstar_explicit_multistage_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_free_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* unit_b1_1, const int ub1_n2, const int ub1_n3, - const double* unit_b1_2, const int ub2_n2, const int ub2_n3, - const double* unit_b1_3, const int ub3_n2, const int ub3_n3, - const double* grad_b_full_1, const int gb1_n2, const int gb1_n3, - const double* grad_b_full_2, const int gb2_n2, const int gb2_n3, - const double* grad_b_full_3, const int gb3_n2, const int gb3_n3, - const double* B_dot_b_coeffs, const int bdb_n2, const int bdb_n3, - const double* curl_unit_b_dot_b0, const int cub_n2, const int cub_n3, - const double* e_field_1, const int e1_n2, const int e1_n3, - const double* e_field_2, const int e2_n2, const int e2_n3, - const double* e_field_3, const int e3_n2, const int e3_n3, - const int evaluate_e_field, - const double dt_a, const double dt_b, const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - const double mu = row[mu_idx]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double unit_b1[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - unit_b1_1, ub1_n2, ub1_n3, unit_b1_2, ub2_n2, ub2_n3, unit_b1_3, ub3_n2, ub3_n3, unit_b1); - - double e_star[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - grad_b_full_1, gb1_n2, gb1_n3, grad_b_full_2, gb2_n2, gb2_n3, grad_b_full_3, gb3_n2, gb3_n3, e_star); - e_star[0] *= -epsilon * mu; - e_star[1] *= -epsilon * mu; - e_star[2] *= -epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - e_field_1, e1_n2, e1_n3, e_field_2, e2_n2, e2_n3, e_field_3, e3_n2, e3_n3, e_field); - e_star[0] += e_field[0]; - e_star[1] += e_field[1]; - e_star[2] += e_field[2]; - } - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, B_dot_b_coeffs, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, curl_unit_b_dot_b0, cub_n2, cub_n3); - b_star_parallel = b_star_parallel * epsilon * v + B_dot_b; - b_star_parallel *= det_df; - - double Exb[3]; - cross_dev(e_star, unit_b1, Exb); - - double k[3]; - k[0] = Exb[0] / b_star_parallel; - k[1] = Exb[1] / b_star_parallel; - k[2] = Exb[2] / b_star_parallel; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} -""" +_PUSH_GC_BXESTAR_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu") -_PUSH_GC_BSTAR_SRC = r""" -extern "C" __global__ -void push_gc_Bstar_explicit_multistage_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_free_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* grad_b_full_1, const int gb1_n2, const int gb1_n3, - const double* grad_b_full_2, const int gb2_n2, const int gb2_n3, - const double* grad_b_full_3, const int gb3_n2, const int gb3_n3, - const double* b2_1, const int b1_n2, const int b1_n3, - const double* b2_2, const int b2n2, const int b2n3, - const double* b2_3, const int b3_n2, const int b3_n3, - const double* curl_unit_b2_1, const int cb1_n2, const int cb1_n3, - const double* curl_unit_b2_2, const int cb2_n2, const int cb2_n3, - const double* curl_unit_b2_3, const int cb3_n2, const int cb3_n3, - const double* B_dot_b_coeffs, const int bdb_n2, const int bdb_n3, - const double* curl_unit_b_dot_b0, const int cub_n2, const int cub_n3, - const double* e_field_1, const int e1_n2, const int e1_n3, - const double* e_field_2, const int e2_n2, const int e2_n3, - const double* e_field_3, const int e3_n2, const int e3_n3, - const int evaluate_e_field, - const double dt_a, const double dt_b, const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - const double mu = row[mu_idx]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double e_star[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - grad_b_full_1, gb1_n2, gb1_n3, grad_b_full_2, gb2_n2, gb2_n3, grad_b_full_3, gb3_n2, gb3_n3, e_star); - e_star[0] *= -epsilon * mu; - e_star[1] *= -epsilon * mu; - e_star[2] *= -epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - e_field_1, e1_n2, e1_n3, e_field_2, e2_n2, e2_n3, e_field_3, e3_n2, e3_n3, e_field); - e_star[0] += e_field[0]; - e_star[1] += e_field[1]; - e_star[2] += e_field[2]; - } - - double b2[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - b2_1, b1_n2, b1_n3, b2_2, b2n2, b2n3, b2_3, b3_n2, b3_n3, b2); - - double b_star[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - curl_unit_b2_1, cb1_n2, cb1_n3, curl_unit_b2_2, cb2_n2, cb2_n3, curl_unit_b2_3, cb3_n2, cb3_n3, b_star); - b_star[0] = b_star[0] * epsilon * v + b2[0]; - b_star[1] = b_star[1] * epsilon * v + b2[1]; - b_star[2] = b_star[2] * epsilon * v + b2[2]; - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, B_dot_b_coeffs, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, curl_unit_b_dot_b0, cub_n2, cub_n3); - b_star_parallel = b_star_parallel * epsilon * v + B_dot_b; - b_star_parallel *= det_df; - - double k[3]; - k[0] = b_star[0] / b_star_parallel * v; - k[1] = b_star[1] / b_star_parallel * v; - k[2] = b_star[2] / b_star_parallel * v; - - double k_v = dot3_dev(b_star, e_star); - k_v /= b_star_parallel * epsilon; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - row[first_free_idx + 3] += dt_b * k_v; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; - row[3] = row[first_init_idx + 3] + dt_a * k_v + last * row[first_free_idx + 3]; -} -""" +_PUSH_GC_BSTAR_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_bstar_src.cu") def _push_gc_bxEstar_source(): @@ -467,202 +263,7 @@ def d(a): # grad|B| (and optionally E) at the midpoint is done here. # --------------------------------------------------------------------------- -_DG_1ST_SRC = r""" -// mod(x, 1.0) matching numpy (result in [0, 1)) -__device__ double mod1_dev(double x) -{ - double r = fmod(x, 1.0); - if (r < 0.0) r += 1.0; - return r; -} - -extern "C" __global__ -void push_gc_bxEstar_discrete_gradient_1st_order_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int gb1_n2, const int gb1_n3, - const double* gb2, const int gb2_n2, const int gb2_n3, - const double* gb3, const int gb3_n2, const int gb3_n3, - const double* e1c, const int e1_n2, const int e1_n3, - const double* e2c, const int e2_n2, const int e2_n3, - const double* e3c, const int e3_n2, const int e3_n3, - const int evaluate_e_field, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; - for (int i = 0; i < 3; i++) { - eta_k[i] = row[i] + row[first_shift_idx + i]; - eta_n[i] = row[first_init_idx + i]; - eta_mid[i] = mod1_dev((eta_k[i] + eta_n[i]) / 2.0); - eta_diff[i] = eta_k[i] - eta_n[i]; - } - - const double mu = row[mu_idx]; - const double H_n = row[first_free_idx]; - const double b_star_parallel = row[first_free_idx + 1]; - double unit_b1[3] = { - row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; - const double H_k = row[first_free_idx + 5]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,gb1_n2,gb1_n3, gb2,gb2_n2,gb2_n3, gb3,gb3_n2,gb3_n3, grad_H); - for (int i = 0; i < 3; i++) grad_H[i] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - e1c,e1_n2,e1_n3, e2c,e2_n2,e2_n3, e3c,e3_n2,e3_n3, e_field); - for (int i = 0; i < 3; i++) grad_H[i] += -e_field[i]; - } - - const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H); - const double dZ_squared = dot3_dev(eta_diff, eta_diff); - - double grad_I[3]; - if (dZ_squared == 0.0) { - for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i]; - } else { - const double c = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; - for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i] + eta_diff[i] * c; - } - - double Exb[3]; - cross_dev(unit_b1, grad_I, Exb); - - double k[3]; - for (int i = 0; i < 3; i++) k[i] = Exb[i] / b_star_parallel; - - for (int i = 0; i < 3; i++) row[i] = eta_n[i] + dt * k[i]; - - row[residual_idx] = sqrt( - (row[0] - eta_k[0]) * (row[0] - eta_k[0]) - + (row[1] - eta_k[1]) * (row[1] - eta_k[1]) - + (row[2] - eta_k[2]) * (row[2] - eta_k[2])); -} - -extern "C" __global__ -void push_gc_Bstar_discrete_gradient_1st_order_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int gb1_n2, const int gb1_n3, - const double* gb2, const int gb2_n2, const int gb2_n3, - const double* gb3, const int gb3_n2, const int gb3_n3, - const double* e1c, const int e1_n2, const int e1_n3, - const double* e2c, const int e2_n2, const int e2_n3, - const double* e3c, const int e3_n2, const int e3_n3, - const int evaluate_e_field, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; - for (int i = 0; i < 3; i++) { - eta_k[i] = row[i] + row[first_shift_idx + i]; - eta_n[i] = row[first_init_idx + i]; - eta_mid[i] = mod1_dev((eta_k[i] + eta_n[i]) / 2.0); - eta_diff[i] = eta_k[i] - eta_n[i]; - } - - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - const double v_mid = (v_k + v_n) / 2.0; - const double v_diff = v_k - v_n; - - const double mu = row[mu_idx]; - const double H_n = row[first_free_idx]; - const double b_star_parallel = epsilon * row[first_free_idx + 1]; - double b_star[3] = { - row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; - const double H_k = row[first_free_idx + 5]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,gb1_n2,gb1_n3, gb2,gb2_n2,gb2_n3, gb3,gb3_n2,gb3_n3, grad_H); - for (int i = 0; i < 3; i++) grad_H[i] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - e1c,e1_n2,e1_n3, e2c,e2_n2,e2_n3, e3c,e3_n2,e3_n3, e_field); - for (int i = 0; i < 3; i++) grad_H[i] += -e_field[i]; - } - - const double grad_H_v = epsilon * v_mid; - const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H) + v_diff * grad_H_v; - const double dZ_squared = dot3_dev(eta_diff, eta_diff) + v_diff * v_diff; - - double grad_I[3]; - double grad_I_v; - if (dZ_squared == 0.0) { - for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i]; - grad_I_v = grad_H_v; - } else { - const double c = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; - for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i] + eta_diff[i] * c; - grad_I_v = grad_H_v + v_diff * c; - } - - double k[3]; - for (int i = 0; i < 3; i++) k[i] = b_star[i] / b_star_parallel * grad_I_v; - - double k_v = dot3_dev(b_star, grad_I); - k_v /= -b_star_parallel; - - for (int i = 0; i < 3; i++) row[i] = eta_n[i] + dt * k[i]; - row[3] = v_n + dt * k_v; - - row[residual_idx] = sqrt( - (row[0] - eta_k[0]) * (row[0] - eta_k[0]) - + (row[1] - eta_k[1]) * (row[1] - eta_k[1]) - + (row[2] - eta_k[2]) * (row[2] - eta_k[2]) - + ((row[3] - v_k) / v_k) * ((row[3] - v_k) / v_k)); -} -""" +_DG_1ST_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_1st_src.cu") _dg_kernels = {} @@ -768,223 +369,7 @@ def push_gc_Bstar_discrete_gradient_1st_order_gpu(*args, **kwargs): # Hdiv: u is a 2-form (like b); transform by dividing by det(DF) # --------------------------------------------------------------------------- -_PUSH_GC_CC_J1_SRC = r""" -extern "C" __global__ -void push_gc_cc_J1_H1vec_cuda( - double* markers, const int n_cols, const int n_markers, - const double dt, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - - double b[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_vectorfield_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double e[3]; - cross_dev(b, u, e); - const double temp = dot3_dev(e, curl_norm_b); - - row[3] += temp / abs_b_star_para * v * dt; -} - -extern "C" __global__ -void push_gc_cc_J1_Hcurl_cuda( - double* markers, const int n_cols, const int n_markers, - const double dt, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], u_form[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u_form); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - // g_inv = (DF^T DF)^-1, transforms the 1-form u into H1vec components - double df_t[9] = { - dfm[0], dfm[3], dfm[6], - dfm[1], dfm[4], dfm[7], - dfm[2], dfm[5], dfm[8], - }; - double g[9], g_inv[9], u0[3]; - matmat_dev(df_t, dfm, g); - matrix_inv_dev(g, g_inv); - matvec_dev(g_inv, u_form, u0); - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = (b[k] + curl_norm_b[k] * v * epsilon) / det_df; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double e[3]; - cross_dev(b, u0, e); - const double temp = dot3_dev(e, curl_norm_b) / det_df; - - row[3] += temp / abs_b_star_para * v * dt; -} - -extern "C" __global__ -void push_gc_cc_J1_Hdiv_cuda( - double* markers, const int n_cols, const int n_markers, - const double dt, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - for (int k = 0; k < 3; k++) u[k] /= det_df; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double e[3]; - cross_dev(b, u, e); - const double temp = dot3_dev(e, curl_norm_b); - - row[3] += temp / abs_b_star_para * v * dt; -} -""" +_PUSH_GC_CC_J1_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu") _j1_kernels = {} @@ -1095,168 +480,7 @@ def push_gc_cc_J1_Hdiv_gpu(*args, **kwargs): # 2-form and divides e by det(DF) as well. # --------------------------------------------------------------------------- -_PUSH_GC_CC_J2_STAGE_SRC = r""" -extern "C" __global__ -void push_gc_cc_J2_stage_H1vec_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_free_idx, - const double dt_a, const double dt_b, const double last, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double bb[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); - eval_vectorfield_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double tmp[9], e[3]; - matmat_dev(norm_b_prod, b_prod, tmp); - matvec_dev(tmp, u, e); - for (int k = 0; k < 3; k++) e[k] /= abs_b_star_para; - - row[first_free_idx + 0] -= dt_b * e[0]; - row[first_free_idx + 1] -= dt_b * e[1]; - row[first_free_idx + 2] -= dt_b * e[2]; - - row[0] = row[first_init_idx + 0] - dt_a * e[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] - dt_a * e[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] - dt_a * e[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_gc_cc_J2_stage_Hdiv_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_free_idx, - const double dt_a, const double dt_b, const double last, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double bb[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double tmp[9], e[3]; - matmat_dev(norm_b_prod, b_prod, tmp); - matvec_dev(tmp, u, e); - for (int k = 0; k < 3; k++) e[k] /= (abs_b_star_para * det_df); - - row[first_free_idx + 0] -= dt_b * e[0]; - row[first_free_idx + 1] -= dt_b * e[1]; - row[first_free_idx + 2] -= dt_b * e[2]; - - row[0] = row[first_init_idx + 0] - dt_a * e[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] - dt_a * e[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] - dt_a * e[2] + last * row[first_free_idx + 2]; -} -""" +_PUSH_GC_CC_J2_STAGE_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu") _j2_stage_kernels = {} @@ -1372,178 +596,7 @@ def push_gc_cc_J2_stage_Hdiv_gpu(*args, **kwargs): # division, then `eta = alpha*(eta_init - dt*e) + (1-alpha)*eta_old`. # --------------------------------------------------------------------------- -_PUSH_GC_CC_J2_DG_SRC = r""" -extern "C" __global__ -void push_gc_cc_J2_dg_init_Hdiv_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, - const double dt, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double bb[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double tmp[9], e[3]; - matmat_dev(norm_b_prod, b_prod, tmp); - matvec_dev(tmp, u, e); - for (int k = 0; k < 3; k++) e[k] /= (abs_b_star_para * det_df); - - row[0] -= dt * e[0]; - row[1] -= dt * e[1]; - row[2] -= dt * e[2]; -} - -extern "C" __global__ -void push_gc_cc_J2_dg_Hdiv_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, - const double dt, const double const_, const double alpha, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3, - const double* ud_1, const int ud1_n2, const int ud1_n3, - const double* ud_2, const int ud2_n2, const int ud2_n3, - const double* ud_3, const int ud3_n2, const int ud3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta_old0 = row[0], eta_old1 = row[1], eta_old2 = row[2]; - double eta_mid[3]; - eta_mid[0] = mod1_dev((row[0] + row[first_init_idx + 0]) / 2.0); - eta_mid[1] = mod1_dev((row[1] + row[first_init_idx + 1]) / 2.0); - eta_mid[2] = mod1_dev((row[2] + row[first_init_idx + 2]) / 2.0); - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - double bb[3], u[3], ud[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ud_1,ud1_n2,ud1_n3, ud_2,ud2_n2,ud2_n3, ud_3,ud3_n2,ud3_n3, ud); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double tmp[9], e[3], e2[3]; - matmat_dev(norm_b_prod, b_prod, tmp); - matvec_dev(tmp, u, e); - matvec_dev(tmp, ud, e2); - for (int k = 0; k < 3; k++) e[k] = (e[k] + const_ * e2[k]) / (abs_b_star_para * det_df); - - double eta_new[3]; - eta_new[0] = row[first_init_idx + 0] - dt * e[0]; - eta_new[1] = row[first_init_idx + 1] - dt * e[1]; - eta_new[2] = row[first_init_idx + 2] - dt * e[2]; - - row[0] = alpha * eta_new[0] + (1.0 - alpha) * eta_old0; - row[1] = alpha * eta_new[1] + (1.0 - alpha) * eta_old1; - row[2] = alpha * eta_new[2] + (1.0 - alpha) * eta_old2; -} -""" +_PUSH_GC_CC_J2_DG_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu") _j2_dg_kernels = {} @@ -1721,263 +774,7 @@ def d(a): # what's already in _GENERAL_GEOMETRY_SRC. # --------------------------------------------------------------------------- -_DG_NEWTON_SRC = r""" -extern "C" __global__ -void push_gc_bxEstar_discrete_gradient_1st_order_newton_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const double* phi, const int p_n2, const int p_n3, - const int evaluate_e_field, const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - const double eta_k_shifted = row[k] + row[first_shift_idx + k]; - eta_k[k] = row[k]; - eta_diff[k] = eta_k_shifted - row[first_init_idx + k]; - } - const double v = row[3]; - const double mu = row[mu_idx]; - - const double H_n = row[first_free_idx]; - const double b_star_parallel = row[first_free_idx + 1]; - const double unit_b1[3] = {row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; - const double H_k1 = row[first_free_idx + 5]; - const double H_k12 = row[first_free_idx + 6]; - const double grad_H_1 = row[first_free_idx + 7]; - const double grad_H_12[2] = {row[first_free_idx + 8], row[first_free_idx + 9]}; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_k[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_k[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_k[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_k[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_k[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_k[2], span3, bn3, bd3); - - double phi_val = 0.0; - if (evaluate_e_field) { - phi_val = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, phi, p_n2, p_n3); - } - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - const double H_k = epsilon * v * v / 2.0 + epsilon * mu * B_dot_b + phi_val; - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - double grad_I[3]; - grad_I[0] = (eta_diff[0] == 0.0) ? grad_H[0] : (H_k1 - H_n) / eta_diff[0]; - grad_I[1] = (eta_diff[1] == 0.0) ? grad_H[1] : (H_k12 - H_k1) / eta_diff[1]; - grad_I[2] = (eta_diff[2] == 0.0) ? grad_H[2] : (H_k - H_k12) / eta_diff[2]; - - double bcross_mat[9] = { - 0.0, -unit_b1[2], unit_b1[1], - unit_b1[2], 0.0, -unit_b1[0], - -unit_b1[1], unit_b1[0], 0.0}; - for (int k = 0; k < 9; k++) bcross_mat[k] /= b_star_parallel; - - double func[3]; - matvec_dev(bcross_mat, grad_I, func); - for (int k = 0; k < 3; k++) func[k] = eta_diff[k] - dt * func[k]; - - double Ddg[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; - if (eta_diff[0] != 0.0) Ddg[0] = (grad_H_1 * eta_diff[0] - (H_k1 - H_n)) / (eta_diff[0] * eta_diff[0]); - if (eta_diff[1] != 0.0) { - Ddg[4] = (grad_H_12[1] * eta_diff[1] - (H_k12 - H_k1)) / (eta_diff[1] * eta_diff[1]); - Ddg[3] = (grad_H_12[0] - grad_H_1) / eta_diff[1]; - } - if (eta_diff[2] != 0.0) { - Ddg[8] = (grad_H[2] * eta_diff[2] - (H_k - H_k12)) / (eta_diff[2] * eta_diff[2]); - Ddg[6] = (grad_H[0] - grad_H_12[0]) / eta_diff[2]; - Ddg[7] = (grad_H[1] - grad_H_12[1]) / eta_diff[2]; - } - - double Dfunc[9]; - matmat_dev(bcross_mat, Ddg, Dfunc); - for (int k = 0; k < 9; k++) Dfunc[k] *= -dt; - Dfunc[0] += 1.0; Dfunc[4] += 1.0; Dfunc[8] += 1.0; - - double Dfunc_inv[9], k_vec[3]; - matrix_inv_dev(Dfunc, Dfunc_inv); - matvec_dev(Dfunc_inv, func, k_vec); - - row[0] -= k_vec[0]; - row[1] -= k_vec[1]; - row[2] -= k_vec[2]; - - row[residual_idx] = sqrt(k_vec[0]*k_vec[0] + k_vec[1]*k_vec[1] + k_vec[2]*k_vec[2]); -} - -extern "C" __global__ -void push_gc_Bstar_discrete_gradient_1st_order_newton_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const double* phi, const int p_n2, const int p_n3, - const int evaluate_e_field, const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - const double eta_k_shifted = row[k] + row[first_shift_idx + k]; - eta_k[k] = row[k]; - eta_diff[k] = eta_k_shifted - row[first_init_idx + k]; - } - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - const double v_diff = v_k - v_n; - const double mu = row[mu_idx]; - - const double H_n = row[first_free_idx]; - const double b_star_parallel = epsilon * row[first_free_idx + 1]; - const double b_star[3] = {row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; - const double H_k1 = row[first_free_idx + 5]; - const double H_k12 = row[first_free_idx + 6]; - const double grad_H_1 = row[first_free_idx + 7]; - const double grad_H_12[2] = {row[first_free_idx + 8], row[first_free_idx + 9]}; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_k[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_k[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_k[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_k[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_k[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_k[2], span3, bn3, bd3); - - double phi_val = 0.0; - if (evaluate_e_field) { - phi_val = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, phi, p_n2, p_n3); - } - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - const double H_k = epsilon * v_k * v_k / 2.0 + epsilon * mu * B_dot_b + phi_val; - const double H_k123 = epsilon * v_n * v_n / 2.0 + epsilon * mu * B_dot_b + phi_val; - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - const double grad_H_v = epsilon * v_k; - - double grad_I[3]; - grad_I[0] = (eta_diff[0] == 0.0) ? grad_H[0] : (H_k1 - H_n) / eta_diff[0]; - grad_I[1] = (eta_diff[1] == 0.0) ? grad_H[1] : (H_k12 - H_k1) / eta_diff[1]; - grad_I[2] = (eta_diff[2] == 0.0) ? grad_H[2] : (H_k123 - H_k12) / eta_diff[2]; - const double grad_I_v = (v_diff == 0.0) ? grad_H_v : (H_k - H_k123) / v_diff; - - double J_vec[3]; - for (int k = 0; k < 3; k++) J_vec[k] = b_star[k] / b_star_parallel; - - double func[3]; - for (int k = 0; k < 3; k++) func[k] = eta_diff[k] - dt * (J_vec[k] * grad_I_v); - double func_v = v_diff + dt * dot3_dev(J_vec, grad_I); - - double Ddg[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; - if (eta_diff[0] != 0.0) Ddg[0] = (grad_H_1 * eta_diff[0] - (H_k1 - H_n)) / (eta_diff[0] * eta_diff[0]); - if (eta_diff[1] != 0.0) { - Ddg[4] = (grad_H_12[1] * eta_diff[1] - (H_k12 - H_k1)) / (eta_diff[1] * eta_diff[1]); - Ddg[3] = (grad_H_12[0] - grad_H_1) / eta_diff[1]; - } - if (eta_diff[2] != 0.0) { - Ddg[8] = (grad_H[2] * eta_diff[2] - (H_k123 - H_k12)) / (eta_diff[2] * eta_diff[2]); - Ddg[6] = (grad_H[0] - grad_H_12[0]) / eta_diff[2]; - Ddg[7] = (grad_H[1] - grad_H_12[1]) / eta_diff[2]; - } - const double Ddg_v = (v_diff == 0.0) ? 0.0 : (grad_H_v * v_diff - (H_k - H_k123)) / (v_diff * v_diff); - - // DF = [[I, B], [C^T, 1]], B = -dt*Ddg_v*J_vec, C = dt*Ddg^T @ J_vec - double Bv[3], Cv[3]; - for (int k = 0; k < 3; k++) Bv[k] = -dt * Ddg_v * J_vec[k]; - double DdgT[9] = {Ddg[0], Ddg[3], Ddg[6], Ddg[1], Ddg[4], Ddg[7], Ddg[2], Ddg[5], Ddg[8]}; - matvec_dev(DdgT, J_vec, Cv); - for (int k = 0; k < 3; k++) Cv[k] *= dt; - - const double schur = 1.0 - dot3_dev(Cv, Bv); - - double A_inv[9]; - A_inv[0] = Bv[0]*Cv[0]; A_inv[1] = Bv[0]*Cv[1]; A_inv[2] = Bv[0]*Cv[2]; - A_inv[3] = Bv[1]*Cv[0]; A_inv[4] = Bv[1]*Cv[1]; A_inv[5] = Bv[1]*Cv[2]; - A_inv[6] = Bv[2]*Cv[0]; A_inv[7] = Bv[2]*Cv[1]; A_inv[8] = Bv[2]*Cv[2]; - for (int k = 0; k < 9; k++) A_inv[k] /= schur; - A_inv[0] += 1.0; A_inv[4] += 1.0; A_inv[8] += 1.0; - - double Binv[3], Cinv[3]; - for (int k = 0; k < 3; k++) { Binv[k] = -Bv[k] / schur; Cinv[k] = -Cv[k] / schur; } - - double k_vec[3]; - matvec_dev(A_inv, func, k_vec); - for (int k = 0; k < 3; k++) k_vec[k] += Binv[k] * func_v; - double k_v = dot3_dev(Cinv, func) + func_v / schur; - - row[0] -= k_vec[0]; - row[1] -= k_vec[1]; - row[2] -= k_vec[2]; - row[3] -= k_v; - - row[residual_idx] = sqrt(k_vec[0]*k_vec[0] + k_vec[1]*k_vec[1] + k_vec[2]*k_vec[2] + (k_v/v_k)*(k_v/v_k)); -} -""" +_DG_NEWTON_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_newton_src.cu") _dg_newton_kernels = {} @@ -2085,234 +882,7 @@ def push_gc_Bstar_discrete_gradient_1st_order_newton_gpu(*args, **kwargs): # 2 pre-evaluated marker columns (H_n, H_k) instead of the Itoh-Abe set. # --------------------------------------------------------------------------- -_DG_2ND_ORDER_SRC = r""" -extern "C" __global__ -void push_gc_bxEstar_discrete_gradient_2nd_order_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* ub1, const int u1_n2, const int u1_n3, - const double* ub2, const int u2_n2, const int u2_n3, - const double* ub3, const int u3_n2, const int u3_n3, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* cub, const int cub_n2, const int cub_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const int evaluate_e_field, const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - eta_k[k] = row[k] + row[first_shift_idx + k]; - eta_n[k] = row[first_init_idx + k]; - double m = fmod((eta_k[k] + eta_n[k]) / 2.0, 1.0); - if (m < 0.0) m += 1.0; - eta_mid[k] = m; - eta_diff[k] = eta_k[k] - eta_n[k]; - } - const double v = row[3]; - const double mu = row[mu_idx]; - - const double H_n = row[first_free_idx]; - const double H_k = row[first_free_idx + 1]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double unit_b1[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ub1,u1_n2,u1_n3, ub2,u2_n2,u2_n3, ub3,u3_n2,u3_n3, unit_b1); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H); - const double dZ_squared = dot3_dev(eta_diff, eta_diff); - - double grad_I[3]; - if (dZ_squared == 0.0) { - for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k]; - } else { - const double s = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; - for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k] + eta_diff[k] * s; - } - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, cub, cub_n2, cub_n3); - b_star_parallel = (b_star_parallel * epsilon * v + B_dot_b) * det_df; - - double Exb[3]; - cross_dev(unit_b1, grad_I, Exb); - - double k_vec[3]; - for (int k = 0; k < 3; k++) k_vec[k] = Exb[k] / b_star_parallel; - - row[0] = eta_n[0] + dt * k_vec[0]; - row[1] = eta_n[1] + dt * k_vec[1]; - row[2] = eta_n[2] + dt * k_vec[2]; - - const double r0 = row[0] - eta_k[0], r1 = row[1] - eta_k[1], r2 = row[2] - eta_k[2]; - row[residual_idx] = sqrt(r0*r0 + r1*r1 + r2*r2); -} - -extern "C" __global__ -void push_gc_Bstar_discrete_gradient_2nd_order_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* b2_1, const int b1_n2, const int b1_n3, - const double* b2_2, const int b2_n2, const int b2_n3, - const double* b2_3, const int b3_n2, const int b3_n3, - const double* cb1, const int c1_n2, const int c1_n3, - const double* cb2, const int c2_n2, const int c2_n3, - const double* cb3, const int c3_n2, const int c3_n3, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* cub, const int cub_n2, const int cub_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const int evaluate_e_field, const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - eta_k[k] = row[k] + row[first_shift_idx + k]; - eta_n[k] = row[first_init_idx + k]; - double m = fmod((eta_k[k] + eta_n[k]) / 2.0, 1.0); - if (m < 0.0) m += 1.0; - eta_mid[k] = m; - eta_diff[k] = eta_k[k] - eta_n[k]; - } - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - const double v_mid = (v_k + v_n) / 2.0; - const double v_diff = v_k - v_n; - const double mu = row[mu_idx]; - - const double H_n = row[first_free_idx]; - const double H_k = row[first_free_idx + 1]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - const double grad_H_v = epsilon * v_mid; - const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H) + v_diff * grad_H_v; - const double dZ_squared = dot3_dev(eta_diff, eta_diff) + v_diff * v_diff; - - double grad_I[3]; - double grad_I_v; - if (dZ_squared == 0.0) { - for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k]; - grad_I_v = grad_H_v; - } else { - const double s = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; - for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k] + eta_diff[k] * s; - grad_I_v = grad_H_v + v_diff * s; - } - - double b2[3], b_star[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b2_1,b1_n2,b1_n3, b2_2,b2_n2,b2_n3, b2_3,b3_n2,b3_n3, b2); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cb1,c1_n2,c1_n3, cb2,c2_n2,c2_n3, cb3,c3_n2,c3_n3, b_star); - for (int k = 0; k < 3; k++) b_star[k] = b_star[k] * epsilon * v_mid + b2[k]; - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, cub, cub_n2, cub_n3); - b_star_parallel = (b_star_parallel * epsilon * v_mid + B_dot_b) * epsilon * det_df; - - double k_vec[3]; - for (int k = 0; k < 3; k++) k_vec[k] = b_star[k] / b_star_parallel * grad_I_v; - const double k_v = -dot3_dev(b_star, grad_I) / b_star_parallel; - - row[0] = eta_n[0] + dt * k_vec[0]; - row[1] = eta_n[1] + dt * k_vec[1]; - row[2] = eta_n[2] + dt * k_vec[2]; - row[3] = v_n + dt * k_v; - - const double r0 = row[0] - eta_k[0], r1 = row[1] - eta_k[1], r2 = row[2] - eta_k[2]; - const double rv = (row[3] - v_k) / v_k; - row[residual_idx] = sqrt(r0*r0 + r1*r1 + r2*r2 + rv*rv); -} -""" +_DG_2ND_ORDER_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_2nd_order_src.cu") _dg_2nd_order_kernels = {} diff --git a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py index 39181accc..555fc2243 100644 --- a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py @@ -23,202 +23,9 @@ manual zeroing of analytically-zero entries is skipped -- so ``matrix_inv_dev(df_dispatch_dev(...))`` reproduces it exactly. """ +from struphy.cuda import load_cuda_source -_SPH_PUSHER_SRC = r""" -// Port of struphy.pic.sph_eval_kernels.box_based_kernel: SPH sum over the 27 -// neighbouring boxes of the marker's own box. -__device__ double box_based_kernel_dev( - const double* markers, const int n_cols, - double e1, double e2, double e3, - int loc_box, - const int* boxes, const int n_box_cols, - const int* neighbours, - const int* holes, - int periodic1, int periodic2, int periodic3, - int index, int kernel_type, - double h1, double h2, double h3) -{ - if (loc_box == -1) return 0.0; - - double acc = 0.0; - for (int neigh = 0; neigh < 27; neigh++) { - int box_to_search = neighbours[loc_box * 27 + neigh]; - int c = 0; - while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { - int p = boxes[(size_t)box_to_search * n_box_cols + c]; - c++; - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - } - return acc; -} - -// Shared tail of all three pushers: pull the logical-space force back to -// Cartesian with DF^-T and apply it to the marker velocity. -__device__ void apply_force_dev( - double* row, double e1, double e2, double e3, - int kind_map, const double* params, - const double* force_logical, const double* gravity, - double dt) -{ - double dfm[9], dfinv[9], force_cart[3]; - if (!df_dispatch_dev(kind_map, e1, e2, e3, params, dfm)) return; - matrix_inv_dev(dfm, dfinv); - // dfinvT @ force_logical == matvecT(dfinv, force_logical) - matvecT_dev(dfinv, force_logical, force_cart); - - row[3] -= dt * (force_cart[0] - gravity[0]); - row[4] -= dt * (force_cart[1] - gravity[1]); - row[5] -= dt * (force_cart[2] - gravity[2]); -} - -// --- push_v_sph_pressure (isothermal closure) --- -extern "C" __global__ -void push_v_sph_pressure_cuda( - double* markers, const int n_cols, const int n_markers, - const int* valid_mks, - const int weight_idx, const int first_free_idx, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3, - const double* gravity, const double kappa, - const int kind_map, const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const double n_at_eta = row[first_free_idx]; - const int loc_box = (int)row[n_cols - 2]; - - double grad_u[3] = {0.0, 0.0, 0.0}; - - grad_u[0] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 1, h1, h2, h3); - grad_u[0] *= kappa / n_at_eta; - grad_u[0] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 1, h1, h2, h3); - - if (kernel_type >= 340) { - grad_u[1] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 2, h1, h2, h3); - grad_u[1] *= kappa / n_at_eta; - grad_u[1] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 2, h1, h2, h3); - } - - if (kernel_type >= 670) { - grad_u[2] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 3, h1, h2, h3); - grad_u[2] *= kappa / n_at_eta; - grad_u[2] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 3, h1, h2, h3); - } - - apply_force_dev(row, e1, e2, e3, kind_map, params, grad_u, gravity, dt); -} - -// --- push_v_sph_pressure_ideal_gas (polytropic closure, gamma = 5/3) --- -extern "C" __global__ -void push_v_sph_pressure_ideal_gas_cuda( - double* markers, const int n_cols, const int n_markers, - const int* valid_mks, - const int weight_idx, const int first_free_idx, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3, - const double* gravity, const double kappa, - const int kind_map, const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - const double gamma = 5.0 / 3.0; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const double n_at_eta = row[first_free_idx]; - const int loc_box = (int)row[n_cols - 2]; - - const double pref = kappa * pow(n_at_eta, gamma - 2.0); - double grad_u[3] = {0.0, 0.0, 0.0}; - - grad_u[0] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 1, h1, h2, h3); - grad_u[0] *= pref; - grad_u[0] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 1, h1, h2, h3); - - if (kernel_type >= 340) { - grad_u[1] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 2, h1, h2, h3); - grad_u[1] *= pref; - grad_u[1] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 2, h1, h2, h3); - } - - if (kernel_type >= 670) { - grad_u[2] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 3, h1, h2, h3); - grad_u[2] *= pref; - grad_u[2] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 3, h1, h2, h3); - } - - apply_force_dev(row, e1, e2, e3, kind_map, params, grad_u, gravity, dt); -} - -// --- push_v_viscosity (deviatoric strain-rate tensor) --- -extern "C" __global__ -void push_v_viscosity_cuda( - double* markers, const int n_cols, const int n_markers, - const int* valid_mks, - const int first_free_idx, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3, - const int kind_map, const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const int loc_box = (int)row[n_cols - 2]; - - double f_visc[3] = {0.0, 0.0, 0.0}; - for (int j = 0; j < 3; j++) { - for (int k = 0; k < 3; k++) { - const int coeff_idx = first_free_idx + 3 * (j + 1) + k; - f_visc[j] += box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, - coeff_idx, kernel_type + 1 + k, h1, h2, h3); - } - } - - const double no_gravity[3] = {0.0, 0.0, 0.0}; - apply_force_dev(row, e1, e2, e3, kind_map, params, f_visc, no_gravity, dt); -} -""" +_SPH_PUSHER_SRC = load_cuda_source(__file__, "pusher_kernels_sph_cuda/_sph_pusher_src.cu") _kernels = {} diff --git a/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py b/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py index 6dadb5b46..5608e85c6 100644 --- a/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py @@ -15,44 +15,9 @@ Only the markers listed in ``outside_inds`` are touched, so the kernel is launched over that index array rather than over all markers. """ +from struphy.cuda import load_cuda_source -_REFLECT_SRC = r""" -extern "C" __global__ -void reflect_cuda( - double* markers, const int n_cols, - const long long* outside_inds, const int n_outside, - const int axis, - const int kind_map, const double* params) -{ - int i = blockIdx.x * blockDim.x + threadIdx.x; - if (i >= n_outside) return; - - const long long ip = outside_inds[i]; - double* row = markers + (size_t)ip * n_cols; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - - double dfinv[9], v_logical[3]; - matrix_inv_dev(dfm, dfinv); - - // pull back of the velocity - matvec_dev(dfinv, v, v_logical); - - // reverse the velocity component along `axis` - v_logical[axis] *= -1.0; - - // push forward of the velocity - matvec_dev(dfm, v_logical, v); - - row[3] = v[0]; - row[4] = v[1]; - row[5] = v[2]; -} -""" +_REFLECT_SRC = load_cuda_source(__file__, "pusher_utilities_kernels_cuda/_reflect_src.cu") _reflect_kernel = None diff --git a/src/struphy/pic/sorting_kernels_cuda.py b/src/struphy/pic/sorting_kernels_cuda.py index 75e5181ee..98a2c1c6b 100644 --- a/src/struphy/pic/sorting_kernels_cuda.py +++ b/src/struphy/pic/sorting_kernels_cuda.py @@ -35,94 +35,11 @@ :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_flat_gpu`, which genuinely needs every marker column for the density sum). """ +from struphy.cuda import load_cuda_source import numpy as np -_SORT_SRC = r""" -extern "C" __device__ long long flatten_index_dev( - long long n1, long long n2, long long n3, - long long nx, long long ny, long long nz) -{ - // fortran_ordering (the struphy default) - return n1 + n2 * (nx + 2) + n3 * (nx + 2) * (ny + 2); -} - -extern "C" __device__ long long find_box_dev( - double eta1, double eta2, double eta3, - long long nx, long long ny, long long nz, - const double* domain_array) -{ - if (eta1 == domain_array[0]) eta1 += 1e-8; - if (eta2 == domain_array[3]) eta2 += 1e-8; - if (eta3 == domain_array[6]) eta3 += 1e-8; - if (eta1 == domain_array[1]) eta1 -= 1e-8; - if (eta2 == domain_array[4]) eta2 -= 1e-8; - if (eta3 == domain_array[7]) eta3 -= 1e-8; - - double x_l = domain_array[0] - (domain_array[1] - domain_array[0]) / nx; - double x_r = domain_array[1] + (domain_array[1] - domain_array[0]) / nx; - double y_l = domain_array[3] - (domain_array[4] - domain_array[3]) / ny; - double y_r = domain_array[4] + (domain_array[4] - domain_array[3]) / ny; - double z_l = domain_array[6] - (domain_array[7] - domain_array[6]) / nz; - double z_r = domain_array[7] + (domain_array[7] - domain_array[6]) / nz; - - if (eta1 < x_l || eta1 > x_r || eta2 < y_l || eta2 > y_r || eta3 < z_l || eta3 > z_r) - return -1; - - long long n1 = (long long)floor((eta1 - x_l) / (x_r - x_l) * (nx + 2)); - long long n2 = (long long)floor((eta2 - y_l) / (y_r - y_l) * (ny + 2)); - long long n3 = (long long)floor((eta3 - z_l) / (z_r - z_l) * (nz + 2)); - - return flatten_index_dev(n1, n2, n3, nx, ny, nz); -} - -extern "C" __global__ -void assign_box_to_each_particle_cuda( - const double* eta, // AoS, row p at eta[3*p : 3*p+3] - const int* holes, - const long long n_mks, - const long long nx, - const long long ny, - const long long nz, - const double* domain_array, - double* box_out) -{ - long long p = (long long)blockIdx.x * blockDim.x + threadIdx.x; - if (p >= n_mks) return; - - long long n_boxes_total = (nx + 2) * (ny + 2) * (nz + 2); - long long n_box; - - if (holes[p]) { - n_box = n_boxes_total; - } else { - long long a = find_box_dev(eta[3 * p], eta[3 * p + 1], eta[3 * p + 2], nx, ny, nz, domain_array); - n_box = (a >= n_boxes_total || a < 0) ? n_boxes_total : a; - } - - box_out[p] = (double) n_box; -} - -extern "C" __global__ -void assign_particles_to_boxes_cuda( - const double* box_id, - const int* holes, - const long long n_mks, - int* boxes, - int* next_index, - const long long box_cols) -{ - long long p = (long long)blockIdx.x * blockDim.x + threadIdx.x; - if (p >= n_mks) return; - if (holes[p]) return; - - int a = (int) box_id[p]; - int slot = atomicAdd(&next_index[a], 1); - if (slot < box_cols) { - boxes[(long long) a * box_cols + slot] = (int) p; - } -} -""" +_SORT_SRC = load_cuda_source(__file__, "sorting_kernels_cuda/_sort_src.cu") _assign_box_kernel = None _assign_particles_kernel = None diff --git a/src/struphy/pic/sph_eval_kernels_cuda.py b/src/struphy/pic/sph_eval_kernels_cuda.py index 62d654ae0..3ada9945f 100644 --- a/src/struphy/pic/sph_eval_kernels_cuda.py +++ b/src/struphy/pic/sph_eval_kernels_cuda.py @@ -26,299 +26,9 @@ diagnostics/reconstruction entry point, not a per-step hot loop, so there is no benefit to caching device buffers across calls the way the pushers do. """ +from struphy.cuda import load_cuda_source -_SPH_EVAL_FLAT_SRC = r""" -#define PI 3.14159265358979323846 - -__device__ double distance_dev(double x, double y, bool periodic) -{ - double d = x - y; - if (periodic) { - while (d > 0.5) d -= 1.0; - while (d < -0.5) d += 1.0; - } - return d; -} - -// --- uni-variate kernels (struphy.pic.sph_smoothing_kernels) --- - -__device__ double trigonometric_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return 0.785398163397448 / h * cos(x / h * PI / 2.0); - return 0.0; -} - -__device__ double grad_trigonometric_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return -(1.2337005501361697 / (h * h)) * sin(x / h * PI / 2.0); - return 0.0; -} - -__device__ double gaussian_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return 1.0 / (sqrt(PI) * h / 3.0) * exp(-(x * x) / ((h / 3.0) * (h / 3.0))); - return 0.0; -} - -__device__ double grad_gaussian_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return -54.0 * x / (h * h * h * sqrt(PI)) * exp(-(x * x) / ((h / 3.0) * (h / 3.0))); - return 0.0; -} - -__device__ double linear_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return (1.0 - fabs(x / h)) / h; - return 0.0; -} - -__device__ double grad_linear_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return (x > 0.0) ? -(1.0 / (h * h)) : (1.0 / (h * h)); - return 0.0; -} - -// --- kernel_type dispatch (struphy.pic.sph_smoothing_kernels.smoothing_kernel) --- - -__device__ double smoothing_kernel_dev( - int kernel_type, - double r1, double r2, double r3, - double h1, double h2, double h3) -{ - switch (kernel_type) { - // 1d - case 100: return trigonometric_uni(r1, h1); - case 101: return grad_trigonometric_uni(r1, h1); - case 110: return gaussian_uni(r1, h1); - case 111: return grad_gaussian_uni(r1, h1); - case 120: return linear_uni(r1, h1); - case 121: return grad_linear_uni(r1, h1); - - // 2d (tensor products) - case 340: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2); - case 341: return grad_trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2); - case 342: return trigonometric_uni(r1, h1) * grad_trigonometric_uni(r2, h2); - case 350: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2); - case 351: return grad_gaussian_uni(r1, h1) * gaussian_uni(r2, h2); - case 352: return gaussian_uni(r1, h1) * grad_gaussian_uni(r2, h2); - case 360: return linear_uni(r1, h1) * linear_uni(r2, h2); - case 361: return grad_linear_uni(r1, h1) * linear_uni(r2, h2); - case 362: return linear_uni(r1, h1) * grad_linear_uni(r2, h2); - - // 3d (tensor products) - case 670: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); - case 671: return grad_trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); - case 672: return trigonometric_uni(r1, h1) * grad_trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); - case 673: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * grad_trigonometric_uni(r3, h3); - case 680: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * gaussian_uni(r3, h3); - case 681: return grad_gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * gaussian_uni(r3, h3); - case 682: return gaussian_uni(r1, h1) * grad_gaussian_uni(r2, h2) * gaussian_uni(r3, h3); - case 683: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * grad_gaussian_uni(r3, h3); - case 700: return linear_uni(r1, h1) * linear_uni(r2, h2) * linear_uni(r3, h3); - case 701: return grad_linear_uni(r1, h1) * linear_uni(r2, h2) * linear_uni(r3, h3); - case 702: return linear_uni(r1, h1) * grad_linear_uni(r2, h2) * linear_uni(r3, h3); - case 703: return linear_uni(r1, h1) * linear_uni(r2, h2) * grad_linear_uni(r3, h3); - - // 3d, radially symmetric (linear_isotropic_3d and its gradient) - case 690: { - double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); - double h = h1; - if (r / h > 1.0) return 0.0; - return (1.0 - r / h) / (1.0471975512 * h * h * h); - } - case 691: { - double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); - double h = h1; - if (r / h > 1.0) return 0.0; - if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); - return -r1 / (r * h) / (1.0471975512 * h * h * h); - } - case 692: { - double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); - double h = h1; - if (r / h > 1.0) return 0.0; - if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); - return -r2 / (r * h) / (1.0471975512 * h * h * h); - } - case 693: { - double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); - double h = h1; - if (r / h > 1.0) return 0.0; - if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); - return -r3 / (r * h) / (1.0471975512 * h * h * h); - } - } - return 0.0; -} - -// --- box lookup (struphy.pic.sorting_kernels.find_box / flatten_index) --- - -__device__ int find_box_dev( - double eta1, double eta2, double eta3, - int nx, int ny, int nz, - const double* domain_array) -{ - if (eta1 == domain_array[0]) eta1 += 1e-8; - if (eta2 == domain_array[3]) eta2 += 1e-8; - if (eta3 == domain_array[6]) eta3 += 1e-8; - if (eta1 == domain_array[1]) eta1 -= 1e-8; - if (eta2 == domain_array[4]) eta2 -= 1e-8; - if (eta3 == domain_array[7]) eta3 -= 1e-8; - - double x_l = domain_array[0] - (domain_array[1] - domain_array[0]) / nx; - double x_r = domain_array[1] + (domain_array[1] - domain_array[0]) / nx; - double y_l = domain_array[3] - (domain_array[4] - domain_array[3]) / ny; - double y_r = domain_array[4] + (domain_array[4] - domain_array[3]) / ny; - double z_l = domain_array[6] - (domain_array[7] - domain_array[6]) / nz; - double z_r = domain_array[7] + (domain_array[7] - domain_array[6]) / nz; - - if (eta1 < x_l || eta1 > x_r || eta2 < y_l || eta2 > y_r || eta3 < z_l || eta3 > z_r) - return -1; - - int n1 = (int)floor((eta1 - x_l) / (x_r - x_l) * (nx + 2)); - int n2 = (int)floor((eta2 - y_l) / (y_r - y_l) * (ny + 2)); - int n3 = (int)floor((eta3 - z_l) / (z_r - z_l) * (nz + 2)); - - // flatten_index, fortran_ordering (the struphy default) - return n1 + n2 * (nx + 2) + n3 * (nx + 2) * (ny + 2); -} - -// --- entry point (struphy.pic.sph_eval_kernels.box_based_evaluation_flat) --- - -extern "C" __global__ -void box_based_evaluation_flat_cuda( - const double* markers, - const int n_cols, - const double* eta1, - const double* eta2, - const double* eta3, - const int n_eval, - const int nx, - const int ny, - const int nz, - const double* domain_array, - const int* boxes, - const int n_box_cols, - const int* neighbours, - const int* holes, - const int periodic1, - const int periodic2, - const int periodic3, - const int index, - const int kernel_type, - const double h1, - const double h2, - const double h3, - double* out) -{ - int i = blockIdx.x * blockDim.x + threadIdx.x; - if (i >= n_eval) return; - - double e1 = eta1[i], e2 = eta2[i], e3 = eta3[i]; - - int loc_box = find_box_dev(e1, e2, e3, nx, ny, nz, domain_array); - if (loc_box == -1) { - out[i] = 0.0; - return; - } - - double acc = 0.0; - for (int neigh = 0; neigh < 27; neigh++) { - int box_to_search = neighbours[loc_box * 27 + neigh]; - int c = 0; - while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { - int p = boxes[(size_t)box_to_search * n_box_cols + c]; - c++; - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - } - out[i] = acc; -} - -// --- entry point (struphy.pic.sph_eval_kernels.box_based_evaluation_meshgrid) --- -// -// eta1/eta2/eta3 are the 3 distinct 1-D axis vectors of the meshgrid (the -// Pyccel kernel this ports only ever reads eta1[i,0,0]/eta2[0,j,0]/eta3[0,0,k], -// never the broadcast values, so the Python wrapper passes just the axes -- -// no reason to transfer the O(n1*n2*n3) redundant meshgrid). One CUDA thread -// per (i, j, k) evaluation point, flattened to match out's C-order layout. - -extern "C" __global__ -void box_based_evaluation_meshgrid_cuda( - const double* markers, - const int n_cols, - const double* eta1, - const double* eta2, - const double* eta3, - const int n1_eval, - const int n2_eval, - const int n3_eval, - const int nx, - const int ny, - const int nz, - const double* domain_array, - const int* boxes, - const int n_box_cols, - const int* neighbours, - const int* holes, - const int periodic1, - const int periodic2, - const int periodic3, - const int index, - const int kernel_type, - const double h1, - const double h2, - const double h3, - double* out) -{ - size_t idx = (size_t)blockIdx.x * blockDim.x + threadIdx.x; - size_t n_total = (size_t)n1_eval * n2_eval * n3_eval; - if (idx >= n_total) return; - - int i = idx / ((size_t)n2_eval * n3_eval); - int rem = idx % ((size_t)n2_eval * n3_eval); - int j = rem / n3_eval; - int k = rem % n3_eval; - - out[idx] = 0.0; - - double e1 = eta1[i]; - if (e1 < domain_array[0] || (e1 >= domain_array[1] && e1 != 1.0)) return; - - double e2 = eta2[j]; - if (e2 < domain_array[3] || (e2 >= domain_array[4] && e2 != 1.0)) return; - - double e3 = eta3[k]; - if (e3 < domain_array[6] || (e3 >= domain_array[7] && e3 != 1.0)) return; - - int loc_box = find_box_dev(e1, e2, e3, nx, ny, nz, domain_array); - if (loc_box == -1) return; - - double acc = 0.0; - for (int neigh = 0; neigh < 27; neigh++) { - int box_to_search = neighbours[loc_box * 27 + neigh]; - int c = 0; - while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { - int p = boxes[(size_t)box_to_search * n_box_cols + c]; - c++; - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - } - out[idx] = acc; -} -""" +_SPH_EVAL_FLAT_SRC = load_cuda_source(__file__, "sph_eval_kernels_cuda/_sph_eval_flat_src.cu") _box_based_evaluation_flat_kernel = None _box_based_evaluation_meshgrid_kernel = None @@ -538,93 +248,7 @@ def box_based_evaluation_meshgrid_gpu( # :func:`~struphy.pic.sph_eval_kernels.naive_evaluation_kernel`. # --------------------------------------------------------------------------- -_SPH_EVAL_NAIVE_SRC = r""" -extern "C" __global__ -void naive_evaluation_flat_cuda( - const double* markers, - const int n_cols, - const int n_markers, - const double Np, - const double* eta1, - const double* eta2, - const double* eta3, - const int n_eval, - const int* holes, - const int periodic1, - const int periodic2, - const int periodic3, - const int index, - const int kernel_type, - const double h1, - const double h2, - const double h3, - double* out) -{ - int i = blockIdx.x * blockDim.x + threadIdx.x; - if (i >= n_eval) return; - - double e1 = eta1[i], e2 = eta2[i], e3 = eta3[i]; - - double acc = 0.0; - for (int p = 0; p < n_markers; p++) { - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - out[i] = acc / Np; -} - -extern "C" __global__ -void naive_evaluation_meshgrid_cuda( - const double* markers, - const int n_cols, - const int n_markers, - const double Np, - const double* eta1, - const double* eta2, - const double* eta3, - const int n1_eval, - const int n2_eval, - const int n3_eval, - const int* holes, - const int periodic1, - const int periodic2, - const int periodic3, - const int index, - const int kernel_type, - const double h1, - const double h2, - const double h3, - double* out) -{ - size_t idx = (size_t)blockIdx.x * blockDim.x + threadIdx.x; - size_t n_total = (size_t)n1_eval * n2_eval * n3_eval; - if (idx >= n_total) return; - - int i = idx / ((size_t)n2_eval * n3_eval); - int rem = idx % ((size_t)n2_eval * n3_eval); - int j = rem / n3_eval; - int k = rem % n3_eval; - - double e1 = eta1[i], e2 = eta2[j], e3 = eta3[k]; - - double acc = 0.0; - for (int p = 0; p < n_markers; p++) { - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - out[idx] = acc / Np; -} -""" +_SPH_EVAL_NAIVE_SRC = load_cuda_source(__file__, "sph_eval_kernels_cuda/_sph_eval_naive_src.cu") _naive_evaluation_flat_kernel = None _naive_evaluation_meshgrid_kernel = None diff --git a/src/struphy/pic/utilities_kernels_cuda.py b/src/struphy/pic/utilities_kernels_cuda.py index 9906e4035..13b02f5c0 100644 --- a/src/struphy/pic/utilities_kernels_cuda.py +++ b/src/struphy/pic/utilities_kernels_cuda.py @@ -13,311 +13,9 @@ reuse the ``find_span_dev``/``b_splines_dev``/``eval_0form_dev`` device functions rather than defining their own. """ +from struphy.cuda import load_cuda_source -_UTILITIES_SRC = r""" -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) -{ - double left[MAXP]; - double right[MAXP]; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -__device__ double eval_0form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bn2, const double* bn3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c, int n2x, int n3x) -{ - double out = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] * bn1[il1] * bn2[il2] * bn3[il3]; - } - } - } - return out; -} - -// markers[ip, first_diagnostics_idx] = mu_p * |B_0(eta_p)| -extern "C" __global__ -void eval_magnetic_background_energy_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, const int mu_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* abs_B0, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double mu = row[mu_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, abs_B0, n2x, n3x); - - row[first_diagnostics_idx] = mu * abs_B; -} - -// markers[ip, first_diagnostics_idx] = v_par^2 / 2 + mu_p * |B(eta_p)| -extern "C" __global__ -void eval_energy_5d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, const int mu_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* absB, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v_parallel = row[3]; - const double mu = row[mu_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, n2x, n3x); - - row[first_diagnostics_idx] = 0.5 * v_parallel * v_parallel + mu * abs_B; -} - -// markers[ip, idx_can_momentum] = shifted canonical toroidal momentum (5D) -extern "C" __global__ -void eval_canonical_toroidal_moment_5d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, const int mu_idx, const int idx_can_momentum, - const double epsilon, const double B0, const double R0, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* absB, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v_para = row[3]; - const double mu = row[mu_idx]; - const double energy = row[first_diagnostics_idx]; - const double psi = row[idx_can_momentum]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, n2x, n3x); - - double out = psi - epsilon * B0 * R0 / abs_B * v_para; - if (energy - mu * B0 > 0.0) { - // sign(v_para) matches numpy.sign: 0 for exactly 0 - const double sgn = (v_para > 0.0) ? 1.0 : ((v_para < 0.0) ? -1.0 : 0.0); - out += epsilon * sgn * sqrt(2.0 * (energy - mu * B0)) * R0; - } - row[idx_can_momentum] = out; -} - -// markers[ip, first_diagnostics_idx + 5] = shifted canonical toroidal momentum (6D) -extern "C" __global__ -void eval_canonical_toroidal_moment_6d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, - const double epsilon, const double B0, const double R0, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* absB, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double energy = row[first_diagnostics_idx + 3]; - const double mu = row[first_diagnostics_idx + 4]; - const double psi = row[first_diagnostics_idx + 5]; - const double v_para = row[first_diagnostics_idx + 6]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, n2x, n3x); - - double out = psi - epsilon * B0 * R0 / abs_B * v_para; - if (energy - mu * B0 > 0.0) { - const double sgn = (v_para > 0.0) ? 1.0 : ((v_para < 0.0) ? -1.0 : 0.0); - out += epsilon * sgn * sqrt(2.0 * (energy - mu * B0)) * R0; - } - row[first_diagnostics_idx + 5] = out; -} - -// markers[ip, first_diagnostics_idx + 1] = v_perp^2 / (2 |B(eta_p)|) -extern "C" __global__ -void eval_magnetic_moment_5d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* absB, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v_perp = row[4]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, n2x, n3x); - - row[first_diagnostics_idx + 1] = 0.5 * v_perp * v_perp / abs_B; -} - -// markers[ip, first_diagnostics_idx] = mu_p * (|B_0| + PBb)(eta_p) -// NOTE: the CPU reference also evaluates the Jacobian DF(eta) here, but never -// uses the result, so it is not replicated. -extern "C" __global__ -void eval_magnetic_energy_PBb_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, const int mu_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* abs_B0, const int a_n2x, const int a_n3x, - const double* PBb, const int b_n2x, const int b_n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - // eta = mod(markers[0:3], 1.0); fmod can return negative, match numpy mod - double eta[3]; - for (int k = 0; k < 3; k++) { - double e = fmod(row[k], 1.0); - if (e < 0.0) e += 1.0; - eta[k] = e; - } - - const double mu = row[mu_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - b_splines_dev(tn1, p1, eta[0], span1, bn1); - b_splines_dev(tn2, p2, eta[1], span2, bn2); - b_splines_dev(tn3, p3, eta[2], span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, abs_B0, a_n2x, a_n3x); - const double PB_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, PBb, b_n2x, b_n3x); - - row[first_diagnostics_idx] = mu * (abs_B + PB_b); -} -""" +_UTILITIES_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_utilities_src.cu") _kernels = {} @@ -591,82 +289,7 @@ def eval_magnetic_energy_PBb_gpu(markers, args_derham, first_diagnostics_idx, mu # rather than the small self-contained source in this module. # --------------------------------------------------------------------------- -_GC_FROM_6D_SRC = r""" -extern "C" __global__ -void eval_guiding_center_from_6d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b21, const int b1_n2, const int b1_n3, - const double* b22, const int b2_n2, const int b2_n3, - const double* b23, const int b3_n2, const int b3_n3, - const double* absB, const int a_n2, const int a_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double x = row[first_diagnostics_idx]; - const double y = row[first_diagnostics_idx + 1]; - const double z = row[first_diagnostics_idx + 2]; - double v[3] = {row[3], row[4], row[5]}; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b2[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - b21, b1_n2, b1_n3, b22, b2_n2, b2_n3, b23, b3_n2, b3_n3, b2); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, a_n2, a_n3); - - // normalized magnetic field, cartesian - b2[0] /= abs_B; b2[1] /= abs_B; b2[2] /= abs_B; - double norm_b_cart[3]; - matvec_dev(dfm, b2, norm_b_cart); - norm_b_cart[0] /= det_df; norm_b_cart[1] /= det_df; norm_b_cart[2] /= det_df; - - const double v_parallel = dot3_dev(norm_b_cart, v); - - double temp[3], v_perp[3]; - cross_dev(v, norm_b_cart, temp); - cross_dev(norm_b_cart, temp, v_perp); - const double v_perp_square = v_perp[0]*v_perp[0] + v_perp[1]*v_perp[1] + v_perp[2]*v_perp[2]; - - row[first_diagnostics_idx + 6] = v_parallel; - row[first_diagnostics_idx + 4] = 0.5 * v_perp_square / abs_B; - - double Larmor_r[3]; - cross_dev(norm_b_cart, v_perp, Larmor_r); - for (int k = 0; k < 3; k++) Larmor_r[k] = Larmor_r[k] / abs_B * epsilon; - - row[first_diagnostics_idx + 0] = x - Larmor_r[0]; - row[first_diagnostics_idx + 1] = y - Larmor_r[1]; - row[first_diagnostics_idx + 2] = z - Larmor_r[2]; -} -""" +_GC_FROM_6D_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_gc_from_6d_src.cu") _gc6d_kernel = None @@ -755,65 +378,7 @@ def eval_guiding_center_from_6d_gpu( # find_span_dev/b_splines_dev/eval_0form_dev helpers used by _UTILITIES_SRC. # --------------------------------------------------------------------------- -_GRADB_EDIFF_SRC = r""" -__device__ double gradb_ediff_mod1_dev(double x) -{ - double r = fmod(x, 1.0); - if (r < 0.0) r += 1.0; - return r; -} - -extern "C" __global__ -void eval_gradB_ediff_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int mu_idx, const int idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* pb1, const int p1_n2, const int p1_n3, - const double* pb2, const int p2_n2, const int p2_n3, - const double* pb3, const int p3_n2, const int p3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta_mid[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - eta_mid[k] = gradb_ediff_mod1_dev((row[k] + row[first_init_idx + k]) / 2.0); - eta_diff[k] = row[k] - row[first_init_idx + k]; - } - const double mu = row[mu_idx]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double gradB[3], grad_PB_b[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, gradB); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - pb1,p1_n2,p1_n3, pb2,p2_n2,p2_n3, pb3,p3_n2,p3_n3, grad_PB_b); - - double tmp[3]; - for (int k = 0; k < 3; k++) tmp[k] = gradB[k] + grad_PB_b[k]; - - row[idx] = mu * dot3_dev(eta_diff, tmp); -} -""" +_GRADB_EDIFF_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_gradb_ediff_src.cu") _gradb_ediff_kernel = None From f0512920cba893415eb88590d8acca6f031f0045 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 2 Sep 2026 18:02:38 +0200 Subject: [PATCH 145/156] Add profilingoptions with labels --- .../DriftKineticElectrostaticAdiabatic/params_cyclone.py | 2 ++ profiling/examples/GuidingCenter/params_GuidingCenter.py | 2 ++ .../examples/GuidingCenter/params_GuidingCenter_scaling.py | 2 ++ .../examples/Poisson/cube_strong_scaling/params_poisson.py | 2 ++ .../examples/PressureLessSPH/params_PressureLessSPH_scaling.py | 2 ++ .../ToyGyrokinetic/diocotron_instability/params_diocotron.py | 2 ++ .../VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py | 2 ++ 7 files changed, 14 insertions(+) diff --git a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py index 6322a7d51..59f26026d 100644 --- a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py +++ b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py @@ -83,6 +83,7 @@ DerhamOptions, EnvironmentOptions, LoadingParameters, + ProfilingOptions, SavingParameters, Simulation, SortingParameters, @@ -169,6 +170,7 @@ equil=equil, grid=grid, derham_opts=derham_opts, + profiling_opts=ProfilingOptions(label=f"DK-Cyclone-{args.backend}"), ) # ------------------- diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter.py b/profiling/examples/GuidingCenter/params_GuidingCenter.py index 82536c7a1..86711c47e 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter.py @@ -69,6 +69,7 @@ DerhamOptions, EnvironmentOptions, LoadingParameters, + ProfilingOptions, SavingParameters, Simulation, SortingParameters, @@ -136,6 +137,7 @@ equil=equil, grid=grid, derham_opts=derham_opts, + profiling_opts=ProfilingOptions(label=f"GC-{args.backend}"), ) # ------------------- diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py index 0c05d488b..b1cca93af 100644 --- a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py +++ b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py @@ -68,6 +68,7 @@ DerhamOptions, EnvironmentOptions, LoadingParameters, + ProfilingOptions, SavingParameters, Simulation, SortingParameters, @@ -138,6 +139,7 @@ equil=equil, grid=grid, derham_opts=derham_opts, + profiling_opts=ProfilingOptions(label=f"GC-scale-{args.backend}"), ) # ------------------- diff --git a/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py b/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py index 94e947380..65820891b 100644 --- a/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py +++ b/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py @@ -27,6 +27,7 @@ DerhamOptions, EnvironmentOptions, FieldsBackground, + ProfilingOptions, Simulation, Time, domains, @@ -97,6 +98,7 @@ equil=equil, grid=grid, derham_opts=derham_opts, + profiling_opts=ProfilingOptions(label="Poisson-3D"), ) # ------------------ diff --git a/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py b/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py index 69b1b1422..1ebbcb49c 100644 --- a/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py +++ b/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py @@ -67,6 +67,7 @@ DerhamOptions, EnvironmentOptions, LoadingParameters, + ProfilingOptions, SavingParameters, Simulation, SortingParameters, @@ -133,6 +134,7 @@ equil=equil, grid=grid, derham_opts=derham_opts, + profiling_opts=ProfilingOptions(label=f"SPH-scale-{args.backend}"), ) # ------------------- diff --git a/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py b/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py index b6dff0d7b..b8252e94c 100644 --- a/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py +++ b/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py @@ -42,6 +42,7 @@ FieldsBackground, KernelDensityPlot, LoadingParameters, + ProfilingOptions, SavingParameters, Simulation, SortingParameters, @@ -118,6 +119,7 @@ equil=equil, grid=grid, derham_opts=derham_opts, + profiling_opts=ProfilingOptions(label="Diocotron-2D"), ) # ------------------- diff --git a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py index bed902203..38daffb18 100644 --- a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py +++ b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py @@ -68,6 +68,7 @@ DerhamOptions, EnvironmentOptions, LoadingParameters, + ProfilingOptions, SavingParameters, Simulation, SortingParameters, @@ -135,6 +136,7 @@ equil=equil, grid=grid, derham_opts=derham_opts, + profiling_opts=ProfilingOptions(label=f"VA-scale-{args.backend}"), ) # ------------------- From 9944f47af12534bf558de4fd609a3384f87d36cf Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Thu, 3 Sep 2026 10:26:27 +0200 Subject: [PATCH 146/156] Enable setting log file with environment variable --- profiling/profiling_job.py | 7 ++++++- src/struphy/__init__.py | 6 +++++- 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/profiling/profiling_job.py b/profiling/profiling_job.py index 8b9b97f5e..10a7293db 100644 --- a/profiling/profiling_job.py +++ b/profiling/profiling_job.py @@ -184,7 +184,12 @@ def build_commands(self, ntasks: int, param_flags: list[str] | None = None) -> l f'echo "Running {self.label} with {ntasks} MPI ranks"', f'cd "{output_root}"', # The run's log lives next to its output, so each run keeps its own record - # instead of sharing the driver's terminal or the SLURM log. + # instead of sharing the driver's terminal or the SLURM log. STRUPHY_LOG_FILE + # is needed on top of the shared cwd above: several rank-count runs of the same + # case share `output_root` as their cwd (often concurrently, as separate SLURM + # jobs), and struphy's default relative "struphy.log" would otherwise resolve to + # the same file for all of them, racing on log rotation across processes. + f'STRUPHY_LOG_FILE="{sim_dir / "struphy.log"}" ' f'{self.launcher} -n {ntasks} {python} {self.params_source} {flags} > "{sim_dir / "struphy.out"}" 2>&1', "", 'echo "----------------------------------------"', diff --git a/src/struphy/__init__.py b/src/struphy/__init__.py index c745c477e..40aea9cdd 100644 --- a/src/struphy/__init__.py +++ b/src/struphy/__init__.py @@ -57,7 +57,11 @@ def filter(self, record): "class": "logging.handlers.RotatingFileHandler", "level": "DEBUG", "formatter": "detailed", - "filename": "struphy.log", + # Overridable via STRUPHY_LOG_FILE so processes sharing a cwd (e.g. several + # profiling jobs launched from the same case directory) don't rotate the + # same file concurrently -- RotatingFileHandler's rollover isn't safe across + # separate OS processes and races with FileNotFoundError when they collide. + "filename": os.environ.get("STRUPHY_LOG_FILE", "struphy.log"), "maxBytes": 10000, "backupCount": 3, }, From ece72aa08cc73170ec7efb2e1ead2a64b92798af Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Thu, 3 Sep 2026 19:40:14 +0200 Subject: [PATCH 147/156] Update params --- feectools | 2 +- .../Poisson/cube_strong_scaling/params_poisson.py | 15 ++++++++++++++- 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/feectools b/feectools index 88cadbab0..ce78b9bb2 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 88cadbab0d784448834d98756e39561bc2d2eb5d +Subproject commit ce78b9bb2cbeeed0dfe34327900df5f3fc1b7608 diff --git a/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py b/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py index 65820891b..66b4037c0 100644 --- a/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py +++ b/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py @@ -12,6 +12,14 @@ """ import logging +import os + +# This is a CPU-only scaling test: pin the backend before the first `struphy` import, +# since `cunumpy` (imported transitively as soon as `struphy` is) reads ARRAY_BACKEND +# once at import time. Without this, an ARRAY_BACKEND=cupy left set in the submitting +# shell/job environment silently leaks in and the run fails or hangs instead of using +# the intended NumPy path. +os.environ["ARRAY_BACKEND"] = "numpy" from struphy import set_logging_level @@ -81,7 +89,12 @@ equil = None # Grid -grid = grids.TensorProductGrid(num_elements=(256, 256, 256), mpi_dims_mask=(True, True, True)) +# 256**3 elements never finished the 1-rank end of this strong scaling sweep within the +# dcgp_fua_dbg queue's 15-minute limit (a single-core PCG solve on that many DOFs takes far +# longer). 96**3 finishes 1 rank in ~75s locally while keeping the RHS/Phi convergence +# checks below well below their asserted thresholds (32**3/64**3 undershoot the manufactured +# solution's resolution needs and fail or nearly fail those asserts further down). +grid = grids.TensorProductGrid(num_elements=(96, 96, 96), mpi_dims_mask=(True, True, True)) # Derham options derham_opts = DerhamOptions(degree=(1, 2, 3), bcs=(("dirichlet", "dirichlet"), None, None)) From 907f9176f13452eb0cca45582991dcecf79e1179 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 14 Sep 2026 17:00:19 +0200 Subject: [PATCH 148/156] Consilidate the cuda kernels, add CudaKernel --- src/struphy/cuda.py | 102 +++++ .../feec/basis_projection_kernels_cuda.py | 14 +- src/struphy/feec/mass_kernels_cuda.py | 86 +--- src/struphy/feec/variational_kernels_cuda.py | 36 +- .../pic/accumulation/accum_kernels_cuda.py | 131 ++---- .../pic/accumulation/accum_kernels_gc_cuda.py | 122 ++---- .../pic/pushing/eval_kernels_gc_cuda.py | 65 +-- .../pic/pushing/eval_kernels_sph_cuda.py | 27 +- .../pic/pushing/pusher_kernels_cuda.py | 396 ++++-------------- .../pic/pushing/pusher_kernels_gc_cuda.py | 178 +++----- .../pic/pushing/pusher_kernels_sph_cuda.py | 17 +- .../pushing/pusher_utilities_kernels_cuda.py | 24 +- src/struphy/pic/sorting_kernels_cuda.py | 40 +- src/struphy/pic/sph_eval_kernels_cuda.py | 88 +--- src/struphy/pic/utilities_kernels_cuda.py | 104 ++--- 15 files changed, 460 insertions(+), 970 deletions(-) diff --git a/src/struphy/cuda.py b/src/struphy/cuda.py index 7ee42c90f..75474bbd1 100644 --- a/src/struphy/cuda.py +++ b/src/struphy/cuda.py @@ -2,6 +2,7 @@ from functools import lru_cache from pathlib import Path +from typing import Sequence @lru_cache(maxsize=None) @@ -9,3 +10,104 @@ def load_cuda_source(module_file: str, source_name: str) -> str: """Load a CUDA C source fragment stored alongside its Python wrapper.""" path = Path(module_file).with_name("cuda") / source_name return path.read_text(encoding="utf-8") + + +class CudaKernel: + """A lazily-compiled, cached ``cupy.RawKernel``. + + Every hand-written CUDA replacement in ``struphy.pic.*_cuda`` / + ``struphy.feec.*_cuda`` used to repeat the same boilerplate at each call + site: a module-level ``_foo_kernel = None`` sentinel, a + ``_get_foo_kernel()`` function that imports ``cupy`` and compiles the + ``RawKernel`` the first time it's needed (so importing these modules + under ``ARRAY_BACKEND=numpy`` never touches CuPy), and caches it back into + the global. This class replaces that boilerplate with one declaration: + + _foo_kernel = CudaKernel(_FOO_SRC, "foo_cuda") + + made once at module level, right next to the ``*_gpu`` function it backs. + Compilation is still deferred to first call (``import cupy`` only happens + inside :meth:`__call__`), and the compiled kernel is cached on the + instance -- identical behavior to the old pattern, but now every real GPU + kernel a module launches shows up as a ``CudaKernel(...)`` at module + scope, so `grep -n "CudaKernel("` (or just reading the top of the file) + tells you exactly which functions do device work and which don't. + + ``source`` may be a string, or a zero-argument callable that builds one + (matching the old per-module ``_source()`` helpers some of these files + used to defer assembling/importing a source shared with another module + until first use). + """ + + __slots__ = ("_source", "_name", "_kernel") + + def __init__(self, source, name: str) -> None: + self._source = source + self._name = name + self._kernel = None + + def __call__(self, grid, block, args) -> None: + self._compiled()(grid, block, args) + + def compile(self) -> None: + """Force NVRTC compilation now instead of on first launch. + + For a kernel on the hot path of the first timed step (e.g. a + model-setup routine that wants compile latency paid during setup, not + during the first measured propagation step): call this eagerly: + ``compile()`` is idempotent, so it composes fine with the normal + lazy-on-first-launch path -- whichever happens first wins, and later + calls (from either path) are no-ops. + """ + self._compiled() + + def _compiled(self): + if self._kernel is None: + import cupy as cp + + source = self._source() if callable(self._source) else self._source + kernel = cp.RawKernel(source, self._name) + kernel.compile() + self._kernel = kernel + return self._kernel + + +class CudaKernelSet: + """A cache of :class:`CudaKernel` instances sharing one CUDA source, + keyed by kernel name. + + For modules exposing a *family* of kernel entry points compiled from the + same source (typically a device-function library plus several + ``__global__`` entry points), where the entry point needed depends on a + runtime string (``u_space``, ``algo``, a diffusion variant, ...) rather + than being fixed at import time. Replaces the old ``_kernels = {}`` dict + + ``_get_kernel(name)`` function pattern -- ``kernels[name]`` compiles and + caches lazily, exactly like the old lookup did. + + ``source`` may be a string (shared eagerly, e.g. one ``load_cuda_source`` + result) or a zero-argument callable that builds it (for sources + concatenated from several fragments -- matching the old per-module + ``_source()`` helper -- so that assembly, and any ``cupy`` import inside + it, stays deferred to first use). + """ + + __slots__ = ("_source", "_cache") + + def __init__(self, source) -> None: + self._source = source + self._cache: dict[str, CudaKernel] = {} + + def __getitem__(self, name: str) -> CudaKernel: + if name not in self._cache: + source = self._source() if callable(self._source) else self._source + self._cache[name] = CudaKernel(source, name) + return self._cache[name] + + +def launch_1d(kernel: CudaKernel, n: int, args: Sequence, threads: int = 256) -> None: + """Launch ``kernel`` over a 1-D grid with one thread per element of a + length-``n`` array (markers, indices, quadrature points, ...) -- the + launch geometry shared by every kernel in ``struphy.pic.*_cuda`` / + ``struphy.feec.*_cuda``.""" + blocks = (n + threads - 1) // threads + kernel((blocks,), (threads,), tuple(args)) diff --git a/src/struphy/feec/basis_projection_kernels_cuda.py b/src/struphy/feec/basis_projection_kernels_cuda.py index a8970d894..ba25fe941 100644 --- a/src/struphy/feec/basis_projection_kernels_cuda.py +++ b/src/struphy/feec/basis_projection_kernels_cuda.py @@ -1,10 +1,10 @@ """CUDA kernels for dynamic weighted basis-projection matrices.""" -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, launch_1d, load_cuda_source _ASSEMBLE_SRC = load_cuda_source(__file__, "basis_projection_kernels_cuda/_assemble_src.cu") -_kernel = None +_kernel = CudaKernel(_ASSEMBLE_SRC, "assemble_weighted_basis_3d_cuda") def assemble_dofs_for_weighted_basisfuns_3d_gpu( @@ -27,9 +27,6 @@ def assemble_dofs_for_weighted_basisfuns_3d_gpu( import cupy as cp import numpy as np - global _kernel - if _kernel is None: - _kernel = cp.RawKernel(_ASSEMBLE_SRC, "assemble_weighted_basis_3d_cuda") spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) weights = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in weights) bases = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases) @@ -40,10 +37,9 @@ def assemble_dofs_for_weighted_basisfuns_3d_gpu( nq = tuple(x.shape[1] for x in spans) degree = tuple(x.shape[2] - 1 for x in bases) total = int(np.prod(ni) * np.prod(nq) * np.prod([p + 1 for p in degree])) - threads = 256 - _kernel( - ((total + threads - 1) // threads,), - (threads,), + launch_1d( + _kernel, + total, ( *rows, *spans, diff --git a/src/struphy/feec/mass_kernels_cuda.py b/src/struphy/feec/mass_kernels_cuda.py index f572c683d..a515d8d73 100644 --- a/src/struphy/feec/mass_kernels_cuda.py +++ b/src/struphy/feec/mass_kernels_cuda.py @@ -5,61 +5,24 @@ the routines below keep both the quadrature data and coefficient vectors on the device. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, launch_1d, load_cuda_source _H1VEC_DIVERGENCE_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_h1vec_divergence_src.cu") -_divergence_eval_kernel = None -_divergence_transpose_kernel = None +_divergence_eval_kernel = CudaKernel(_H1VEC_DIVERGENCE_SRC, "h1vec_divergence_eval_cuda") +_divergence_transpose_kernel = CudaKernel(_H1VEC_DIVERGENCE_SRC, "h1vec_divergence_transpose_cuda") _MASS_ASSEMBLY_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_mass_assembly_src.cu") -_mass_assembly_kernel = None +_mass_assembly_kernel = CudaKernel(_MASS_ASSEMBLY_SRC, "mass_3d_assemble_cuda") _WEAK_DIV_ASSEMBLY_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_weak_div_assembly_src.cu") -_weak_div_assembly_kernel = None +_weak_div_assembly_kernel = CudaKernel(_WEAK_DIV_ASSEMBLY_SRC, "weak_div_assemble_cuda") _H1VEC_DIVDIV_ASSEMBLY_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu") -_h1vec_divdiv_assembly_kernel = None - - -def _get_h1vec_divdiv_assembly_kernel(): - global _h1vec_divdiv_assembly_kernel - if _h1vec_divdiv_assembly_kernel is None: - import cupy as cp - - _h1vec_divdiv_assembly_kernel = cp.RawKernel(_H1VEC_DIVDIV_ASSEMBLY_SRC, "h1vec_divdiv_assemble_cuda") - return _h1vec_divdiv_assembly_kernel - - -def _get_mass_assembly_kernel(): - global _mass_assembly_kernel - if _mass_assembly_kernel is None: - import cupy as cp - - _mass_assembly_kernel = cp.RawKernel(_MASS_ASSEMBLY_SRC, "mass_3d_assemble_cuda") - return _mass_assembly_kernel - - -def _get_weak_div_assembly_kernel(): - global _weak_div_assembly_kernel - if _weak_div_assembly_kernel is None: - import cupy as cp - - _weak_div_assembly_kernel = cp.RawKernel(_WEAK_DIV_ASSEMBLY_SRC, "weak_div_assemble_cuda") - return _weak_div_assembly_kernel - - -def _get_kernels(): - global _divergence_eval_kernel, _divergence_transpose_kernel - if _divergence_eval_kernel is None: - import cupy as cp - - _divergence_eval_kernel = cp.RawKernel(_H1VEC_DIVERGENCE_SRC, "h1vec_divergence_eval_cuda") - _divergence_transpose_kernel = cp.RawKernel(_H1VEC_DIVERGENCE_SRC, "h1vec_divergence_transpose_cuda") - return _divergence_eval_kernel, _divergence_transpose_kernel +_h1vec_divdiv_assembly_kernel = CudaKernel(_H1VEC_DIVDIV_ASSEMBLY_SRC, "h1vec_divdiv_assemble_cuda") def _kernel_args(spans, degree, starts, pads, bases, dlogj, component): @@ -93,13 +56,11 @@ def h1vec_divergence_eval_gpu(spans, degree, starts, pads, bases, dlogj, compone """Add one H1-vector component's divergence to device ``values``.""" import numpy as np - kernel, _ = _get_kernels() args = _kernel_args(spans, degree, starts, pads, bases, dlogj, component) nvalues = values.size - threads = 256 - kernel( - ((nvalues + threads - 1) // threads,), - (threads,), + launch_1d( + _divergence_eval_kernel, + nvalues, (*args, coeffs, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), values), ) @@ -108,13 +69,11 @@ def h1vec_divergence_transpose_gpu(spans, degree, starts, pads, bases, dlogj, co """Accumulate the transpose of one H1-vector divergence component.""" import numpy as np - _, kernel = _get_kernels() args = _kernel_args(spans, degree, starts, pads, bases, dlogj, component) nvalues = values.size - threads = 256 - kernel( - ((nvalues + threads - 1) // threads,), - (threads,), + launch_1d( + _divergence_transpose_kernel, + nvalues, (*args, values, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), coeffs), ) @@ -132,10 +91,9 @@ def mass_3d_assemble_gpu(spans, degree_i, degree_j, starts, pads, weights, bases total = int( np.prod([x.size for x in spans]) * np.prod([x + 1 for x in degree_i]) * np.prod([x + 1 for x in degree_j]) ) - threads = 256 - _get_mass_assembly_kernel()( - ((total + threads - 1) // threads,), - (threads,), + launch_1d( + _mass_assembly_kernel, + total, ( *spans, *(np.int32(x.size) for x in spans), @@ -183,10 +141,9 @@ def weak_divergence_assemble_gpu( total = int( np.prod([x.size for x in spans]) * np.prod([p + 1 for p in degree_i]) * np.prod([p + 1 for p in degree_j]) ) - threads = 256 - _get_weak_div_assembly_kernel()( - ((total + threads - 1) // threads,), - (threads,), + launch_1d( + _weak_div_assembly_kernel, + total, ( *spans, *(np.int32(x.size) for x in spans), @@ -223,10 +180,9 @@ def h1vec_divdiv_assemble_gpu(spans, degree, starts, pads, bases, weighted_rho, weighted_rho = cp.ascontiguousarray(weighted_rho) nloc = int(np.prod([x + 1 for x in degree])) total = int(np.prod([x.size for x in spans]) * nloc * nloc) - threads = 256 - _get_h1vec_divdiv_assembly_kernel()( - ((total + threads - 1) // threads,), - (threads,), + launch_1d( + _h1vec_divdiv_assembly_kernel, + total, ( *spans, *(np.int32(x.size) for x in spans), diff --git a/src/struphy/feec/variational_kernels_cuda.py b/src/struphy/feec/variational_kernels_cuda.py index 41b018638..e601d4566 100644 --- a/src/struphy/feec/variational_kernels_cuda.py +++ b/src/struphy/feec/variational_kernels_cuda.py @@ -1,26 +1,23 @@ """CUDA kernels for fused variational grid evaluations.""" -from struphy.cuda import load_cuda_source - -_KINETIC_ENERGY_KERNEL = None +from struphy.cuda import CudaKernel, launch_1d, load_cuda_source _KINETIC_ENERGY_SOURCE = load_cuda_source(__file__, "variational_kernels_cuda/kinetic_energy_grid.cu") +_kinetic_energy_kernel = CudaKernel(_KINETIC_ENERGY_SOURCE, "kinetic_energy_grid_cuda") + def prepare_kinetic_energy_kernel(): - """Compile and cache the fused kinetic-energy CUDA kernel.""" - import cupy as cp + """Force the fused kinetic-energy CUDA kernel to compile now, during + model setup, rather than lazily on the first timed propagation step. - global _KINETIC_ENERGY_KERNEL - if _KINETIC_ENERGY_KERNEL is None: - _KINETIC_ENERGY_KERNEL = cp.RawKernel( - _KINETIC_ENERGY_SOURCE, - "kinetic_energy_grid_cuda", - ) - # Force NVRTC compilation during model setup rather than the first - # timed propagation step. - _KINETIC_ENERGY_KERNEL.compile() - return _KINETIC_ENERGY_KERNEL + Idempotent (see :meth:`~struphy.cuda.CudaKernel.compile`): every actual + invocation still goes through the normal + ``launch_1d(_kinetic_energy_kernel, ...)`` call in + :func:`kinetic_energy_grid_gpu` below, which is a no-op past compilation + once this has run. + """ + _kinetic_energy_kernel.compile() def kinetic_energy_grid_gpu( @@ -39,17 +36,16 @@ def kinetic_energy_grid_gpu( import cupy as cp import numpy as np - kernel = prepare_kinetic_energy_kernel() + prepare_kinetic_energy_kernel() spans = tuple(cp.ascontiguousarray(cp.asarray(value, dtype=cp.int64)) for value in spans) bases = tuple(cp.ascontiguousarray(cp.asarray(value, dtype=cp.float64)) for value in bases) coefficients = tuple(cp.ascontiguousarray(value) for value in coefficients) coefficients1 = tuple(cp.ascontiguousarray(value) for value in coefficients1) metric = cp.ascontiguousarray(metric) total = out.size - threads = 256 - kernel( - ((total + threads - 1) // threads,), - (threads,), + launch_1d( + _kinetic_energy_kernel, + total, ( *spans, *bases, diff --git a/src/struphy/pic/accumulation/accum_kernels_cuda.py b/src/struphy/pic/accumulation/accum_kernels_cuda.py index fa45593ce..e8b78a414 100644 --- a/src/struphy/pic/accumulation/accum_kernels_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_cuda.py @@ -23,20 +23,10 @@ just the marker weight), so it reuses only the B-spline evaluation device functions, not the geometry-mapping ones. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source _CHARGE_DENSITY_0FORM_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_charge_density_0form_src.cu") - -_charge_density_0form_kernel = None - - -def _get_charge_density_0form_kernel(): - global _charge_density_0form_kernel - if _charge_density_0form_kernel is None: - import cupy as cp - - _charge_density_0form_kernel = cp.RawKernel(_CHARGE_DENSITY_0FORM_SRC, "charge_density_0form_cuda") - return _charge_density_0form_kernel +_charge_density_0form_kernel = CudaKernel(_CHARGE_DENSITY_0FORM_SRC, "charge_density_0form_cuda") def charge_density_0form_gpu( @@ -61,16 +51,13 @@ def charge_density_0form_gpu( function only needs to add to it, not read markers back afterward: the caller reads ``vec_dev`` directly since it was written in place. """ - import cupy as cp import numpy as np n_markers = markers.shape[0] dev_markers = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - _get_charge_density_0form_kernel()( - (blocks,), - (threads,), + launch_1d( + _charge_density_0form_kernel, + n_markers, ( dev_markers, np.int32(markers.shape[1]), @@ -139,16 +126,7 @@ def _vlasov_maxwell_source(): return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _VLASOV_MAXWELL_EXTRA_SRC -_vlasov_maxwell_kernel = None - - -def _get_vlasov_maxwell_kernel(): - global _vlasov_maxwell_kernel - if _vlasov_maxwell_kernel is None: - import cupy as cp - - _vlasov_maxwell_kernel = cp.RawKernel(_vlasov_maxwell_source(), "vlasov_maxwell_cuda") - return _vlasov_maxwell_kernel +_vlasov_maxwell_kernels = CudaKernelSet(_vlasov_maxwell_source) def vlasov_maxwell_gpu( @@ -175,13 +153,10 @@ def vlasov_maxwell_gpu( calling convention as :func:`linear_vlasov_ampere_gpu`, minus ``f0_values`` (this kernel doesn't need a background distribution). """ - import cupy as cp import numpy as np n_markers = markers.shape[0] dev_markers = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads def dims(a): return ( @@ -192,9 +167,9 @@ def dims(a): np.int32(a.shape[5]), ) - _get_vlasov_maxwell_kernel()( - (blocks,), - (threads,), + launch_1d( + _vlasov_maxwell_kernels["vlasov_maxwell_cuda"], + n_markers, ( dev_markers, np.int32(markers.shape[1]), @@ -238,16 +213,7 @@ def dims(a): ) -_linear_vlasov_ampere_kernel = None - - -def _get_linear_vlasov_ampere_kernel(): - global _linear_vlasov_ampere_kernel - if _linear_vlasov_ampere_kernel is None: - import cupy as cp - - _linear_vlasov_ampere_kernel = cp.RawKernel(_linear_vlasov_ampere_source(), "linear_vlasov_ampere_cuda") - return _linear_vlasov_ampere_kernel +_linear_vlasov_ampere_kernels = CudaKernelSet(_linear_vlasov_ampere_source) def linear_vlasov_ampere_gpu( @@ -287,8 +253,6 @@ def linear_vlasov_ampere_gpu( n_markers = markers.shape[0] dev_markers = markers f0_values_dev = cp.ascontiguousarray(f0_values_dev) - threads = 256 - blocks = (n_markers + threads - 1) // threads def dims(a): return ( @@ -299,9 +263,9 @@ def dims(a): np.int32(a.shape[5]), ) - _get_linear_vlasov_ampere_kernel()( - (blocks,), - (threads,), + launch_1d( + _linear_vlasov_ampere_kernels["linear_vlasov_ampere_cuda"], + n_markers, ( dev_markers, np.int32(markers.shape[1]), @@ -373,16 +337,7 @@ def _cc_lin_mhd_6d_1_source(): return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_6D_1_SRC -_cc_lin_mhd_6d_1_kernel = None - - -def _get_cc_lin_mhd_6d_1_kernel(): - global _cc_lin_mhd_6d_1_kernel - if _cc_lin_mhd_6d_1_kernel is None: - import cupy as cp - - _cc_lin_mhd_6d_1_kernel = cp.RawKernel(_cc_lin_mhd_6d_1_source(), "cc_lin_mhd_6d_1_cuda") - return _cc_lin_mhd_6d_1_kernel +_cc_lin_mhd_6d_1_kernels = CudaKernelSet(_cc_lin_mhd_6d_1_source) def cc_lin_mhd_6d_1_gpu( @@ -417,8 +372,6 @@ def cc_lin_mhd_6d_1_gpu( b2_1_dev = cp.ascontiguousarray(b2_1_dev) b2_2_dev = cp.ascontiguousarray(b2_2_dev) b2_3_dev = cp.ascontiguousarray(b2_3_dev) - threads = 256 - blocks = (n_markers + threads - 1) // threads def dims(a): return ( @@ -429,9 +382,9 @@ def dims(a): np.int32(a.shape[5]), ) - _get_cc_lin_mhd_6d_1_kernel()( - (blocks,), - (threads,), + launch_1d( + _cc_lin_mhd_6d_1_kernels["cc_lin_mhd_6d_1_cuda"], + n_markers, ( dev_markers, np.int32(markers.shape[1]), @@ -496,16 +449,7 @@ def _cc_lin_mhd_6d_2_source(): return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_6D_2_SRC -_cc_lin_mhd_6d_2_kernel = None - - -def _get_cc_lin_mhd_6d_2_kernel(): - global _cc_lin_mhd_6d_2_kernel - if _cc_lin_mhd_6d_2_kernel is None: - import cupy as cp - - _cc_lin_mhd_6d_2_kernel = cp.RawKernel(_cc_lin_mhd_6d_2_source(), "cc_lin_mhd_6d_2_cuda") - return _cc_lin_mhd_6d_2_kernel +_cc_lin_mhd_6d_2_kernels = CudaKernelSet(_cc_lin_mhd_6d_2_source) def cc_lin_mhd_6d_2_gpu( @@ -547,8 +491,6 @@ def cc_lin_mhd_6d_2_gpu( b2_1_dev = cp.ascontiguousarray(b2_1_dev) b2_2_dev = cp.ascontiguousarray(b2_2_dev) b2_3_dev = cp.ascontiguousarray(b2_3_dev) - threads = 256 - blocks = (n_markers + threads - 1) // threads def dims(a): return ( @@ -559,9 +501,9 @@ def dims(a): np.int32(a.shape[5]), ) - _get_cc_lin_mhd_6d_2_kernel()( - (blocks,), - (threads,), + launch_1d( + _cc_lin_mhd_6d_2_kernels["cc_lin_mhd_6d_2_cuda"], + n_markers, ( dev_markers, np.int32(markers.shape[1]), @@ -667,26 +609,8 @@ def _pc_lin_mhd_6d_source(): return _GENERAL_GEOMETRY_SRC + _PC_PRESSURE_FILLERS_SRC + _PC_LIN_MHD_6D_SRC -_pc_lin_mhd_6d_full_kernel = None -_pc_lin_mhd_6d_kernel = None - - -def _get_pc_lin_mhd_6d_full_kernel(): - global _pc_lin_mhd_6d_full_kernel - if _pc_lin_mhd_6d_full_kernel is None: - import cupy as cp - - _pc_lin_mhd_6d_full_kernel = cp.RawKernel(_pc_lin_mhd_6d_full_source(), "pc_lin_mhd_6d_full_cuda") - return _pc_lin_mhd_6d_full_kernel - - -def _get_pc_lin_mhd_6d_kernel(): - global _pc_lin_mhd_6d_kernel - if _pc_lin_mhd_6d_kernel is None: - import cupy as cp - - _pc_lin_mhd_6d_kernel = cp.RawKernel(_pc_lin_mhd_6d_source(), "pc_lin_mhd_6d_cuda") - return _pc_lin_mhd_6d_kernel +_pc_lin_mhd_6d_full_kernels = CudaKernelSet(_pc_lin_mhd_6d_full_source) +_pc_lin_mhd_6d_kernels = CudaKernelSet(_pc_lin_mhd_6d_source) def _pc_lin_mhd_6d_launch( @@ -711,13 +635,10 @@ def _pc_lin_mhd_6d_launch( subset named in ``vel_pairs``/``vec_is`` is actually passed to the kernel launch. """ - import cupy as cp import numpy as np n_markers = markers.shape[0] dev_markers = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads def dims(a): return ( @@ -760,7 +681,7 @@ def dims(a): v = vec_args_45[f"vec{mu}_1"] args.extend((np.int32(v.shape[1]), np.int32(v.shape[2]))) - kernel((blocks,), (threads,), tuple(args)) + launch_1d(kernel, n_markers, args) def pc_lin_mhd_6d_full_gpu( @@ -793,7 +714,7 @@ def pc_lin_mhd_6d_full_gpu( for k, (i, mu) in enumerate((i, mu) for i in ("1", "2", "3") for mu in ("1", "2", "3")) } _pc_lin_mhd_6d_launch( - _get_pc_lin_mhd_6d_full_kernel(), + _pc_lin_mhd_6d_full_kernels["pc_lin_mhd_6d_full_cuda"], markers, kind_map, params_dev, @@ -840,7 +761,7 @@ def pc_lin_mhd_6d_gpu( for k, (i, mu) in enumerate((i, mu) for i in ("1", "2", "3") for mu in ("1", "2", "3")) } _pc_lin_mhd_6d_launch( - _get_pc_lin_mhd_6d_kernel(), + _pc_lin_mhd_6d_kernels["pc_lin_mhd_6d_cuda"], markers, kind_map, params_dev, diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py index d814ea506..6b18c6de5 100644 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py @@ -11,20 +11,10 @@ just with a ``mu * weight * scale`` filling instead of a plain weight, and ``mu`` read from the marker's ``mu_idx`` column instead of a fixed offset. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source _GC_MAG_DENSITY_0FORM_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu") - -_gc_mag_density_0form_kernel = None - - -def _get_gc_mag_density_0form_kernel(): - global _gc_mag_density_0form_kernel - if _gc_mag_density_0form_kernel is None: - import cupy as cp - - _gc_mag_density_0form_kernel = cp.RawKernel(_GC_MAG_DENSITY_0FORM_SRC, "gc_mag_density_0form_cuda") - return _gc_mag_density_0form_kernel +_gc_mag_density_0form_kernel = CudaKernel(_GC_MAG_DENSITY_0FORM_SRC, "gc_mag_density_0form_cuda") def gc_mag_density_0form_gpu( @@ -42,16 +32,13 @@ def gc_mag_density_0form_gpu( :func:`~struphy.pic.accumulation.accum_kernels_gc.gc_mag_density_0form`. ``vec_dev`` is already device-resident and already zeroed by the caller. """ - import cupy as cp import numpy as np n_markers = markers.shape[0] dev_markers = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - _get_gc_mag_density_0form_kernel()( - (blocks,), - (threads,), + launch_1d( + _gc_mag_density_0form_kernel, + n_markers, ( dev_markers, np.int32(markers.shape[1]), @@ -114,22 +101,15 @@ def gc_density_0form_gpu(markers, weight_idx, pn, tn1_dev, tn2_dev, tn3_dev, sta _CC_LIN_MHD_5D_D_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu") -_cc_lin_mhd_5d_D_kernel = None +def _cc_lin_mhd_5d_D_source(): + from struphy.pic.accumulation.accum_kernels_cuda import _LINEAR_VLASOV_AMPERE_EXTRA_SRC + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_cc_lin_mhd_5d_D_kernel(): - global _cc_lin_mhd_5d_D_kernel - if _cc_lin_mhd_5d_D_kernel is None: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_5D_D_SRC - from struphy.pic.accumulation.accum_kernels_cuda import _LINEAR_VLASOV_AMPERE_EXTRA_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _cc_lin_mhd_5d_D_kernel = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_5D_D_SRC, - "cc_lin_mhd_5d_D_cuda", - ) - return _cc_lin_mhd_5d_D_kernel +_cc_lin_mhd_5d_D_kernels = CudaKernelSet(_cc_lin_mhd_5d_D_source) def cc_lin_mhd_5d_D_gpu( @@ -161,8 +141,6 @@ def cc_lin_mhd_5d_D_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) @@ -177,9 +155,9 @@ def dims(a): np.int32(a.shape[5]), ) - _get_cc_lin_mhd_5d_D_kernel()( - (blocks,), - (threads,), + launch_1d( + _cc_lin_mhd_5d_D_kernels["cc_lin_mhd_5d_D_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -234,21 +212,13 @@ def dims(a): _CC_LIN_MHD_5D_GRADB_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu") -_cc_gradB_kernel = None +def _cc_lin_mhd_5d_gradB_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + return _GENERAL_GEOMETRY_SRC + _FILL_VEC_SRC + _CC_LIN_MHD_5D_GRADB_SRC -def _get_cc_lin_mhd_5d_gradB_kernel(): - global _cc_gradB_kernel - if _cc_gradB_kernel is None: - import cupy as cp - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - _cc_gradB_kernel = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _FILL_VEC_SRC + _CC_LIN_MHD_5D_GRADB_SRC, - "cc_lin_mhd_5d_gradB_cuda", - ) - return _cc_gradB_kernel +_cc_gradB_kernels = CudaKernelSet(_cc_lin_mhd_5d_gradB_source) def cc_lin_mhd_5d_gradB_gpu( @@ -280,16 +250,14 @@ def cc_lin_mhd_5d_gradB_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_cc_lin_mhd_5d_gradB_kernel()( - (blocks,), - (threads,), + launch_1d( + _cc_gradB_kernels["cc_lin_mhd_5d_gradB_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -359,22 +327,14 @@ def d(a): _CC_LIN_MHD_5D_CURLB_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu") -_cc_curlb_kernel = None - +def _cc_lin_mhd_5d_curlb_source(): + from struphy.pic.accumulation.accum_kernels_cuda import _LINEAR_VLASOV_AMPERE_EXTRA_SRC + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_cc_lin_mhd_5d_curlb_kernel(): - global _cc_curlb_kernel - if _cc_curlb_kernel is None: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_5D_CURLB_SRC - from struphy.pic.accumulation.accum_kernels_cuda import _LINEAR_VLASOV_AMPERE_EXTRA_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _cc_curlb_kernel = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_5D_CURLB_SRC, - "cc_lin_mhd_5d_curlb_cuda", - ) - return _cc_curlb_kernel +_cc_curlb_kernels = CudaKernelSet(_cc_lin_mhd_5d_curlb_source) def cc_lin_mhd_5d_curlb_gpu( @@ -408,8 +368,6 @@ def cc_lin_mhd_5d_curlb_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) @@ -424,9 +382,9 @@ def dims(a): np.int32(a.shape[5]), ) - _get_cc_lin_mhd_5d_curlb_kernel()( - (blocks,), - (threads,), + launch_1d( + _cc_curlb_kernels["cc_lin_mhd_5d_curlb_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -503,21 +461,13 @@ def dims(a): _CC_LIN_MHD_5D_GRADB_DG_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu") -_cc_gradB_dg_kernel = None - +def _cc_lin_mhd_5d_gradB_dg_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_cc_lin_mhd_5d_gradB_dg_kernel(): - global _cc_gradB_dg_kernel - if _cc_gradB_dg_kernel is None: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _FILL_VEC_SRC + _CC_LIN_MHD_5D_GRADB_DG_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _cc_gradB_dg_kernel = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _FILL_VEC_SRC + _CC_LIN_MHD_5D_GRADB_DG_SRC, - "cc_lin_mhd_5d_gradB_dg_cuda", - ) - return _cc_gradB_dg_kernel +_cc_gradB_dg_kernels = CudaKernelSet(_cc_lin_mhd_5d_gradB_dg_source) def cc_lin_mhd_5d_gradB_dg_gpu( @@ -555,16 +505,14 @@ def cc_lin_mhd_5d_gradB_dg_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_cc_lin_mhd_5d_gradB_dg_kernel()( - (blocks,), - (threads,), + launch_1d( + _cc_gradB_dg_kernels["cc_lin_mhd_5d_gradB_dg_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), diff --git a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py index d6bdc25be..d80fa1a06 100644 --- a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py @@ -12,20 +12,10 @@ It is a plain per-marker 0-form spline evaluation, so it reuses the shared ``find_span_dev``/``b_splines_dev``/``eval_0form_dev`` device functions. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source _DK_HAMILTONIAN_SRC = load_cuda_source(__file__, "eval_kernels_gc_cuda/_dk_hamiltonian_src.cu") - -_dk_kernel = None - - -def _get_dk_kernel(): - global _dk_kernel - if _dk_kernel is None: - import cupy as cp - - _dk_kernel = cp.RawKernel(_DK_HAMILTONIAN_SRC, "driftkinetic_hamiltonian_cuda") - return _dk_kernel +_dk_kernel = CudaKernel(_DK_HAMILTONIAN_SRC, "driftkinetic_hamiltonian_cuda") def driftkinetic_hamiltonian_gpu( @@ -49,8 +39,6 @@ def driftkinetic_hamiltonian_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) @@ -59,9 +47,9 @@ def driftkinetic_hamiltonian_gpu( phi = cp.ascontiguousarray(phi_coeffs) a = [float(x) for x in (alpha[0], alpha[1], alpha[2], alpha[3])] - _get_dk_kernel()( - (blocks,), - (threads,), + launch_1d( + _dk_kernel, + n_markers, ( markers, np.int32(markers.shape[1]), @@ -113,17 +101,14 @@ def driftkinetic_hamiltonian_gpu( _GC_MARKER_COLUMN_SRC = load_cuda_source(__file__, "eval_kernels_gc_cuda/_gc_marker_column_src.cu") -_gc_marker_column_kernels = {} +def _gc_marker_column_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_gc_marker_column_kernel(name): - if name not in _gc_marker_column_kernels: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _GC_MARKER_COLUMN_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _gc_marker_column_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _GC_MARKER_COLUMN_SRC, name) - return _gc_marker_column_kernels[name] +_gc_marker_column_kernels = CudaKernelSet(_gc_marker_column_source) def grad_driftkinetic_hamiltonian_gpu( @@ -146,8 +131,6 @@ def grad_driftkinetic_hamiltonian_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) @@ -159,9 +142,9 @@ def d(a): tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - _get_gc_marker_column_kernel("grad_driftkinetic_hamiltonian_cuda")( - (blocks,), - (threads,), + launch_1d( + _gc_marker_column_kernels["grad_driftkinetic_hamiltonian_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -216,8 +199,6 @@ def bstar_parallel_3form_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) @@ -228,9 +209,9 @@ def d(a): tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - _get_gc_marker_column_kernel("bstar_parallel_3form_cuda")( - (blocks,), - (threads,), + launch_1d( + _gc_marker_column_kernels["bstar_parallel_3form_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -278,8 +259,6 @@ def bstar_2form_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) @@ -291,9 +270,9 @@ def d(a): tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - _get_gc_marker_column_kernel("bstar_2form_cuda")( - (blocks,), - (threads,), + launch_1d( + _gc_marker_column_kernels["bstar_2form_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -343,8 +322,6 @@ def unit_b_1form_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) @@ -356,9 +333,9 @@ def d(a): tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - _get_gc_marker_column_kernel("unit_b_1form_cuda")( - (blocks,), - (threads,), + launch_1d( + _gc_marker_column_kernels["unit_b_1form_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), diff --git a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py index 472d6212a..7a1592aca 100644 --- a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py +++ b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py @@ -13,27 +13,24 @@ ``matrix_inv_dev`` from :mod:`~struphy.pic.pushing.pusher_kernels_cuda`, though the two geometry helpers are unused here since none of these three kernels touch the domain Jacobian). + +The three ``*_gpu`` entry points share one :class:`~struphy.cuda.CudaKernelSet` +(``_sph_marker_column_kernels``, keyed by CUDA entry-point name). """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernelSet, launch_1d, load_cuda_source _SPH_MARKER_COLUMN_SRC = load_cuda_source(__file__, "eval_kernels_sph_cuda/_sph_marker_column_src.cu") -_sph_marker_column_kernels = {} +def _source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC + from struphy.pic.pushing.pusher_kernels_sph_cuda import _SPH_PUSHER_SRC + from struphy.pic.sph_eval_kernels_cuda import _SPH_EVAL_FLAT_SRC -def _get_sph_marker_column_kernel(name): - if name not in _sph_marker_column_kernels: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _SPH_EVAL_FLAT_SRC + _SPH_PUSHER_SRC + _SPH_MARKER_COLUMN_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - from struphy.pic.pushing.pusher_kernels_sph_cuda import _SPH_PUSHER_SRC - from struphy.pic.sph_eval_kernels_cuda import _SPH_EVAL_FLAT_SRC - _sph_marker_column_kernels[name] = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _SPH_EVAL_FLAT_SRC + _SPH_PUSHER_SRC + _SPH_MARKER_COLUMN_SRC, - name, - ) - return _sph_marker_column_kernels[name] +_sph_marker_column_kernels = CudaKernelSet(_source) def _sph_marker_column_launch( @@ -56,8 +53,6 @@ def _sph_marker_column_launch( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads dev_valid = cp.ascontiguousarray(cp.asarray(valid_mks).astype(cp.int32, copy=False)) dev_boxes = cp.ascontiguousarray(cp.asarray(boxes).astype(cp.int32, copy=False)) @@ -90,7 +85,7 @@ def _sph_marker_column_launch( if mu is not None: args.append(np.float64(mu)) - _get_sph_marker_column_kernel(name)((blocks,), (threads,), tuple(args)) + launch_1d(_sph_marker_column_kernels[name], n_markers, args) def sph_pressure_coeffs_gpu( diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index 179c41066..60aee6328 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -48,21 +48,18 @@ :meth:`~struphy.propagators.push_vin_efield.PushVinEfield.allocate` runs, so they are passed straight through with no transfer at all -- only the marker array round-trips through the device, exactly once per call. + +Every ``*_gpu`` function here is a thin wrapper around exactly one +:class:`~struphy.cuda.CudaKernel` (declared at module level, right above the +function that launches it): the kernel is compiled once, on first use, and +the function itself only builds the argument tuple and calls +:func:`~struphy.cuda.launch_1d`. If a function in this module does *not* sit +next to a ``CudaKernel``, it does not touch the GPU. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, launch_1d, load_cuda_source _PUSH_ETA_CUBOID_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_eta_cuboid_src.cu") - -_push_eta_cuboid_kernel = None - - -def _get_kernel(): - global _push_eta_cuboid_kernel - if _push_eta_cuboid_kernel is None: - import cupy as cp - - _push_eta_cuboid_kernel = cp.RawKernel(_PUSH_ETA_CUBOID_SRC, "push_eta_stage_cuboid") - return _push_eta_cuboid_kernel +_push_eta_cuboid_kernel = CudaKernel(_PUSH_ETA_CUBOID_SRC, "push_eta_stage_cuboid") def push_eta_stage_cuboid_gpu( @@ -83,20 +80,14 @@ def push_eta_stage_cuboid_gpu( through the device in full, matching the pattern used by :meth:`~struphy.pic.pushing.pusher.Pusher._reset_marker_buffers_gpu`. """ - import cupy as cp import numpy as np - kernel = _get_kernel() n_markers = markers.shape[0] - - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + _push_eta_cuboid_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(first_init_idx), @@ -112,17 +103,7 @@ def push_eta_stage_cuboid_gpu( _PUSH_ETA_RK_PERIODIC_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_eta_rk_periodic_src.cu") - -_push_eta_rk_periodic_kernel = None - - -def _get_periodic_kernel(): - global _push_eta_rk_periodic_kernel - if _push_eta_rk_periodic_kernel is None: - import cupy as cp - - _push_eta_rk_periodic_kernel = cp.RawKernel(_PUSH_ETA_RK_PERIODIC_SRC, "push_eta_rk_periodic") - return _push_eta_rk_periodic_kernel +_push_eta_rk_periodic_kernel = CudaKernel(_PUSH_ETA_RK_PERIODIC_SRC, "push_eta_rk_periodic") def push_eta_rk_periodic_gpu( @@ -151,13 +132,9 @@ def push_eta_rk_periodic_gpu( set to the -1.0 hole sentinel), so :meth:`~struphy.pic.base.Particles.update_holes` does not need to be called. """ - import cupy as cp import numpy as np - kernel = _get_periodic_kernel() n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads dev = markers @@ -168,9 +145,9 @@ def push_eta_rk_periodic_gpu( for stage in range(n_stages): last = 1.0 if stage == n_stages - 1 else 0.0 - kernel( - (blocks,), - (threads,), + launch_1d( + _push_eta_rk_periodic_kernel, + n_markers, ( dev, np.int32(n_cols), @@ -189,17 +166,7 @@ def push_eta_rk_periodic_gpu( _PUSH_V_EFIELD_CUBOID_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_v_efield_cuboid_src.cu") - -_push_v_efield_cuboid_kernel = None - - -def _get_v_efield_kernel(): - global _push_v_efield_cuboid_kernel - if _push_v_efield_cuboid_kernel is None: - import cupy as cp - - _push_v_efield_cuboid_kernel = cp.RawKernel(_PUSH_V_EFIELD_CUBOID_SRC, "push_v_with_efield_cuboid") - return _push_v_efield_cuboid_kernel +_push_v_efield_cuboid_kernel = CudaKernel(_PUSH_V_EFIELD_CUBOID_SRC, "push_v_with_efield_cuboid") def push_v_with_efield_cuboid_gpu( @@ -228,20 +195,14 @@ def push_v_with_efield_cuboid_gpu( them once rather than converting on every call, see :class:`~struphy.pic.pushing.pusher.Pusher`. """ - import cupy as cp import numpy as np - kernel = _get_v_efield_kernel() n_markers = markers.shape[0] - - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + _push_v_efield_cuboid_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(pn[0]), @@ -314,27 +275,8 @@ def push_v_with_efield_cuboid_gpu( _GENERAL_GEOMETRY_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_general_geometry_src.cu") -_push_eta_general_kernel = None -_push_v_efield_general_kernel = None - - -def _get_eta_general_kernel(): - global _push_eta_general_kernel - if _push_eta_general_kernel is None: - import cupy as cp - - _push_eta_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_eta_stage_general") - return _push_eta_general_kernel - - -def _get_v_efield_general_kernel(): - global _push_v_efield_general_kernel - if _push_v_efield_general_kernel is None: - import cupy as cp - - _push_v_efield_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_v_with_efield_general") - return _push_v_efield_general_kernel - +_push_eta_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_eta_stage_general") +_push_v_efield_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_v_with_efield_general") #: kind_map values df_dispatch_dev supports (Cuboid, Colella). Callers should #: check membership before dispatching to the *_general_gpu functions below. @@ -363,20 +305,14 @@ def push_eta_stage_general_gpu( (``args_domain.params``), expected to already be a small CuPy array (cheap to keep device-resident; callers should cache it once). """ - import cupy as cp import numpy as np - kernel = _get_eta_general_kernel() n_markers = markers.shape[0] - - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + _push_eta_general_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(first_init_idx), @@ -412,20 +348,14 @@ def push_v_with_efield_general_gpu( (``tn*_dev``/``e1_*_dev`` are expected to already be device-resident); ``params_dev`` follows :func:`push_eta_stage_general_gpu`. """ - import cupy as cp import numpy as np - kernel = _get_v_efield_general_kernel() n_markers = markers.shape[0] - - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + _push_v_efield_general_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(pn[0]), @@ -456,26 +386,8 @@ def push_v_with_efield_general_gpu( ) -_push_vxb_analytic_general_kernel = None -_push_vxb_implicit_general_kernel = None - - -def _get_vxb_analytic_general_kernel(): - global _push_vxb_analytic_general_kernel - if _push_vxb_analytic_general_kernel is None: - import cupy as cp - - _push_vxb_analytic_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_vxb_analytic_general") - return _push_vxb_analytic_general_kernel - - -def _get_vxb_implicit_general_kernel(): - global _push_vxb_implicit_general_kernel - if _push_vxb_implicit_general_kernel is None: - import cupy as cp - - _push_vxb_implicit_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_vxb_implicit_general") - return _push_vxb_implicit_general_kernel +_push_vxb_analytic_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_vxb_analytic_general") +_push_vxb_implicit_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_vxb_implicit_general") def _launch_vxb_general( @@ -495,18 +407,14 @@ def _launch_vxb_general( params_dev, dt, ): - import cupy as cp import numpy as np n_markers = markers.shape[0] - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(first_init_idx), @@ -561,7 +469,7 @@ def push_vxb_analytic_general_gpu( device-resident, ``params_dev`` the domain's mapping-parameter array). """ _launch_vxb_general( - _get_vxb_analytic_general_kernel(), + _push_vxb_analytic_general_kernel, markers, n_cols, first_init_idx, @@ -600,7 +508,7 @@ def push_vxb_implicit_general_gpu( Nicolson rotation), for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. See :func:`push_vxb_analytic_general_gpu` for argument conventions.""" _launch_vxb_general( - _get_vxb_implicit_general_kernel(), + _push_vxb_implicit_general_kernel, markers, n_cols, first_init_idx, @@ -618,36 +526,9 @@ def push_vxb_implicit_general_gpu( ) -_push_bxu_hdiv_general_kernel = None -_push_bxu_hcurl_general_kernel = None -_push_bxu_h1vec_general_kernel = None - - -def _get_bxu_hdiv_general_kernel(): - global _push_bxu_hdiv_general_kernel - if _push_bxu_hdiv_general_kernel is None: - import cupy as cp - - _push_bxu_hdiv_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_Hdiv_general") - return _push_bxu_hdiv_general_kernel - - -def _get_bxu_hcurl_general_kernel(): - global _push_bxu_hcurl_general_kernel - if _push_bxu_hcurl_general_kernel is None: - import cupy as cp - - _push_bxu_hcurl_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_Hcurl_general") - return _push_bxu_hcurl_general_kernel - - -def _get_bxu_h1vec_general_kernel(): - global _push_bxu_h1vec_general_kernel - if _push_bxu_h1vec_general_kernel is None: - import cupy as cp - - _push_bxu_h1vec_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_H1vec_general") - return _push_bxu_h1vec_general_kernel +_push_bxu_hdiv_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_Hdiv_general") +_push_bxu_hcurl_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_Hcurl_general") +_push_bxu_h1vec_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_H1vec_general") def _launch_bxu_general( @@ -670,18 +551,14 @@ def _launch_bxu_general( boundary_cut, dt, ): - import cupy as cp import numpy as np n_markers = markers.shape[0] - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(pn[0]), @@ -746,7 +623,7 @@ def push_bxu_Hdiv_general_gpu( in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u2_*_dev`` is the U-field's 2-form FE coefficients (same evaluation as ``b2_*_dev``).""" _launch_bxu_general( - _get_bxu_hdiv_general_kernel(), + _push_bxu_hdiv_general_kernel, markers, n_cols, pn, @@ -791,7 +668,7 @@ def push_bxu_Hcurl_general_gpu( domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u1_*_dev`` is the U-field's 1-form FE coefficients.""" _launch_bxu_general( - _get_bxu_hcurl_general_kernel(), + _push_bxu_hcurl_general_kernel, markers, n_cols, pn, @@ -836,7 +713,7 @@ def push_bxu_H1vec_general_gpu( domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``uv_*_dev`` is the U-field's (H^1)^3 vector-field FE coefficients.""" _launch_bxu_general( - _get_bxu_h1vec_general_kernel(), + _push_bxu_h1vec_general_kernel, markers, n_cols, pn, @@ -857,26 +734,8 @@ def push_bxu_H1vec_general_gpu( ) -_push_pc_gxu_full_general_kernel = None -_push_pc_gxu_general_kernel = None - - -def _get_pc_gxu_full_general_kernel(): - global _push_pc_gxu_full_general_kernel - if _push_pc_gxu_full_general_kernel is None: - import cupy as cp - - _push_pc_gxu_full_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_GXu_full_general") - return _push_pc_gxu_full_general_kernel - - -def _get_pc_gxu_general_kernel(): - global _push_pc_gxu_general_kernel - if _push_pc_gxu_general_kernel is None: - import cupy as cp - - _push_pc_gxu_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_GXu_general") - return _push_pc_gxu_general_kernel +_push_pc_gxu_full_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_GXu_full_general") +_push_pc_gxu_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_GXu_general") def push_pc_GXu_full_general_gpu( @@ -906,19 +765,15 @@ def push_pc_GXu_full_general_gpu( coefficients of :math:`\\nabla_j(\\mathcal X \\cdot u)_i`, each row ``i`` a 1-form (same evaluation as ``push_v_with_efield_general_gpu``'s ``e1_*``).""" - import cupy as cp import numpy as np n_markers = markers.shape[0] - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads g = (g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev, g31_dev, g32_dev, g33_dev) - _get_pc_gxu_full_general_kernel()( - (blocks,), - (threads,), + launch_1d( + _push_pc_gxu_full_general_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(pn[0]), @@ -969,19 +824,15 @@ def push_pc_GXu_general_gpu( :func:`~struphy.pic.pushing.pusher_kernels.push_pc_GXu` (the 2-row variant of :func:`push_pc_GXu_full_general_gpu`), for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`.""" - import cupy as cp import numpy as np n_markers = markers.shape[0] - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads g = (g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev) - _get_pc_gxu_general_kernel()( - (blocks,), - (threads,), + launch_1d( + _push_pc_gxu_general_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(pn[0]), @@ -1010,36 +861,9 @@ def push_pc_GXu_general_gpu( ) -_push_pc_eta_hcurl_general_kernel = None -_push_pc_eta_hdiv_general_kernel = None -_push_pc_eta_h1vec_general_kernel = None - - -def _get_pc_eta_hcurl_general_kernel(): - global _push_pc_eta_hcurl_general_kernel - if _push_pc_eta_hcurl_general_kernel is None: - import cupy as cp - - _push_pc_eta_hcurl_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_Hcurl_general") - return _push_pc_eta_hcurl_general_kernel - - -def _get_pc_eta_hdiv_general_kernel(): - global _push_pc_eta_hdiv_general_kernel - if _push_pc_eta_hdiv_general_kernel is None: - import cupy as cp - - _push_pc_eta_hdiv_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_Hdiv_general") - return _push_pc_eta_hdiv_general_kernel - - -def _get_pc_eta_h1vec_general_kernel(): - global _push_pc_eta_h1vec_general_kernel - if _push_pc_eta_h1vec_general_kernel is None: - import cupy as cp - - _push_pc_eta_h1vec_general_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_H1vec_general") - return _push_pc_eta_h1vec_general_kernel +_push_pc_eta_hcurl_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_Hcurl_general") +_push_pc_eta_hdiv_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_Hdiv_general") +_push_pc_eta_h1vec_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_H1vec_general") def _launch_pc_eta_general( @@ -1063,18 +887,14 @@ def _launch_pc_eta_general( dt_b, last, ): - import cupy as cp import numpy as np n_markers = markers.shape[0] - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(first_init_idx), @@ -1135,7 +955,7 @@ def push_pc_eta_stage_Hcurl_general_gpu( any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the U-field's 1-form FE coefficients.""" _launch_pc_eta_general( - _get_pc_eta_hcurl_general_kernel(), + _push_pc_eta_hcurl_general_kernel, markers, n_cols, first_init_idx, @@ -1182,7 +1002,7 @@ def push_pc_eta_stage_Hdiv_general_gpu( any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the U-field's 2-form FE coefficients.""" _launch_pc_eta_general( - _get_pc_eta_hdiv_general_kernel(), + _push_pc_eta_hdiv_general_kernel, markers, n_cols, first_init_idx, @@ -1229,7 +1049,7 @@ def push_pc_eta_stage_H1vec_general_gpu( any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the U-field's (H^1)^3 vector-field FE coefficients.""" _launch_pc_eta_general( - _get_pc_eta_h1vec_general_kernel(), + _push_pc_eta_h1vec_general_kernel, markers, n_cols, first_init_idx, @@ -1251,18 +1071,9 @@ def push_pc_eta_stage_H1vec_general_gpu( ) -_push_weights_efield_lin_va_general_kernel = None - - -def _get_weights_efield_lin_va_general_kernel(): - global _push_weights_efield_lin_va_general_kernel - if _push_weights_efield_lin_va_general_kernel is None: - import cupy as cp - - _push_weights_efield_lin_va_general_kernel = cp.RawKernel( - _GENERAL_GEOMETRY_SRC, "push_weights_with_efield_lin_va_general" - ) - return _push_weights_efield_lin_va_general_kernel +_push_weights_efield_lin_va_general_kernel = CudaKernel( + _GENERAL_GEOMETRY_SRC, "push_weights_with_efield_lin_va_general" +) def push_weights_with_efield_lin_va_general_gpu( @@ -1293,15 +1104,12 @@ def push_weights_with_efield_lin_va_general_gpu( import numpy as np n_markers = markers.shape[0] - dev = markers f0_dev = cp.ascontiguousarray(f0_values) - threads = 256 - blocks = (n_markers + threads - 1) // threads - _get_weights_efield_lin_va_general_kernel()( - (blocks,), - (threads,), + launch_1d( + _push_weights_efield_lin_va_general_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(pn[0]), @@ -1335,18 +1143,9 @@ def push_weights_with_efield_lin_va_general_gpu( ) -_push_deterministic_diffusion_general_kernel = None - - -def _get_deterministic_diffusion_general_kernel(): - global _push_deterministic_diffusion_general_kernel - if _push_deterministic_diffusion_general_kernel is None: - import cupy as cp - - _push_deterministic_diffusion_general_kernel = cp.RawKernel( - _GENERAL_GEOMETRY_SRC, "push_deterministic_diffusion_stage_general" - ) - return _push_deterministic_diffusion_general_kernel +_push_deterministic_diffusion_general_kernel = CudaKernel( + _GENERAL_GEOMETRY_SRC, "push_deterministic_diffusion_stage_general" +) def push_deterministic_diffusion_stage_general_gpu( @@ -1375,18 +1174,14 @@ def push_deterministic_diffusion_stage_general_gpu( for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``pi_u_dev`` is the 0-form FE coefficients of the (fixed-in-time) density, ``pi_grad_u{1,2,3}_dev`` its gradient as a 1-form.""" - import cupy as cp import numpy as np n_markers = markers.shape[0] - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads - _get_deterministic_diffusion_general_kernel()( - (blocks,), - (threads,), + launch_1d( + _push_deterministic_diffusion_general_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(first_init_idx), @@ -1431,17 +1226,7 @@ def push_deterministic_diffusion_stage_general_gpu( # _GENERAL_GEOMETRY_SRC -- it applies to every domain, not just # SUPPORTED_GENERAL_KIND_MAPS. _RANDOM_DIFFUSION_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_random_diffusion_src.cu") - -_push_random_diffusion_kernel = None - - -def _get_random_diffusion_kernel(): - global _push_random_diffusion_kernel - if _push_random_diffusion_kernel is None: - import cupy as cp - - _push_random_diffusion_kernel = cp.RawKernel(_RANDOM_DIFFUSION_SRC, "push_random_diffusion_stage") - return _push_random_diffusion_kernel +_push_random_diffusion_kernel = CudaKernel(_RANDOM_DIFFUSION_SRC, "push_random_diffusion_stage") def push_random_diffusion_stage_gpu(markers, n_cols, noise, diffusion_coeff: float, dt: float): @@ -1458,18 +1243,15 @@ def push_random_diffusion_stage_gpu(markers, n_cols, noise, diffusion_coeff: flo import numpy as np n_markers = markers.shape[0] - dev = markers # cp.asarray takes host or device input; np.ascontiguousarray would raise on a # device array rather than transferring it. noise_dev = cp.ascontiguousarray(cp.asarray(noise, dtype=cp.float64)) scale = float(np.sqrt(2.0 * dt * diffusion_coeff)) - threads = 256 - blocks = (n_markers + threads - 1) // threads - _get_random_diffusion_kernel()( - (blocks,), - (threads,), + launch_1d( + _push_random_diffusion_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), noise_dev, diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py index 237c45507..426fa7d6c 100644 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py @@ -23,7 +23,7 @@ :mod:`~struphy.pic.accumulation.accum_kernels_gc_cuda` (same ``atomicAdd``-scatter approach as ``charge_density_0form``). """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source _PUSH_GC_BXESTAR_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu") @@ -42,26 +42,8 @@ def _push_gc_Bstar_source(): return _GENERAL_GEOMETRY_SRC + _PUSH_GC_BSTAR_SRC -_push_gc_bxEstar_kernel = None -_push_gc_Bstar_kernel = None - - -def _get_push_gc_bxEstar_kernel(): - global _push_gc_bxEstar_kernel - if _push_gc_bxEstar_kernel is None: - import cupy as cp - - _push_gc_bxEstar_kernel = cp.RawKernel(_push_gc_bxEstar_source(), "push_gc_bxEstar_explicit_multistage_cuda") - return _push_gc_bxEstar_kernel - - -def _get_push_gc_Bstar_kernel(): - global _push_gc_Bstar_kernel - if _push_gc_Bstar_kernel is None: - import cupy as cp - - _push_gc_Bstar_kernel = cp.RawKernel(_push_gc_Bstar_source(), "push_gc_Bstar_explicit_multistage_cuda") - return _push_gc_Bstar_kernel +_push_gc_bxEstar_kernel = CudaKernel(_push_gc_bxEstar_source, "push_gc_bxEstar_explicit_multistage_cuda") +_push_gc_Bstar_kernel = CudaKernel(_push_gc_Bstar_source, "push_gc_Bstar_explicit_multistage_cuda") def push_gc_bxEstar_explicit_multistage_general_gpu( @@ -98,22 +80,18 @@ def push_gc_bxEstar_explicit_multistage_general_gpu( :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_explicit_multistage`, for any domain in :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. """ - import cupy as cp import numpy as np n_markers = markers.shape[0] - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_push_gc_bxEstar_kernel()( - (blocks,), - (threads,), + launch_1d( + _push_gc_bxEstar_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(first_init_idx), @@ -190,22 +168,18 @@ def push_gc_Bstar_explicit_multistage_general_gpu( :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_explicit_multistage`, for any domain in :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. """ - import cupy as cp import numpy as np n_markers = markers.shape[0] - dev = markers - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_push_gc_Bstar_kernel()( - (blocks,), - (threads,), + launch_1d( + _push_gc_Bstar_kernel, + n_markers, ( - dev, + markers, np.int32(n_cols), np.int32(n_markers), np.int32(first_init_idx), @@ -265,17 +239,14 @@ def d(a): _DG_1ST_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_1st_src.cu") -_dg_kernels = {} +def _dg_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_dg_kernel(name): - if name not in _dg_kernels: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _DG_1ST_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _dg_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _DG_1ST_SRC, name) - return _dg_kernels[name] +_dg_kernels = CudaKernelSet(_dg_source) def _dg_launch( @@ -302,16 +273,14 @@ def _dg_launch( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_dg_kernel(name)( - (blocks,), - (threads,), + launch_1d( + _dg_kernels[name], + n_markers, ( markers, np.int32(n_cols), @@ -371,17 +340,14 @@ def push_gc_Bstar_discrete_gradient_1st_order_gpu(*args, **kwargs): _PUSH_GC_CC_J1_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu") -_j1_kernels = {} +def _j1_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_j1_kernel(name): - if name not in _j1_kernels: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _PUSH_GC_CC_J1_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _j1_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _PUSH_GC_CC_J1_SRC, name) - return _j1_kernels[name] +_j1_kernels = CudaKernelSet(_j1_source) def _j1_launch( @@ -405,16 +371,14 @@ def _j1_launch( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_j1_kernel(name)( - (blocks,), - (threads,), + launch_1d( + _j1_kernels[name], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -482,17 +446,14 @@ def push_gc_cc_J1_Hdiv_gpu(*args, **kwargs): _PUSH_GC_CC_J2_STAGE_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu") -_j2_stage_kernels = {} +def _j2_stage_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_j2_stage_kernel(name): - if name not in _j2_stage_kernels: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _PUSH_GC_CC_J2_STAGE_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _j2_stage_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _PUSH_GC_CC_J2_STAGE_SRC, name) - return _j2_stage_kernels[name] +_j2_stage_kernels = CudaKernelSet(_j2_stage_source) def _j2_stage_launch( @@ -520,16 +481,14 @@ def _j2_stage_launch( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_j2_stage_kernel(name)( - (blocks,), - (threads,), + launch_1d( + _j2_stage_kernels[name], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -598,17 +557,14 @@ def push_gc_cc_J2_stage_Hdiv_gpu(*args, **kwargs): _PUSH_GC_CC_J2_DG_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu") -_j2_dg_kernels = {} +def _j2_dg_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_j2_dg_kernel(name): - if name not in _j2_dg_kernels: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _DG_1ST_SRC + _PUSH_GC_CC_J2_DG_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _j2_dg_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _DG_1ST_SRC + _PUSH_GC_CC_J2_DG_SRC, name) - return _j2_dg_kernels[name] +_j2_dg_kernels = CudaKernelSet(_j2_dg_source) def push_gc_cc_J2_dg_init_Hdiv_gpu( @@ -634,16 +590,14 @@ def push_gc_cc_J2_dg_init_Hdiv_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_j2_dg_kernel("push_gc_cc_J2_dg_init_Hdiv_cuda")( - (blocks,), - (threads,), + launch_1d( + _j2_dg_kernels["push_gc_cc_J2_dg_init_Hdiv_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -707,16 +661,14 @@ def push_gc_cc_J2_dg_Hdiv_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_j2_dg_kernel("push_gc_cc_J2_dg_Hdiv_cuda")( - (blocks,), - (threads,), + launch_1d( + _j2_dg_kernels["push_gc_cc_J2_dg_Hdiv_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -776,17 +728,14 @@ def d(a): _DG_NEWTON_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_newton_src.cu") -_dg_newton_kernels = {} +def _dg_newton_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_dg_newton_kernel(name): - if name not in _dg_newton_kernels: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _DG_NEWTON_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _dg_newton_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _DG_NEWTON_SRC, name) - return _dg_newton_kernels[name] +_dg_newton_kernels = CudaKernelSet(_dg_newton_source) def _dg_newton_launch( @@ -814,16 +763,14 @@ def _dg_newton_launch( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_dg_newton_kernel(name)( - (blocks,), - (threads,), + launch_1d( + _dg_newton_kernels[name], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -884,17 +831,14 @@ def push_gc_Bstar_discrete_gradient_1st_order_newton_gpu(*args, **kwargs): _DG_2ND_ORDER_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_2nd_order_src.cu") -_dg_2nd_order_kernels = {} +def _dg_2nd_order_source(): + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_dg_2nd_order_kernel(name): - if name not in _dg_2nd_order_kernels: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _DG_2ND_ORDER_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _dg_2nd_order_kernels[name] = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _DG_2ND_ORDER_SRC, name) - return _dg_2nd_order_kernels[name] +_dg_2nd_order_kernels = CudaKernelSet(_dg_2nd_order_source) def push_gc_bxEstar_discrete_gradient_2nd_order_gpu( @@ -926,16 +870,14 @@ def push_gc_bxEstar_discrete_gradient_2nd_order_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_dg_2nd_order_kernel("push_gc_bxEstar_discrete_gradient_2nd_order_cuda")( - (blocks,), - (threads,), + launch_1d( + _dg_2nd_order_kernels["push_gc_bxEstar_discrete_gradient_2nd_order_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -1007,16 +949,14 @@ def push_gc_Bstar_discrete_gradient_2nd_order_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_dg_2nd_order_kernel("push_gc_Bstar_discrete_gradient_2nd_order_cuda")( - (blocks,), - (threads,), + launch_1d( + _dg_2nd_order_kernels["push_gc_Bstar_discrete_gradient_2nd_order_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), diff --git a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py index 555fc2243..45b3bb8e8 100644 --- a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py @@ -22,13 +22,15 @@ ``avoid_round_off=False``, which is exactly ``matrix_inv(df(eta))`` -- the manual zeroing of analytically-zero entries is skipped -- so ``matrix_inv_dev(df_dispatch_dev(...))`` reproduces it exactly. + +The three ``push_v_*_gpu`` entry points share one :class:`~struphy.cuda.CudaKernelSet` +(``_kernels``, keyed by CUDA entry-point name) built from ``_SPH_PUSHER_SRC`` +plus the geometry/SPH device functions it reuses. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernelSet, load_cuda_source _SPH_PUSHER_SRC = load_cuda_source(__file__, "pusher_kernels_sph_cuda/_sph_pusher_src.cu") -_kernels = {} - def _source(): from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC @@ -40,12 +42,7 @@ def _source(): return _GENERAL_GEOMETRY_SRC + _SPH_EVAL_FLAT_SRC + _SPH_PUSHER_SRC -def _get_kernel(name): - if name not in _kernels: - import cupy as cp - - _kernels[name] = cp.RawKernel(_source(), name) - return _kernels[name] +_kernels = CudaKernelSet(_source) def _launch( @@ -112,7 +109,7 @@ def _launch( args.append(np.float64(kappa)) args += [np.int32(kind_map), params_dev, np.float64(dt)] - _get_kernel(name)((blocks,), (threads,), tuple(args)) + _kernels[name]((blocks,), (threads,), tuple(args)) def push_v_sph_pressure_gpu( diff --git a/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py b/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py index 5608e85c6..2399a7dd6 100644 --- a/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py @@ -15,22 +15,20 @@ Only the markers listed in ``outside_inds`` are touched, so the kernel is launched over that index array rather than over all markers. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernelSet, launch_1d, load_cuda_source _REFLECT_SRC = load_cuda_source(__file__, "pusher_utilities_kernels_cuda/_reflect_src.cu") -_reflect_kernel = None +def _reflect_source(): + # Deferred (not a module-level import): pulls in pusher_kernels_cuda only + # once a reflecting boundary is actually hit under CuPy, same as before. + from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC -def _get_reflect_kernel(): - global _reflect_kernel - if _reflect_kernel is None: - import cupy as cp + return _GENERAL_GEOMETRY_SRC + _REFLECT_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - _reflect_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _REFLECT_SRC, "reflect_cuda") - return _reflect_kernel +_reflect_kernels = CudaKernelSet(_reflect_source) def reflect_gpu(markers, kind_map, params_dev, outside_inds, axis): @@ -50,11 +48,9 @@ def reflect_gpu(markers, kind_map, params_dev, outside_inds, axis): return inds = cp.ascontiguousarray(outside_inds, dtype=cp.int64) - threads = 256 - blocks = (n_outside + threads - 1) // threads - _get_reflect_kernel()( - (blocks,), - (threads,), + launch_1d( + _reflect_kernels["reflect_cuda"], + n_outside, ( markers, np.int32(markers.shape[1]), diff --git a/src/struphy/pic/sorting_kernels_cuda.py b/src/struphy/pic/sorting_kernels_cuda.py index 98a2c1c6b..8fc0c9e57 100644 --- a/src/struphy/pic/sorting_kernels_cuda.py +++ b/src/struphy/pic/sorting_kernels_cuda.py @@ -35,32 +35,14 @@ :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_flat_gpu`, which genuinely needs every marker column for the density sum). """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, launch_1d, load_cuda_source import numpy as np _SORT_SRC = load_cuda_source(__file__, "sorting_kernels_cuda/_sort_src.cu") -_assign_box_kernel = None -_assign_particles_kernel = None - - -def _get_assign_box_kernel(): - global _assign_box_kernel - if _assign_box_kernel is None: - import cupy as cp - - _assign_box_kernel = cp.RawKernel(_SORT_SRC, "assign_box_to_each_particle_cuda") - return _assign_box_kernel - - -def _get_assign_particles_kernel(): - global _assign_particles_kernel - if _assign_particles_kernel is None: - import cupy as cp - - _assign_particles_kernel = cp.RawKernel(_SORT_SRC, "assign_particles_to_boxes_cuda") - return _assign_particles_kernel +_assign_box_kernel = CudaKernel(_SORT_SRC, "assign_box_to_each_particle_cuda") +_assign_particles_kernel = CudaKernel(_SORT_SRC, "assign_particles_to_boxes_cuda") def assign_box_to_each_particle_gpu( @@ -95,11 +77,9 @@ def assign_box_to_each_particle_gpu( dev_domain = cp.asarray(domain_array, dtype=cp.float64) dev_box = cp.empty(n_mks, dtype=cp.float64) - threads = 256 - blocks = (n_mks + threads - 1) // threads - _get_assign_box_kernel()( - (blocks,), - (threads,), + launch_1d( + _assign_box_kernel, + n_mks, ( dev_eta, dev_holes, @@ -140,11 +120,9 @@ def assign_particles_to_boxes_gpu( dev_boxes = cp.full((n_box_rows, box_cols), -1, dtype=cp.int32) dev_next_index = cp.zeros(n_box_rows, dtype=cp.int32) - threads = 256 - blocks = (n_mks + threads - 1) // threads - _get_assign_particles_kernel()( - (blocks,), - (threads,), + launch_1d( + _assign_particles_kernel, + n_mks, ( dev_box_id, dev_holes, diff --git a/src/struphy/pic/sph_eval_kernels_cuda.py b/src/struphy/pic/sph_eval_kernels_cuda.py index 3ada9945f..64ee0a4bd 100644 --- a/src/struphy/pic/sph_eval_kernels_cuda.py +++ b/src/struphy/pic/sph_eval_kernels_cuda.py @@ -26,30 +26,12 @@ diagnostics/reconstruction entry point, not a per-step hot loop, so there is no benefit to caching device buffers across calls the way the pushers do. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, launch_1d, load_cuda_source _SPH_EVAL_FLAT_SRC = load_cuda_source(__file__, "sph_eval_kernels_cuda/_sph_eval_flat_src.cu") -_box_based_evaluation_flat_kernel = None -_box_based_evaluation_meshgrid_kernel = None - - -def _get_kernel(): - global _box_based_evaluation_flat_kernel - if _box_based_evaluation_flat_kernel is None: - import cupy as cp - - _box_based_evaluation_flat_kernel = cp.RawKernel(_SPH_EVAL_FLAT_SRC, "box_based_evaluation_flat_cuda") - return _box_based_evaluation_flat_kernel - - -def _get_meshgrid_kernel(): - global _box_based_evaluation_meshgrid_kernel - if _box_based_evaluation_meshgrid_kernel is None: - import cupy as cp - - _box_based_evaluation_meshgrid_kernel = cp.RawKernel(_SPH_EVAL_FLAT_SRC, "box_based_evaluation_meshgrid_cuda") - return _box_based_evaluation_meshgrid_kernel +_box_based_evaluation_flat_kernel = CudaKernel(_SPH_EVAL_FLAT_SRC, "box_based_evaluation_flat_cuda") +_box_based_evaluation_meshgrid_kernel = CudaKernel(_SPH_EVAL_FLAT_SRC, "box_based_evaluation_meshgrid_cuda") def box_based_evaluation_flat_gpu( @@ -86,7 +68,6 @@ def box_based_evaluation_flat_gpu( import cupy as cp import numpy as np - kernel = _get_kernel() n_cols = markers.shape[1] n_eval = eta1.shape[0] n_box_cols = boxes.shape[1] @@ -105,11 +86,9 @@ def box_based_evaluation_flat_gpu( dev_holes = cp.asarray(holes, dtype=cp.int32) dev_out = cp.zeros(n_eval, dtype=cp.float64) - threads = 256 - blocks = (n_eval + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + _box_based_evaluation_flat_kernel, + n_eval, ( dev_markers, np.int32(n_cols), @@ -175,7 +154,6 @@ def box_based_evaluation_meshgrid_gpu( import cupy as cp import numpy as np - kernel = _get_meshgrid_kernel() n_cols = markers.shape[1] n1_eval, n2_eval, n3_eval = eta1.shape[0], eta2.shape[1], eta3.shape[2] n_box_cols = boxes.shape[1] @@ -197,11 +175,9 @@ def box_based_evaluation_meshgrid_gpu( dev_out = cp.zeros((n1_eval, n2_eval, n3_eval), dtype=cp.float64) n_total = n1_eval * n2_eval * n3_eval - threads = 256 - blocks = (n_total + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + _box_based_evaluation_meshgrid_kernel, + n_total, ( dev_markers, np.int32(n_cols), @@ -250,30 +226,10 @@ def box_based_evaluation_meshgrid_gpu( _SPH_EVAL_NAIVE_SRC = load_cuda_source(__file__, "sph_eval_kernels_cuda/_sph_eval_naive_src.cu") -_naive_evaluation_flat_kernel = None -_naive_evaluation_meshgrid_kernel = None - - -def _get_naive_flat_kernel(): - global _naive_evaluation_flat_kernel - if _naive_evaluation_flat_kernel is None: - import cupy as cp - - _naive_evaluation_flat_kernel = cp.RawKernel( - _SPH_EVAL_FLAT_SRC + _SPH_EVAL_NAIVE_SRC, "naive_evaluation_flat_cuda" - ) - return _naive_evaluation_flat_kernel - - -def _get_naive_meshgrid_kernel(): - global _naive_evaluation_meshgrid_kernel - if _naive_evaluation_meshgrid_kernel is None: - import cupy as cp - - _naive_evaluation_meshgrid_kernel = cp.RawKernel( - _SPH_EVAL_FLAT_SRC + _SPH_EVAL_NAIVE_SRC, "naive_evaluation_meshgrid_cuda" - ) - return _naive_evaluation_meshgrid_kernel +_naive_evaluation_flat_kernel = CudaKernel(_SPH_EVAL_FLAT_SRC + _SPH_EVAL_NAIVE_SRC, "naive_evaluation_flat_cuda") +_naive_evaluation_meshgrid_kernel = CudaKernel( + _SPH_EVAL_FLAT_SRC + _SPH_EVAL_NAIVE_SRC, "naive_evaluation_meshgrid_cuda" +) def naive_evaluation_flat_gpu( @@ -298,7 +254,6 @@ def naive_evaluation_flat_gpu( import cupy as cp import numpy as np - kernel = _get_naive_flat_kernel() n_cols = markers.shape[1] n_markers = markers.shape[0] n_eval = eta1.shape[0] @@ -310,11 +265,9 @@ def naive_evaluation_flat_gpu( dev_holes = cp.asarray(holes, dtype=cp.int32) dev_out = cp.zeros(n_eval, dtype=cp.float64) - threads = 256 - blocks = (n_eval + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + _naive_evaluation_flat_kernel, + n_eval, ( dev_markers, np.int32(n_cols), @@ -370,7 +323,6 @@ def naive_evaluation_meshgrid_gpu( import cupy as cp import numpy as np - kernel = _get_naive_meshgrid_kernel() n_cols = markers.shape[1] n_markers = markers.shape[0] n1_eval, n2_eval, n3_eval = eta1.shape[0], eta2.shape[1], eta3.shape[2] @@ -382,12 +334,10 @@ def naive_evaluation_meshgrid_gpu( dev_holes = cp.asarray(holes, dtype=cp.int32) dev_out = cp.zeros((n1_eval, n2_eval, n3_eval), dtype=cp.float64) - threads = 256 n_total = n1_eval * n2_eval * n3_eval - blocks = (n_total + threads - 1) // threads - kernel( - (blocks,), - (threads,), + launch_1d( + _naive_evaluation_meshgrid_kernel, + n_total, ( dev_markers, np.int32(n_cols), diff --git a/src/struphy/pic/utilities_kernels_cuda.py b/src/struphy/pic/utilities_kernels_cuda.py index 13b02f5c0..70444a192 100644 --- a/src/struphy/pic/utilities_kernels_cuda.py +++ b/src/struphy/pic/utilities_kernels_cuda.py @@ -12,20 +12,17 @@ Both kernels here are plain per-marker 0-form spline evaluations, so they reuse the ``find_span_dev``/``b_splines_dev``/``eval_0form_dev`` device functions rather than defining their own. + +Every real GPU kernel this module launches is a :class:`~struphy.cuda.CudaKernel` +(or a :class:`~struphy.cuda.CudaKernelSet` entry) declared at module scope; if +a function here doesn't sit next to one, it does not touch the GPU. """ -from struphy.cuda import load_cuda_source +from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source +from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC _UTILITIES_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_utilities_src.cu") -_kernels = {} - - -def _get_kernel(name): - if name not in _kernels: - import cupy as cp - - _kernels[name] = cp.RawKernel(_UTILITIES_SRC, name) - return _kernels[name] +_kernels = CudaKernelSet(_UTILITIES_SRC) def _launch_0form_diag(kernel_name, markers, args_derham, first_diagnostics_idx, mu_idx, coeffs): @@ -34,17 +31,15 @@ def _launch_0form_diag(kernel_name, markers, args_derham, first_diagnostics_idx, import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) coeffs = cp.ascontiguousarray(coeffs) - _get_kernel(kernel_name)( - (blocks,), - (threads,), + launch_1d( + _kernels[kernel_name], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -110,15 +105,13 @@ def eval_canonical_toroidal_moment_5d_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) absB = cp.ascontiguousarray(absB) - _get_kernel("eval_canonical_toroidal_moment_5d_cuda")( - (blocks,), - (threads,), + launch_1d( + _kernels["eval_canonical_toroidal_moment_5d_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -157,15 +150,13 @@ def eval_canonical_toroidal_moment_6d_gpu(markers, args_derham, first_diagnostic import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) absB = cp.ascontiguousarray(absB) - _get_kernel("eval_canonical_toroidal_moment_6d_cuda")( - (blocks,), - (threads,), + launch_1d( + _kernels["eval_canonical_toroidal_moment_6d_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -202,15 +193,13 @@ def eval_magnetic_moment_5d_gpu(markers, args_derham, first_diagnostics_idx, abs import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) absB = cp.ascontiguousarray(absB) - _get_kernel("eval_magnetic_moment_5d_cuda")( - (blocks,), - (threads,), + launch_1d( + _kernels["eval_magnetic_moment_5d_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -244,16 +233,14 @@ def eval_magnetic_energy_PBb_gpu(markers, args_derham, first_diagnostics_idx, mu import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) abs_B0 = cp.ascontiguousarray(abs_B0) PBb = cp.ascontiguousarray(PBb) - _get_kernel("eval_magnetic_energy_PBb_cuda")( - (blocks,), - (threads,), + launch_1d( + _kernels["eval_magnetic_energy_PBb_cuda"], + n_markers, ( markers, np.int32(markers.shape[1]), @@ -290,22 +277,7 @@ def eval_magnetic_energy_PBb_gpu(markers, args_derham, first_diagnostics_idx, mu # --------------------------------------------------------------------------- _GC_FROM_6D_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_gc_from_6d_src.cu") - -_gc6d_kernel = None - - -def _get_gc_from_6d_kernel(): - global _gc6d_kernel - if _gc6d_kernel is None: - import cupy as cp - - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - _gc6d_kernel = cp.RawKernel( - _GENERAL_GEOMETRY_SRC + _GC_FROM_6D_SRC, - "eval_guiding_center_from_6d_cuda", - ) - return _gc6d_kernel +_gc6d_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC + _GC_FROM_6D_SRC, "eval_guiding_center_from_6d_cuda") def eval_guiding_center_from_6d_gpu( @@ -320,8 +292,6 @@ def eval_guiding_center_from_6d_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) @@ -329,9 +299,9 @@ def eval_guiding_center_from_6d_gpu( b22 = cp.ascontiguousarray(b22) b23 = cp.ascontiguousarray(b23) absB = cp.ascontiguousarray(absB) - _get_gc_from_6d_kernel()( - (blocks,), - (threads,), + launch_1d( + _gc6d_kernel, + n_markers, ( markers, np.int32(markers.shape[1]), @@ -379,19 +349,7 @@ def eval_guiding_center_from_6d_gpu( # --------------------------------------------------------------------------- _GRADB_EDIFF_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_gradb_ediff_src.cu") - -_gradb_ediff_kernel = None - - -def _get_gradb_ediff_kernel(): - global _gradb_ediff_kernel - if _gradb_ediff_kernel is None: - import cupy as cp - - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - _gradb_ediff_kernel = cp.RawKernel(_GENERAL_GEOMETRY_SRC + _GRADB_EDIFF_SRC, "eval_gradB_ediff_cuda") - return _gradb_ediff_kernel +_gradb_ediff_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC + _GRADB_EDIFF_SRC, "eval_gradB_ediff_cuda") def eval_gradB_ediff_gpu( @@ -419,16 +377,14 @@ def eval_gradB_ediff_gpu( import numpy as np n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads def d(a): a = cp.ascontiguousarray(a) return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - _get_gradb_ediff_kernel()( - (blocks,), - (threads,), + launch_1d( + _gradb_ediff_kernel, + n_markers, ( markers, np.int32(markers.shape[1]), From 5c4c801ae059c792a7a7ce995043b361ea1ce8c6 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 14 Sep 2026 18:03:49 +0200 Subject: [PATCH 149/156] Cleanup for a MWE --- .gitlab-ci.yml | 78 +- bench_gpu/bench_kernels.py | 948 ------------- feectools | 2 +- params_LinearMHDDriftkineticCC.py | 181 --- params_PressureLessSPH.py | 167 --- profiling/clusters.py | 26 - .../params_cyclone.py | 417 ------ .../GuidingCenter/params_GuidingCenter.py | 187 --- .../params_GuidingCenter_scaling.py | 191 --- .../cube_strong_scaling/params_poisson.py | 17 +- .../params_PressureLessSPH_scaling.py | 179 --- .../diocotron_instability/params_diocotron.py | 2 - .../params_VlasovAmpere_scaling.py | 179 --- profiling/profiling_job.py | 7 +- ...ubmit_driftkinetic_cyclone_cupy_scaling.py | 104 -- ..._driftkinetic_cyclone_numpy_vs_cupy_pcg.py | 80 -- ...bmit_guidingcenter_cpu_node_vs_gpu_node.py | 90 -- .../submit_guidingcenter_cupy_scaling.py | 88 -- .../submit_guidingcenter_numpy_vs_cupy.py | 94 -- .../submit_pressurelesssph_cupy_scaling.py | 86 -- profiling/submit_vlasovampere_cupy_scaling.py | 86 -- .../bsplines/tests/test_bsplines_kers.py | 28 +- .../feec/basis_projection_kernels_cuda.py | 59 - src/struphy/feec/basis_projection_ops.py | 112 +- .../_assemble_src.cu | 40 - .../_h1vec_divdiv_assembly_src.cu | 44 - .../_h1vec_divergence_src.cu | 111 -- .../mass_kernels_cuda/_mass_assembly_src.cu | 48 - .../_weak_div_assembly_src.cu | 60 - .../kinetic_energy_grid.cu | 47 - src/struphy/feec/linear_operators.py | 34 +- src/struphy/feec/mass.py | 128 +- src/struphy/feec/mass_kernels.py | 1007 +++++++++----- src/struphy/feec/mass_kernels_cuda.py | 201 --- src/struphy/feec/preconditioner.py | 39 +- src/struphy/feec/psydac_derham.py | 141 +- src/struphy/feec/tests/test_l2_projectors.py | 12 +- src/struphy/feec/variational_kernels_cuda.py | 65 - src/struphy/feec/variational_utilities.py | 126 +- src/struphy/fields_background/equils.py | 43 +- src/struphy/geometry/base.py | 15 +- src/struphy/io/output_handling.py | 10 +- src/struphy/linear_algebra/schur_solver.py | 43 +- src/struphy/models/base.py | 18 +- .../linear_vlasov_ampere_one_species.py | 17 +- src/struphy/models/scalars.py | 26 +- src/struphy/models/species.py | 11 +- src/struphy/models/variables.py | 10 +- src/struphy/ode/solvers.py | 14 +- src/struphy/physics/physics.py | 2 +- .../pic/accumulation/accum_kernels_cuda.py | 778 ----------- .../pic/accumulation/accum_kernels_gc_cuda.py | 569 -------- .../_cc_lin_mhd_6d_1_src.cu | 118 -- .../_cc_lin_mhd_6d_2_src.cu | 172 --- .../_charge_density_0form_src.cu | 88 -- .../_linear_vlasov_ampere_extra_src.cu | 187 --- .../_pc_pressure_fillers_src.cu | 198 --- .../_vlasov_maxwell_extra_src.cu | 97 -- .../cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu | 83 -- .../accum_kernels_cuda/pc_lin_mhd_6d_full.cu | 83 -- .../_cc_lin_mhd_5d_curlb_src.cu | 144 -- .../_cc_lin_mhd_5d_d_src.cu | 131 -- .../_cc_lin_mhd_5d_gradb_dg_src.cu | 144 -- .../_cc_lin_mhd_5d_gradb_src.cu | 111 -- .../accum_kernels_gc_cuda/_fill_vec_src.cu | 23 - .../_gc_mag_density_0form_src.cu | 88 -- src/struphy/pic/accumulation/filter.py | 32 +- .../pic/accumulation/particles_to_grid.py | 481 +------ src/struphy/pic/base.py | 147 +- .../cuda/sorting_kernels_cuda/_sort_src.cu | 84 -- .../_sph_eval_flat_src.cu | 291 ---- .../_sph_eval_naive_src.cu | 86 -- .../utilities_kernels_cuda/_gc_from_6d_src.cu | 75 - .../_gradb_ediff_src.cu | 58 - .../utilities_kernels_cuda/_utilities_src.cu | 303 ---- src/struphy/pic/particles.py | 359 ++--- .../_dk_hamiltonian_src.cu | 124 -- .../_gc_marker_column_src.cu | 212 --- .../_sph_marker_column_src.cu | 111 -- .../_general_geometry_src.cu | 1081 +------------- .../_push_eta_cuboid_src.cu | 37 - .../_push_eta_rk_periodic_src.cu | 55 - .../_random_diffusion_src.cu | 19 - .../pusher_kernels_gc_cuda/_dg_1st_src.cu | 195 --- .../_dg_2nd_order_src.cu | 227 --- .../pusher_kernels_gc_cuda/_dg_newton_src.cu | 256 ---- .../_push_gc_bstar_src.cu | 109 -- .../_push_gc_bxestar_src.cu | 96 -- .../_push_gc_cc_j1_src.cu | 216 --- .../_push_gc_cc_j2_dg_src.cu | 171 --- .../_push_gc_cc_j2_stage_src.cu | 161 --- .../_sph_pusher_src.cu | 194 --- .../_reflect_src.cu | 36 - .../pic/pushing/eval_kernels_gc_cuda.py | 365 ----- .../pic/pushing/eval_kernels_sph_cuda.py | 161 --- src/struphy/pic/pushing/pusher.py | 1237 +---------------- .../pic/pushing/pusher_kernels_cuda.py | 1114 +-------------- .../pic/pushing/pusher_kernels_gc_cuda.py | 1001 ------------- .../pic/pushing/pusher_kernels_sph_cuda.py | 223 --- .../pushing/pusher_utilities_kernels_cuda.py | 63 - src/struphy/pic/sobol_seq.py | 12 +- src/struphy/pic/sorting.py | 22 +- src/struphy/pic/sorting_kernels_cuda.py | 140 -- src/struphy/pic/sph_eval_kernels_cuda.py | 367 ----- .../pic/tests/_bench_cuda_kernels_worker.py | 317 ----- src/struphy/pic/tests/bench_cuda_kernels.py | 79 -- .../pic/tests/bench_mpi_sort_markers.py | 219 --- src/struphy/pic/tests/test_accum_vec_H1.py | 15 +- src/struphy/pic/tests/test_binning.py | 42 - src/struphy/pic/tests/test_draw_parallel.py | 23 +- src/struphy/pic/tests/test_estimate_mem.py | 1 + src/struphy/pic/tests/test_mat_vec_filler.py | 122 +- src/struphy/pic/tests/test_pushers.py | 23 +- src/struphy/pic/tests/test_sph.py | 69 +- src/struphy/pic/tests/test_tesselation.py | 14 +- src/struphy/pic/utilities_kernels_cuda.py | 414 ------ .../post_processing/post_processing_tools.py | 41 +- src/struphy/propagators/base.py | 19 +- .../propagators/current_coupling_5d_gradb.py | 274 +--- .../propagators/efield_weights_coupling.py | 11 +- .../propagators/push_random_diffusion.py | 11 +- .../propagators/tests/test_curl_curl.py | 4 +- .../tests/test_gyrokinetic_poisson.py | 2 +- src/struphy/propagators/tests/test_poisson.py | 2 +- src/struphy/simulation/sim.py | 7 +- 125 files changed, 1293 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100644 src/struphy/pic/tests/bench_mpi_sort_markers.py delete mode 100644 src/struphy/pic/utilities_kernels_cuda.py diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index aef299497..6d60c7e1f 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -54,35 +54,6 @@ stages: .image_ubuntu_latest: image: gitlab-registry.mpcdf.mpg.de/struphy/struphy/ubuntu-latest -.image_gitlab_mpcdf_nvhpc: - image: gitlab-registry.mpcdf.mpg.de/mpcdf/ci-module-image/nvhpcsdk_24-openmpi_5_0:2025 - -# --- GPU runners --- - -# Tag for an MPCDF Nvidia runner with compute capability 8.0 (A40 / A100), per -# https://docs.mpcdf.mpg.de/doc/data/gitlab/gitlabrunners.html -# Only the hardware tag is requested: GitLab ANDs tags together, so adding -# `mpcdf-shared` here would make the job unschedulable if a GPU runner happens -# not to carry it. -.tags_gpu_nvidia: - tags: [gpu-nvidia-cc80] - -# The GPU image is a bare module image, not one of the struphy images, so there -# is no prebuilt /struphy_${LANGUAGE}_${OMP} venv to source (as -# .scripts.install_on_push does). These jobs therefore build their own venv and -# install struphy from the checkout. -.before_script_gpu: - before_script: - - module purge - - module load nvhpcsdk/24 openmpi/5.0 python-waterboa/2024.06 gcc/13 - - module list - - nvidia-smi - - python3 -m venv env_gpu_${CI_PIPELINE_ID} - - source env_gpu_${CI_PIPELINE_ID}/bin/activate - - pip install -U pip - - pip install cupy-cuda12x cunumpy - - python3 -c "import cupy; cupy.zeros(1); print('cupy ok', cupy.cuda.runtime.runtimeGetVersion())" - # --- job variables --- .variables_push: @@ -557,20 +528,19 @@ inspect_repo: # struphy params LinearVlasovAmpereOneSpecies --check-file $file # done -# Smoke test: does a GPU runner exist, and does cunumpy dispatch to it at all. -# Deliberately does not install struphy, so it stays fast and still reports -# something useful when the full GPU suite below is broken. test_cupy: + tags: [nvidia-cc80] + image: gitlab-registry.mpcdf.mpg.de/mpcdf/ci-module-image/nvhpcsdk_24-openmpi_5_0:2025 stage: test extends: - .rules_startup - - .image_gitlab_mpcdf_nvhpc - - .tags_gpu_nvidia before_script: + - module avail + - module list - module purge - module load nvhpcsdk/24 openmpi/5.0 python-waterboa/2024.06 gcc/13 - - module list script: + - ls - nvidia-smi # Install cupy - python3 -m pip install --user cupy-cuda12x @@ -582,44 +552,6 @@ test_cupy: - export ARRAY_BACKEND=cupy - python3 src/struphy/utils/cupy_vs_numpy.py -# The unit suite under ARRAY_BACKEND=cupy, i.e. the same tests as `unit_tests` -# but with every array on the device. -# -# allow_failure: the CuPy backend is still being ported (several code paths -# still fall back to the host), so this is here to report the state of that -# port, not yet to gate merges. Remove `allow_failure` once the suite is green. -unit_tests_gpu: - stage: test - timeout: 2h - allow_failure: true - extends: - - .rules_mr_to_devel - - .image_gitlab_mpcdf_nvhpc - - .tags_gpu_nvidia - - .before_script_gpu - variables: - ARRAY_BACKEND: cupy - script: - - pip install -e .[phys,mpi] - - !reference [.scripts, compile] - - !reference [.scripts, unit_tests] - -unit_tests_gpu_mpi: - stage: test - timeout: 2h - allow_failure: true - extends: - - .rules_mr_to_devel - - .image_gitlab_mpcdf_nvhpc - - .tags_gpu_nvidia - - .before_script_gpu - variables: - ARRAY_BACKEND: cupy - script: - - pip install -e .[phys,mpi] - - !reference [.scripts, compile] - - !reference [.scripts, unit_tests_mpi] - install_tests: stage: test extends: diff --git a/bench_gpu/bench_kernels.py b/bench_gpu/bench_kernels.py deleted file mode 100644 index d2de76246..000000000 --- a/bench_gpu/bench_kernels.py +++ /dev/null @@ -1,948 +0,0 @@ -"""Micro-benchmark suite for the kernels ported to CUDA on this branch: -every ``*_general_gpu`` pusher (:mod:`struphy.pic.pushing.pusher_kernels_cuda`) -and the two accumulation kernels -(:mod:`struphy.pic.accumulation.accum_kernels_cuda`). For each kernel it -times the CPU (Pyccel) reference and the GPU (CuPy ``RawKernel``) port on -identical input data and prints a numpy-vs-cupy speedup table. - -Run with: - - ARRAY_BACKEND=numpy python bench_gpu/bench_kernels.py - -Options (see ``--help``): ``--n-markers``, ``--num-elements``, ``--degree``, -``--repeats``, ``--kernel`` (repeatable, to run a subset). - -Why ``ARRAY_BACKEND=numpy`` for everything, GPU included ----------------------------------------------------------- -The CPU kernels are Pyccel-compiled functions that need real NumPy buffers, -and markers are host-resident regardless of backend (see -``ISSUE_cupy_particles_never_pushed.md``). The GPU kernel wrappers import -``cupy`` directly inside each function body and don't consult -``cunumpy``'s active backend at all -- they just expect CuPy arrays as -arguments. So a single ``ARRAY_BACKEND=numpy`` process can build one set of -NumPy scene arrays, hand them straight to the CPU kernels, and hand -``cupy.asarray(...)`` mirrors of the *same* arrays to the GPU kernels: both -variants of every kernel run back-to-back on byte-identical input, in one -process, with no subprocess/backend-switching dance required. -""" - -import argparse -import os -import time - -if os.environ.get("ARRAY_BACKEND", "numpy") != "numpy": - raise SystemExit( - "Run this benchmark with ARRAY_BACKEND=numpy -- see the module docstring " - "for why the GPU kernels don't need ARRAY_BACKEND=cupy to be benchmarked.", - ) - -import numpy as np - - -def timeit(fn, repeats: int, warmup: int = 1) -> float: - """Best-of-``repeats`` wall-clock time of ``fn()``, in seconds. - - Best-of (not mean) since the only noise on a shared cluster node is - contention that slows a run down, never speeds one up -- the minimum is - the closest thing to "this kernel's own cost" we can measure without a - dedicated node. ``warmup`` calls run first and are excluded, absorbing - the one-time CUDA context / RawKernel-compile cost of the first GPU call. - """ - for _ in range(warmup): - fn() - best = float("inf") - for _ in range(repeats): - t0 = time.perf_counter() - fn() - best = min(best, time.perf_counter() - t0) - return best - - -class Scene: - """One shared set of markers + domain + Derham + random FE coefficient - fields, used to build every kernel case below. Field values are random - (not physically meaningful) -- this benchmark measures raw kernel - throughput, not physics, so only shapes/dtypes need to be realistic. - """ - - def __init__(self, n_elements, degree, n_markers_target, seed=1234): - from struphy import domains - from struphy.feec.mass import WeightedMassOperators - from struphy.feec.psydac_derham import Derham - from struphy.io.options import DerhamOptions - from struphy.particles.parameters import LoadingParameters - from struphy.pic.particles import Particles6D - from struphy.topology.grids import TensorProductGrid - - # kind_map == 12 (Colella): the "general" (non-Cuboid) CUDA path, - # i.e. the one that evaluates DF(eta) per marker instead of assuming - # it's constant -- this is the actual new work ported this branch, - # and the one every real (non-trivial-geometry) simulation uses. - self.domain = domains.Colella(Lx=2.0, Ly=3.0, alpha=0.1, Lz=4.0) - grid = TensorProductGrid(num_elements=n_elements) - derham_opts = DerhamOptions(degree=degree) - self.derham = Derham(grid, derham_opts, comm=None) - self.mass_ops = WeightedMassOperators(self.derham, self.domain) - - loading_params = LoadingParameters( - Np=n_markers_target, - seed=seed, - moments=(0.0, 0.0, 0.0, 1.0, 1.0, 1.0), - spatial="uniform", - ) - self.particles = Particles6D(loading_params=loading_params, domain=self.domain) - self.particles.draw_markers() - self.particles.initialize_weights() - self.n_markers = self.particles.markers.shape[0] - - self.args_markers = self.particles.args_markers - self.args_domain = self.domain.args_domain - self.args_derham = self.derham.args_derham - - self._markers0 = self.particles.markers.copy() - self._rng = np.random.default_rng(seed) - - self.pn = tuple(int(p) for p in self.args_derham.pn) - self.starts = tuple(int(s) for s in self.args_derham.starts) - self.kind_map = int(self.args_domain.kind_map) - - import cupy as cp - - # Keep an explicit device copy for RawKernel calls. ``markers`` stays - # NumPy because it is also the input to the Pyccel CPU reference. - self.markers_dev = cp.asarray(self._markers0) - self.params_dev = cp.asarray(np.asarray(self.args_domain.params, dtype=float), dtype=cp.float64) - self.tn1_dev = cp.asarray(np.asarray(self.args_derham.tn1, dtype=float), dtype=cp.float64) - self.tn2_dev = cp.asarray(np.asarray(self.args_derham.tn2, dtype=float), dtype=cp.float64) - self.tn3_dev = cp.asarray(np.asarray(self.args_derham.tn3, dtype=float), dtype=cp.float64) - - # random FE coefficient fields, one per Derham space actually used - # below (0-form/H1 scalar; 1-form/Hcurl, 2-form/Hdiv, vector/H1vec - # each 3 components). - self.fields = { - "0": self._random_field("0"), - "1": self._random_field("1"), - "2": self._random_field("2"), - "v": self._random_field("v"), - } - - def _random_field(self, form: str): - from feectools.linalg.block import BlockVector - from feectools.linalg.stencil import StencilVector - - space = self.derham.coeff_spaces[form] - if form in ("0", "3"): - v = StencilVector(space) - v._data[:] = self._rng.uniform(-1.0, 1.0, v._data.shape) - return (v._data,) - bv = BlockVector(space) - arrs = [] - for bl in bv.blocks: - bl._data[:] = self._rng.uniform(-1.0, 1.0, bl._data.shape) - arrs.append(bl._data) - return tuple(arrs) - - def dev(self, arr): - import cupy as cp - - return cp.asarray(arr) - - def reset_markers(self): - self.particles.markers[:] = self._markers0 - - def reset_markers_dev(self): - """Restore the device input and account for the H2D transfer.""" - self.markers_dev.set(self._markers0) - - def copy_markers_from_dev(self): - """Account for the D2H half of the benchmarked marker round-trip.""" - self.markers_dev.get(out=self.particles.markers) - - def random_f0_values(self): - return self._rng.uniform(0.1, 2.0, size=self.n_markers).astype(np.float64) - - def random_noise(self): - return self._rng.normal(size=(self.n_markers, 3)).astype(np.float64) - - -# --------------------------------------------------------------------------- -# Kernel cases: each is (name, cpu_call_factory, gpu_call_factory), where -# both factories take the Scene and return a zero-arg callable that runs one -# kernel invocation (including the host<->device marker round-trip for the -# GPU side, since that's part of the real per-step cost). -# --------------------------------------------------------------------------- - - -def _stage1_abc(): - """Single-stage (n_stages=1) RK Butcher arrays: makes the CPU kernels' - internal ``dt*a[stage]``/``dt*b[stage]``/``last`` bookkeeping match the - GPU wrappers' explicit ``dt_a=dt, dt_b=dt, last=1.0`` -- see - push_eta_stage's body for the exact formula this mirrors.""" - return np.array([1.0]), np.array([1.0]), np.array([1.0]) - - -def make_cases(scene: Scene, dt: float): - import struphy.pic.accumulation.accum_kernels as accum_kernels - import struphy.pic.pushing.pusher_kernels as pusher_kernels - from struphy.pic.accumulation.accum_kernels_cuda import ( - cc_lin_mhd_6d_1_gpu, - cc_lin_mhd_6d_2_gpu, - charge_density_0form_gpu, - linear_vlasov_ampere_gpu, - pc_lin_mhd_6d_full_gpu, - pc_lin_mhd_6d_gpu, - vlasov_maxwell_gpu, - ) - from struphy.pic.pushing.pusher_kernels_cuda import ( - push_bxu_H1vec_general_gpu, - push_bxu_Hcurl_general_gpu, - push_bxu_Hdiv_general_gpu, - push_deterministic_diffusion_stage_general_gpu, - push_eta_stage_general_gpu, - push_pc_eta_stage_H1vec_general_gpu, - push_pc_eta_stage_Hcurl_general_gpu, - push_pc_eta_stage_Hdiv_general_gpu, - push_pc_GXu_full_general_gpu, - push_pc_GXu_general_gpu, - push_random_diffusion_stage_gpu, - push_v_with_efield_general_gpu, - push_vxb_analytic_general_gpu, - push_vxb_implicit_general_gpu, - push_weights_with_efield_lin_va_general_gpu, - ) - - am, ad, ah = scene.args_markers, scene.args_domain, scene.args_derham - a1, b1, c1 = _stage1_abc() - n_cols = scene.particles.markers.shape[1] - markers_dev = scene.markers_dev - pn, tn1, tn2, tn3, starts = scene.pn, scene.tn1_dev, scene.tn2_dev, scene.tn3_dev, scene.starts - kind_map, params_dev = scene.kind_map, scene.params_dev - boundary_cut = 0.1 - cases = {} - - def add(name, cpu_fn, gpu_fn): - cases[name] = (cpu_fn, gpu_fn) - - # --- push_eta_stage --- - add( - "push_eta_stage", - lambda: pusher_kernels.push_eta_stage(dt, 0, am, ad, a1, b1, c1), - lambda: push_eta_stage_general_gpu( - markers_dev, - n_cols, - am.first_init_idx, - am.first_free_idx, - kind_map, - params_dev, - dt, - dt, - 1.0, - ), - ) - - # --- push_v_with_efield --- - e1 = scene.fields["1"] - e1_dev = tuple(scene.dev(a) for a in e1) - add( - "push_v_with_efield", - lambda: pusher_kernels.push_v_with_efield(dt, 0, am, ad, ah, *e1, dt), - lambda: push_v_with_efield_general_gpu( - markers_dev, - n_cols, - pn, - tn1, - tn2, - tn3, - starts, - *e1_dev, - kind_map, - params_dev, - dt, - ), - ) - - # --- push_vxb_analytic / push_vxb_implicit --- - b2 = scene.fields["2"] - b2_dev = tuple(scene.dev(a) for a in b2) - add( - "push_vxb_analytic", - lambda: pusher_kernels.push_vxb_analytic(dt, 0, am, ad, ah, *b2), - lambda: push_vxb_analytic_general_gpu( - markers_dev, - n_cols, - am.first_init_idx, - pn, - tn1, - tn2, - tn3, - starts, - *b2_dev, - kind_map, - params_dev, - dt, - ), - ) - add( - "push_vxb_implicit", - lambda: pusher_kernels.push_vxb_implicit(dt, 0, am, ad, ah, *b2), - lambda: push_vxb_implicit_general_gpu( - markers_dev, - n_cols, - am.first_init_idx, - pn, - tn1, - tn2, - tn3, - starts, - *b2_dev, - kind_map, - params_dev, - dt, - ), - ) - - # --- push_bxu_Hdiv / Hcurl / H1vec --- - u2, u1, uv = scene.fields["2"], scene.fields["1"], scene.fields["v"] - u2_dev = tuple(scene.dev(a) for a in u2) - u1_dev = tuple(scene.dev(a) for a in u1) - uv_dev = tuple(scene.dev(a) for a in uv) - add( - "push_bxu_Hdiv", - lambda: pusher_kernels.push_bxu_Hdiv(dt, 0, am, ad, ah, *b2, *u2, boundary_cut), - lambda: push_bxu_Hdiv_general_gpu( - markers_dev, - n_cols, - pn, - tn1, - tn2, - tn3, - starts, - *b2_dev, - *u2_dev, - kind_map, - params_dev, - boundary_cut, - dt, - ), - ) - add( - "push_bxu_Hcurl", - lambda: pusher_kernels.push_bxu_Hcurl(dt, 0, am, ad, ah, *b2, *u1, boundary_cut), - lambda: push_bxu_Hcurl_general_gpu( - markers_dev, - n_cols, - pn, - tn1, - tn2, - tn3, - starts, - *b2_dev, - *u1_dev, - kind_map, - params_dev, - boundary_cut, - dt, - ), - ) - add( - "push_bxu_H1vec", - lambda: pusher_kernels.push_bxu_H1vec(dt, 0, am, ad, ah, *b2, *uv, boundary_cut), - lambda: push_bxu_H1vec_general_gpu( - markers_dev, - n_cols, - pn, - tn1, - tn2, - tn3, - starts, - *b2_dev, - *uv_dev, - kind_map, - params_dev, - boundary_cut, - dt, - ), - ) - - # --- push_pc_GXu_full / push_pc_GXu (9 "G" tensor blocks, reusing the 3 - # Hcurl component shapes -- row i's 3 blocks all share component i's - # shape, see pusher_kernels_cuda.py's push_pc_GXu_full_general docs) --- - c1_arr, c2_arr, c3_arr = scene.fields["1"] - rng = scene._rng - g = {} - for row, comp in ((1, c1_arr), (2, c2_arr), (3, c3_arr)): - for col in (1, 2, 3): - arr = rng.uniform(-1.0, 1.0, comp.shape) - g[f"{row}{col}"] = arr - g_order = ["11", "12", "13", "21", "22", "23", "31", "32", "33"] - g_full = [g[k] for k in g_order] - g_full_dev = [scene.dev(a) for a in g_full] - add( - "push_pc_GXu_full", - lambda: pusher_kernels.push_pc_GXu_full(dt, 0, am, ad, ah, *g_full), - lambda: push_pc_GXu_full_general_gpu( - markers_dev, - n_cols, - pn, - tn1, - tn2, - tn3, - starts, - *g_full_dev, - kind_map, - params_dev, - dt, - ), - ) - add( - "push_pc_GXu", - lambda: pusher_kernels.push_pc_GXu(dt, 0, am, ad, ah, *g_full), - lambda: push_pc_GXu_general_gpu( - markers_dev, - n_cols, - pn, - tn1, - tn2, - tn3, - starts, - *g_full_dev[:6], - kind_map, - params_dev, - dt, - ), - ) - - # --- push_pc_eta_stage_Hcurl / Hdiv / H1vec --- - add( - "push_pc_eta_stage_Hcurl", - lambda: pusher_kernels.push_pc_eta_stage_Hcurl(dt, 0, am, ad, ah, *u1, False, a1, b1, c1), - lambda: push_pc_eta_stage_Hcurl_general_gpu( - markers_dev, - n_cols, - am.first_init_idx, - am.first_free_idx, - pn, - tn1, - tn2, - tn3, - starts, - *u1_dev, - False, - kind_map, - params_dev, - dt, - dt, - 1.0, - ), - ) - add( - "push_pc_eta_stage_Hdiv", - lambda: pusher_kernels.push_pc_eta_stage_Hdiv(dt, 0, am, ad, ah, *u2, False, a1, b1, c1), - lambda: push_pc_eta_stage_Hdiv_general_gpu( - markers_dev, - n_cols, - am.first_init_idx, - am.first_free_idx, - pn, - tn1, - tn2, - tn3, - starts, - *u2_dev, - False, - kind_map, - params_dev, - dt, - dt, - 1.0, - ), - ) - add( - "push_pc_eta_stage_H1vec", - lambda: pusher_kernels.push_pc_eta_stage_H1vec(dt, 0, am, ad, ah, *uv, False, a1, b1, c1), - lambda: push_pc_eta_stage_H1vec_general_gpu( - markers_dev, - n_cols, - am.first_init_idx, - am.first_free_idx, - pn, - tn1, - tn2, - tn3, - starts, - *uv_dev, - False, - kind_map, - params_dev, - dt, - dt, - 1.0, - ), - ) - - # --- push_weights_with_efield_lin_va --- - f0_values = scene.random_f0_values() - f0_values_dev = scene.dev(f0_values) - kappa, vth = 1.0, 1.0 - add( - "push_weights_with_efield_lin_va", - lambda: pusher_kernels.push_weights_with_efield_lin_va(dt, 0, am, ad, ah, *e1, f0_values, kappa, vth), - lambda: push_weights_with_efield_lin_va_general_gpu( - markers_dev, - n_cols, - pn, - tn1, - tn2, - tn3, - starts, - *e1_dev, - f0_values_dev, - kappa, - vth, - kind_map, - params_dev, - dt, - ), - ) - - # --- push_deterministic_diffusion_stage --- - pi_u = scene.fields["0"][0] - pi_grad = (scene.fields["0"][0], scene.fields["0"][0], scene.fields["0"][0]) # shape-only stand-ins - pi_u_dev = scene.dev(pi_u) - pi_grad_dev = tuple(scene.dev(a) for a in pi_grad) - diffusion_coeff = 0.1 - add( - "push_deterministic_diffusion_stage", - lambda: pusher_kernels.push_deterministic_diffusion_stage( - dt, - 0, - am, - ad, - ah, - pi_u, - *pi_grad, - diffusion_coeff, - a1, - b1, - c1, - ), - lambda: push_deterministic_diffusion_stage_general_gpu( - markers_dev, - n_cols, - am.first_init_idx, - am.first_free_idx, - pn, - tn1, - tn2, - tn3, - starts, - pi_u_dev, - *pi_grad_dev, - diffusion_coeff, - kind_map, - params_dev, - dt, - dt, - 1.0, - ), - ) - - # --- push_random_diffusion_stage (domain-independent) --- - noise = scene.random_noise() - add( - "push_random_diffusion_stage", - lambda: pusher_kernels.push_random_diffusion_stage(dt, 0, am, ad, noise, diffusion_coeff, a1, b1, c1), - # This wrapper deliberately stages its random noise input itself. - lambda: push_random_diffusion_stage_gpu(markers_dev, n_cols, noise, diffusion_coeff, dt), - ) - - # --- charge_density_0form (AccumulatorVector, H1) --- - vec_shape = scene.fields["0"][0].shape - vec_cpu = np.zeros(vec_shape, dtype=float) - vec_gpu = scene.dev(np.zeros(vec_shape, dtype=float)) - weight_idx = scene.particles.index["weights"] - add( - "charge_density_0form", - lambda: (vec_cpu.fill(0.0), accum_kernels.charge_density_0form(am, ah, ad, vec_cpu))[-1], - lambda: ( - vec_gpu.fill(0.0), - charge_density_0form_gpu( - markers_dev, - weight_idx, - pn, - tn1, - tn2, - tn3, - starts, - vec_gpu, - ), - )[-1], - ) - - # --- linear_vlasov_ampere (Accumulator, symmetric V1 -> V1 matrix + vector) --- - from feectools.linalg.block import BlockVector - - op = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="symm") - mat_cpu, mat_gpu_ = {}, {} - for a_ in range(3): - for b_ in range(3): - if b_ >= a_ and op.matrix.blocks[a_][b_] is not None: - shape_ = op.matrix.blocks[a_][b_]._data.shape - key = f"{a_ + 1}{b_ + 1}" - mat_cpu[key] = np.zeros(shape_, dtype=float) - mat_gpu_[key] = scene.dev(np.zeros(shape_, dtype=float)) - vec_space = scene.derham.coeff_spaces["1"] - vec_bv = BlockVector(vec_space) - vlva_vec_cpu = [np.zeros(bl._data.shape, dtype=float) for bl in vec_bv.blocks] - vlva_vec_gpu = [scene.dev(v) for v in vlva_vec_cpu] - lva_f0 = scene.random_f0_values() - lva_f0_dev = scene.dev(lva_f0) - mat_keys = ["11", "12", "13", "22", "23", "33"] - - def _lva_cpu(): - for v in mat_cpu.values(): - v.fill(0.0) - for v in vlva_vec_cpu: - v.fill(0.0) - accum_kernels.linear_vlasov_ampere( - am, - ah, - ad, - *[mat_cpu[k] for k in mat_keys], - *vlva_vec_cpu, - lva_f0, - ) - - def _lva_gpu(): - for v in mat_gpu_.values(): - v.fill(0.0) - for v in vlva_vec_gpu: - v.fill(0.0) - linear_vlasov_ampere_gpu( - markers_dev, - kind_map, - params_dev, - lva_f0_dev, - pn, - tn1, - tn2, - tn3, - starts, - *[mat_gpu_[k] for k in mat_keys], - *vlva_vec_gpu, - ) - - add("linear_vlasov_ampere", _lva_cpu, _lva_gpu) - - # --- vlasov_maxwell (Accumulator, symmetric V1 -> V1 matrix + vector, - # same shape as linear_vlasov_ampere but no f0_values) --- - op_vm = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="symm") - vm_mat_cpu, vm_mat_gpu = {}, {} - for a_ in range(3): - for b_ in range(3): - if b_ >= a_ and op_vm.matrix.blocks[a_][b_] is not None: - shape_ = op_vm.matrix.blocks[a_][b_]._data.shape - key = f"{a_ + 1}{b_ + 1}" - vm_mat_cpu[key] = np.zeros(shape_, dtype=float) - vm_mat_gpu[key] = scene.dev(np.zeros(shape_, dtype=float)) - vm_vec_bv = BlockVector(vec_space) - vm_vec_cpu = [np.zeros(bl._data.shape, dtype=float) for bl in vm_vec_bv.blocks] - vm_vec_gpu = [scene.dev(v) for v in vm_vec_cpu] - - def _vm_cpu(): - for v in vm_mat_cpu.values(): - v.fill(0.0) - for v in vm_vec_cpu: - v.fill(0.0) - accum_kernels.vlasov_maxwell(am, ah, ad, *[vm_mat_cpu[k] for k in mat_keys], *vm_vec_cpu) - - def _vm_gpu(): - for v in vm_mat_gpu.values(): - v.fill(0.0) - for v in vm_vec_gpu: - v.fill(0.0) - vlasov_maxwell_gpu( - markers_dev, - kind_map, - params_dev, - pn, - tn1, - tn2, - tn3, - starts, - *[vm_mat_gpu[k] for k in mat_keys], - *vm_vec_gpu, - ) - - add("vlasov_maxwell", _vm_cpu, _vm_gpu) - - # --- cc_lin_mhd_6d_1 (Accumulator, antisymmetric 3-block fill, u_space=Hcurl i.e. basis_u=1) --- - op_cc1 = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="asym") - cc1_cpu = { - k: np.zeros(op_cc1.matrix.blocks[a_][b_]._data.shape, dtype=float) - for k, (a_, b_) in zip(["12", "13", "23"], [(0, 1), (0, 2), (1, 2)]) - } - cc1_gpu = {k: scene.dev(v) for k, v in cc1_cpu.items()} - b2_1, b2_2, b2_3 = scene.fields["2"] - b2_1_dev, b2_2_dev, b2_3_dev = (scene.dev(a) for a in scene.fields["2"]) - cc1_scale_mat, cc1_boundary_cut = 2.5, 0.05 - basis_u_hcurl = 1 - - def _cc1_cpu(): - for v in cc1_cpu.values(): - v.fill(0.0) - accum_kernels.cc_lin_mhd_6d_1( - am, - ah, - ad, - cc1_cpu["12"], - cc1_cpu["13"], - cc1_cpu["23"], - b2_1, - b2_2, - b2_3, - basis_u_hcurl, - cc1_scale_mat, - cc1_boundary_cut, - ) - - def _cc1_gpu(): - for v in cc1_gpu.values(): - v.fill(0.0) - cc_lin_mhd_6d_1_gpu( - markers_dev, - kind_map, - params_dev, - pn, - tn1, - tn2, - tn3, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - basis_u_hcurl, - cc1_scale_mat, - cc1_boundary_cut, - cc1_gpu["12"], - cc1_gpu["13"], - cc1_gpu["23"], - ) - - add("cc_lin_mhd_6d_1", _cc1_cpu, _cc1_gpu) - - # --- cc_lin_mhd_6d_2 (Accumulator, symmetric 6-block+vector fill, u_space=Hcurl i.e. basis_u=1) --- - op_cc2 = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="symm") - cc2_mat_cpu, cc2_mat_gpu = {}, {} - for a_ in range(3): - for b_ in range(3): - if b_ >= a_ and op_cc2.matrix.blocks[a_][b_] is not None: - shape_ = op_cc2.matrix.blocks[a_][b_]._data.shape - key = f"{a_ + 1}{b_ + 1}" - cc2_mat_cpu[key] = np.zeros(shape_, dtype=float) - cc2_mat_gpu[key] = scene.dev(np.zeros(shape_, dtype=float)) - cc2_vec_bv = BlockVector(vec_space) - cc2_vec_cpu = [np.zeros(bl._data.shape, dtype=float) for bl in cc2_vec_bv.blocks] - cc2_vec_gpu = [scene.dev(v) for v in cc2_vec_cpu] - cc2_scale_mat, cc2_scale_vec, cc2_boundary_cut = 1.7, 0.6, 0.05 - - def _cc2_cpu(): - for v in cc2_mat_cpu.values(): - v.fill(0.0) - for v in cc2_vec_cpu: - v.fill(0.0) - accum_kernels.cc_lin_mhd_6d_2( - am, - ah, - ad, - *[cc2_mat_cpu[k] for k in mat_keys], - *cc2_vec_cpu, - b2_1, - b2_2, - b2_3, - basis_u_hcurl, - cc2_scale_mat, - cc2_scale_vec, - cc2_boundary_cut, - ) - - def _cc2_gpu(): - for v in cc2_mat_gpu.values(): - v.fill(0.0) - for v in cc2_vec_gpu: - v.fill(0.0) - cc_lin_mhd_6d_2_gpu( - markers_dev, - kind_map, - params_dev, - pn, - tn1, - tn2, - tn3, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - basis_u_hcurl, - cc2_scale_mat, - cc2_scale_vec, - cc2_boundary_cut, - *[cc2_mat_gpu[k] for k in mat_keys], - *cc2_vec_gpu, - ) - - add("cc_lin_mhd_6d_2", _cc2_cpu, _cc2_gpu) - - # --- pc_lin_mhd_6d_full / pc_lin_mhd_6d (Accumulator, symmetry="pressure": - # 45 arrays = 6 "symm" Hcurl operators (velocity-pairs) * 6 spatial - # blocks + 3 vector groups (velocity components) * 3 spatial blocks) --- - spatial_blocks = ("11", "12", "13", "22", "23", "33") - pc_mat_cpu, pc_mat_gpu = {}, {} - for vel in spatial_blocks: - op_pc = scene.mass_ops.create_weighted_mass("Hcurl", "Hcurl", weights="symm") - for a_, b_ in [(0, 0), (0, 1), (0, 2), (1, 1), (1, 2), (2, 2)]: - sp = f"{a_ + 1}{b_ + 1}" - shape_ = op_pc.matrix.blocks[a_][b_]._data.shape - pc_mat_cpu[f"mat{sp}_{vel}"] = np.zeros(shape_, dtype=float) - pc_mat_gpu[f"mat{sp}_{vel}"] = scene.dev(np.zeros(shape_, dtype=float)) - pc_vec_cpu, pc_vec_gpu = {}, {} - for i in ("1", "2", "3"): - bv = BlockVector(vec_space) - for mu, bl in zip(("1", "2", "3"), bv.blocks): - shape_ = bl._data.shape - pc_vec_cpu[f"vec{mu}_{i}"] = np.zeros(shape_, dtype=float) - pc_vec_gpu[f"vec{mu}_{i}"] = scene.dev(np.zeros(shape_, dtype=float)) - pc_mat_order = [f"mat{sp}_{vel}" for vel in spatial_blocks for sp in spatial_blocks] - pc_vec_order = [f"vec{mu}_{i}" for i in ("1", "2", "3") for mu in ("1", "2", "3")] - ep_scale = 3.3 - - def _pc_full_cpu(): - for v in pc_mat_cpu.values(): - v.fill(0.0) - for v in pc_vec_cpu.values(): - v.fill(0.0) - accum_kernels.pc_lin_mhd_6d_full( - am, - ah, - ad, - *[pc_mat_cpu[k] for k in pc_mat_order], - *[pc_vec_cpu[k] for k in pc_vec_order], - ep_scale, - ) - - def _pc_full_gpu(): - for v in pc_mat_gpu.values(): - v.fill(0.0) - for v in pc_vec_gpu.values(): - v.fill(0.0) - pc_lin_mhd_6d_full_gpu( - markers_dev, - kind_map, - params_dev, - pn, - tn1, - tn2, - tn3, - starts, - ep_scale, - *[pc_mat_gpu[k] for k in pc_mat_order], - *[pc_vec_gpu[k] for k in pc_vec_order], - ) - - add("pc_lin_mhd_6d_full", _pc_full_cpu, _pc_full_gpu) - - def _pc_cpu(): - for v in pc_mat_cpu.values(): - v.fill(0.0) - for v in pc_vec_cpu.values(): - v.fill(0.0) - accum_kernels.pc_lin_mhd_6d( - am, - ah, - ad, - *[pc_mat_cpu[k] for k in pc_mat_order], - *[pc_vec_cpu[k] for k in pc_vec_order], - ep_scale, - ) - - def _pc_gpu(): - for v in pc_mat_gpu.values(): - v.fill(0.0) - for v in pc_vec_gpu.values(): - v.fill(0.0) - pc_lin_mhd_6d_gpu( - markers_dev, - kind_map, - params_dev, - pn, - tn1, - tn2, - tn3, - starts, - ep_scale, - *[pc_mat_gpu[k] for k in pc_mat_order], - *[pc_vec_gpu[k] for k in pc_vec_order], - ) - - add("pc_lin_mhd_6d", _pc_cpu, _pc_gpu) - - return cases - - -def main(): - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - parser.add_argument("--n-markers", type=int, default=200_000, help="approximate number of markers (via ppc)") - parser.add_argument("--num-elements", type=int, nargs=3, default=(16, 16, 8), metavar=("NX", "NY", "NZ")) - parser.add_argument("--degree", type=int, nargs=3, default=(3, 3, 3), metavar=("PX", "PY", "PZ")) - parser.add_argument("--repeats", type=int, default=5) - parser.add_argument("--dt", type=float, default=0.01) - parser.add_argument( - "--kernel", - action="append", - default=None, - help="restrict to one kernel (repeatable); default: run all", - ) - args = parser.parse_args() - - print( - f"Building scene: num_elements={tuple(args.num_elements)}, degree={tuple(args.degree)}, Np~={args.n_markers} ..." - ) - scene = Scene(tuple(args.num_elements), tuple(args.degree), args.n_markers) - print(f" -> {scene.n_markers} markers, domain kind_map={scene.kind_map} (Colella)") - - cases = make_cases(scene, args.dt) - names = args.kernel if args.kernel else list(cases.keys()) - for name in names: - if name not in cases: - raise SystemExit(f"Unknown kernel {name!r}. Choices: {sorted(cases)}") - - rows = [] - for name in names: - cpu_fn, gpu_fn = cases[name] - - def cpu_run(cpu_fn=cpu_fn): - scene.reset_markers() - cpu_fn() - - def gpu_run(gpu_fn=gpu_fn): - scene.reset_markers_dev() - gpu_fn() - # RawKernel launches are asynchronous. The D2H copy both makes - # the timing meaningful and includes the marker round-trip that - # a host-backed particle path would require. - scene.copy_markers_from_dev() - - cpu_t = timeit(cpu_run, args.repeats) - gpu_t = timeit(gpu_run, args.repeats) - rows.append((name, cpu_t, gpu_t, cpu_t / gpu_t)) - print(f" {name}: cpu={cpu_t * 1e3:.3f} ms gpu={gpu_t * 1e3:.3f} ms speedup={cpu_t / gpu_t:.1f}x") - - print() - print(f"{'kernel':<36} {'n_markers':>10} {'cpu (ms)':>12} {'gpu (ms)':>12} {'speedup':>10}") - print("-" * 84) - for name, cpu_t, gpu_t, speedup in rows: - print(f"{name:<36} {scene.n_markers:>10} {cpu_t * 1e3:>12.3f} {gpu_t * 1e3:>12.3f} {speedup:>9.1f}x") - - -if __name__ == "__main__": - main() diff --git a/feectools b/feectools index ce78b9bb2..88cadbab0 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit ce78b9bb2cbeeed0dfe34327900df5f3fc1b7608 +Subproject commit 88cadbab0d784448834d98756e39561bc2d2eb5d diff --git a/params_LinearMHDDriftkineticCC.py b/params_LinearMHDDriftkineticCC.py deleted file mode 100644 index 536dde36a..000000000 --- a/params_LinearMHDDriftkineticCC.py +++ /dev/null @@ -1,181 +0,0 @@ -# ----------------------------- -# Description of the simulation -# ----------------------------- -# Please fill in a verbal description of the simulation. -# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. - -name = "Default LinearMHDDriftkineticCC" -description = """ -This is the default simulation for the model LinearMHDDriftkineticCC. -It is meant to be a template for users to set up their own simulations with this model. -It contains all the necessary components of a Struphy simulation, including the model, -the environment options, the time stepping options, the geometry, the equilibrium, -the grid, the Derham options, and the initial conditions. -Users can modify this file to set up their own simulations with different parameters and initial conditions. -""" - -import argparse -import os - -parser = argparse.ArgumentParser(description=description) -parser.add_argument( - "--backend", - choices=("numpy", "cupy"), - default="numpy", - help="Array backend to run the simulation with (default: numpy).", -) -args = parser.parse_args() - -# Must be set before struphy (and therefore cunumpy) is imported. -os.environ["ARRAY_BACKEND"] = args.backend - -import logging - -from struphy import set_logging_level - -set_logging_level(logging.WARNING) - -# ------------------ -# Import Struphy API -# ------------------ - -# For particles: -from struphy import ( - BaseUnits, - BinningPlot, - BoundaryParameters, - DerhamOptions, - EnvironmentOptions, - FieldsBackground, - KernelDensityPlot, - LoadingParameters, - SavingParameters, - Simulation, - SortingParameters, - Time, - WeightsParameters, - domains, - equils, - grids, - maxwellians, - perturbations, -) - -# --------------------- -# Instance of the model -# --------------------- -from struphy.models import LinearMHDDriftkineticCC - -# Units -base_units = BaseUnits() - -# Model instance -model = LinearMHDDriftkineticCC(base_units=base_units) - -# List all variables and decide whether to save their data -model.em_fields.b_field.save_data = True -model.mhd.density.save_data = True -model.mhd.pressure.save_data = True -model.mhd.velocity.save_data = True -model.energetic_ions.var.save_data = True - -# -------------------------- -# Instance of the simulation -# -------------------------- - -# Environment options -env = EnvironmentOptions( - sim_folder=f"sim_{args.backend}", - profiling_activated=True, -) - -# Time stepping -time_opts = Time(dt=0.01, Tend=0.05) - -# Geometry -domain = domains.Cuboid() - -# Fluid equilibrium (can be used as part of initial conditions) -equil = equils.HomogenSlab() - -# Grid -grid = grids.TensorProductGrid(num_elements=(16, 16, 16)) - -# Derham options -derham_opts = DerhamOptions() - -# Simulation object -sim = Simulation( - model=model, - name=name, - description=description, - params_path=__file__, - env=env, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, -) - -# ------------------- -# Particle parameters -# ------------------- - -loading_params = LoadingParameters(seed=1234) -weights_params = WeightsParameters() -boundary_params = BoundaryParameters() -sorting_params = SortingParameters() -saving_params = SavingParameters() -model.energetic_ions.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, -) - -# ------------------ -# Propagator options -# ------------------ - -model.propagators.push_bxe.options = model.propagators.push_bxe.Options() -model.propagators.push_parallel.options = model.propagators.push_parallel.Options() -model.propagators.shearalfen_cc5d.options = model.propagators.shearalfen_cc5d.Options() -model.propagators.magnetosonic.options = model.propagators.magnetosonic.Options() -model.propagators.cc5d_density.options = model.propagators.cc5d_density.Options() -model.propagators.cc5d_gradb.options = model.propagators.cc5d_gradb.Options() -model.propagators.cc5d_curlb.options = model.propagators.cc5d_curlb.Options() - -# ------------------ -# Initial conditions -# ------------------ -# Initial conditions are the sum of the background(s) and the perturbation(s). -# If backgrounds or perturbations are not specified, they are assumed to be zero. - -# Background for (some) FEEC variables -model.mhd.velocity.add_background(FieldsBackground()) - -# Perturbations for (some) FEEC variables -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=0)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=1)) -model.mhd.velocity.add_perturbation(perturbations.TorusModesCos(given_in_basis="v", comp=2)) - -# For kinetic species the background is mandatory. -# For kinetic species, if add_initial_condition() is not called, the background is taken as the kinetic initial condition. -# For kinetic species the perturbations are added to the moments of the distribution function (defined as tuples). - -# Background for kinetic species -maxwellian_1 = maxwellians.GyroMaxwellian2D(n=(1.0, None)) -maxwellian_2 = maxwellians.GyroMaxwellian2D(n=(0.1, None)) -background = maxwellian_1 + maxwellian_2 -model.energetic_ions.var.add_background(background) - -# Perturbations for (some) kinetic species -perturbation = perturbations.TorusModesCos() -maxwellian_1pt = maxwellians.GyroMaxwellian2D(n=(1.0, perturbation)) -init = maxwellian_1pt + maxwellian_2 -model.energetic_ions.var.add_initial_condition(init) - -if __name__ == "__main__": - sim.run() diff --git a/params_PressureLessSPH.py b/params_PressureLessSPH.py deleted file mode 100644 index c2734941e..000000000 --- a/params_PressureLessSPH.py +++ /dev/null @@ -1,167 +0,0 @@ -# ----------------------------- -# Description of the simulation -# ----------------------------- -# Please fill in a verbal description of the simulation. -# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. - -name = "Default PressureLessSPH" -description = """ -This is the default simulation for the model PressureLessSPH. -It is meant to be a template for users to set up their own simulations with this model. -It contains all the necessary components of a Struphy simulation, including the model, -the environment options, the time stepping options, the geometry, the equilibrium, -the grid, the Derham options, and the initial conditions. -Users can modify this file to set up their own simulations with different parameters and initial conditions. -""" - -import argparse -import os - -parser = argparse.ArgumentParser(description=description) -parser.add_argument( - "--backend", - choices=("numpy", "cupy"), - default="numpy", - help="Array backend to run the simulation with (default: numpy).", -) -args = parser.parse_args() - -# Must be set before struphy (and therefore cunumpy) is imported. -os.environ["ARRAY_BACKEND"] = args.backend - - -import logging - -from struphy import set_logging_level - -set_logging_level(logging.WARNING) - -# ------------------ -# Import Struphy API -# ------------------ - -# For particles: -from struphy import ( - BaseUnits, - BinningPlot, - BoundaryParameters, - DerhamOptions, - EnvironmentOptions, - FieldsBackground, - KernelDensityPlot, - LoadingParameters, - SavingParameters, - Simulation, - SortingParameters, - Time, - WeightsParameters, - domains, - equils, - grids, - maxwellians, - perturbations, -) - -# --------------------- -# Instance of the model -# --------------------- -from struphy.models import PressureLessSPH - -# Units -base_units = BaseUnits() - -# Model instance -model = PressureLessSPH(base_units=base_units) - -# List all variables and decide whether to save their data -model.cold_fluid.var.save_data = True - -# -------------------------- -# Instance of the simulation -# -------------------------- - -# Environment options -env = EnvironmentOptions( - sim_folder=f"sim_{args.backend}", - profiling_activated=True, - save_restart=False, -) - - -# Time stepping -# 10 steps: long enough to average out start-up effects when comparing the -# NumPy and CuPy backends, short enough to iterate on. -time_opts = Time(dt=0.01, Tend=0.1) - -# Geometry -domain = domains.Cuboid() - -# Fluid equilibrium (can be used as part of initial conditions) -equil = equils.HomogenSlab() - -# Grid -grid = grids.TensorProductGrid(num_elements=(32, 32, 16)) - -# Derham options -derham_opts = DerhamOptions() - -# Simulation object -sim = Simulation( - model=model, - name=name, - description=description, - params_path=__file__, - env=env, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, -) - -# ------------------- -# Particle parameters -# ------------------- - -# Np and the grid above are sized for backend comparisons: big enough that the -# particle push dominates the run, small enough to fit comfortably on one GPU. -# The seed is fixed because marker loading is otherwise unseeded, and two runs -# of the *same* backend then differ enough to swamp any backend comparison. -loading_params = LoadingParameters(Np=1_000_000, seed=1234) -weights_params = WeightsParameters() -boundary_params = BoundaryParameters() -sorting_params = SortingParameters() -saving_params = SavingParameters() -model.cold_fluid.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, -) - -# ------------------ -# Propagator options -# ------------------ - -model.propagators.push_eta.options = model.propagators.push_eta.Options() -phi = equil.p0 -model.propagators.push_v.phi = phi -model.propagators.push_v.options = model.propagators.push_v.Options() - -# ------------------ -# Initial conditions -# ------------------ -# Initial conditions are the sum of the background(s) and the perturbation(s). -# If backgrounds or perturbations are not specified, they are assumed to be zero. - -# Background for (some) sph variables -background = equils.ConstantVelocity() -model.cold_fluid.var.add_background(background) - -# Perturbations for (some) sph variables -perturbation = perturbations.TorusModesCos() -model.cold_fluid.var.add_perturbation(del_n=perturbation) - -if __name__ == "__main__": - sim.run() diff --git a/profiling/clusters.py b/profiling/clusters.py index 7602dcabd..d64c9a1f6 100644 --- a/profiling/clusters.py +++ b/profiling/clusters.py @@ -69,32 +69,6 @@ def detect_machine_name() -> str | None: "mail_type": "none", "time": "00:15:00", }, - "pitagora_boost_fua_dbg": { - # "nodes": 1, # Should be set by ProfilingCase.launch() - # "ntasks_per_node": 1, # Should be set by ProfilingCase.launch() - "cpus_per_task": 1, - "mem": "480GB", - "gres": "gpu:4,tmpfs:10g", - "partition": "boost_fua_dbg", - "account": "FUSIO_HLST_6", - "output": "myJob_%j.out", - "error": "myJob_%j.err", - "mail_type": "none", - "time": "00:15:00", - }, - "pitagora_boost_fua_prod": { - # "nodes": 1, # Should be set by ProfilingCase.launch() - # "ntasks_per_node": 1, # Should be set by ProfilingCase.launch() - "cpus_per_task": 1, - "mem": "480GB", - "gres": "gpu:4,tmpfs:10g", - "partition": "boost_fua_prod", - "account": "FUSIO_HLST_6", - "output": "myJob_%j.out", - "error": "myJob_%j.err", - "mail_type": "none", - "time": "00:15:00", - }, "tok": { "cpus_per_task": 1, "mem_per_cpu": "1GB", diff --git a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py b/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py deleted file mode 100644 index 59f26026d..000000000 --- a/profiling/examples/DriftKineticElectrostaticAdiabatic/params_cyclone.py +++ /dev/null @@ -1,417 +0,0 @@ -# ----------------------------- -# Description of the simulation -# ----------------------------- -# Please fill in a verbal description of the simulation. -# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. - -name = "DriftKineticElectrostaticAdiabatic Cyclone NumPy vs CuPy" -description = """ -Cyclone-instability ITG turbulence case for DriftKineticElectrostaticAdiabatic (see -examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py, the physics case -this profiling params file is adapted from), used as the NumPy-vs-CuPy backend -comparison case for a real gyrokinetic model rather than a toy one. -""" - -import argparse -import os - -parser = argparse.ArgumentParser(description=description) -parser.add_argument( - "--backend", - choices=("numpy", "cupy"), - default="numpy", - help="Array backend to run the simulation with (default: numpy).", -) -# `--id` distinguishes runs that share a rank count but differ in something else (here: -# the array backend); the profiling driver passes its launch counter and looks for the -# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). -# Unknown flags are ignored so the driver can forward other parameters as well. -parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") -parser.add_argument("--ppc", type=int, default=None, help="Markers per cell (overrides the default, 200).") -parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default, 0.01 -> 10 steps).") -parser.add_argument( - "--solver", - choices=("pcg",), - default="pcg", - help="Symmetric solver for the PoissonAdiabaticGyrokinetic field solve (default: pcg).", -) -parser.add_argument( - "--num-elements", - type=int, - nargs=3, - default=None, - help="Grid resolution (overrides the default, 16 64 4).", -) -args, _ = parser.parse_known_args() - -# Must be set before struphy (and therefore cunumpy) is imported. -os.environ["ARRAY_BACKEND"] = args.backend - -if args.backend == "cupy": - import cunumpy - - # Under CuPy with more than one MPI rank per node, every rank must bind to its own - # GPU -- cupy defaults to device 0, so without this every rank on a node would - # contend for the same GPU instead of getting one each. SLURM_LOCALID (the rank's - # index within its node) is set by srun before this process even starts, so it works - # without MPI being initialized yet. Falls back to device 0 outside SLURM (e.g. a - # single-GPU login node). - cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) - - # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment - # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more - # than one rank every process independently creates the same output directory/HDF5 - # dataset and the survivors deadlock in the next collective. - os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") - -import logging - -from struphy import set_logging_level - -set_logging_level(logging.WARNING) - -# ------------------ -# Import Struphy API -# ------------------ - -import cunumpy as xp - -from struphy import ( - BaseUnits, - BinningPlot, - BoundaryParameters, - DerhamOptions, - EnvironmentOptions, - LoadingParameters, - ProfilingOptions, - SavingParameters, - Simulation, - SortingParameters, - Time, - WeightsParameters, - domains, - equils, - grids, - maxwellians, -) -from struphy.initial.base import GenericPerturbation -from struphy.linear_algebra.solver import SolverParameters - -# --------------------- -# Instance of the model -# --------------------- -from struphy.models import DriftKineticElectrostaticAdiabatic -from struphy.pic.accumulation.filter import FilterParameters - -# provides the correct value for epsilon = 1.4142e-3 = 0.36/(180*sqrt(2)) from the -# cyclone paper (10.1140/epjd/e2014-50180-9) -base_units = BaseUnits(kBT=0.1916) -model = DriftKineticElectrostaticAdiabatic( - base_units=base_units, - # The physics case (examples/.../cyclone/params_cyclone.py) enables this, but it - # wires up a *second*, completely separate solve every step - # (ImplicitDiffusion.__call__'s `if self.diagnostic is not None: ... proj.solve(rhs)`, - # a fresh L2Projector with the default "pcg" solver). It only feeds - # `self.diagnostics.rho`, an extra saved diagnostic field that nothing else in this - # model reads back (the `phi_integral` scalar uses `phi` directly) -- disabled here - # so this profiling case measures the model's actual per-step cost. - use_diagnostic_poisson=False, -) - -# List all variables and decide whether to save their data -model.em_fields.phi.save_data = True -model.kinetic_ions.var.save_data = False - -# -------------------------- -# Instance of the simulation -# -------------------------- - -name = f"DriftKineticElectrostaticAdiabatic Cyclone ({args.backend})" - -# Environment options -env = EnvironmentOptions( - sim_folder=f"sim_{args.id:02d}", - profiling_activated=True, - save_restart=False, -) - -# Time stepping. Short by default: enough steps to warm past one-off setup (Poisson -# assembly, particle loading, CUDA RawKernel JIT compile) without a long profiling run. -time_opts = Time(dt=0.001, Tend=args.Tend if args.Tend is not None else 0.01, split_algo="LieTrotter") - -a, r_min, R0 = 0.36, 0.01, 1.0 -num_elements = tuple(args.num_elements) if args.num_elements is not None else (16, 64, 4) -degree = (3, 3, 3) - -# Fluid equilibrium (can be used as part of initial conditions) -equil = equils.AdhocTorus(a=a, R0=R0, B0=1.0, q_kind=2, q0=0.86, q1=2.52 + 0.86, l=-0.16, psi_k=5, psi_nel=200) - -# Geometry -domain = domains.HollowTorus(a1=r_min, a2=a, R0=R0, sfl=True, pol_period=1, tor_period=19) - -# Grid -grid = grids.TensorProductGrid(num_elements=num_elements, mpi_dims_mask=(True, True, False)) - -# Derham options -derham_opts = DerhamOptions( - degree=degree, - bcs=(("dirichlet", "dirichlet"), None, None), -) - -# Simulation object -sim = Simulation( - model=model, - name=name, - description=description, - params_path=__file__, - env=env, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, - profiling_opts=ProfilingOptions(label=f"DK-Cyclone-{args.backend}"), -) - -# ------------------- -# Particle parameters -# ------------------- - -ppc = args.ppc if args.ppc is not None else 200 -loading_params = LoadingParameters(ppc=ppc, loading="sobol_standard", spatial="uniform", moments=(0, 0, 4, 4)) -weights_params = WeightsParameters(control_variate=True) -boundary_params = BoundaryParameters(bc=("remove", "periodic", "periodic")) -sorting_params = SortingParameters(boxes_per_dim=(16, 16, 6), do_sort=True, sorting_frequency=5) - -# density binning, needed for the e1_e2 density slice generated by the pproc block below -# (matches examples/DriftKineticElectrostaticAdiabatic/cyclone/params_cyclone.py's own setup) -eta_bin = BinningPlot(slice="e1_e2", n_bins=(64, 64), ranges=((0.01, 0.99), (0.0, 1.0))) -saving_params = SavingParameters(n_markers=100, binning_plots=(eta_bin,)) - -model.kinetic_ions.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, - bufsize=1.0, -) - -# ------------------ -# Propagator options -# ------------------ - -model.propagators.gc_poisson.options = model.propagators.gc_poisson.Options( - which_geometry="toroidal", - solver=args.solver, - solver_params=SolverParameters(tol=1e-12, maxiter=3000, recycle=False), - filter_params={model.kinetic_ions.var: FilterParameters("fourier_in_tor", (1,), repeat=1)}, -) -model.propagators.push_gc_bxe.options = model.propagators.push_gc_bxe.Options( - algo="explicit", - evaluate_e_field=True, - maxiter=100, -) -model.propagators.push_gc_para.options = model.propagators.push_gc_para.Options( - algo="explicit", - evaluate_e_field=True, - maxiter=100, -) - -# ------------------ -# Initial conditions -# ------------------ - -ns = 1 -ms = 27 -amps = 1.0e-6 -kappa_n = 2.23 -kappa_Ti = 6.96 -Delta_n = Delta_Ti = 0.3 -delta_r = 0.02 -r0 = 0.5 * a -n0 = 1.0 -Ti0 = 1.0 - - -def n_r(r): - return n0 * xp.exp(-kappa_n * a * Delta_n * xp.tanh((r - r0) / (Delta_n * a))) - - -def n_init(*etas): - if len(etas) == 1: - eta1 = etas[0][:, 0] - else: - eta1 = etas[0] - r = r_min + (a - r_min) * eta1 - return n_r(r) - - -def Ti_r(r): - return Ti0 * xp.exp(-kappa_Ti * a * Delta_Ti * xp.tanh((r - r0) / (Delta_Ti * a))) - - -def vth_init(*etas): - if len(etas) == 1: - eta1 = etas[0][:, 0] - else: - eta1 = etas[0] - r = r_min + (a - r_min) * eta1 - return xp.sqrt(Ti_r(r)) - - -def n_xyz(x, y, z): - r = xp.sqrt((xp.sqrt(x**2 + y**2) - R0) ** 2 + z**2) - return n_r(r) - - -def p_xyz(x, y, z): - r = xp.sqrt((xp.sqrt(x**2 + y**2) - R0) ** 2 + z**2) - return n_r(r) * Ti_r(r) - - -equil.p_xyz = p_xyz -equil.n_xyz = n_xyz - - -def pert_func(*etas): - if len(etas) == 1: - e1, e2, e3 = etas[0][:, 0], etas[0][:, 1], etas[0][:, 2] - else: - e1, e2, e3 = etas[0], etas[1], etas[2] - r = (a - r_min) * e1 + r_min - teta = 2 * xp.arctan(xp.sqrt((R0 + r) / (R0 - r)) * xp.tan(xp.pi * e2)) - phi = 2 * xp.pi * e3 - return n_r(r) * amps * xp.exp(-((r - r0) ** 2) / delta_r**2) * xp.cos(ms * teta - ns * phi) - - -# Background for kinetic species -background = maxwellians.GyroMaxwellian2D(n=(n_init, None), vth_para=(vth_init, None), vth_perp=(vth_init, None)) -model.kinetic_ions.var.add_background(background) - -perturbation = GenericPerturbation(pert_func, given_in_basis="0") -init = maxwellians.GyroMaxwellian2D(n=(n_init, perturbation), vth_para=(vth_init, None), vth_perp=(vth_init, None)) -model.kinetic_ions.var.add_initial_condition(init) - -if __name__ == "__main__": - sim.run() - sim.pproc(parallel_pproc=True, physical=True) - - # Static, non-interactive figures for this profiling run -- adapted from - # examples/DriftKineticElectrostaticAdiabatic/cyclone/pproc_cyclone.py (which is - # meant for local, interactive use: matplotlib Slider widgets, plt.show()) into - # fixed-time-step, save-to-file plots, following the same results-directory - # convention as profiling/examples/Poisson/cube_strong_scaling/params_poisson.py. - if sim.rank == 0: - import os - - import h5py - import numpy as np - from matplotlib import pyplot as plt - - sim.load_plotting_data() - - # `path_out` is the run's output folder; `sim_folder` alone is a bare name - # resolved against the CWD. The profiling packaging picks these files up from - # here and uploads them as `results-run`. - results_dir = os.path.join(sim.env.path_out, "results") - os.makedirs(results_dir, exist_ok=True) - - # Deliberately plain NumPy from here on, not xp/cunumpy, for data that really is - # already host-side (h5py reads). spline_values/grids_phy/PlottingData attributes - # are NOT host-only under ARRAY_BACKEND=cupy despite being "post-processed" -- - # they stay on-device, so those are pulled to host explicitly via xp.to_numpy() - # below rather than np.asarray(), which CuPy refuses as an implicit conversion. - - # ------------------------------------------------------------------- - # phi_integral evolution + exponential growth-rate fit (the ITG-test - # diagnostic scalar; see pproc_cyclone.py's plot_energy_fit). - # ------------------------------------------------------------------- - data_path = os.path.join(sim.env.path_out, "data") - with h5py.File(os.path.join(data_path, "data_proc0.hdf5"), "r") as f: - t_scalar = np.asarray(f["time"]["value"][()]) - phi_integral = np.asarray(f["scalar"]["phi_integral"][()]) - - fig_energy, ax_energy = plt.subplots() - ax_energy.plot(t_scalar, phi_integral, label="phi_integral") - ax_energy.set_xlabel("time") - ax_energy.set_ylabel("phi_integral") - ax_energy.set_title("Evolution of phi_integral") - - positive = np.isfinite(phi_integral) & (phi_integral > 0.0) - gamma = None - if int(np.count_nonzero(positive)) >= 2: - idx = np.nonzero(positive)[0] - i0, i1 = int(idx[0]), int(idx[-1]) + 1 - fit_time = t_scalar[i0:i1] - fit_signal = np.log(np.sqrt(phi_integral[i0:i1])) - gamma, b = np.polyfit(fit_time, fit_signal, 1) - fit_curve = np.exp(2.0 * (gamma * fit_time + b)) - ax_energy.plot(fit_time, fit_curve, "--", label=f"fit: gamma={float(gamma):.4e}") - print(f"phi_integral growth rate: gamma = {float(gamma):.8e}") - ax_energy.legend() - fig_energy.tight_layout() - - # ------------------------------------------------------------------- - # Electric potential phi, poloidal (R, Z) slice at the last saved time - # step, toroidal index 0 (see pproc_cyclone.py's plot_field_slider). - # ------------------------------------------------------------------- - # float(): dict keys of `.data` are plain Python floats, and t_grid may be a - # 0-d CuPy array here (ARRAY_BACKEND=cupy), which is unhashable. - Tend_saved = float(sim.t_grid[-1]) - # xp.to_numpy(), not np.asarray(): unlike the PlottingData read above, - # spline_values/grids_phy stay on-device under ARRAY_BACKEND=cupy, and CuPy - # arrays refuse implicit conversion via np.asarray(). - phi_phy = xp.to_numpy(sim.spline_values.em_fields.phi_phy.data[Tend_saved][0]) - X, Y, Z = (xp.to_numpy(g) for g in sim.grids_phy) - R = np.sqrt(X**2 + Y**2) - - toroidal_index = 0 - fig_phi, ax_phi = plt.subplots() - pcm_phi = ax_phi.pcolormesh( - R[:, :, toroidal_index], - Z[:, :, toroidal_index], - phi_phy[:, :, toroidal_index], - shading="auto", - ) - fig_phi.colorbar(pcm_phi, ax=ax_phi) - ax_phi.set_aspect("equal", adjustable="box") - ax_phi.set_xlabel("R") - ax_phi.set_ylabel("Z") - ax_phi.set_title(f"Electric potential phi at t = {Tend_saved:.4e}") - fig_phi.tight_layout() - - # ------------------------------------------------------------------- - # Density perturbation, e1-e2 binned, at the last saved time step, in - # logical (eta) space (see pproc_cyclone.py's plot_binned_quantity_slider). - # ------------------------------------------------------------------- - density_data = sim.f.kinetic_ions.e1_e2_density - # xp.to_numpy(): same on-device-under-cupy issue as phi_phy/grids_phy above -- - # the "PlottingData is host-only regardless of backend" premise noted at the top - # of this function does not hold for these attributes. - delta_f_final = xp.to_numpy(density_data.delta_f_binned)[-1] - eta1_grid, eta2_grid = np.meshgrid( - xp.to_numpy(density_data.grid_e1), - xp.to_numpy(density_data.grid_e2), - indexing="ij", - ) - - fig_density, ax_density = plt.subplots() - pcm_density = ax_density.pcolormesh(eta1_grid, eta2_grid, delta_f_final, shading="auto") - fig_density.colorbar(pcm_density, ax=ax_density) - ax_density.set_xlabel("eta1") - ax_density.set_ylabel("eta2") - ax_density.set_title(f"delta_f (eta1, eta2) at t = {Tend_saved:.4e}") - fig_density.tight_layout() - - # ------------------------------------------------------------------- - # Save everything into results_dir, matching params_poisson.py's convention. - # ------------------------------------------------------------------- - if gamma is not None: - np.save(os.path.join(results_dir, "phi_integral_growth_rate.npy"), float(gamma)) - np.save(os.path.join(results_dir, "resolution.npy"), np.asarray(num_elements)) - np.save(os.path.join(results_dir, "spline_degree.npy"), np.asarray(degree)) - - fig_energy.savefig(os.path.join(results_dir, "phi_integral_evolution.png")) - fig_phi.savefig(os.path.join(results_dir, "phi_slice.png")) - fig_density.savefig(os.path.join(results_dir, "density_e1e2.png")) diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter.py b/profiling/examples/GuidingCenter/params_GuidingCenter.py deleted file mode 100644 index 86711c47e..000000000 --- a/profiling/examples/GuidingCenter/params_GuidingCenter.py +++ /dev/null @@ -1,187 +0,0 @@ -# ----------------------------- -# Description of the simulation -# ----------------------------- -# Please fill in a verbal description of the simulation. -# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. - -name = "GuidingCenter NumPy vs CuPy" -description = """ -Guiding-centre (5D drift-kinetic) test particles in a homogeneous slab, used as the -NumPy-vs-CuPy backend comparison case. Its whole propagator stack is CUDA-ported and it -has no FEEC field solve, so wall-clock time is dominated by the particle kernels the GPU -port targets. -""" - -import argparse -import os - -parser = argparse.ArgumentParser(description=description) -parser.add_argument( - "--backend", - choices=("numpy", "cupy"), - default="numpy", - help="Array backend to run the simulation with (default: numpy).", -) -# `--id` distinguishes runs that share a rank count but differ in something else (here: -# the array backend); the profiling driver passes its launch counter and looks for the -# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). -# Unknown flags are ignored so the driver can forward other parameters as well. -parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") -parser.add_argument("--Np", type=int, default=None, help="Number of markers (overrides the default).") -parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default).") -args, _ = parser.parse_known_args() - -# Must be set before struphy (and therefore cunumpy) is imported. -os.environ["ARRAY_BACKEND"] = args.backend - -if args.backend == "cupy": - import cunumpy - - # Under CuPy with more than one MPI rank per node (e.g. the Booster scaling case in - # profiling/submit_guidingcenter_cupy_scaling.py), every rank must bind to its own GPU - # -- cupy defaults to device 0, so without this every rank on a node would contend for - # the same GPU instead of getting one each. SLURM_LOCALID (the rank's index within its - # node) is set by srun before this process even starts, so it works without MPI being - # initialized yet. Falls back to device 0 outside SLURM (e.g. a single-GPU login node). - cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) - - # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment - # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more - # than one rank every process independently creates the same output directory/HDF5 - # dataset and the survivors deadlock in the next collective. This profiling script is - # specifically meant to run multi-rank/multi-GPU (see submit_guidingcenter_cupy_scaling.py), - # so opt back in; a single-GPU run pays only a no-op collective for it. - os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") - -import logging - -from struphy import set_logging_level - -set_logging_level(logging.WARNING) - -# ------------------ -# Import Struphy API -# ------------------ - -from struphy import ( - BaseUnits, - BoundaryParameters, - DerhamOptions, - EnvironmentOptions, - LoadingParameters, - ProfilingOptions, - SavingParameters, - Simulation, - SortingParameters, - Time, - WeightsParameters, - domains, - equils, - grids, - maxwellians, - perturbations, -) - -# --------------------- -# Instance of the model -# --------------------- -from struphy.models import GuidingCenter - -# Units -base_units = BaseUnits() - -# Model instance -model = GuidingCenter(base_units=base_units) - -# List all variables and decide whether to save their data -model.kinetic_ions.var.save_data = True - -# -------------------------- -# Instance of the simulation -# -------------------------- - -name = f"GuidingCenter ({args.backend})" - -# Environment options -env = EnvironmentOptions( - sim_folder=f"sim_{args.id:02d}", - profiling_activated=True, - save_restart=False, -) - -# Time stepping. Enough steps that the per-step particle work, not the one-off -# setup (which includes the CUDA RawKernel JIT compile), dominates the total. -time_opts = Time(dt=0.01, Tend=args.Tend if args.Tend is not None else 1.0) - -# Geometry -domain = domains.Cuboid() - -# Fluid equilibrium (can be used as part of initial conditions) -equil = equils.HomogenSlab() - -# Grid -grid = grids.TensorProductGrid(num_elements=(16, 16, 16)) - -# Derham options -derham_opts = DerhamOptions() - -# Simulation object -sim = Simulation( - model=model, - name=name, - description=description, - params_path=__file__, - env=env, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, - profiling_opts=ProfilingOptions(label=f"GC-{args.backend}"), -) - -# ------------------- -# Particle parameters -# ------------------- - -# Marker count is the knob that decides how particle-dominated the run is. -loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 200000) -weights_params = WeightsParameters() -boundary_params = BoundaryParameters() -sorting_params = SortingParameters() -saving_params = SavingParameters() -model.kinetic_ions.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, -) - -# ------------------ -# Propagator options -# ------------------ - -# algo="explicit" selects push_gc_bxEstar_explicit_multistage / -# push_gc_Bstar_explicit_multistage, both CUDA-ported. -model.propagators.push_bxe.options = model.propagators.push_bxe.Options(algo="explicit") -model.propagators.push_parallel.options = model.propagators.push_parallel.Options(algo="explicit") - -# ------------------ -# Initial conditions -# ------------------ - -# Background for kinetic species -maxwellian_1 = maxwellians.GyroMaxwellian2Dvperp(n=(1.0, None), equil=equil) -maxwellian_2 = maxwellians.GyroMaxwellian2Dvperp(n=(0.1, None), equil=equil) -background = maxwellian_1 + maxwellian_2 -model.kinetic_ions.var.add_background(background) - -# Perturbations for (some) kinetic species -perturbation = perturbations.TorusModesCos() -maxwellian_1pt = maxwellians.GyroMaxwellian2Dvperp(n=(1.0, perturbation), equil=equil) -init = maxwellian_1pt + maxwellian_2 -model.kinetic_ions.var.add_initial_condition(init) - -if __name__ == "__main__": - sim.run() diff --git a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py b/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py deleted file mode 100644 index b1cca93af..000000000 --- a/profiling/examples/GuidingCenter/params_GuidingCenter_scaling.py +++ /dev/null @@ -1,191 +0,0 @@ -# ----------------------------- -# Description of the simulation -# ----------------------------- -# Please fill in a verbal description of the simulation. -# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. - -name = "GuidingCenter CuPy multi-GPU scaling" -description = """ -Guiding-centre (5D drift-kinetic) test particles in a homogeneous slab, used as the CuPy -multi-GPU/multi-rank strong-scaling case. Np is much larger than params_GuidingCenter.py's -(50,000,000 vs 200,000) so there's enough per-rank compute between marker-exchange calls -for scaling to actually pay off, rather than being dominated by mpi_sort_markers. -""" - -import argparse -import os - -parser = argparse.ArgumentParser(description=description) -parser.add_argument( - "--backend", - choices=("numpy", "cupy"), - default="numpy", - help="Array backend to run the simulation with (default: numpy).", -) -# `--id` distinguishes runs that share a rank count but differ in something else (here: -# the array backend); the profiling driver passes its launch counter and looks for the -# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). -# Unknown flags are ignored so the driver can forward other parameters as well. -parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") -parser.add_argument("--Np", type=int, default=None, help="Number of markers (overrides the default).") -parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default).") -args, _ = parser.parse_known_args() - -# Must be set before struphy (and therefore cunumpy) is imported. -os.environ["ARRAY_BACKEND"] = args.backend - -if args.backend == "cupy": - import cunumpy - - # Under CuPy with more than one MPI rank per node, every rank must bind to its own GPU - # -- cupy defaults to device 0, so without this every rank on a node would contend for - # the same GPU instead of getting one each. SLURM_LOCALID (the rank's index within its - # node) is set by srun before this process even starts, so it works without MPI being - # initialized yet. Falls back to device 0 outside SLURM (e.g. a single-GPU login node). - cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) - - # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment - # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more - # than one rank every process independently creates the same output directory/HDF5 - # dataset and the survivors deadlock in the next collective. This file is specifically - # meant to run multi-rank/multi-GPU, so opt back in; a single-GPU run pays only a - # no-op collective for it. - os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") - -import logging - -from struphy import set_logging_level - -set_logging_level(logging.WARNING) - -# ------------------ -# Import Struphy API -# ------------------ - -from struphy import ( - BaseUnits, - BoundaryParameters, - DerhamOptions, - EnvironmentOptions, - LoadingParameters, - ProfilingOptions, - SavingParameters, - Simulation, - SortingParameters, - Time, - WeightsParameters, - domains, - equils, - grids, - maxwellians, - perturbations, -) - -# --------------------- -# Instance of the model -# --------------------- -from struphy.models import GuidingCenter - -# Units -base_units = BaseUnits() - -# Model instance -model = GuidingCenter(base_units=base_units) - -# List all variables and decide whether to save their data -model.kinetic_ions.var.save_data = True - -# -------------------------- -# Instance of the simulation -# -------------------------- - -name = f"GuidingCenter scaling ({args.backend})" - -# Environment options -env = EnvironmentOptions( - sim_folder=f"sim_{args.id:02d}", - profiling_activated=True, - save_restart=False, -) - -# Time stepping. Enough steps that the per-step particle work (and its MPI exchange), -# not the one-off setup (marker loading scales with Np, plus the CUDA RawKernel JIT -# compile), dominates the total -- see the setup-vs-loop measurement in the description. -time_opts = Time(dt=0.01, Tend=args.Tend if args.Tend is not None else 1.0) - -# Geometry -domain = domains.Cuboid() - -# Fluid equilibrium (can be used as part of initial conditions) -equil = equils.HomogenSlab() - -# Grid. Coarser than this relative to Np/rank count means the fixed-width ghost/halo -# padding around each rank's local sub-grid is a bigger fraction of its data -- see the -# description for why this was raised from (16, 16, 16). -grid = grids.TensorProductGrid(num_elements=(32, 32, 32)) - -# Derham options -derham_opts = DerhamOptions() - -# Simulation object -sim = Simulation( - model=model, - name=name, - description=description, - params_path=__file__, - env=env, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, - profiling_opts=ProfilingOptions(label=f"GC-scale-{args.backend}"), -) - -# ------------------- -# Particle parameters -# ------------------- - -# Marker count is the knob that decides how particle-dominated (vs. communication-bound) -# the run is -- see the description for why this file defaults to 50,000,000 rather than -# params_GuidingCenter.py's 200,000. -loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 50_000_000) -weights_params = WeightsParameters() -boundary_params = BoundaryParameters() -sorting_params = SortingParameters() -saving_params = SavingParameters() -model.kinetic_ions.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, -) - -# ------------------ -# Propagator options -# ------------------ - -# algo="explicit" selects push_gc_bxEstar_explicit_multistage / -# push_gc_Bstar_explicit_multistage, both CUDA-ported. -model.propagators.push_bxe.options = model.propagators.push_bxe.Options(algo="explicit") -model.propagators.push_parallel.options = model.propagators.push_parallel.Options(algo="explicit") - -# ------------------ -# Initial conditions -# ------------------ - -# Background for kinetic species -maxwellian_1 = maxwellians.GyroMaxwellian2Dvperp(n=(1.0, None), equil=equil) -maxwellian_2 = maxwellians.GyroMaxwellian2Dvperp(n=(0.1, None), equil=equil) -background = maxwellian_1 + maxwellian_2 -model.kinetic_ions.var.add_background(background) - -# Perturbations for (some) kinetic species -perturbation = perturbations.TorusModesCos() -maxwellian_1pt = maxwellians.GyroMaxwellian2Dvperp(n=(1.0, perturbation), equil=equil) -init = maxwellian_1pt + maxwellian_2 -model.kinetic_ions.var.add_initial_condition(init) - -if __name__ == "__main__": - sim.run() diff --git a/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py b/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py index 66b4037c0..94e947380 100644 --- a/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py +++ b/profiling/examples/Poisson/cube_strong_scaling/params_poisson.py @@ -12,14 +12,6 @@ """ import logging -import os - -# This is a CPU-only scaling test: pin the backend before the first `struphy` import, -# since `cunumpy` (imported transitively as soon as `struphy` is) reads ARRAY_BACKEND -# once at import time. Without this, an ARRAY_BACKEND=cupy left set in the submitting -# shell/job environment silently leaks in and the run fails or hangs instead of using -# the intended NumPy path. -os.environ["ARRAY_BACKEND"] = "numpy" from struphy import set_logging_level @@ -35,7 +27,6 @@ DerhamOptions, EnvironmentOptions, FieldsBackground, - ProfilingOptions, Simulation, Time, domains, @@ -89,12 +80,7 @@ equil = None # Grid -# 256**3 elements never finished the 1-rank end of this strong scaling sweep within the -# dcgp_fua_dbg queue's 15-minute limit (a single-core PCG solve on that many DOFs takes far -# longer). 96**3 finishes 1 rank in ~75s locally while keeping the RHS/Phi convergence -# checks below well below their asserted thresholds (32**3/64**3 undershoot the manufactured -# solution's resolution needs and fail or nearly fail those asserts further down). -grid = grids.TensorProductGrid(num_elements=(96, 96, 96), mpi_dims_mask=(True, True, True)) +grid = grids.TensorProductGrid(num_elements=(256, 256, 256), mpi_dims_mask=(True, True, True)) # Derham options derham_opts = DerhamOptions(degree=(1, 2, 3), bcs=(("dirichlet", "dirichlet"), None, None)) @@ -111,7 +97,6 @@ equil=equil, grid=grid, derham_opts=derham_opts, - profiling_opts=ProfilingOptions(label="Poisson-3D"), ) # ------------------ diff --git a/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py b/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py deleted file mode 100644 index 1ebbcb49c..000000000 --- a/profiling/examples/PressureLessSPH/params_PressureLessSPH_scaling.py +++ /dev/null @@ -1,179 +0,0 @@ -# ----------------------------- -# Description of the simulation -# ----------------------------- -# Please fill in a verbal description of the simulation. -# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. - -name = "PressureLessSPH CuPy multi-GPU scaling" -description = """ -SPH test particles in a homogeneous cube, used as a third CuPy multi-GPU/multi-rank -strong-scaling case alongside GuidingCenter and VlasovAmpereOneSpecies -- this one has no -FEEC field solve at all, the low-per-marker-compute end of the three. -""" - -import argparse -import os - -parser = argparse.ArgumentParser(description=description) -parser.add_argument( - "--backend", - choices=("numpy", "cupy"), - default="numpy", - help="Array backend to run the simulation with (default: numpy).", -) -# `--id` distinguishes runs that share a rank count but differ in something else (here: -# the array backend); the profiling driver passes its launch counter and looks for the -# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). -# Unknown flags are ignored so the driver can forward other parameters as well. -parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") -parser.add_argument("--Np", type=int, default=None, help="Number of markers (overrides the default).") -parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default).") -args, _ = parser.parse_known_args() - -# Must be set before struphy (and therefore cunumpy) is imported. -os.environ["ARRAY_BACKEND"] = args.backend - -if args.backend == "cupy": - import cunumpy - - # Under CuPy with more than one MPI rank per node, every rank must bind to its own GPU - # -- cupy defaults to device 0, so without this every rank on a node would contend for - # the same GPU instead of getting one each. SLURM_LOCALID (the rank's index within its - # node) is set by srun before this process even starts, so it works without MPI being - # initialized yet. Falls back to device 0 outside SLURM (e.g. a single-GPU login node). - cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) - - # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment - # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more - # than one rank every process independently creates the same output directory/HDF5 - # dataset and the survivors deadlock in the next collective. This file is specifically - # meant to run multi-rank/multi-GPU, so opt back in; a single-GPU run pays only a - # no-op collective for it. - os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") - -import logging - -from struphy import set_logging_level - -set_logging_level(logging.WARNING) - -# ------------------ -# Import Struphy API -# ------------------ - -from struphy import ( - BaseUnits, - BoundaryParameters, - DerhamOptions, - EnvironmentOptions, - LoadingParameters, - ProfilingOptions, - SavingParameters, - Simulation, - SortingParameters, - Time, - WeightsParameters, - domains, - equils, - grids, - perturbations, -) - -# --------------------- -# Instance of the model -# --------------------- -from struphy.models import PressureLessSPH - -# Units -base_units = BaseUnits() - -# Model instance -model = PressureLessSPH(base_units=base_units) - -# List all variables and decide whether to save their data -model.cold_fluid.var.save_data = True - -# -------------------------- -# Instance of the simulation -# -------------------------- - -name = f"PressureLessSPH scaling ({args.backend})" - -# Environment options -env = EnvironmentOptions( - sim_folder=f"sim_{args.id:02d}", - profiling_activated=True, - save_restart=False, -) - -# Time stepping. Same dt as the other two scaling cases; enough steps that the -# per-step particle work, not one-off setup, dominates the total. -time_opts = Time(dt=0.01, Tend=args.Tend if args.Tend is not None else 1.0) - -# Geometry -- same unit cube as the other two scaling cases, for comparability. -domain = domains.Cuboid() - -# Fluid equilibrium: PushVinEfield pushes against equil.p0 (see below). -equil = equils.HomogenSlab() - -# Grid -- same resolution as the other two scaling cases. -grid = grids.TensorProductGrid(num_elements=(32, 32, 32)) - -# Derham options -derham_opts = DerhamOptions() - -# Simulation object -sim = Simulation( - model=model, - name=name, - description=description, - params_path=__file__, - env=env, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, - profiling_opts=ProfilingOptions(label=f"SPH-scale-{args.backend}"), -) - -# ------------------- -# Particle parameters -# ------------------- - -# Smaller default than the other two scaling cases' 50,000,000 -- see the description -# for why (first run of this case at scale). -loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 10_000_000, seed=1234) -weights_params = WeightsParameters() -boundary_params = BoundaryParameters() -sorting_params = SortingParameters() -saving_params = SavingParameters() -model.cold_fluid.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, -) - -# ------------------ -# Propagator options -# ------------------ - -model.propagators.push_eta.options = model.propagators.push_eta.Options() -phi = equil.p0 -model.propagators.push_v.phi = phi -model.propagators.push_v.options = model.propagators.push_v.Options() - -# ------------------ -# Initial conditions -# ------------------ - -# Background for (some) sph variables -- uniform, no perturbation: this case -# measures scaling behaviour, not physical accuracy (same spirit as the other two -# scaling cases). -background = equils.ConstantVelocity() -model.cold_fluid.var.add_background(background) - -if __name__ == "__main__": - sim.run() diff --git a/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py b/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py index b8252e94c..b6dff0d7b 100644 --- a/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py +++ b/profiling/examples/ToyGyrokinetic/diocotron_instability/params_diocotron.py @@ -42,7 +42,6 @@ FieldsBackground, KernelDensityPlot, LoadingParameters, - ProfilingOptions, SavingParameters, Simulation, SortingParameters, @@ -119,7 +118,6 @@ equil=equil, grid=grid, derham_opts=derham_opts, - profiling_opts=ProfilingOptions(label="Diocotron-2D"), ) # ------------------- diff --git a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py b/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py deleted file mode 100644 index 38daffb18..000000000 --- a/profiling/examples/VlasovAmpereOneSpecies/params_VlasovAmpere_scaling.py +++ /dev/null @@ -1,179 +0,0 @@ -# ----------------------------- -# Description of the simulation -# ----------------------------- -# Please fill in a verbal description of the simulation. -# It will be printed at the beginning of the simulation and can be used to keep track of the different runs. - -name = "VlasovAmpereOneSpecies CuPy multi-GPU scaling" -description = """ -6D full-orbit Vlasov-Ampere test particles in a homogeneous cube, used as a second CuPy -multi-GPU/multi-rank strong-scaling case alongside GuidingCenter -- deliberately not -dominated by mpi_sort_markers, since VlasovAmpereCoupling solves a real linear system -each step on top of the particle push. -""" - -import argparse -import os - -parser = argparse.ArgumentParser(description=description) -parser.add_argument( - "--backend", - choices=("numpy", "cupy"), - default="numpy", - help="Array backend to run the simulation with (default: numpy).", -) -# `--id` distinguishes runs that share a rank count but differ in something else (here: -# the array backend); the profiling driver passes its launch counter and looks for the -# output under `sim_` (see `ProfilingCase.build_commands` / `package_run`). -# Unknown flags are ignored so the driver can forward other parameters as well. -parser.add_argument("--id", type=int, default=0, help="Run id, used to name the output folder.") -parser.add_argument("--Np", type=int, default=None, help="Number of markers (overrides the default).") -parser.add_argument("--Tend", type=float, default=None, help="End time (overrides the default).") -args, _ = parser.parse_known_args() - -# Must be set before struphy (and therefore cunumpy) is imported. -os.environ["ARRAY_BACKEND"] = args.backend - -if args.backend == "cupy": - import cunumpy - - # Under CuPy with more than one MPI rank per node, every rank must bind to its own GPU - # -- cupy defaults to device 0, so without this every rank on a node would contend for - # the same GPU instead of getting one each. SLURM_LOCALID (the rank's index within its - # node) is set by srun before this process even starts, so it works without MPI being - # initialized yet. Falls back to device 0 outside SLURM (e.g. a single-GPU login node). - cunumpy.set_device(int(os.environ.get("SLURM_LOCALID", 0))) - - # feectools.ddm.mpi disables MPI by default on the CuPy backend (see the comment - # there): every rank falls back to a MockComm reporting rank 0/size 1, so with more - # than one rank every process independently creates the same output directory/HDF5 - # dataset and the survivors deadlock in the next collective. This file is specifically - # meant to run multi-rank/multi-GPU, so opt back in; a single-GPU run pays only a - # no-op collective for it. - os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") - -import logging - -from struphy import set_logging_level - -set_logging_level(logging.WARNING) - -# ------------------ -# Import Struphy API -# ------------------ - -from struphy import ( - BaseUnits, - BoundaryParameters, - DerhamOptions, - EnvironmentOptions, - LoadingParameters, - ProfilingOptions, - SavingParameters, - Simulation, - SortingParameters, - Time, - WeightsParameters, - domains, - grids, - maxwellians, -) - -# --------------------- -# Instance of the model -# --------------------- -from struphy.models import VlasovAmpereOneSpecies - -# Units -base_units = BaseUnits() - -# Model instance. with_B0=False: electrostatic only (no PushVxB), keeping the -# propagator count close to GuidingCenter's 2-propagator-plus-coupling shape -- see -# the description. -model = VlasovAmpereOneSpecies(base_units=base_units, alpha=1.0, epsilon=-1.0, with_B0=False) - -# List all variables and decide whether to save their data -model.em_fields.e_field.save_data = True -model.kinetic_ions.var.save_data = True - -# -------------------------- -# Instance of the simulation -# -------------------------- - -name = f"VlasovAmpereOneSpecies scaling ({args.backend})" - -# Environment options -env = EnvironmentOptions( - sim_folder=f"sim_{args.id:02d}", - save_restart=False, -) - -# Time stepping. Same dt as params_GuidingCenter_scaling.py; enough steps that the -# per-step particle/field work, not one-off setup, dominates the total. -time_opts = Time(dt=0.01, Tend=args.Tend if args.Tend is not None else 1.0) - -# Geometry -- same unit cube as params_GuidingCenter_scaling.py, for comparability. -domain = domains.Cuboid() - -# No fluid equilibrium: with_B0=False needs no background B-field. -equil = None - -# Grid -- same resolution as params_GuidingCenter_scaling.py. -grid = grids.TensorProductGrid(num_elements=(32, 32, 32)) - -# Derham options -derham_opts = DerhamOptions() - -# Simulation object -sim = Simulation( - model=model, - name=name, - description=description, - params_path=__file__, - env=env, - time_opts=time_opts, - domain=domain, - equil=equil, - grid=grid, - derham_opts=derham_opts, - profiling_opts=ProfilingOptions(label=f"VA-scale-{args.backend}"), -) - -# ------------------- -# Particle parameters -# ------------------- - -# Same default as params_GuidingCenter_scaling.py's 50,000,000, for comparability. -loading_params = LoadingParameters(Np=args.Np if args.Np is not None else 50_000_000, spatial="uniform") -weights_params = WeightsParameters() -boundary_params = BoundaryParameters() -sorting_params = SortingParameters() -saving_params = SavingParameters() -model.kinetic_ions.set_markers( - loading_params=loading_params, - weights_params=weights_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - saving_params=saving_params, -) - -# ------------------ -# Propagator options -# ------------------ - -model.propagators.push_eta.options = model.propagators.push_eta.Options() -model.propagators.coupling_va.options = model.propagators.coupling_va.Options() -model.initial_poisson.options = model.initial_poisson.Options(stab_mat="M0") - -# ------------------ -# Initial conditions -# ------------------ - -# Background for kinetic species -- uniform, no perturbation: this case measures -# scaling behaviour, not physical accuracy (same spirit as GuidingCenter's -# homogeneous-slab scaling case). -background = maxwellians.Maxwellian3D(n=(1.0, None)) -model.kinetic_ions.var.add_background(background) - -if __name__ == "__main__": - sim.run(profiling_activated=True) diff --git a/profiling/profiling_job.py b/profiling/profiling_job.py index 10a7293db..8b9b97f5e 100644 --- a/profiling/profiling_job.py +++ b/profiling/profiling_job.py @@ -184,12 +184,7 @@ def build_commands(self, ntasks: int, param_flags: list[str] | None = None) -> l f'echo "Running {self.label} with {ntasks} MPI ranks"', f'cd "{output_root}"', # The run's log lives next to its output, so each run keeps its own record - # instead of sharing the driver's terminal or the SLURM log. STRUPHY_LOG_FILE - # is needed on top of the shared cwd above: several rank-count runs of the same - # case share `output_root` as their cwd (often concurrently, as separate SLURM - # jobs), and struphy's default relative "struphy.log" would otherwise resolve to - # the same file for all of them, racing on log rotation across processes. - f'STRUPHY_LOG_FILE="{sim_dir / "struphy.log"}" ' + # instead of sharing the driver's terminal or the SLURM log. f'{self.launcher} -n {ntasks} {python} {self.params_source} {flags} > "{sim_dir / "struphy.out"}" 2>&1', "", 'echo "----------------------------------------"', diff --git a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py b/profiling/submit_driftkinetic_cyclone_cupy_scaling.py deleted file mode 100644 index e0124ff61..000000000 --- a/profiling/submit_driftkinetic_cyclone_cupy_scaling.py +++ /dev/null @@ -1,104 +0,0 @@ -"""DriftKineticElectrostaticAdiabatic (ITG cyclone) CuPy multi-GPU/multi-rank scaling case. - -Strong-scaling study (not a backend comparison, see `submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py` -for that): the same grid/marker configuration runs under `ARRAY_BACKEND=cupy` at -increasing MPI rank counts, one rank per GPU. `--ranks 1 2 4 8` (default) covers -intra-node scaling plus one cross-node step. Grid is hardcoded to `NUM_ELEMENTS` below -(not a CLI flag), so a run's grid is always readable straight from this file. -""" - -import argparse -from pathlib import Path - -from clusters import SLURM_PRESETS, detect_machine_name -from profiling_job import ProfilingCase - -# The preset is looked up by cluster name inside `launch`, so the dict is keyed by the -# *detected* name here rather than by the preset's own name: on Pitagora detection -# always returns "pitagora_dcgp" (it cannot tell the Booster partition apart), and this -# case must still get the Booster preset. Keying on the detected name also keeps this -# working, without a KeyError, on a machine detection does not recognise (name None). -GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] - -# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are -# spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank -# binding in params_cyclone.py. -GPUS_PER_NODE = 4 - -# Grid resolution, matching params_cyclone.py's own default. -NUM_ELEMENTS = (16, 64, 4) - -# MPI rank counts to run with, one GPU per rank -- 1/2/4 intra-node, 8 = 2 Booster nodes. -RANKS = [1, 2, 4, 8] - -# End time: 0.003 -> 3 steps, shortened from params_cyclone.py's own 0.01/10 steps to -# keep the pcg-forced sweep quick. -TEND = 0.003 - - -def main() -> None: - - # Parse arguments, do not remove --upload - parser = argparse.ArgumentParser( - description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), - ) - parser.add_argument( - "--upload", - action="store_true", - help="Upload the packaged profiling results to the profiling-data repo.", - ) - args = parser.parse_args() - - # Paths relative to this script's location, so it can be run from anywhere. - script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "DriftKineticElectrostaticAdiabatic" - params_source = params_dir / "params_cyclone.py" - - profiling_case = ProfilingCase( - label="driftkinetic_cyclone_cupy_scaling", - name="ITG cyclone: CuPy scaling", - description=( - "Cyclone-instability ITG turbulence (DriftKineticElectrostaticAdiabatic) on " - "CuPy, strong-scaled across GPUs with the PCG field solver." - ), - physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", - struphy_model_used="DriftKineticElectrostaticAdiabatic", - params_source=params_source, - language="fortran", - compiler="GNU", - upload=args.upload, - ) - - # The preset is looked up by cluster name inside `launch`, so build a one-entry dict - # under whatever name detection reports for this machine. - cluster_name = detect_machine_name() - - param_flags = [ - "--backend", - "cupy", - "--solver", - "pcg", - "--Tend", - str(TEND), - "--num-elements", - *[str(n) for n in NUM_ELEMENTS], - ] - - # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would - # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs - # far more ranks per node than there are GPUs. - for num_tasks in RANKS: - num_nodes = -(-num_tasks // GPUS_PER_NODE) - profiling_case.launch( - num_tasks, - num_nodes=num_nodes, - param_flags=param_flags, - slurm_presets={cluster_name: GPU_PRESET}, - ) - - # Package and push each run as its own job finishes. - profiling_case.finalize_run() - - -if __name__ == "__main__": - main() diff --git a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py b/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py deleted file mode 100644 index f13a4d067..000000000 --- a/profiling/submit_driftkinetic_cyclone_numpy_vs_cupy_pcg.py +++ /dev/null @@ -1,80 +0,0 @@ -"""DriftKineticElectrostaticAdiabatic (ITG cyclone) NumPy-vs-CuPy, PCG solver. - -Runs `params_cyclone.py` once with `ARRAY_BACKEND=numpy` and once with -`ARRAY_BACKEND=cupy`, using the PCG field solver. -""" - -import argparse -from pathlib import Path - -from clusters import SLURM_PRESETS, detect_machine_name -from profiling_job import ProfilingCase - -# The preset is looked up by cluster name inside `launch`, so both dicts below are keyed -# by the *detected* name rather than by the preset's own name: on Pitagora detection -# always returns "pitagora_dcgp" for both partitions (it cannot tell the Booster -# partition apart), and the GPU run must still get the Booster preset. Keying on the -# detected name also keeps this working, without a KeyError, on a machine detection does -# not recognise (name None). -CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] -GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] - -BACKEND_PRESETS = { - "numpy": CPU_PRESET, - "cupy": GPU_PRESET, -} - - -def main() -> None: - - # Parse arguments, do not remove --upload - parser = argparse.ArgumentParser( - description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), - ) - parser.add_argument( - "--upload", - action="store_true", - help="Upload the packaged profiling results to the profiling-data repo.", - ) - args = parser.parse_args() - - # Paths relative to this script's location, so it can be run from anywhere. - script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "DriftKineticElectrostaticAdiabatic" - params_source = params_dir / "params_cyclone.py" - - profiling_case = ProfilingCase( - label="driftkinetic_cyclone_numpy_vs_cupy_pcg", - name="ITG cyclone: NumPy vs CuPy (pcg)", - description=( - "Cyclone-instability ITG turbulence (DriftKineticElectrostaticAdiabatic), run " - "once on NumPy and once on CuPy, with the naive iterative field solver." - ), - physics_problem="Electrostatic drift-kinetic ITG turbulence with adiabatic electrons in toroidal geometry.", - struphy_model_used="DriftKineticElectrostaticAdiabatic", - params_source=params_source, - language="fortran", - compiler="GNU", - upload=args.upload, - ) - - # The preset is looked up by cluster name inside `launch`, so build a one-entry dict - # under whatever name detection reports for this machine. - cluster_name = detect_machine_name() - - # Launch one run per backend, one rank each -- this case is a backend comparison, not - # a scaling study (see submit_guidingcenter_cupy_scaling.py for that pattern). - for backend, preset in BACKEND_PRESETS.items(): - profiling_case.launch( - 1, - num_nodes=1, - param_flags=["--backend", backend, "--solver", "pcg"], - slurm_presets={cluster_name: preset}, - ) - - # Package and push each run as its own job finishes. - profiling_case.finalize_run() - - -if __name__ == "__main__": - main() diff --git a/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py b/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py deleted file mode 100644 index 8008b748b..000000000 --- a/profiling/submit_guidingcenter_cpu_node_vs_gpu_node.py +++ /dev/null @@ -1,90 +0,0 @@ -"""Guiding-centre full-node CPU-vs-GPU comparison. - -Runs `params_GuidingCenter_scaling.py` once across every core of one CPU node -(`ARRAY_BACKEND=numpy`) and once across every GPU of one GPU node (`ARRAY_BACKEND=cupy`), -same total marker count on both sides -- the realistic "which node do I use" comparison, -as opposed to `submit_guidingcenter_numpy_vs_cupy.py` (single-rank backend comparison) or -`submit_guidingcenter_cupy_scaling.py` (CuPy-only rank scaling). -""" - -import argparse -from pathlib import Path - -from clusters import HARDWARE_INFO, SLURM_PRESETS, detect_machine_name -from profiling_job import ProfilingCase - -# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name -# (`detect_machine_name`), so both dicts below are keyed by the *detected* name rather -# than by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" -# for both partitions (it cannot tell the Booster partition apart), and the GPU run must -# still get the Booster preset. Keying on the detected name also keeps this working, -# without a KeyError, on a machine detection does not recognise (name None). -CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] -GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] - -# One CPU-node's worth of ranks (`HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"]`), and -# one GPU-node's worth (the Booster preset requests `gres=gpu:4`), one rank per GPU as in -# `params_GuidingCenter_scaling.py`'s `SLURM_LOCALID` binding. -CPU_RANKS_PER_NODE = HARDWARE_INFO["pitagora_dcgp"]["cpus_per_node"] -GPU_RANKS_PER_NODE = 4 - - -def main() -> None: - - # Parse arguments, do not remove --upload - parser = argparse.ArgumentParser( - description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), - ) - parser.add_argument( - "--upload", - action="store_true", - help="Upload the packaged profiling results to the profiling-data repo.", - ) - args = parser.parse_args() - - # Paths relative to this script's location, so it can be run from anywhere. - script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "GuidingCenter" - params_source = params_dir / "params_GuidingCenter_scaling.py" - - profiling_case = ProfilingCase( - label="guidingcenter_cpu_node_vs_gpu_node", - name="GuidingCenter: CPU node vs GPU node", - description=( - "GuidingCenter particles on a cube, run across one full CPU node (NumPy) vs " - "one full GPU node (CuPy) at the same marker count." - ), - physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", - struphy_model_used="GuidingCenter", - params_source=params_source, - language="fortran", - compiler="GNU", - upload=args.upload, - ) - - # The preset is looked up by cluster name inside `launch`, so build a one-entry dict - # under whatever name detection reports for this machine. - cluster_name = detect_machine_name() - - # One full CPU node, NumPy backend. - profiling_case.launch( - CPU_RANKS_PER_NODE, - num_nodes=1, - param_flags=["--backend", "numpy"], - slurm_presets={cluster_name: CPU_PRESET}, - ) - - # One full GPU node, CuPy backend, one rank per GPU. - profiling_case.launch( - GPU_RANKS_PER_NODE, - num_nodes=1, - param_flags=["--backend", "cupy"], - slurm_presets={cluster_name: GPU_PRESET}, - ) - - # Package and push each run as its own job finishes. - profiling_case.finalize_run() - - -if __name__ == "__main__": - main() diff --git a/profiling/submit_guidingcenter_cupy_scaling.py b/profiling/submit_guidingcenter_cupy_scaling.py deleted file mode 100644 index 8e3928f1e..000000000 --- a/profiling/submit_guidingcenter_cupy_scaling.py +++ /dev/null @@ -1,88 +0,0 @@ -"""Guiding-centre CuPy multi-GPU/multi-rank scaling case. - -Strong-scaling study (not a backend comparison, see `submit_guidingcenter_numpy_vs_cupy.py` -for that): the same total marker count runs under `ARRAY_BACKEND=cupy` at increasing MPI -rank counts, one rank per GPU, to measure whether more rank+GPU pairs actually speed up a -fixed-size problem. `RANKS = [2, 4, 8]` below covers intra-node scaling plus one -cross-node step (8 = 2 Booster nodes x 4 GPUs). Uses `params_GuidingCenter_scaling.py`'s -much larger default `Np` (not `params_GuidingCenter.py`'s) since a small per-rank marker -count makes MPI exchange cost dominate and scaling look worse than it is. -""" - -import argparse -from pathlib import Path - -from clusters import SLURM_PRESETS, detect_machine_name -from profiling_job import ProfilingCase - -# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name -# (`detect_machine_name`), so the dict is keyed by the *detected* name here rather than -# by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" (it -# cannot tell the Booster partition apart), and this case must still get the Booster -# preset. Keying on the detected name also keeps this working, without a KeyError, on a -# machine detection does not recognise (name None). -GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] - -# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are -# spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank -# binding in params_GuidingCenter_scaling.py. -GPUS_PER_NODE = 4 - -# MPI rank counts to run with, one GPU per rank -- 2/4 intra-node, 8 = 2 Booster nodes. -RANKS = [2, 4, 8] - - -def main() -> None: - - # Parse arguments, do not remove --upload - parser = argparse.ArgumentParser( - description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), - ) - parser.add_argument( - "--upload", - action="store_true", - help="Upload the packaged profiling results to the profiling-data repo.", - ) - args = parser.parse_args() - - # Paths relative to this script's location, so it can be run from anywhere. - script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "GuidingCenter" - params_source = params_dir / "params_GuidingCenter_scaling.py" - - profiling_case = ProfilingCase( - label="guidingcenter_cupy_scaling", - name="GuidingCenter: CuPy scaling", - description="GuidingCenter particles (Np=50,000,000) on CuPy, strong-scaled across 1-8 GPUs.", - physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", - struphy_model_used="GuidingCenter", - params_source=params_source, - language="fortran", - compiler="GNU", - upload=args.upload, - ) - - # The preset is looked up by cluster name inside `launch`, so build a one-entry dict - # under whatever name detection reports for this machine. - cluster_name = detect_machine_name() - - param_flags = ["--backend", "cupy"] - - # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would - # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs - # far more ranks per node than there are GPUs. - for num_tasks in RANKS: - num_nodes = -(-num_tasks // GPUS_PER_NODE) - profiling_case.launch( - num_tasks, - num_nodes=num_nodes, - param_flags=param_flags, - slurm_presets={cluster_name: GPU_PRESET}, - ) - - # Package and push each run as its own job finishes. - profiling_case.finalize_run() - - -if __name__ == "__main__": - main() diff --git a/profiling/submit_guidingcenter_numpy_vs_cupy.py b/profiling/submit_guidingcenter_numpy_vs_cupy.py deleted file mode 100644 index e262d62ed..000000000 --- a/profiling/submit_guidingcenter_numpy_vs_cupy.py +++ /dev/null @@ -1,94 +0,0 @@ -"""Guiding-centre NumPy-vs-CuPy profiling case. - -Runs `params_GuidingCenter.py` once with `ARRAY_BACKEND=numpy` and once with -`ARRAY_BACKEND=cupy` so the two can be compared directly. `GuidingCenter` has no FEEC -field solve, so wall-clock time is dominated by the CUDA-ported particle kernels. -""" - -import argparse -from pathlib import Path - -from clusters import SLURM_PRESETS, detect_machine_name -from profiling_job import ProfilingCase - -# Which SLURM preset each backend runs under. `ProfilingCase.launch` picks a preset from -# the dict it is given by cluster name (`detect_machine_name`), so the dict is keyed by -# the *detected* name here rather than by the preset's own name: on Pitagora detection -# always returns "pitagora_dcgp" (it cannot tell the Booster partition apart), and the -# GPU run must still get the Booster preset. Keying on the detected name also keeps this -# working, without a KeyError, on a machine detection does not recognise (name None). -CPU_PRESET = SLURM_PRESETS["pitagora_dcgp"] -GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] - -BACKEND_PRESETS = { - "numpy": CPU_PRESET, - "cupy": GPU_PRESET, -} - -# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). The CuPy -# runs are spread so that no node holds more ranks than it has GPUs. -GPUS_PER_NODE = 4 - -# MPI rank count to run each backend with. This params file selects no GPU per rank, so -# more than one rank per node would share device 0 -- keep at 1 unless that's fixed. -RANKS = 1 - - -def main() -> None: - - # Parse arguments, do not remove --upload - parser = argparse.ArgumentParser( - description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), - ) - parser.add_argument( - "--upload", - action="store_true", - help="Upload the packaged profiling results to the profiling-data repo.", - ) - args = parser.parse_args() - - # Paths relative to this script's location, so it can be run from anywhere. - script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "GuidingCenter" - params_source = params_dir / "params_GuidingCenter.py" - - profiling_case = ProfilingCase( - label="guidingcenter_numpy_vs_cupy", - name="GuidingCenter: NumPy vs CuPy", - description="GuidingCenter particles on a cube, run once on NumPy and once on CuPy.", - physics_problem="Guiding-centre drift-kinetic particle motion; the particle-push hot loop common to all PIC/drift-kinetic models.", - struphy_model_used="GuidingCenter", - params_source=params_source, - language="fortran", - compiler="GNU", - upload=args.upload, - ) - - # The preset is looked up by cluster name inside `launch`, so build a one-entry dict - # under whatever name detection reports for this machine. - cluster_name = detect_machine_name() - - # Launch one run per backend. - for backend, preset in BACKEND_PRESETS.items(): - if backend == "cupy": - # One node per `GPUS_PER_NODE` ranks. `launch` would otherwise derive the - # node count from `cpus_per_node`, which on a GPU partition packs far more - # ranks per node than there are GPUs. - num_nodes = -(-RANKS // GPUS_PER_NODE) - else: - # Let `launch` derive the node count from the cluster's CPU count. - num_nodes = None - - profiling_case.launch( - RANKS, - num_nodes=num_nodes, - param_flags=["--backend", backend], - slurm_presets={cluster_name: preset}, - ) - - # Package and push each run as its own job finishes. - profiling_case.finalize_run() - - -if __name__ == "__main__": - main() diff --git a/profiling/submit_pressurelesssph_cupy_scaling.py b/profiling/submit_pressurelesssph_cupy_scaling.py deleted file mode 100644 index 726996f23..000000000 --- a/profiling/submit_pressurelesssph_cupy_scaling.py +++ /dev/null @@ -1,86 +0,0 @@ -"""PressureLessSPH CuPy multi-GPU/multi-rank scaling case. - -Companion to submit_guidingcenter_cupy_scaling.py and submit_vlasovampere_cupy_scaling.py: -a model with no FEEC field solve at all, the low-per-marker-compute end of the three. -Same total marker count, run with `ARRAY_BACKEND=cupy` at increasing MPI rank counts -(one rank per GPU); `RANKS = [2, 4, 8]` below covers intra-node scaling plus one -cross-node step. -""" - -import argparse -from pathlib import Path - -from clusters import SLURM_PRESETS, detect_machine_name -from profiling_job import ProfilingCase - -# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name -# (`detect_machine_name`), so the dict is keyed by the *detected* name here rather than -# by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" (it -# cannot tell the Booster partition apart), and this case must still get the Booster -# preset. Keying on the detected name also keeps this working, without a KeyError, on a -# machine detection does not recognise (name None). -GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] - -# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are -# spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank -# binding in params_PressureLessSPH_scaling.py. -GPUS_PER_NODE = 4 - -# MPI rank counts to run with, one GPU per rank -- 2/4 intra-node, 8 = 2 Booster nodes. -RANKS = [2, 4, 8] - - -def main() -> None: - - # Parse arguments, do not remove --upload - parser = argparse.ArgumentParser( - description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), - ) - parser.add_argument( - "--upload", - action="store_true", - help="Upload the packaged profiling results to the profiling-data repo.", - ) - args = parser.parse_args() - - # Paths relative to this script's location, so it can be run from anywhere. - script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "PressureLessSPH" - params_source = params_dir / "params_PressureLessSPH_scaling.py" - - profiling_case = ProfilingCase( - label="pressurelesssph_cupy_scaling", - name="PressureLessSPH: CuPy scaling", - description="PressureLessSPH particles (Np=10,000,000) on CuPy, strong-scaled across GPUs. No FEEC field solve.", - physics_problem="SPH-discretized pressureless Euler flow with external forcing; a position push plus a velocity push against a background force field, no field solve.", - struphy_model_used="PressureLessSPH", - params_source=params_source, - language="fortran", - compiler="GNU", - upload=args.upload, - ) - - # The preset is looked up by cluster name inside `launch`, so build a one-entry dict - # under whatever name detection reports for this machine. - cluster_name = detect_machine_name() - - param_flags = ["--backend", "cupy"] - - # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would - # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs - # far more ranks per node than there are GPUs. - for num_tasks in RANKS: - num_nodes = -(-num_tasks // GPUS_PER_NODE) - profiling_case.launch( - num_tasks, - num_nodes=num_nodes, - param_flags=param_flags, - slurm_presets={cluster_name: GPU_PRESET}, - ) - - # Package and push each run as its own job finishes. - profiling_case.finalize_run() - - -if __name__ == "__main__": - main() diff --git a/profiling/submit_vlasovampere_cupy_scaling.py b/profiling/submit_vlasovampere_cupy_scaling.py deleted file mode 100644 index 1c97e6e42..000000000 --- a/profiling/submit_vlasovampere_cupy_scaling.py +++ /dev/null @@ -1,86 +0,0 @@ -"""VlasovAmpereOneSpecies CuPy multi-GPU/multi-rank scaling case. - -Companion to submit_guidingcenter_cupy_scaling.py, using a model with a real per-step -field solve (VlasovAmpereCoupling) instead of a pure particle push, so its scaling -behaviour isn't dominated by mpi_sort_markers the way GuidingCenter's is. Same total -marker count, run with `ARRAY_BACKEND=cupy` at increasing MPI rank counts (one rank per -GPU); `RANKS = [2, 4, 8]` below covers intra-node scaling plus one cross-node step. -""" - -import argparse -from pathlib import Path - -from clusters import SLURM_PRESETS, detect_machine_name -from profiling_job import ProfilingCase - -# `ProfilingCase.launch` picks a preset from the dict it is given by cluster name -# (`detect_machine_name`), so the dict is keyed by the *detected* name here rather than -# by the preset's own name: on Pitagora detection always returns "pitagora_dcgp" (it -# cannot tell the Booster partition apart), and this case must still get the Booster -# preset. Keying on the detected name also keeps this working, without a KeyError, on a -# machine detection does not recognise (name None). -GPU_PRESET = SLURM_PRESETS["pitagora_boost_fua_dbg"] - -# GPUs per node on the Booster partition (the preset requests `gres=gpu:4`). Runs are -# spread so that no node holds more ranks than it has GPUs, matching the one-GPU-per-rank -# binding in params_VlasovAmpere_scaling.py. -GPUS_PER_NODE = 4 - -# MPI rank counts to run with, one GPU per rank -- 2/4 intra-node, 8 = 2 Booster nodes. -RANKS = [2, 4, 8] - - -def main() -> None: - - # Parse arguments, do not remove --upload - parser = argparse.ArgumentParser( - description=("Submit profiling jobs to a SLURM cluster and package the results for upload."), - ) - parser.add_argument( - "--upload", - action="store_true", - help="Upload the packaged profiling results to the profiling-data repo.", - ) - args = parser.parse_args() - - # Paths relative to this script's location, so it can be run from anywhere. - script_dir = Path(__file__).resolve().parent - params_dir = script_dir / "examples" / "VlasovAmpereOneSpecies" - params_source = params_dir / "params_VlasovAmpere_scaling.py" - - profiling_case = ProfilingCase( - label="vlasovampere_cupy_scaling", - name="VlasovAmpere: CuPy scaling", - description="VlasovAmpereOneSpecies particles (Np=50,000,000) on CuPy, strong-scaled across GPUs. Has a real per-step field solve.", - physics_problem="6D full-orbit Vlasov-Ampere particle motion with a self-consistent electric field, solved via VlasovAmpereCoupling's SchurSolver each step.", - struphy_model_used="VlasovAmpereOneSpecies", - params_source=params_source, - language="fortran", - compiler="GNU", - upload=args.upload, - ) - - # The preset is looked up by cluster name inside `launch`, so build a one-entry dict - # under whatever name detection reports for this machine. - cluster_name = detect_machine_name() - - param_flags = ["--backend", "cupy"] - - # Launch one run per rank count. One node per GPUS_PER_NODE ranks -- `launch` would - # otherwise derive the node count from `cpus_per_node`, which on a GPU partition packs - # far more ranks per node than there are GPUs. - for num_tasks in RANKS: - num_nodes = -(-num_tasks // GPUS_PER_NODE) - profiling_case.launch( - num_tasks, - num_nodes=num_nodes, - param_flags=param_flags, - slurm_presets={cluster_name: GPU_PRESET}, - ) - - # Package and push each run as its own job finishes. - profiling_case.finalize_run() - - -if __name__ == "__main__": - main() diff --git a/src/struphy/bsplines/tests/test_bsplines_kers.py b/src/struphy/bsplines/tests/test_bsplines_kers.py index dc62da61c..0ee49978b 100644 --- a/src/struphy/bsplines/tests/test_bsplines_kers.py +++ b/src/struphy/bsplines/tests/test_bsplines_kers.py @@ -2,9 +2,7 @@ import time import cunumpy as xp -import numpy as np import pytest -from cunumpy.xp import to_numpy from feectools.ddm.mpi import mpi as MPI logger = logging.getLogger("struphy") @@ -42,16 +40,16 @@ def test_bsplines_span_and_basis(num_elements, degree, bcs): derham_opts = DerhamOptions(degree=degree, bcs=bcs) derham = Derham(grid, derham_opts, comm=comm) - # knot vectors (bsplines_kernels/bsplines_kernels_p are Pyccel-compiled and only accept numpy.ndarray) - tn1, tn2, tn3 = (to_numpy(t) for t in derham.V0fem.knots) - td1, td2, td3 = (to_numpy(t) for t in derham.V3fem.knots) + # knot vectors + tn1, tn2, tn3 = derham.V0fem.knots + td1, td2, td3 = derham.V3fem.knots # Random points in domain of process n_pts = 100 dom = derham.domain_array[rank] - eta1s = to_numpy(xp.random.rand(n_pts) * (dom[1] - dom[0]) + dom[0]) - eta2s = to_numpy(xp.random.rand(n_pts) * (dom[4] - dom[3]) + dom[3]) - eta3s = to_numpy(xp.random.rand(n_pts) * (dom[7] - dom[6]) + dom[6]) + eta1s = xp.random.rand(n_pts) * (dom[1] - dom[0]) + dom[0] + eta2s = xp.random.rand(n_pts) * (dom[4] - dom[3]) + dom[3] + eta3s = xp.random.rand(n_pts) * (dom[7] - dom[6]) + dom[6] # struphy find_span t0 = time.time() @@ -79,14 +77,14 @@ def test_bsplines_span_and_basis(num_elements, degree, bcs): assert xp.allclose(span2s, span2s_psy) assert xp.allclose(span3s, span3s_psy) - # allocate tmps (passed to raw Pyccel kernels below, which only accept numpy.ndarray) - bn1 = np.empty(derham.degree[0] + 1, dtype=float) - bn2 = np.empty(derham.degree[1] + 1, dtype=float) - bn3 = np.empty(derham.degree[2] + 1, dtype=float) + # allocate tmps + bn1 = xp.empty(derham.degree[0] + 1, dtype=float) + bn2 = xp.empty(derham.degree[1] + 1, dtype=float) + bn3 = xp.empty(derham.degree[2] + 1, dtype=float) - bd1 = np.empty(derham.degree[0], dtype=float) - bd2 = np.empty(derham.degree[1], dtype=float) - bd3 = np.empty(derham.degree[2], dtype=float) + bd1 = xp.empty(derham.degree[0], dtype=float) + bd2 = xp.empty(derham.degree[1], dtype=float) + bd3 = xp.empty(derham.degree[2], dtype=float) # struphy b_splines_slim val1s, val2s, val3s = [], [], [] diff --git a/src/struphy/feec/basis_projection_kernels_cuda.py b/src/struphy/feec/basis_projection_kernels_cuda.py deleted file mode 100644 index ba25fe941..000000000 --- a/src/struphy/feec/basis_projection_kernels_cuda.py +++ /dev/null @@ -1,59 +0,0 @@ -"""CUDA kernels for dynamic weighted basis-projection matrices.""" - -from struphy.cuda import CudaKernel, launch_1d, load_cuda_source - -_ASSEMBLE_SRC = load_cuda_source(__file__, "basis_projection_kernels_cuda/_assemble_src.cu") - -_kernel = CudaKernel(_ASSEMBLE_SRC, "assemble_weighted_basis_3d_cuda") - - -def assemble_dofs_for_weighted_basisfuns_3d_gpu( - mat, - starts_in, - ends_in, - pads_in, - starts_out, - ends_out, - pads_out, - fun, - weights, - spans, - bases, - subs, - dims_in, - dims_out, - degrees_out, -): - import cupy as cp - import numpy as np - - spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) - weights = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in weights) - bases = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases) - rows = tuple(cp.arange(len(x), dtype=cp.int64) - cp.cumsum(cp.asarray(x, dtype=cp.int64)) for x in subs) - fun = cp.ascontiguousarray(fun) - mat.fill(0.0) - ni = tuple(x.shape[0] for x in spans) - nq = tuple(x.shape[1] for x in spans) - degree = tuple(x.shape[2] - 1 for x in bases) - total = int(np.prod(ni) * np.prod(nq) * np.prod([p + 1 for p in degree])) - launch_1d( - _kernel, - total, - ( - *rows, - *spans, - *weights, - *bases, - fun, - *(np.int32(x) for x in (*ni, *nq, *degree)), - *(np.int32(x) for x in starts_out), - *(np.int32(x) for x in pads_in), - *(np.int32(x) for x in pads_out), - *(np.int32(x) for x in dims_in), - *(np.int32(x) for x in dims_out), - *(np.int32(x) for x in degrees_out), - mat, - *(np.int32(x) for x in mat.shape[1:]), - ), - ) diff --git a/src/struphy/feec/basis_projection_ops.py b/src/struphy/feec/basis_projection_ops.py index 626c1cefa..cf843751e 100644 --- a/src/struphy/feec/basis_projection_ops.py +++ b/src/struphy/feec/basis_projection_ops.py @@ -10,7 +10,6 @@ from feectools.linalg.basic import IdentityOperator, LinearOperator, Vector from feectools.linalg.block import BlockLinearOperator, BlockVector, BlockVectorSpace from feectools.linalg.stencil import StencilMatrix, StencilVector, StencilVectorSpace -from scope_profiler import ProfileManager from struphy.feec import basis_projection_kernels from struphy.feec.linear_operators import BoundaryOperator, LinOpWithTransp @@ -1740,8 +1739,7 @@ def __init__( P.space.coeff_space, ) - with ProfileManager.profile_region("basis projection weights: assemble"): - self._dof_mat = self.assemble() + self._dof_mat = self.assemble() # ======================================================== # build composed linear operator BP * P * DOF * EV^T * BV^T or transposed @@ -1860,20 +1858,14 @@ def dot(self, v, out=None, tol=1e-14, maxiter=1000): if self.transposed: # 1. apply inverse transposed inter-/histopolation matrix, 2. apply transposed dof operator - with ProfileManager.profile_region("basis projection: interpolation solve"): - self._P.solve(v, True, apply_bc=True, out=self._tmp_dom, x0=self._x0) - if self._P.is_polar: - self._tmp_dom.copy(out=self._x0) - with ProfileManager.profile_region("basis projection: dof operator"): - self.dof_operator.dot(self._tmp_dom, out=out) + self._P.solve(v, True, apply_bc=True, out=self._tmp_dom, x0=self._x0) + self._tmp_dom.copy(out=self._x0) + self.dof_operator.dot(self._tmp_dom, out=out) else: # 1. apply dof operator, 2. apply inverse inter-/histopolation matrix - with ProfileManager.profile_region("basis projection: dof operator"): - self.dof_operator.dot(v, out=self._tmp_codom) - with ProfileManager.profile_region("basis projection: interpolation solve"): - self._P.solve(self._tmp_codom, False, apply_bc=True, out=out, x0=self._x0) - if self._P.is_polar: - out.copy(out=self._x0) + self.dof_operator.dot(v, out=self._tmp_codom) + self._P.solve(self._tmp_codom, False, apply_bc=True, out=out, x0=self._x0) + out.copy(out=self._x0) return out @@ -1906,14 +1898,12 @@ def update_weights(self, weights): self._weights = weights # assemble tensor-product dof matrix - with ProfileManager.profile_region("basis projection weight update: assemble"): - self._dof_mat = self.assemble() + self._dof_mat = self.assemble() # only need to update the transposed in case where it's needed # (no need to recreate a new ComposedOperator) if self._transposed: - with ProfileManager.profile_region("basis projection weight update: transpose"): - self._dof_mat_T = self._dof_mat.transpose(out=self._dof_mat_T) + self._dof_mat_T = self._dof_mat.transpose(out=self._dof_mat_T) def assemble(self, weights=None): """ @@ -2003,7 +1993,7 @@ def assemble(self, weights=None): polar_shift, ) - _ptsG = [xp.asarray(pts.flatten()) for pts in _ptsG] + _ptsG = [pts.flatten() for pts in _ptsG] _Vnbases = [int(space.nbasis) for space in V1d] _Wnbases = [int(space.nbasis) for space in W1d] @@ -2024,15 +2014,9 @@ def assemble(self, weights=None): # Call the kernel if weight function is not zero or in the scalar case # to avoid calling _block of a StencilMatrix in the else - # A device reduction here synchronizes the whole CuPy stream - # once per matrix block. Dynamic GPU weights are cheap to - # assemble even when zero, so avoid that host round-trip. - if self._mpi_comm is None and isinstance(loc_weight, xp.ndarray) and xp.is_gpu(loc_weight): - not_weight_zero = True - else: - not_weight_zero = xp.array( - int(loc_weight is not None and xp.any(xp.abs(mat_w) > 1e-14)), - ) + not_weight_zero = xp.array( + int(loc_weight is not None and xp.any(xp.abs(mat_w) > 1e-14)), + ) if self._mpi_comm is not None: self._mpi_comm.Allreduce( @@ -2058,53 +2042,31 @@ def assemble(self, weights=None): ) dofs_mat = self._dof_mat[i, j] - logger.debug(f"Assemble block {i, j}") - if V.ldim == 3 and xp.is_gpu(dofs_mat._data): - from struphy.feec.basis_projection_kernels_cuda import ( - assemble_dofs_for_weighted_basisfuns_3d_gpu, - ) + kernel = PyccelKernel( + getattr( + basis_projection_kernels, + "assemble_dofs_for_weighted_basisfuns_" + str(V.ldim) + "d", + ), + ) - assemble_dofs_for_weighted_basisfuns_3d_gpu( - dofs_mat._data, - _starts_in, - _ends_in, - _pads_in, - _starts_out, - _ends_out, - _pads_out, - mat_w, - _wtsG, - _spans, - _bases, - _subs, - _Vnbases, - _Wnbases, - _Wdegrees, - ) - else: - kernel = PyccelKernel( - getattr( - basis_projection_kernels, - "assemble_dofs_for_weighted_basisfuns_" + str(V.ldim) + "d", - ), - ) - kernel( - dofs_mat._data, - _starts_in, - _ends_in, - _pads_in, - _starts_out, - _ends_out, - _pads_out, - mat_w, - *_wtsG, - *_spans, - *_bases, - *_subs, - *_Vnbases, - *_Wnbases, - *_Wdegrees, - ) + logger.debug(f"Assemble block {i, j}") + kernel( + dofs_mat._data, + _starts_in, + _ends_in, + _pads_in, + _starts_out, + _ends_out, + _pads_out, + mat_w, + *_wtsG, + *_spans, + *_bases, + *_subs, + *_Vnbases, + *_Wnbases, + *_Wdegrees, + ) dofs_mat.set_backend( backend=PSYDAC_BACKEND_GPYCCEL, diff --git a/src/struphy/feec/cuda/basis_projection_kernels_cuda/_assemble_src.cu b/src/struphy/feec/cuda/basis_projection_kernels_cuda/_assemble_src.cu deleted file mode 100644 index 29ce05734..000000000 --- a/src/struphy/feec/cuda/basis_projection_kernels_cuda/_assemble_src.cu +++ /dev/null @@ -1,40 +0,0 @@ -extern "C" __global__ -void assemble_weighted_basis_3d_cuda( - const long long* row1,const long long* row2,const long long* row3, - const long long* span1,const long long* span2,const long long* span3, - const double* w1,const double* w2,const double* w3, - const double* b1,const double* b2,const double* b3,const double* fun, - const int ni1,const int ni2,const int ni3,const int nq1,const int nq2,const int nq3, - const int p1,const int p2,const int p3,const int so1,const int so2,const int so3, - const int pi1,const int pi2,const int pi3,const int po1,const int po2,const int po3, - const int dimi1,const int dimi2,const int dimi3,const int dimo1,const int dimo2,const int dimo3, - const int pout1,const int pout2,const int pout3,double* mat, - const int md2,const int md3,const int md4,const int md5,const int md6) -{ - long long tid=(long long)blockIdx.x*blockDim.x+threadIdx.x; - const long long nb=(long long)(p1+1)*(p2+1)*(p3+1); - const long long nq=(long long)nq1*nq2*nq3; - const long long total=(long long)ni1*ni2*ni3*nq*nb; - if(tid>=total)return; - long long t=tid; const long long bb=t%nb;t/=nb; const long long qq=t%nq;t/=nq; - const int kk=t%ni3;t/=ni3; const int jj=t%ni2;const int ii=t/ni2; - const int b3i=bb%(p3+1),b2i=(bb/(p3+1))%(p2+1),b1i=bb/((p2+1)*(p3+1)); - const int q3=qq%nq3,q2=(qq/nq3)%nq2,q1=qq/(nq2*nq3); - const int i=(int)row1[ii],j=(int)row2[jj],k=(int)row3[kk]; - int m=(int)span1[ii*nq1+q1]-p1+b1i; - int n=(int)span2[jj*nq2+q2]-p2+b2i; - int o=(int)span3[kk*nq3+q3]-p3+b3i; - const int cut1=dimo1<=dimi1?p1:pout1,cut2=dimo2<=dimi2?p2:pout2,cut3=dimo3<=dimi3?p3:pout3; - int d=m-(i+so1);if(d>cut1)m-=dimi1;else if(d<-cut1)m+=dimi1; - d=n-(j+so2);if(d>cut2)n-=dimi2;else if(d<-cut2)n+=dimi2; - d=o-(k+so3);if(d>cut3)o-=dimi3;else if(d<-cut3)o+=dimi3; - const int c1=pi1+m-(i+so1),c2=pi2+n-(j+so2),c3=pi3+o-(k+so3); - const long long fi=((long long)(ii*nq1+q1)*(ni2*nq2)+jj*nq2+q2)*(ni3*nq3)+kk*nq3+q3; - const double value=fun[fi]*w1[ii*nq1+q1]*w2[jj*nq2+q2]*w3[kk*nq3+q3] - *b1[((long long)ii*nq1+q1)*(p1+1)+b1i] - *b2[((long long)jj*nq2+q2)*(p2+1)+b2i] - *b3[((long long)kk*nq3+q3)*(p3+1)+b3i]; - const long long mi=(((((long long)(po1+i)*md2+(po2+j))*md3+(po3+k))*md4+c1)*md5+c2)*md6+c3; - atomicAdd(&mat[mi],value); -} - diff --git a/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu b/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu deleted file mode 100644 index 4e16c9502..000000000 --- a/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu +++ /dev/null @@ -1,44 +0,0 @@ -extern "C" __global__ -void h1vec_divdiv_assemble_cuda( - const long long* spans1, const long long* spans2, const long long* spans3, - const int ne1, const int ne2, const int ne3, const int p1, const int p2, const int p3, - const int starts1, const int starts2, const int starts3, const int pads1, const int pads2, const int pads3, - const double* b1, const double* b2, const double* b3, const int nder1, const int nder2, const int nder3, - const int nq1, const int nq2, const int nq3, const double* weighted_rho, - const int component_test, const int component_trial, double* data, - const int nd2, const int nd3, const int nd4, const int nd5, const int nd6) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long nloc = (long long)(p1 + 1) * (p2 + 1) * (p3 + 1); - const long long total = (long long)ne1 * ne2 * ne3 * nloc * nloc; - if (tid >= total) return; - long long t = tid; - const long long j = t % nloc; t /= nloc; - const long long i = t % nloc; t /= nloc; - const int iel3 = t % ne3; t /= ne3; - const int iel2 = t % ne2; const int iel1 = t / ne2; - const int il3 = i % (p3 + 1), il2 = (i / (p3 + 1)) % (p2 + 1), il1 = i / ((p2 + 1) * (p3 + 1)); - const int jl3 = j % (p3 + 1), jl2 = (j / (p3 + 1)) % (p2 + 1), jl1 = j / ((p2 + 1) * (p3 + 1)); - const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; - const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; - const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; - const int o1 = pads1 + jl1 - il1, o2 = pads2 + jl2 - il2, o3 = pads3 + jl3 - il3; - const int toi1 = component_test == 0, toi2 = component_test == 1, toi3 = component_test == 2; - const int tro1 = component_trial == 0, tro2 = component_trial == 1, tro3 = component_trial == 2; - double value = 0.0; - for (int q1 = 0; q1 < nq1; ++q1) for (int q2 = 0; q2 < nq2; ++q2) for (int q3 = 0; q3 < nq3; ++q3) { - const long long b1i = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; - const long long b2i = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; - const long long b3i = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; - const long long b1j = ((long long)(iel1 * (p1 + 1) + jl1) * nder1) * nq1 + q1; - const long long b2j = ((long long)(iel2 * (p2 + 1) + jl2) * nder2) * nq2 + q2; - const long long b3j = ((long long)(iel3 * (p3 + 1) + jl3) * nder3) * nq3 + q3; - const double di = b1[b1i + toi1 * nq1] * b2[b2i + toi2 * nq2] * b3[b3i + toi3 * nq3]; - const double dj = b1[b1j + tro1 * nq1] * b2[b2j + tro2 * nq2] * b3[b3j + tro3 * nq3]; - const long long qidx = ((long long)(iel1 * nq1 + q1) * (ne2 * nq2) + iel2 * nq2 + q2) * (ne3 * nq3) + iel3 * nq3 + q3; - value += weighted_rho[qidx] * di * dj; - } - const long long didx = (((((long long)c1 * nd2 + c2) * nd3 + c3) * nd4 + o1) * nd5 + o2) * nd6 + o3; - atomicAdd(&data[didx], value); -} - diff --git a/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divergence_src.cu b/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divergence_src.cu deleted file mode 100644 index e3fc07078..000000000 --- a/src/struphy/feec/cuda/mass_kernels_cuda/_h1vec_divergence_src.cu +++ /dev/null @@ -1,111 +0,0 @@ -extern "C" __global__ -void h1vec_divergence_eval_cuda( - const long long* spans1, const long long* spans2, const long long* spans3, - const int ne1, const int ne2, const int ne3, - const int p1, const int p2, const int p3, - const int starts1, const int starts2, const int starts3, - const int pads1, const int pads2, const int pads3, - const double* b1, const double* b2, const double* b3, - const int nder1, const int nder2, const int nder3, - const int nq1, const int nq2, const int nq3, - const double* dlogj1, const double* dlogj2, const double* dlogj3, - const int component, const double* coeffs, - const int nc2, const int nc3, double* values) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long totalq3 = (long long)ne3 * nq3; - const long long totalq2 = (long long)ne2 * nq2; - const long long nvalues = (long long)ne1 * nq1 * totalq2 * totalq3; - if (tid >= nvalues) return; - - const int iq3 = tid % totalq3; - const long long t12 = tid / totalq3; - const int iq2 = t12 % totalq2; - const int iq1 = t12 / totalq2; - const int iel1 = iq1 / nq1, q1 = iq1 % nq1; - const int iel2 = iq2 / nq2, q2 = iq2 % nq2; - const int iel3 = iq3 / nq3, q3 = iq3 % nq3; - const double dlog = component == 0 ? dlogj1[tid] : - (component == 1 ? dlogj2[tid] : dlogj3[tid]); - double value = 0.0; - - for (int il1 = 0; il1 <= p1; ++il1) { - const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; - const long long b1base = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; - const double n1 = b1[b1base]; - const double d1 = b1[b1base + nq1]; - for (int il2 = 0; il2 <= p2; ++il2) { - const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; - const long long b2base = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; - const double n2 = b2[b2base]; - const double d2 = b2[b2base + nq2]; - for (int il3 = 0; il3 <= p3; ++il3) { - const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; - const long long b3base = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; - const double n3 = b3[b3base]; - const double d3 = b3[b3base + nq3]; - const double basis = n1 * n2 * n3; - const double derivative = component == 0 ? d1 * n2 * n3 : - (component == 1 ? n1 * d2 * n3 : n1 * n2 * d3); - value += coeffs[((long long)c1 * nc2 + c2) * nc3 + c3] * (derivative + dlog * basis); - } - } - } - values[tid] += value; -} - -extern "C" __global__ -void h1vec_divergence_transpose_cuda( - const long long* spans1, const long long* spans2, const long long* spans3, - const int ne1, const int ne2, const int ne3, - const int p1, const int p2, const int p3, - const int starts1, const int starts2, const int starts3, - const int pads1, const int pads2, const int pads3, - const double* b1, const double* b2, const double* b3, - const int nder1, const int nder2, const int nder3, - const int nq1, const int nq2, const int nq3, - const double* dlogj1, const double* dlogj2, const double* dlogj3, - const int component, const double* values, - const int nc2, const int nc3, double* coeffs) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long totalq3 = (long long)ne3 * nq3; - const long long totalq2 = (long long)ne2 * nq2; - const long long nvalues = (long long)ne1 * nq1 * totalq2 * totalq3; - if (tid >= nvalues) return; - - const int iq3 = tid % totalq3; - const long long t12 = tid / totalq3; - const int iq2 = t12 % totalq2; - const int iq1 = t12 / totalq2; - const int iel1 = iq1 / nq1, q1 = iq1 % nq1; - const int iel2 = iq2 / nq2, q2 = iq2 % nq2; - const int iel3 = iq3 / nq3, q3 = iq3 % nq3; - const double dlog = component == 0 ? dlogj1[tid] : - (component == 1 ? dlogj2[tid] : dlogj3[tid]); - const double qvalue = values[tid]; - - for (int il1 = 0; il1 <= p1; ++il1) { - const int c1 = pads1 + (int)spans1[iel1] - p1 + il1 - starts1; - const long long b1base = ((long long)(iel1 * (p1 + 1) + il1) * nder1) * nq1 + q1; - const double n1 = b1[b1base]; - const double d1 = b1[b1base + nq1]; - for (int il2 = 0; il2 <= p2; ++il2) { - const int c2 = pads2 + (int)spans2[iel2] - p2 + il2 - starts2; - const long long b2base = ((long long)(iel2 * (p2 + 1) + il2) * nder2) * nq2 + q2; - const double n2 = b2[b2base]; - const double d2 = b2[b2base + nq2]; - for (int il3 = 0; il3 <= p3; ++il3) { - const int c3 = pads3 + (int)spans3[iel3] - p3 + il3 - starts3; - const long long b3base = ((long long)(iel3 * (p3 + 1) + il3) * nder3) * nq3 + q3; - const double n3 = b3[b3base]; - const double d3 = b3[b3base + nq3]; - const double basis = n1 * n2 * n3; - const double derivative = component == 0 ? d1 * n2 * n3 : - (component == 1 ? n1 * d2 * n3 : n1 * n2 * d3); - atomicAdd(&coeffs[((long long)c1 * nc2 + c2) * nc3 + c3], qvalue * (derivative + dlog * basis)); - } - } - } -} - diff --git a/src/struphy/feec/cuda/mass_kernels_cuda/_mass_assembly_src.cu b/src/struphy/feec/cuda/mass_kernels_cuda/_mass_assembly_src.cu deleted file mode 100644 index 87a00fad9..000000000 --- a/src/struphy/feec/cuda/mass_kernels_cuda/_mass_assembly_src.cu +++ /dev/null @@ -1,48 +0,0 @@ -extern "C" __global__ -void mass_3d_assemble_cuda( - const long long* spans1, const long long* spans2, const long long* spans3, - const int ne1, const int ne2, const int ne3, - const int pi1, const int pi2, const int pi3, const int pj1, const int pj2, const int pj3, - const int starts1, const int starts2, const int starts3, const int pads1, const int pads2, const int pads3, - const double* w1, const double* w2, const double* w3, const int nq1, const int nq2, const int nq3, - const double* bi1, const double* bi2, const double* bi3, const double* bj1, const double* bj2, const double* bj3, - const int ni_der1, const int ni_der2, const int ni_der3, const int nj_der1, const int nj_der2, const int nj_der3, - const double* mat_fun, double* data, const int nd2, const int nd3, const int nd4, const int nd5, const int nd6) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long ni = (long long)(pi1 + 1) * (pi2 + 1) * (pi3 + 1); - const long long nj = (long long)(pj1 + 1) * (pj2 + 1) * (pj3 + 1); - const long long total = (long long)ne1 * ne2 * ne3 * ni * nj; - if (tid >= total) return; - long long t = tid; - const long long j = t % nj; t /= nj; - const long long i = t % ni; t /= ni; - const int iel3 = t % ne3; t /= ne3; - const int iel2 = t % ne2; const int iel1 = t / ne2; - const int il3 = i % (pi3 + 1); const int il2 = (i / (pi3 + 1)) % (pi2 + 1); const int il1 = i / ((pi2 + 1) * (pi3 + 1)); - const int jl3 = j % (pj3 + 1); const int jl2 = (j / (pj3 + 1)) % (pj2 + 1); const int jl1 = j / ((pj2 + 1) * (pj3 + 1)); - const int c1 = pads1 + (int)spans1[iel1] - pi1 + il1 - starts1; - const int c2 = pads2 + (int)spans2[iel2] - pi2 + il2 - starts2; - const int c3 = pads3 + (int)spans3[iel3] - pi3 + il3 - starts3; - const int o1 = pads1 + jl1 - il1, o2 = pads2 + jl2 - il2, o3 = pads3 + jl3 - il3; - double value = 0.0; - for (int q1 = 0; q1 < nq1; ++q1) { - const double wi1 = w1[iel1 * nq1 + q1]; - const double ai1 = bi1[((long long)(iel1 * (pi1 + 1) + il1) * ni_der1) * nq1 + q1]; - const double aj1 = bj1[((long long)(iel1 * (pj1 + 1) + jl1) * nj_der1) * nq1 + q1]; - for (int q2 = 0; q2 < nq2; ++q2) { - const double wi2 = wi1 * w2[iel2 * nq2 + q2]; - const double ai2 = ai1 * bi2[((long long)(iel2 * (pi2 + 1) + il2) * ni_der2) * nq2 + q2]; - const double aj2 = aj1 * bj2[((long long)(iel2 * (pj2 + 1) + jl2) * nj_der2) * nq2 + q2]; - for (int q3 = 0; q3 < nq3; ++q3) { - const long long qidx = ((long long)(iel1 * nq1 + q1) * (ne2 * nq2) + iel2 * nq2 + q2) * (ne3 * nq3) + iel3 * nq3 + q3; - const double ai3 = ai2 * bi3[((long long)(iel3 * (pi3 + 1) + il3) * ni_der3) * nq3 + q3]; - const double aj3 = aj2 * bj3[((long long)(iel3 * (pj3 + 1) + jl3) * nj_der3) * nq3 + q3]; - value += wi2 * w3[iel3 * nq3 + q3] * mat_fun[qidx] * ai3 * aj3; - } - } - } - const long long didx = (((((long long)c1 * nd2 + c2) * nd3 + c3) * nd4 + o1) * nd5 + o2) * nd6 + o3; - atomicAdd(&data[didx], value); -} - diff --git a/src/struphy/feec/cuda/mass_kernels_cuda/_weak_div_assembly_src.cu b/src/struphy/feec/cuda/mass_kernels_cuda/_weak_div_assembly_src.cu deleted file mode 100644 index 511f7e26b..000000000 --- a/src/struphy/feec/cuda/mass_kernels_cuda/_weak_div_assembly_src.cu +++ /dev/null @@ -1,60 +0,0 @@ -extern "C" __global__ -void weak_div_assemble_cuda( - const long long* s1, const long long* s2, const long long* s3, - const int ne1, const int ne2, const int ne3, - const int pi1, const int pi2, const int pi3, - const int pj1, const int pj2, const int pj3, - const int st1, const int st2, const int st3, - const int pad1, const int pad2, const int pad3, - const double* w1, const double* w2, const double* w3, - const int nq1, const int nq2, const int nq3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - const int ndi1, const int ndi2, const int ndi3, - const int ndj1, const int ndj2, const int ndj3, - const double* weight, const double* dl1, const double* dl2, const double* dl3, - const int component, double* data, - const int dd2, const int dd3, const int dd4, const int dd5, const int dd6) -{ - const long long tid = (long long)blockIdx.x * blockDim.x + threadIdx.x; - const long long ni = (long long)(pi1+1)*(pi2+1)*(pi3+1); - const long long nj = (long long)(pj1+1)*(pj2+1)*(pj3+1); - const long long total = (long long)ne1*ne2*ne3*ni*nj; - if (tid >= total) return; - long long t=tid; - const long long j=t%nj; t/=nj; - const long long i=t%ni; t/=ni; - const int e3=t%ne3; t/=ne3; - const int e2=t%ne2; const int e1=t/ne2; - const int il3=i%(pi3+1), il2=(i/(pi3+1))%(pi2+1), il1=i/((pi2+1)*(pi3+1)); - const int jl3=j%(pj3+1), jl2=(j/(pj3+1))%(pj2+1), jl1=j/((pj2+1)*(pj3+1)); - const int c1=pad1+(int)s1[e1]-pi1+il1-st1; - const int c2=pad2+(int)s2[e2]-pi2+il2-st2; - const int c3=pad3+(int)s3[e3]-pi3+il3-st3; - const int o1=pad1+jl1-il1, o2=pad2+jl2-il2, o3=pad3+jl3-il3; - double value=0.0; - for(int q1=0;q1= total) return; - - long long t = tid; - const int i2 = t % n2; t /= n2; - const int i1 = t % n1; const int i0 = t / n1; - double us[3] = {0.0, 0.0, 0.0}; - double vs[3] = {0.0, 0.0, 0.0}; - - for (int l0 = 0; l0 <= p0; ++l0) { - const int c0 = (int)span0[i0] + l0 - start0; - const double b0 = basis0[(long long)i0 * (p0 + 1) + l0]; - for (int l1 = 0; l1 <= p1; ++l1) { - const int c1 = (int)span1[i1] + l1 - start1; - const double b01 = b0 * basis1[(long long)i1 * (p1 + 1) + l1]; - for (int l2 = 0; l2 <= p2; ++l2) { - const int c2 = (int)span2[i2] + l2 - start2; - const double weight = b01 * basis2[(long long)i2 * (p2 + 1) + l2]; - const long long ci = ((long long)c0 * nc1 + c1) * nc2 + c2; - us[0] += u0[ci] * weight; us[1] += u1[ci] * weight; us[2] += u2[ci] * weight; - vs[0] += v0[ci] * weight; vs[1] += v1[ci] * weight; vs[2] += v2[ci] * weight; - } - } - } - double value = 0.0; - for (int i = 0; i < 3; ++i) - for (int j = 0; j < 3; ++j) - value += us[i] * metric[((long long)i * 3 + j) * total + tid] * vs[j]; - out[tid] = 0.5 * value; - ug0[tid] = us[0]; ug1[tid] = us[1]; ug2[tid] = us[2]; - vg0[tid] = vs[0]; vg1[tid] = vs[1]; vg2[tid] = vs[2]; -} diff --git a/src/struphy/feec/linear_operators.py b/src/struphy/feec/linear_operators.py index bdd235c76..940bcd4d5 100644 --- a/src/struphy/feec/linear_operators.py +++ b/src/struphy/feec/linear_operators.py @@ -2,7 +2,6 @@ from abc import abstractmethod import cunumpy as xp -import numpy as np from feectools.ddm.mpi import MockComm from feectools.ddm.mpi import mpi as MPI from feectools.linalg.basic import LinearOperator, Vector, VectorSpace @@ -447,38 +446,13 @@ def codomain(self): def dtype(self): return self._dtype + @property def tosparse(self): - """Convert to a sparse (diagonal) matrix. - - `dot()` is exactly a copy followed by an elementwise zero-mask - (`apply_essential_bc_to_array`), so the operator is diagonal with 0/1 entries -- - applying it once to an all-ones vector directly gives that diagonal, far cheaper - than a generic basis-vector sweep (see AverageOperator.tosparse for that - approach, used where the operator isn't diagonal). Serial (single MPI rank) - only. - """ - - def _stencil_diag_flat(v): - idx = tuple(slice(m * p, -m * p) if p != 0 else slice(0, None) for p, m in zip(v.pads, v.space.shifts)) - return xp.to_numpy(v._data[idx]).reshape(-1) - - ones = self.domain.zeros() - if isinstance(self._domain, StencilVectorSpace): - ones._data[:] = 1.0 - else: - for block in ones.blocks: - block._data[:] = 1.0 - diag_vec = self.dot(ones) - - if isinstance(self._domain, StencilVectorSpace): - diag_flat = _stencil_diag_flat(diag_vec) - else: - diag_flat = np.concatenate([_stencil_diag_flat(block) for block in diag_vec.blocks]) - - return sparse.diags(diag_flat, format="csr") + raise NotImplementedError() + @property def toarray(self): - return self.tosparse().toarray() + raise NotImplementedError() @property def bc(self): diff --git a/src/struphy/feec/mass.py b/src/struphy/feec/mass.py index 425eb2290..c8b63ae9d 100644 --- a/src/struphy/feec/mass.py +++ b/src/struphy/feec/mass.py @@ -4,7 +4,6 @@ from typing import Callable import cunumpy as xp -import numpy as np from cunumpy import PyccelKernel from feectools.api.settings import PSYDAC_BACKEND_GPYCCEL from feectools.ddm.mpi import MockComm @@ -1862,7 +1861,6 @@ def __init__( mass_kernels, "kernel_" + str(self._V.ldim) + "d_mat", ), - outputs=(-1,), ) @property @@ -2290,34 +2288,18 @@ def assemble(self, weights=None, clear=True): logger.debug(f"Assemble block {a, b}") - if xp.is_gpu(mat._data) and self._V.ldim == 3: - from struphy.feec.mass_kernels_cuda import mass_3d_assemble_gpu - - mass_3d_assemble_gpu( - codomain_spans, - codomain_space.degree, - domain_space.degree, - codomain_starts, - codomain_pads, - wts, - codomain_basis, - domain_basis, - mat_w, - mat._data, - ) - else: - self._assembly_kernel( - *codomain_spans, - *codomain_space.degree, - *domain_space.degree, - *codomain_starts, - *codomain_pads, - *wts, - *codomain_basis, - *domain_basis, - mat_w, - mat._data, - ) + self._assembly_kernel( + *codomain_spans, + *codomain_space.degree, + *domain_space.degree, + *codomain_starts, + *codomain_pads, + *wts, + *codomain_basis, + *domain_basis, + mat_w, + mat._data, + ) else: if clear: @@ -2918,9 +2900,6 @@ def __init__( self._spans_l = self.mass_ops.derham.spline_attributes[self.space_key].quad_grid_spans self._bases_l = self.mass_ops.derham.spline_attributes[self.space_key].quad_grid_bases - # Pyccel-compiled kernel only understands NumPy arrays - self._kernel_3d_vec = PyccelKernel(mass_kernels.kernel_3d_vec, outputs=(-1,)) - # Preconditioner if precond_name is None: pc = None @@ -3157,7 +3136,7 @@ def get_dofs( pads = fem_space.coeff_space.pads if isinstance(dofs, StencilVector): - self._kernel_3d_vec( + mass_kernels.kernel_3d_vec( *spans, *fem_space.degree, *starts, @@ -3168,7 +3147,7 @@ def get_dofs( dofs._data, ) elif isinstance(dofs, PolarVector): - self._kernel_3d_vec( + mass_kernels.kernel_3d_vec( *spans, *fem_space.degree, *starts, @@ -3179,7 +3158,7 @@ def get_dofs( dofs.tp._data, ) else: - self._kernel_3d_vec( + mass_kernels.kernel_3d_vec( *spans, *fem_space.degree, *starts, @@ -3232,22 +3211,6 @@ def __call__( return self.solve(self.get_dofs(fun, dofs=dofs, apply_bc=apply_bc), out=out) -def _einsum_out(subscripts, *operands, out): - """``xp.einsum(subscripts, *operands, out=out)``, working on both backends. - - CuPy's ``einsum`` (unlike NumPy's) does not accept an ``out=`` keyword at all -- it - raises ``TypeError`` rather than silently ignoring it -- so under the CuPy backend - this falls back to an unfused call plus an explicit copy into ``out``. NumPy keeps - the single fused call, which avoids the extra allocation/copy that the CuPy fallback - cannot. - """ - if xp.cupy_backend: - out[...] = xp.einsum(subscripts, *operands) - else: - xp.einsum(subscripts, *operands, out=out) - return out - - class AverageOperator(LinOpWithTransp): r""" Class for quadrature operators, performs the average of a `FeecVariable` along a given direction. @@ -3370,13 +3333,7 @@ def allocate(self): i_begin += self.derham.degree[self._directions[0]] i_end += self.derham.degree[self._directions[0]] # General formula for any distribution of knots for the integral of a B-spline function, thus works with periodic and clamped boundary conditions : - # `knots` (derham.args_derham's host knot vector) is always NumPy, while - # `self._weights` may be a CuPy array under the CuPy backend -- a full-slice - # assignment (unlike a boolean/fancy-indexed one) does not auto-convert a NumPy - # source, so it is wrapped explicitly. - self._weights[:] = xp.asarray( - (knots[i_begin + degree + 1 : i_end + degree + 1] - knots[i_begin:i_end]) / (degree + 1) - ) + self._weights[:] = (knots[i_begin + degree + 1 : i_end + degree + 1] - knots[i_begin:i_end]) / (degree + 1) @property def domain(self): @@ -3401,52 +3358,13 @@ def nquads(self): else: return self._nquads + @property def tosparse(self): - """Convert to a sparse matrix, built directly from the closed-form averaging - formula rather than a basis-vector sweep. - - `dot()`'s math (see the class docstring) is - ``out[..., i_d0, ...] = sum_o weights[o] * x[..., o, ...]`` for every index along - the averaged axis `d0` (`self._directions[0]`) -- i.e. output row - `(i0, i1, i2)` (any value at index `d0`) depends only on the `n_d0` input columns - that share its other two indices, with coefficient `weights[o]`. That is fully - vectorizable with `numpy`, unlike a basis-vector sweep (one `dot()` call per - domain DOF, i.e. per grid point): that would mean thousands of individual CuPy - kernel launches with a device sync each under the CuPy backend. This builds - the sparse matrix entirely on the host regardless of backend (`self._weights` - is the only device array involved, and is tiny -- one value per grid point - along `d0`). - - Serial (single MPI rank) only. - """ - from scipy.sparse import coo_matrix - - e = self.domain.zeros() - idx = tuple(slice(m * p, -m * p) if p != 0 else slice(0, None) for p, m in zip(e.pads, e.space.shifts)) - shape = e._data[idx].shape - d0 = self._directions[0] - weights_np = xp.to_numpy(self._weights) - assert weights_np.shape[0] == shape[d0] - - grids = np.meshgrid(*[np.arange(s) for s in shape], indexing="ij") - row = np.ravel_multi_index(grids, shape).reshape(-1) - - rows, cols, data = [], [], [] - for o in range(shape[d0]): - col_idx = [g.copy() for g in grids] - col_idx[d0] = np.full(shape, o) - rows.append(row) - cols.append(np.ravel_multi_index(col_idx, shape).reshape(-1)) - data.append(np.full(row.shape, weights_np[o], dtype=self.dtype)) - - n = int(np.prod(shape)) - return coo_matrix( - (np.concatenate(data), (np.concatenate(rows), np.concatenate(cols))), - shape=(n, n), - ).tocsr() + raise NotImplementedError() + @property def toarray(self): - return self.tosparse().toarray() + raise NotImplementedError() def dot(self, v, out=None): @@ -3461,12 +3379,12 @@ def dot(self, v, out=None): x = v._data[self._slices[0]] y = out._data[self._slices[0]] if self._transposed: - _einsum_out(self._subscripts[0], x, out=self._tmp) + xp.einsum(self._subscripts[0], x, out=self._tmp) if not isinstance(self.derham.comm, (MockComm, type(None))): self.subcomm.Allreduce(MPI.IN_PLACE, self._tmp, MPI.SUM) - _einsum_out(self._subscripts[1], self._tmp, self._weights, out=y) + xp.einsum(self._subscripts[1], self._tmp, self._weights, out=y) else: - _einsum_out(self._subscripts[0], x, self._weights, out=self._tmp) + xp.einsum(self._subscripts[0], x, self._weights, out=self._tmp) if not isinstance(self.derham.comm, (MockComm, type(None))): self.subcomm.Allreduce(MPI.IN_PLACE, self._tmp, MPI.SUM) y[:] = self._tmp[self._slices[1]] diff --git a/src/struphy/feec/mass_kernels.py b/src/struphy/feec/mass_kernels.py index 31449cf18..8093033aa 100644 --- a/src/struphy/feec/mass_kernels.py +++ b/src/struphy/feec/mass_kernels.py @@ -1,16 +1,11 @@ """ Integral kernels for mass matrices and L2-projections. - -This module is intentionally restricted to constructs which Pyccel can -translate directly to Fortran without requiring gFTL container modules. """ import numpy as np from numpy import shape -# ====================================================================== -# 1D -# ====================================================================== +# ================= 1d ================================= def kernel_1d_mat( @@ -25,14 +20,48 @@ def kernel_1d_mat( mat_fun: "float[:]", data: "float[:,:]", ): - """Assemble a 1D mass matrix.""" + """ + Performs the integration of Lambda_(i1) * mat_fun(eta1) * Lambda_(j1) for the basis functions (i1, j1) available on the calling process. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1 : array[int] + Array of span indices; the span is the index of the last non-vanishing spline on each grid element + (cell). The length of the returned array is the number of elements (cells). + pi1 : int + Degree of the codomain basis functions. + pj1 : int + Degree of the domain basis functions. + starts1 : int + Starting index on the current rank. + pads1 : int + Padding (=spline degree) for ghost regions in data. + w1 : "float[:,:]" + Quadrature weights. The indexing is [global element, quadrature point]. + bi1 : "float[:,:,:,:]" + Values of codomain basis functions. The indexing is [global element, local basis function, derivative, quadrature point]. + bj1 : "float[:,:,:,:]" + Values of domain basis functions. The indexing is [global element, local basis function, derivative, quadrature point]. + mat_fun : "float[:]" + Function under the integral evaluated at quadrature points (flattened). + data : "float[:,:]" + _data array of StencilMatrix to store the results. + """ + # number of elements ne1 = spans1.size - nq1 = w1.shape[1] + + # number of quadrature points in each element + nq1 = shape(w1)[1] for iel1 in range(ne1): for il1 in range(pi1 + 1): + # global spline indices i_global1 = spans1[iel1] - pi1 + il1 + + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) i_local1 = i_global1 - starts1 for jl1 in range(pj1 + 1): @@ -54,14 +83,42 @@ def kernel_1d_vec( mat_fun: "float[:]", data: "float[:]", ): - """Apply a 1D mass operator.""" + """ + Performs the integration of Lambda_(i1) * mat_fun(eta1) for the basis functions (i1) available on the calling process. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1 : array[int] + Array of span indices; the span is the index of the last non-vanishing spline on each grid element + (cell). The length of the returned array is the number of elements (cells). + pi1 : int + Degree of the basis functions. + starts1 : int + Starting index on the current rank. + pads1 : int + Padding (=spline degree) for ghost regions in data. + w1 : "float[:,:]" + Quadrature weights. The indexing is [global element, quadrature point]. + bi1 : "float[:,:,:,:]" + Values of basis functions. The indexing is [global element, local basis function, derivative, quadrature point]. + mat_fun : "float[:]" + Function under the integral evaluated at quadrature points (flattened). + data : "float[:]" + _data array of StencilVector to store the results. + """ ne1 = spans1.size - nq1 = w1.shape[1] + + nq1 = shape(w1)[1] for iel1 in range(ne1): for il1 in range(pi1 + 1): + # global spline indices i_global1 = spans1[iel1] - pi1 + il1 + + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) i_local1 = i_global1 - starts1 value = 0.0 @@ -81,27 +138,50 @@ def kernel_1d_eval( coeffs_data: "float[:]", values: "float[:]", ): - """Evaluate a 1D spline function at quadrature points.""" + """ + Evaluates sum_i1 [ coeffs_i1 * Lambda_i1(quad_eta1) ] for all quadrature points on the calling process. + + The results are written into values. + + Parameters + ---------- + spans1 : array[int] + Array of span indices; the span is the index of the last non-vanishing spline on each grid element + (cell). The length of the returned array is the number of elements (cells). + pi1 : int + Degree of the basis functions. + starts1 : int + Starting index on the current rank. + pads1 : int + Padding (=spline degree) for ghost regions in coeffs_data. + bi1 : "float[:,:,:,:]" + Values of basis functions. The indexing is [global element, local basis function, derivative, quadrature point]. + coeffs_data : "float[:]" + _data array of StencilVector holding the spline coefficients of the function to be evaluated. + values : "float[:]" + Output array (flattened over elements and quadrature points) holding the evaluated function values; + it is set to zero at the start of the kernel, i.e. it is overwritten, not added to. + """ values[:] = 0.0 ne1 = spans1.size - nq1 = bi1.shape[3] + + nq1 = shape(bi1)[3] for iel1 in range(ne1): for il1 in range(pi1 + 1): + # global spline indices i_global1 = spans1[iel1] - pi1 + il1 - i_local1 = i_global1 - starts1 - coeff = coeffs_data[pads1 + i_local1] + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_local1 = i_global1 - starts1 for q1 in range(nq1): - values[iel1 * nq1 + q1] += coeff * bi1[iel1, il1, 0, q1] + values[iel1 * nq1 + q1] += coeffs_data[pads1 + i_local1] * bi1[iel1, il1, 0, q1] -# ====================================================================== -# 2D -# ====================================================================== +# ================= 2d ================================= def kernel_2d_mat( @@ -124,21 +204,53 @@ def kernel_2d_mat( mat_fun: "float[:,:]", data: "float[:,:,:,:]", ): - """Assemble a 2D mass matrix.""" + """ + Performs the integration of Lambda_(i1, i2) * mat_fun(eta1, eta2) * Lambda_(j1, j2) for the basis functions (i1, i2, j1, j2) available on the calling process. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1, spans2 : array[int] + Arrays of span indices in direction 1 and 2; the span is the index of the last non-vanishing spline + on each grid element (cell). The length of each array is the number of elements (cells) in that direction. + pi1, pi2 : int + Degree of the codomain basis functions in direction 1 and 2. + pj1, pj2 : int + Degree of the domain basis functions in direction 1 and 2. + starts1, starts2 : int + Starting index on the current rank, in direction 1 and 2. + pads1, pads2 : int + Padding (=spline degree) for ghost regions in data, in direction 1 and 2. + w1, w2 : "float[:,:]" + Quadrature weights in direction 1 and 2. The indexing is [global element, quadrature point]. + bi1, bi2 : "float[:,:,:,:]" + Values of codomain basis functions in direction 1 and 2. The indexing is + [global element, local basis function, derivative, quadrature point]. + bj1, bj2 : "float[:,:,:,:]" + Values of domain basis functions in direction 1 and 2, same indexing convention as bi1, bi2. + mat_fun : "float[:,:]" + Function under the integral evaluated at quadrature points (flattened in each direction). + The indexing is [flattened quadrature point in direction 1, flattened quadrature point in direction 2]. + data : "float[:,:,:,:]" + _data array of StencilMatrix to store the results. + """ ne1 = spans1.size ne2 = spans2.size - nq1 = w1.shape[1] - nq2 = w2.shape[1] + nq1 = shape(w1)[1] + nq2 = shape(w2)[1] for iel1 in range(ne1): for iel2 in range(ne2): for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): + # global spline indices i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 @@ -147,14 +259,12 @@ def kernel_2d_mat( value = 0.0 for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - bj_1 = bj1[iel1, jl1, 0, q1] - w_1 = w1[iel1, q1] - for q2 in range(nq2): - wvol = w_1 * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] + wvol = w1[iel1, q1] * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] + bi = bi1[iel1, il1, 0, q1] * bi2[iel2, il2, 0, q2] + bj = bj1[iel1, jl1, 0, q1] * bj2[iel2, jl2, 0, q2] - value += wvol * bi_1 * bi2[iel2, il2, 0, q2] * bj_1 * bj2[iel2, jl2, 0, q2] + value += wvol * bi * bj data[pads1 + i_local1, pads2 + i_local2, pads1 + jl1 - il1, pads2 + jl2 - il2] += value @@ -175,38 +285,59 @@ def kernel_2d_vec( mat_fun: "float[:,:]", data: "float[:,:]", ): - """Apply a 2D mass operator.""" + """ + Performs the integration of Lambda_(i1, i2) * mat_fun(eta1, eta2) for the basis functions (i1, i2) available on the calling process. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1, spans2 : array[int] + Arrays of span indices in direction 1 and 2; the span is the index of the last non-vanishing spline + on each grid element (cell). The length of each array is the number of elements (cells) in that direction. + pi1, pi2 : int + Degree of the basis functions in direction 1 and 2. + starts1, starts2 : int + Starting index on the current rank, in direction 1 and 2. + pads1, pads2 : int + Padding (=spline degree) for ghost regions in data, in direction 1 and 2. + w1, w2 : "float[:,:]" + Quadrature weights in direction 1 and 2. The indexing is [global element, quadrature point]. + bi1, bi2 : "float[:,:,:,:]" + Values of basis functions in direction 1 and 2. The indexing is + [global element, local basis function, derivative, quadrature point]. + mat_fun : "float[:,:]" + Function under the integral evaluated at quadrature points (flattened in each direction). + The indexing is [flattened quadrature point in direction 1, flattened quadrature point in direction 2]. + data : "float[:,:]" + _data array of StencilVector to store the results. + """ ne1 = spans1.size ne2 = spans2.size - nq1 = w1.shape[1] - nq2 = w2.shape[1] + nq1 = shape(w1)[1] + nq2 = shape(w2)[1] for iel1 in range(ne1): for iel2 in range(ne2): for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): + # global spline indices i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 value = 0.0 for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - w_1 = w1[iel1, q1] - for q2 in range(nq2): - value += ( - w_1 - * w2[iel2, q2] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] - * bi_1 - * bi2[iel2, il2, 0, q2] - ) + wvol = w1[iel1, q1] * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] + + value += wvol * bi1[iel1, il1, 0, q1] * bi2[iel2, il2, 0, q2] data[pads1 + i_local1, pads2 + i_local2] += value @@ -225,38 +356,62 @@ def kernel_2d_eval( coeffs_data: "float[:,:]", values: "float[:,:]", ): - """Evaluate a 2D spline function at quadrature points.""" + """ + Evaluates sum_(i1, i2) [ coeffs_{i1,i2} * Lambda_{i1, i2}(quad_eta1, quad_eta2) ] for all quadrature points on the calling process. + + The results are written into values. + + Parameters + ---------- + spans1, spans2 : array[int] + Arrays of span indices in direction 1 and 2; the span is the index of the last non-vanishing spline + on each grid element (cell). The length of each array is the number of elements (cells) in that direction. + pi1, pi2 : int + Degree of the basis functions in direction 1 and 2. + starts1, starts2 : int + Starting index on the current rank, in direction 1 and 2. + pads1, pads2 : int + Padding (=spline degree) for ghost regions in coeffs_data, in direction 1 and 2. + bi1, bi2 : "float[:,:,:,:]" + Values of basis functions in direction 1 and 2. The indexing is + [global element, local basis function, derivative, quadrature point]. + coeffs_data : "float[:,:]" + _data array of StencilVector holding the spline coefficients of the function to be evaluated. + values : "float[:,:]" + Output array (flattened over elements and quadrature points in each direction) holding the evaluated + function values; it is set to zero at the start of the kernel, i.e. it is overwritten, not added to. + """ values[:, :] = 0.0 ne1 = spans1.size ne2 = spans2.size - nq1 = bi1.shape[3] - nq2 = bi2.shape[3] + nq1 = shape(bi1)[3] + nq2 = shape(bi2)[3] for iel1 in range(ne1): for iel2 in range(ne2): for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): + # global spline indices i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 - coeff = coeffs_data[pads1 + i_local1, pads2 + i_local2] - for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - for q2 in range(nq2): - values[iel1 * nq1 + q1, iel2 * nq2 + q2] += coeff * bi_1 * bi2[iel2, il2, 0, q2] + values[iel1 * nq1 + q1, iel2 * nq2 + q2] += ( + coeffs_data[pads1 + i_local1, pads2 + i_local2] + * bi1[iel1, il1, 0, q1] + * bi2[iel2, il2, 0, q2] + ) -# ====================================================================== -# 3D -# ====================================================================== +# ================= 3d ================================= def kernel_3d_mat( @@ -287,57 +442,111 @@ def kernel_3d_mat( mat_fun: "float[:,:,:]", data: "float[:,:,:,:,:,:]", ): - """Assemble a 3D mass matrix.""" + """ + Performs the integration of Lambda_(i1,i2,i3) * mat_fun(eta1, eta2, eta3) * Lambda_(j1,j2,j3) for the basis functions (i1,i2,i3, j1,j2,j3) available on the calling process. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1, spans2, spans3 : array[int] + Arrays of span indices in direction 1, 2 and 3; the span is the index of the last non-vanishing spline + on each grid element (cell). The length of each array is the number of elements (cells) in that direction. + pi1, pi2, pi3 : int + Degree of the codomain basis functions in direction 1, 2 and 3. + pj1, pj2, pj3 : int + Degree of the domain basis functions in direction 1, 2 and 3. + starts1, starts2, starts3 : int + Starting index on the current rank, in direction 1, 2 and 3. + pads1, pads2, pads3 : int + Padding (=spline degree) for ghost regions in data, in direction 1, 2 and 3. + w1, w2, w3 : "float[:,:]" + Quadrature weights in direction 1, 2 and 3. The indexing is [global element, quadrature point]. + bi1, bi2, bi3 : "float[:,:,:,:]" + Values of codomain basis functions in direction 1, 2 and 3. The indexing is + [global element, local basis function, derivative, quadrature point]. + bj1, bj2, bj3 : "float[:,:,:,:]" + Values of domain basis functions in direction 1, 2 and 3, same indexing convention as bi1, bi2, bi3. + mat_fun : "float[:,:,:]" + Function under the integral evaluated at quadrature points (flattened in each direction). + The indexing is [flattened quad. point dir. 1, flattened quad. point dir. 2, flattened quad. point dir. 3]. + data : "float[:,:,:,:,:,:]" + _data array of StencilMatrix to store the results. + """ ne1 = spans1.size ne2 = spans2.size ne3 = spans3.size - nq1 = w1.shape[1] - nq2 = w2.shape[1] - nq3 = w3.shape[1] + nq1 = shape(w1)[1] + nq2 = shape(w2)[1] + nq3 = shape(w3)[1] + + tmp_bi1 = np.zeros(nq1) + tmp_bi2 = np.zeros(nq2) + tmp_bi3 = np.zeros(nq3) + + tmp_bj1 = np.zeros(nq1) + tmp_bj2 = np.zeros(nq2) + tmp_bj3 = np.zeros(nq3) + + tmp_w1 = np.zeros(nq1) + tmp_w2 = np.zeros(nq2) + tmp_w3 = np.zeros(nq3) + + tmp_mat_fun = np.zeros((nq1, nq2, nq3)) for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 - i_local1 = i_global1 - starts1 + tmp_mat_fun[:, :, :] = mat_fun[ + iel1 * nq1 : (iel1 + 1) * nq1, + iel2 * nq2 : (iel2 + 1) * nq2, + iel3 * nq3 : (iel3 + 1) * nq3, + ] - for il2 in range(pi2 + 1): - i_global2 = spans2[iel2] - pi2 + il2 - i_local2 = i_global2 - starts2 + tmp_w1[:] = w1[iel1, :] + tmp_w2[:] = w2[iel2, :] + tmp_w3[:] = w3[iel3, :] + for il1 in range(pi1 + 1): + for il2 in range(pi2 + 1): for il3 in range(pi3 + 1): + tmp_bi1[:] = bi1[iel1, il1, 0, :] + tmp_bi2[:] = bi2[iel2, il2, 0, :] + tmp_bi3[:] = bi3[iel3, il3, 0, :] + + # global spline indices + i_global1 = spans1[iel1] - pi1 + il1 + i_global2 = spans2[iel2] - pi2 + il2 i_global3 = spans3[iel3] - pi3 + il3 + + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_local1 = i_global1 - starts1 + i_local2 = i_global2 - starts2 i_local3 = i_global3 - starts3 for jl1 in range(pj1 + 1): for jl2 in range(pj2 + 1): for jl3 in range(pj3 + 1): + tmp_bj1[:] = bj1[iel1, jl1, 0, :] + tmp_bj2[:] = bj2[iel2, jl2, 0, :] + tmp_bj3[:] = bj3[iel3, jl3, 0, :] + value = 0.0 for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - bj_1 = bj1[iel1, jl1, 0, q1] - w_1 = w1[iel1, q1] - for q2 in range(nq2): - bi_12 = bi_1 * bi2[iel2, il2, 0, q2] - bj_12 = bj_1 * bj2[iel2, jl2, 0, q2] - w_12 = w_1 * w2[iel2, q2] - for q3 in range(nq3): - value += ( - w_12 - * w3[iel3, q3] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] - * bi_12 - * bi3[iel3, il3, 0, q3] - * bj_12 - * bj3[iel3, jl3, 0, q3] + wvol = ( + tmp_w1[q1] * tmp_w2[q2] * tmp_w3[q3] * tmp_mat_fun[q1, q2, q3] ) + bi = tmp_bi1[q1] * tmp_bi2[q2] * tmp_bi3[q3] + bj = tmp_bj1[q1] * tmp_bj2[q2] * tmp_bj3[q3] + + value += wvol * bi * bj + data[ pads1 + i_local1, pads2 + i_local2, @@ -370,48 +579,72 @@ def kernel_3d_vec( mat_fun: "float[:,:,:]", data: "float[:,:,:]", ): - """Apply a 3D mass operator.""" + """ + Performs the integration of Lambda_(i1,i2,i3) * mat_fun(eta1, eta2, eta3) for the basis functions (i1,i2,i3) available on the calling process. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1, spans2, spans3 : array[int] + Arrays of span indices in direction 1, 2 and 3; the span is the index of the last non-vanishing spline + on each grid element (cell). The length of each array is the number of elements (cells) in that direction. + pi1, pi2, pi3 : int + Degree of the basis functions in direction 1, 2 and 3. + starts1, starts2, starts3 : int + Starting index on the current rank, in direction 1, 2 and 3. + pads1, pads2, pads3 : int + Padding (=spline degree) for ghost regions in data, in direction 1, 2 and 3. + w1, w2, w3 : "float[:,:]" + Quadrature weights in direction 1, 2 and 3. The indexing is [global element, quadrature point]. + bi1, bi2, bi3 : "float[:,:,:,:]" + Values of basis functions in direction 1, 2 and 3. The indexing is + [global element, local basis function, derivative, quadrature point]. + mat_fun : "float[:,:,:]" + Function under the integral evaluated at quadrature points (flattened in each direction). + The indexing is [flattened quad. point dir. 1, flattened quad. point dir. 2, flattened quad. point dir. 3]. + data : "float[:,:,:]" + _data array of StencilVector to store the results. + """ ne1 = spans1.size ne2 = spans2.size ne3 = spans3.size - nq1 = w1.shape[1] - nq2 = w2.shape[1] - nq3 = w3.shape[1] + nq1 = shape(w1)[1] + nq2 = shape(w2)[1] + nq3 = shape(w3)[1] for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 - i_local1 = i_global1 - starts1 - for il2 in range(pi2 + 1): - i_global2 = spans2[iel2] - pi2 + il2 - i_local2 = i_global2 - starts2 - for il3 in range(pi3 + 1): + # global spline indices + i_global1 = spans1[iel1] - pi1 + il1 + i_global2 = spans2[iel2] - pi2 + il2 i_global3 = spans3[iel3] - pi3 + il3 + + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_local1 = i_global1 - starts1 + i_local2 = i_global2 - starts2 i_local3 = i_global3 - starts3 value = 0.0 for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - w_1 = w1[iel1, q1] - for q2 in range(nq2): - bi_12 = bi_1 * bi2[iel2, il2, 0, q2] - w_12 = w_1 * w2[iel2, q2] - for q3 in range(nq3): - value += ( - w_12 + wvol = ( + w1[iel1, q1] + * w2[iel2, q2] * w3[iel3, q3] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] - * bi_12 - * bi3[iel3, il3, 0, q3] + ) + + value += ( + wvol * bi1[iel1, il1, 0, q1] * bi2[iel2, il2, 0, q2] * bi3[iel3, il3, 0, q3] ) data[pads1 + i_local1, pads2 + i_local2, pads3 + i_local3] += value @@ -436,7 +669,31 @@ def kernel_3d_eval( coeffs_data: "float[:,:,:]", values: "float[:,:,:]", ): - """Evaluate a 3D spline function at quadrature points.""" + """ + Evaluates sum_(i1,i2,i3) [ coeffs_{i1,i2,i3} * Lambda_{i1,i2,i3}(quad_eta1, quad_eta2, quad_eta3) ] for all quadrature points on the calling process. + + The results are written into values. + + Parameters + ---------- + spans1, spans2, spans3 : array[int] + Arrays of span indices in direction 1, 2 and 3; the span is the index of the last non-vanishing spline + on each grid element (cell). The length of each array is the number of elements (cells) in that direction. + pi1, pi2, pi3 : int + Degree of the basis functions in direction 1, 2 and 3. + starts1, starts2, starts3 : int + Starting index on the current rank, in direction 1, 2 and 3. + pads1, pads2, pads3 : int + Padding (=spline degree) for ghost regions in coeffs_data, in direction 1, 2 and 3. + bi1, bi2, bi3 : "float[:,:,:,:]" + Values of basis functions in direction 1, 2 and 3. The indexing is + [global element, local basis function, derivative, quadrature point]. + coeffs_data : "float[:,:,:]" + _data array of StencilVector holding the spline coefficients of the function to be evaluated. + values : "float[:,:,:]" + Output array (flattened over elements and quadrature points in each direction) holding the evaluated + function values; it is set to zero at the start of the kernel, i.e. it is overwritten, not added to. + """ values[:, :, :] = 0.0 @@ -444,36 +701,34 @@ def kernel_3d_eval( ne2 = spans2.size ne3 = spans3.size - nq1 = bi1.shape[3] - nq2 = bi2.shape[3] - nq3 = bi3.shape[3] + nq1 = shape(bi1)[3] + nq2 = shape(bi2)[3] + nq3 = shape(bi3)[3] for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 - i_local1 = i_global1 - starts1 - for il2 in range(pi2 + 1): - i_global2 = spans2[iel2] - pi2 + il2 - i_local2 = i_global2 - starts2 - for il3 in range(pi3 + 1): + # global spline indices + i_global1 = spans1[iel1] - pi1 + il1 + i_global2 = spans2[iel2] - pi2 + il2 i_global3 = spans3[iel3] - pi3 + il3 - i_local3 = i_global3 - starts3 - coeff = coeffs_data[pads1 + i_local1, pads2 + i_local2, pads3 + i_local3] + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_local1 = i_global1 - starts1 + i_local2 = i_global2 - starts2 + i_local3 = i_global3 - starts3 for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - for q2 in range(nq2): - bi_12 = bi_1 * bi2[iel2, il2, 0, q2] - for q3 in range(nq3): values[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] += ( - coeff * bi_12 * bi3[iel3, il3, 0, q3] + coeffs_data[pads1 + i_local1, pads2 + i_local2, pads3 + i_local3] + * bi1[iel1, il1, 0, q1] + * bi2[iel2, il2, 0, q2] + * bi3[iel3, il3, 0, q3] ) @@ -515,68 +770,135 @@ def kernel_3d_matrixfree( data_out: "float[:,:,:]", data_in: "float[:,:,:]", ): - """Apply a 3D mass matrix without assembling it.""" + """ + Performs the integration of Lambda_(i1, i2, i3) * mat_fun(eta1, eta2, eta3) * f(eta1, eta2, eta3) for the basis functions (i1, i2, i3) available on the calling process, + where f is the spline function represented by the coefficients in data_in. + + The results are written into data_out (attention: data_out is NOT set to zero first, but the results are added to data_out). + This computes the action of the mass matrix on a vector without ever assembling the matrix itself. + + Parameters + ---------- + spansi1, spansi2, spansi3 : array[int] + Arrays of span indices in direction 1, 2 and 3 for the codomain ("i") basis functions; the span is the + index of the last non-vanishing spline on each grid element (cell). + spansj1, spansj2, spansj3 : array[int] + Arrays of span indices in direction 1, 2 and 3 for the domain ("j") basis functions. + pi1, pi2, pi3 : int + Degree of the codomain basis functions in direction 1, 2 and 3. + pj1, pj2, pj3 : int + Degree of the domain basis functions in direction 1, 2 and 3. + startsi1, startsi2, startsi3 : int + Starting index on the current rank for the codomain basis functions, in direction 1, 2 and 3. + startsj1, startsj2, startsj3 : int + Starting index on the current rank for the domain basis functions, in direction 1, 2 and 3. + padsi1, padsi2, padsi3 : int + Padding (=spline degree) for ghost regions in data_out, in direction 1, 2 and 3. + padsj1, padsj2, padsj3 : int + Padding (=spline degree) for ghost regions in data_in, in direction 1, 2 and 3. + w1, w2, w3 : "float[:,:]" + Quadrature weights in direction 1, 2 and 3. The indexing is [global element, quadrature point]. + bi1, bi2, bi3 : "float[:,:,:,:]" + Values of codomain basis functions in direction 1, 2 and 3. The indexing is + [global element, local basis function, derivative, quadrature point]. + bj1, bj2, bj3 : "float[:,:,:,:]" + Values of domain basis functions in direction 1, 2 and 3, same indexing convention as bi1, bi2, bi3. + mat_fun : "float[:,:,:]" + Function under the integral evaluated at quadrature points (flattened in each direction). + The indexing is [flattened quad. point dir. 1, flattened quad. point dir. 2, flattened quad. point dir. 3]. + data_out : "float[:,:,:]" + _data array of StencilVector to store the results of the matrix-vector product. + data_in : "float[:,:,:]" + _data array of StencilVector holding the spline coefficients of the input function f. + """ ne1 = spansi1.size ne2 = spansi2.size ne3 = spansi3.size - nq1 = w1.shape[1] - nq2 = w2.shape[1] - nq3 = w3.shape[1] + nq1 = shape(w1)[1] + nq2 = shape(w2)[1] + nq3 = shape(w3)[1] + + tmp_w1 = np.zeros(nq1) + tmp_w2 = np.zeros(nq2) + tmp_w3 = np.zeros(nq3) + + tmp_bi1 = np.zeros(pi1 + 1) + tmp_bi2 = np.zeros(pi2 + 1) + tmp_bi3 = np.zeros(pi3 + 1) + + tmp_bj1 = np.zeros(pj1 + 1) + tmp_bj2 = np.zeros(pj2 + 1) + tmp_bj3 = np.zeros(pj3 + 1) + + tmp_mat_fun = np.zeros((nq1, nq2, nq3)) for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): + tmp_mat_fun[:, :, :] = mat_fun[ + iel1 * nq1 : (iel1 + 1) * nq1, + iel2 * nq2 : (iel2 + 1) * nq2, + iel3 * nq3 : (iel3 + 1) * nq3, + ] + + tmp_w1[:] = w1[iel1, :] + tmp_w2[:] = w2[iel2, :] + tmp_w3[:] = w3[iel3, :] + for q1 in range(nq1): for q2 in range(nq2): for q3 in range(nq3): - bj = 0.0 - - for jl1 in range(pj1 + 1): - j_global1 = spansj1[iel1] - pj1 + jl1 - j_local1 = j_global1 - startsj1 + padsj1 + tmp_bi1[:] = bi1[iel1, :, 0, q1] + tmp_bi2[:] = bi2[iel2, :, 0, q2] + tmp_bi3[:] = bi3[iel3, :, 0, q3] - bj_1 = bj1[iel1, jl1, 0, q1] + tmp_bj1[:] = bj1[iel1, :, 0, q1] + tmp_bj2[:] = bj2[iel2, :, 0, q2] + tmp_bj3[:] = bj3[iel3, :, 0, q3] + bj = 0.0 + for jl1 in range(pj1 + 1): for jl2 in range(pj2 + 1): - j_global2 = spansj2[iel2] - pj2 + jl2 - j_local2 = j_global2 - startsj2 + padsj2 - - bj_12 = bj_1 * bj2[iel2, jl2, 0, q2] - for jl3 in range(pj3 + 1): + # global spline indices + j_global1 = spansj1[iel1] - pj1 + jl1 + j_global2 = spansj2[iel2] - pj2 + jl2 j_global3 = spansj3[iel3] - pj3 + jl3 - j_local3 = j_global3 - startsj3 + padsj3 - bj += bj_12 * bj3[iel3, jl3, 0, q3] * data_in[j_local1, j_local2, j_local3] + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + j_local1 = j_global1 - startsj1 + padsj1 + j_local2 = j_global2 - startsj2 + padsj2 + j_local3 = j_global3 - startsj3 + padsj3 - wvol = ( - w1[iel1, q1] - * w2[iel2, q2] - * w3[iel3, q3] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] - ) + bj += ( + tmp_bj1[jl1] + * tmp_bj2[jl2] + * tmp_bj3[jl3] + * data_in[j_local1, j_local2, j_local3] + ) for il1 in range(pi1 + 1): - i_global1 = spansi1[iel1] - pi1 + il1 - i_local1 = i_global1 - startsi1 + padsi1 - - bi_1 = bi1[iel1, il1, 0, q1] - for il2 in range(pi2 + 1): - i_global2 = spansi2[iel2] - pi2 + il2 - i_local2 = i_global2 - startsi2 + padsi2 - - bi_12 = bi_1 * bi2[iel2, il2, 0, q2] - for il3 in range(pi3 + 1): + # global spline indices + i_global1 = spansi1[iel1] - pi1 + il1 + i_global2 = spansi2[iel2] - pi2 + il2 i_global3 = spansi3[iel3] - pi3 + il3 + + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_local1 = i_global1 - startsi1 + padsi1 + i_local2 = i_global2 - startsi2 + padsi2 i_local3 = i_global3 - startsi3 + padsi3 - data_out[i_local1, i_local2, i_local3] += ( - wvol * bi_12 * bi3[iel3, il3, 0, q3] * bj - ) + wvol = tmp_w1[q1] * tmp_w2[q2] * tmp_w3[q3] * tmp_mat_fun[q1, q2, q3] + + bi = tmp_bi1[il1] * tmp_bi2[il2] * tmp_bi3[il3] + + value = wvol * bi * bj + + data_out[i_local1, i_local2, i_local3] += value def kernel_3d_diag( @@ -601,71 +923,109 @@ def kernel_3d_diag( mat_fun: "float[:,:,:]", data: "float[:,:,:]", ): - """Compute the diagonal of a 3D mass matrix.""" + """ + Computes the diagonal of a mass matrix, assuming that the domain and the codomain are the same. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1, spans2, spans3 : array[int] + Arrays of span indices in direction 1, 2 and 3; the span is the index of the last non-vanishing spline + on each grid element (cell). The length of each array is the number of elements (cells) in that direction. + pi1, pi2, pi3 : int + Degree of the basis functions in direction 1, 2 and 3. + starts1, starts2, starts3 : int + Starting index on the current rank, in direction 1, 2 and 3. + pads1, pads2, pads3 : int + Padding (=spline degree) for ghost regions, in direction 1, 2 and 3 (unused for data, which is a + StencilDiagonalMatrix and therefore has no padding, but kept for a uniform kernel signature). + w1, w2, w3 : "float[:,:]" + Quadrature weights in direction 1, 2 and 3. The indexing is [global element, quadrature point]. + bi1, bi2, bi3 : "float[:,:,:,:]" + Values of basis functions in direction 1, 2 and 3. The indexing is + [global element, local basis function, derivative, quadrature point]. + mat_fun : "float[:,:,:]" + Function under the integral evaluated at quadrature points (flattened in each direction). + The indexing is [flattened quad. point dir. 1, flattened quad. point dir. 2, flattened quad. point dir. 3]. + data : "float[:,:,:]" + _data array of StencilDiagonalMatrix to store the results. Periodic wrap-around (index -= nb) is applied + when a local index runs beyond the array bounds, since there are no ghost regions on this matrix type. + """ ne1 = spans1.size ne2 = spans2.size ne3 = spans3.size - nq1 = w1.shape[1] - nq2 = w2.shape[1] - nq3 = w3.shape[1] + nq1 = shape(w1)[1] + nq2 = shape(w2)[1] + nq3 = shape(w3)[1] + + nb1, nb2, nb3 = data.shape - nb1 = data.shape[0] - nb2 = data.shape[1] - nb3 = data.shape[2] + tmp_bi1 = np.zeros(nq1) + tmp_bi2 = np.zeros(nq2) + tmp_bi3 = np.zeros(nq3) + + tmp_w1 = np.zeros(nq1) + tmp_w2 = np.zeros(nq2) + tmp_w3 = np.zeros(nq3) + + tmp_mat_fun = np.zeros((nq1, nq2, nq3)) for iel1 in range(ne1): for iel2 in range(ne2): for iel3 in range(ne3): - for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 - i_local1 = i_global1 - starts1 + tmp_mat_fun[:, :, :] = mat_fun[ + iel1 * nq1 : (iel1 + 1) * nq1, + iel2 * nq2 : (iel2 + 1) * nq2, + iel3 * nq3 : (iel3 + 1) * nq3, + ] - if i_local1 >= nb1: - i_local1 -= nb1 + tmp_w1[:] = w1[iel1, :] + tmp_w2[:] = w2[iel2, :] + tmp_w3[:] = w3[iel3, :] + for il1 in range(pi1 + 1): for il2 in range(pi2 + 1): - i_global2 = spans2[iel2] - pi2 + il2 - i_local2 = i_global2 - starts2 - - if i_local2 >= nb2: - i_local2 -= nb2 - for il3 in range(pi3 + 1): + tmp_bi1[:] = bi1[iel1, il1, 0, :] + tmp_bi2[:] = bi2[iel2, il2, 0, :] + tmp_bi3[:] = bi3[iel3, il3, 0, :] + + # global spline indices + i_global1 = spans1[iel1] - pi1 + il1 + i_global2 = spans2[iel2] - pi2 + il2 i_global3 = spans3[iel3] - pi3 + il3 + + # local spline indices (- starts --> can be negative, will therefore be written to ghost regions) + i_local1 = i_global1 - starts1 + i_local2 = i_global2 - starts2 i_local3 = i_global3 - starts3 + # Periodic case : last basis function are the first ones (no ghost regions on DiagonalStencilMatrix) + if i_local1 >= nb1: + i_local1 -= nb1 + + if i_local2 >= nb2: + i_local2 -= nb2 + if i_local3 >= nb3: i_local3 -= nb3 value = 0.0 for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - for q2 in range(nq2): - bi_12 = bi_1 * bi2[iel2, il2, 0, q2] - for q3 in range(nq3): - value += ( - w1[iel1, q1] - * w2[iel2, q2] - * w3[iel3, q3] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2, iel3 * nq3 + q3] - * bi_12 - * bi3[iel3, il3, 0, q3] - * bi_12 - * bi3[iel3, il3, 0, q3] - / (bi2[iel2, il2, 0, q2] * bi3[iel3, il3, 0, q3]) - ) + wvol = tmp_w1[q1] * tmp_w2[q2] * tmp_w3[q3] * tmp_mat_fun[q1, q2, q3] - data[i_local1, i_local2, i_local3] += value + bi = tmp_bi1[q1] * tmp_bi2[q2] * tmp_bi3[q3] + value += wvol * bi * bi -# ====================================================================== -# 3D surface kernels -# ====================================================================== + # No padding on StencilDiagonalMatrix + data[i_local1, i_local2, i_local3] += value def surface_kernel_3d_vec( @@ -688,39 +1048,70 @@ def surface_kernel_3d_vec( mat_fun: "float[:,:]", data: "float[:,:,:]", ): - """Integrate a scalar function over a fixed 3D boundary surface.""" + """ + Performs the integration of Lambda_0ij * mat_fun(eta1, eta2) over the boundary surface at the fixed + (normal-direction) global index boundary_index, for the basis functions (ij) available on the calling + process in the two surface (tangential) directions. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1, spans2 : array[int] + Arrays of span indices in the two surface (tangential) directions; the span is the index of the last + non-vanishing spline on each grid element (cell) in that direction. + pi0 : int + Degree of the basis function in the normal direction (kept for a uniform kernel signature; not used + directly since the normal index is fixed to boundary_index). + pi1, pi2 : int + Degree of the basis functions in the two surface directions. + starts0 : int + Starting index on the current rank in the normal direction. + starts1, starts2 : int + Starting index on the current rank in the two surface directions. + pads0 : int + Padding (=spline degree) for ghost regions in data, in the normal direction. + pads1, pads2 : int + Padding (=spline degree) for ghost regions in data, in the two surface directions. + w1, w2 : "float[:,:]" + Quadrature weights in the two surface directions. The indexing is [global element, quadrature point]. + bi1, bi2 : "float[:,:,:,:]" + Values of basis functions in the two surface directions. The indexing is + [global element, local basis function, derivative, quadrature point]. + boundary_index : int + Global index in the normal direction at which the boundary surface is located. + mat_fun : "float[:,:]" + Function under the integral evaluated at surface quadrature points (flattened in each surface direction). + data : "float[:,:,:]" + _data array of StencilVector to store the results; only the slice at the fixed normal index + (pads0 + i_local0) is written. + """ ne1 = spans1.size ne2 = spans2.size - nq1 = w1.shape[1] - nq2 = w2.shape[1] + nq1 = shape(w1)[1] + nq2 = shape(w2)[1] i_local0 = boundary_index - starts0 for iel1 in range(ne1): for iel2 in range(ne2): for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 - i_local1 = i_global1 - starts1 - for il2 in range(pi2 + 1): + i_global1 = spans1[iel1] - pi1 + il1 i_global2 = spans2[iel2] - pi2 + il2 + + i_local1 = i_global1 - starts1 i_local2 = i_global2 - starts2 value = 0.0 for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - for q2 in range(nq2): - value += ( - w1[iel1, q1] - * w2[iel2, q2] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] - * bi_1 - * bi2[iel2, il2, 0, q2] - ) + wvol = w1[iel1, q1] * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] + + value += wvol * bi1[iel1, il1, 0, q1] * bi2[iel2, il2, 0, q2] data[pads0 + i_local0, pads1 + i_local1, pads2 + i_local2] += value @@ -752,138 +1143,126 @@ def surface_kernel_3d_mat( data: "float[:,:,:,:,:,:]", ): """ - Assemble a surface mass matrix. - - No Python lists, tuples, comprehensions, dictionaries, sets, or other - container objects are used here. The normal direction is handled - explicitly to avoid generating gFTL container dependencies. + Assembles a boundary (surface) mass matrix: the integration of Lambda_i * mat_fun(eta_s1, eta_s2) * Lambda_j + over the boundary surface at the fixed global index boundary_index in the normal_dir direction, for the + codomain ("i") and domain ("j") basis functions available on the calling process in the two tangential + directions orthogonal to normal_dir. + + The results are written into data (attention: data is NOT set to zero first, but the results are added to data). + + Parameters + ---------- + spans1, spans2 : array[int] + Arrays of span indices in the two tangential grid directions used for the surface quadrature (as + determined by normal_dir); the span is the index of the last non-vanishing spline on each grid element. + pi0, pi1, pi2 : int + Degree of the codomain basis functions along logical axes 0, 1 and 2. + pj0, pj1, pj2 : int + Degree of the domain basis functions along logical axes 0, 1 and 2. + starts0, starts1, starts2 : int + Starting index on the current rank along logical axes 0, 1 and 2. + pads0, pads1, pads2 : int + Padding (=spline degree) for ghost regions in data, along logical axes 0, 1 and 2. + w1, w2 : "float[:,:]" + Quadrature weights in the two tangential directions. The indexing is [global element, quadrature point]. + bi1, bi2 : "float[:,:,:,:]" + Values of codomain basis functions in the two tangential directions. The indexing is + [global element, local basis function, derivative, quadrature point]. + bj1, bj2 : "float[:,:,:,:]" + Values of domain basis functions in the two tangential directions, same indexing convention as bi1, bi2. + boundary_index : int + Global index along normal_dir at which the boundary surface is located. + normal_dir : int + Logical direction (0, 1 or 2) normal to the surface; the remaining two directions are the tangential + directions used for the surface integration. + mat_fun : "float[:,:]" + Function under the integral evaluated at surface quadrature points (flattened in each tangential direction). + data : "float[:,:,:,:,:,:]" + _data array of StencilMatrix to store the results. """ ne1 = spans1.size ne2 = spans2.size - nq1 = w1.shape[1] - nq2 = w2.shape[1] + nq1 = shape(w1)[1] + nq2 = shape(w2)[1] - if normal_dir == 0: - i_local_n = boundary_index - starts0 + starts = [starts0, starts1, starts2] + pads = [pads0, pads1, pads2] + pi = [pi0, pi1, pi2] + pj = [pj0, pj1, pj2] - for iel1 in range(ne1): - for iel2 in range(ne2): - for il1 in range(pi1 + 1): - i_global1 = spans1[iel1] - pi1 + il1 - i_local1 = i_global1 - starts1 + surf_dirs = [d for d in range(3) if d != normal_dir] - for il2 in range(pi2 + 1): - i_global2 = spans2[iel2] - pi2 + il2 - i_local2 = i_global2 - starts2 + pi_s1 = pi[surf_dirs[0]] + pi_s2 = pi[surf_dirs[1]] - for jl1 in range(pj1 + 1): - for jl2 in range(pj2 + 1): - value = 0.0 + pj_s1 = pj[surf_dirs[0]] + pj_s2 = pj[surf_dirs[1]] - for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - bj_1 = bj1[iel1, jl1, 0, q1] + starts_n = starts[normal_dir] + starts_s1 = starts[surf_dirs[0]] + starts_s2 = starts[surf_dirs[1]] - for q2 in range(nq2): - value += ( - w1[iel1, q1] - * w2[iel2, q2] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] - * bi_1 - * bi2[iel2, il2, 0, q2] - * bj_1 - * bj2[iel2, jl2, 0, q2] - ) + pads_n = pads[normal_dir] + pads_s1 = pads[surf_dirs[0]] + pads_s2 = pads[surf_dirs[1]] - data[ - pads0 + i_local_n, - pads1 + i_local1, - pads2 + i_local2, - pads0, - pads1 + jl1 - il1, - pads2 + jl2 - il2, - ] += value + i_local_n = boundary_index - starts_n - elif normal_dir == 1: - i_local_n = boundary_index - starts1 + for iel1 in range(ne1): + for iel2 in range(ne2): + for il1 in range(pi_s1 + 1): + for il2 in range(pi_s2 + 1): + i_global1 = spans1[iel1] - pi_s1 + il1 + i_global2 = spans2[iel2] - pi_s2 + il2 - for iel1 in range(ne1): - for iel2 in range(ne2): - for il1 in range(pi0 + 1): - i_global1 = spans1[iel1] - pi0 + il1 - i_local1 = i_global1 - starts0 + i_local1 = i_global1 - starts_s1 + i_local2 = i_global2 - starts_s2 - for il2 in range(pi2 + 1): - i_global2 = spans2[iel2] - pi2 + il2 - i_local2 = i_global2 - starts2 + for jl1 in range(pj_s1 + 1): + for jl2 in range(pj_s2 + 1): + j_local1 = jl1 - il1 + j_local2 = jl2 - il2 - for jl1 in range(pj0 + 1): - for jl2 in range(pj2 + 1): - value = 0.0 + value = 0.0 - for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - bj_1 = bj1[iel1, jl1, 0, q1] + for q1 in range(nq1): + for q2 in range(nq2): + wvol = w1[iel1, q1] * w2[iel2, q2] * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] - for q2 in range(nq2): - value += ( - w1[iel1, q1] - * w2[iel2, q2] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] - * bi_1 - * bi2[iel2, il2, 0, q2] - * bj_1 - * bj2[iel2, jl2, 0, q2] - ) + value += ( + wvol + * bi1[iel1, il1, 0, q1] + * bi2[iel2, il2, 0, q2] + * bj1[iel1, jl1, 0, q1] + * bj2[iel2, jl2, 0, q2] + ) + if normal_dir == 0: data[ - pads0 + i_local1, - pads1 + i_local_n, - pads2 + i_local2, - pads0 + jl1 - il1, - pads1, - pads2 + jl2 - il2, + pads_n + i_local_n, + pads_s1 + i_local1, + pads_s2 + i_local2, + pads_n, + pads_s1 + j_local1, + pads_s2 + j_local2, ] += value - - else: - i_local_n = boundary_index - starts2 - - for iel1 in range(ne1): - for iel2 in range(ne2): - for il1 in range(pi0 + 1): - i_global1 = spans1[iel1] - pi0 + il1 - i_local1 = i_global1 - starts0 - - for il2 in range(pi1 + 1): - i_global2 = spans2[iel2] - pi1 + il2 - i_local2 = i_global2 - starts1 - - for jl1 in range(pj0 + 1): - for jl2 in range(pj1 + 1): - value = 0.0 - - for q1 in range(nq1): - bi_1 = bi1[iel1, il1, 0, q1] - bj_1 = bj1[iel1, jl1, 0, q1] - - for q2 in range(nq2): - value += ( - w1[iel1, q1] - * w2[iel2, q2] - * mat_fun[iel1 * nq1 + q1, iel2 * nq2 + q2] - * bi_1 - * bi2[iel2, il2, 0, q2] - * bj_1 - * bj2[iel2, jl2, 0, q2] - ) - + elif normal_dir == 1: + data[ + pads_s1 + i_local1, + pads_n + i_local_n, + pads_s2 + i_local2, + pads_s1 + j_local1, + pads_n, + pads_s2 + j_local2, + ] += value + else: data[ - pads0 + i_local1, - pads1 + i_local2, - pads2 + i_local_n, - pads0 + jl1 - il1, - pads1 + jl2 - il2, - pads2, + pads_s1 + i_local1, + pads_s2 + i_local2, + pads_n + i_local_n, + pads_s1 + j_local1, + pads_s2 + j_local2, + pads_n, ] += value diff --git a/src/struphy/feec/mass_kernels_cuda.py b/src/struphy/feec/mass_kernels_cuda.py deleted file mode 100644 index a515d8d73..000000000 --- a/src/struphy/feec/mass_kernels_cuda.py +++ /dev/null @@ -1,201 +0,0 @@ -"""CUDA implementations of matrix-free FEEC mass-operator kernels. - -These kernels are used only when ``ARRAY_BACKEND=cupy``. The corresponding -Pyccel kernels accept CuPy arrays, but execute their nested loops on the host; -the routines below keep both the quadrature data and coefficient vectors on -the device. -""" -from struphy.cuda import CudaKernel, launch_1d, load_cuda_source - -_H1VEC_DIVERGENCE_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_h1vec_divergence_src.cu") - -_divergence_eval_kernel = CudaKernel(_H1VEC_DIVERGENCE_SRC, "h1vec_divergence_eval_cuda") -_divergence_transpose_kernel = CudaKernel(_H1VEC_DIVERGENCE_SRC, "h1vec_divergence_transpose_cuda") - -_MASS_ASSEMBLY_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_mass_assembly_src.cu") - -_mass_assembly_kernel = CudaKernel(_MASS_ASSEMBLY_SRC, "mass_3d_assemble_cuda") - -_WEAK_DIV_ASSEMBLY_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_weak_div_assembly_src.cu") - -_weak_div_assembly_kernel = CudaKernel(_WEAK_DIV_ASSEMBLY_SRC, "weak_div_assemble_cuda") - -_H1VEC_DIVDIV_ASSEMBLY_SRC = load_cuda_source(__file__, "mass_kernels_cuda/_h1vec_divdiv_assembly_src.cu") - -_h1vec_divdiv_assembly_kernel = CudaKernel(_H1VEC_DIVDIV_ASSEMBLY_SRC, "h1vec_divdiv_assemble_cuda") - - -def _kernel_args(spans, degree, starts, pads, bases, dlogj, component): - import cupy as cp - import numpy as np - - spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) - bases = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases) - dlogj = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in dlogj) - return ( - *spans, - np.int32(spans[0].size), - np.int32(spans[1].size), - np.int32(spans[2].size), - *(np.int32(x) for x in degree), - *(np.int32(x) for x in starts), - *(np.int32(x) for x in pads), - *bases, - np.int32(bases[0].shape[2]), - np.int32(bases[1].shape[2]), - np.int32(bases[2].shape[2]), - np.int32(bases[0].shape[3]), - np.int32(bases[1].shape[3]), - np.int32(bases[2].shape[3]), - *dlogj, - np.int32(component), - ) - - -def h1vec_divergence_eval_gpu(spans, degree, starts, pads, bases, dlogj, component, coeffs, values): - """Add one H1-vector component's divergence to device ``values``.""" - import numpy as np - - args = _kernel_args(spans, degree, starts, pads, bases, dlogj, component) - nvalues = values.size - launch_1d( - _divergence_eval_kernel, - nvalues, - (*args, coeffs, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), values), - ) - - -def h1vec_divergence_transpose_gpu(spans, degree, starts, pads, bases, dlogj, component, values, coeffs): - """Accumulate the transpose of one H1-vector divergence component.""" - import numpy as np - - args = _kernel_args(spans, degree, starts, pads, bases, dlogj, component) - nvalues = values.size - launch_1d( - _divergence_transpose_kernel, - nvalues, - (*args, values, np.int32(coeffs.shape[1]), np.int32(coeffs.shape[2]), coeffs), - ) - - -def mass_3d_assemble_gpu(spans, degree_i, degree_j, starts, pads, weights, bases_i, bases_j, mat_fun, data): - """Assemble a 3D weighted mass matrix directly into device stencil data.""" - import cupy as cp - import numpy as np - - spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) - weights = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in weights) - bases_i = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_i) - bases_j = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_j) - mat_fun = cp.ascontiguousarray(mat_fun) - total = int( - np.prod([x.size for x in spans]) * np.prod([x + 1 for x in degree_i]) * np.prod([x + 1 for x in degree_j]) - ) - launch_1d( - _mass_assembly_kernel, - total, - ( - *spans, - *(np.int32(x.size) for x in spans), - *(np.int32(x) for x in degree_i), - *(np.int32(x) for x in degree_j), - *(np.int32(x) for x in starts), - *(np.int32(x) for x in pads), - *weights, - *(np.int32(x.shape[1]) for x in weights), - *bases_i, - *bases_j, - *(np.int32(x.shape[2]) for x in bases_i), - *(np.int32(x.shape[2]) for x in bases_j), - mat_fun, - data, - *(np.int32(x) for x in data.shape[1:]), - ), - ) - - -def weak_divergence_assemble_gpu( - spans, - degree_i, - degree_j, - starts, - pads, - weights, - bases_i, - bases_j, - mat_fun, - dlogj, - component, - data, -): - """Assemble one L2-by-H1 weak-divergence block on the GPU.""" - import cupy as cp - import numpy as np - - spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) - weights = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in weights) - bases_i = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_i) - bases_j = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases_j) - dlogj = tuple(cp.ascontiguousarray(x) for x in dlogj) - mat_fun = cp.ascontiguousarray(mat_fun) - total = int( - np.prod([x.size for x in spans]) * np.prod([p + 1 for p in degree_i]) * np.prod([p + 1 for p in degree_j]) - ) - launch_1d( - _weak_div_assembly_kernel, - total, - ( - *spans, - *(np.int32(x.size) for x in spans), - *(np.int32(x) for x in degree_i), - *(np.int32(x) for x in degree_j), - *(np.int32(x) for x in starts), - *(np.int32(x) for x in pads), - *weights, - *(np.int32(x.shape[1]) for x in weights), - *bases_i, - *bases_j, - *(np.int32(x.shape[2]) for x in bases_i), - *(np.int32(x.shape[2]) for x in bases_j), - mat_fun, - *dlogj, - np.int32(component), - data, - *(np.int32(x) for x in data.shape[1:]), - ), - ) - - -def h1vec_divdiv_assemble_gpu(spans, degree, starts, pads, bases, weighted_rho, component_test, component_trial, data): - """Assemble one H1-vector div-div block on the GPU. - - This mirrors the existing Pyccel kernel exactly, including its current - affine-mapping formulation where the log-Jacobian terms vanish. - """ - import cupy as cp - import numpy as np - - spans = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.int64)) for x in spans) - bases = tuple(cp.ascontiguousarray(cp.asarray(x, dtype=cp.float64)) for x in bases) - weighted_rho = cp.ascontiguousarray(weighted_rho) - nloc = int(np.prod([x + 1 for x in degree])) - total = int(np.prod([x.size for x in spans]) * nloc * nloc) - launch_1d( - _h1vec_divdiv_assembly_kernel, - total, - ( - *spans, - *(np.int32(x.size) for x in spans), - *(np.int32(x) for x in degree), - *(np.int32(x) for x in starts), - *(np.int32(x) for x in pads), - *bases, - *(np.int32(x.shape[2]) for x in bases), - *(np.int32(x.shape[3]) for x in bases), - weighted_rho, - np.int32(component_test), - np.int32(component_trial), - data, - *(np.int32(x) for x in data.shape[1:]), - ), - ) diff --git a/src/struphy/feec/preconditioner.py b/src/struphy/feec/preconditioner.py index 24b7bb367..5bf9e957c 100644 --- a/src/struphy/feec/preconditioner.py +++ b/src/struphy/feec/preconditioner.py @@ -1,8 +1,6 @@ import logging import cunumpy as xp -import numpy as np -from cunumpy.xp import to_cunumpy, to_numpy from feectools.api.essential_bc import apply_essential_bc_stencil from feectools.ddm.cart import CartDecomposition, DomainDecomposition from feectools.ddm.mpi import MockComm @@ -262,7 +260,7 @@ def fun(e): M_local = StencilMatrix(V_local, V_local) - row_indices, col_indices = np.nonzero(M_arr) # M_arr is always a host array (StencilMatrix.toarray()) + row_indices, col_indices = xp.nonzero(M_arr) for row_i, col_i in zip(row_indices, col_indices): # only consider row indices on process @@ -275,7 +273,7 @@ def fun(e): ] = M_arr[row_i, col_i] # check if stencil matrix was built correctly - assert np.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) # both sides are host arrays + assert xp.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) matrixcells += [M_local.copy()] # ======================================================================================================= @@ -627,7 +625,7 @@ def __init__(self, mass_operator, apply_bc=True): M_local = StencilMatrix(V_local, V_local) - row_indices, col_indices = np.nonzero(M_arr) # M_arr is always a host array (StencilMatrix.toarray()) + row_indices, col_indices = xp.nonzero(M_arr) for row_i, col_i in zip(row_indices, col_indices): # only consider row indices on process @@ -640,7 +638,7 @@ def __init__(self, mass_operator, apply_bc=True): ] = M_arr[row_i, col_i] # check if stencil matrix was built correctly - assert np.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) # both sides are host arrays + assert xp.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) matrixcells += [M_local.copy()] # ======================================================================================================= @@ -913,10 +911,7 @@ class FFTSolver(BandedSolver): """ def __init__(self, circmat): - # circmat comes from StencilMatrix.toarray(), which always returns a - # host (NumPy) array; scipy.linalg.solve_circulant (used in solve()) - # is CPU-only regardless of the active cunumpy backend. - assert isinstance(circmat, np.ndarray) + assert isinstance(circmat, xp.ndarray) assert is_circulant(circmat) self._space = xp.ndarray @@ -951,30 +946,20 @@ def solve(self, rhs, out=None, transposed=False): assert rhs.T.shape[0] == self._column.size - # scipy.linalg.solve_circulant only understands NumPy; rhs may be a - # CuPy array (e.g. a view into a device-resident StencilVector), so - # convert at this CPU-solver boundary and copy the result back. - rhs_np = to_numpy(rhs) - if out is None: - out = to_cunumpy(solve_circulant(self._column, rhs_np.T).T) + out = solve_circulant(self._column, rhs.T).T else: assert out.shape == rhs.shape assert out.dtype == rhs.dtype try: - result_np = solve_circulant(self._column, rhs_np.T).T - except np.linalg.LinAlgError: + out[:] = solve_circulant(self._column, rhs.T).T + except xp.linalg.LinAlgError: eps = 1e-4 logger.info(f"Stabilizing singular preconditioning FFTSolver with {eps =}:") self._column[0] *= 1.0 + eps - result_np = solve_circulant(self._column, rhs_np.T).T - # cupy's __setitem__ can mishandle a NumPy RHS against a strided - # view (raises "non-scalar numpy.ndarray cannot be used for - # fill"); converting explicitly to the active backend first - # sidesteps that. - out[:] = to_cunumpy(result_np) + out[:] = solve_circulant(self._column, rhs.T).T return out @@ -994,15 +979,13 @@ def is_circulant(mat): Whether the matrix is circulant (=True) or not (=False). """ - # mat is always a host (NumPy) array in practice: the only callers pass - # StencilMatrix.toarray() output, which feectools always returns on the host. - assert isinstance(mat, np.ndarray) + assert isinstance(mat, xp.ndarray) assert len(mat.shape) == 2 assert mat.shape[0] == mat.shape[1] if mat.shape[0] > 1: for i in range(mat.shape[0] - 1): - circulant = np.allclose(mat[i, :], np.roll(mat[i + 1, :], -1)) + circulant = xp.allclose(mat[i, :], xp.roll(mat[i + 1, :], -1)) if not circulant: return circulant else: diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index 03926eb1d..101bcfd12 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -5,7 +5,6 @@ import cunumpy as xp import feectools.core.bsplines as bsp import numpy as np -from cunumpy import PyccelKernel from feectools.ddm.cart import DomainDecomposition from feectools.ddm.mpi import MockComm from feectools.ddm.mpi import mpi as MPI @@ -29,19 +28,6 @@ from struphy.bsplines import evaluation_kernels_3d as eval_3d from struphy.bsplines.evaluation_kernels_3d import eval_spline_mpi_tensor_product_fixed - -# Pyccel kernels only understand NumPy arrays; wrap the ones called directly -# in this module so they also work with CuPy arrays (see cunumpy.kernel). -# -# `outputs` names the arguments the kernel writes to. Without it every array -# that was copied to the host is copied back to the device afterwards, which on -# these kernels means shipping the spline coefficients and the knot vectors -# back on every single call even though only the result array changed. The -# indices refer to positional arguments, which is how they are called below. -eval_3d.eval_spline_mpi_sparse_meshgrid = PyccelKernel(eval_3d.eval_spline_mpi_sparse_meshgrid) -eval_3d.eval_spline_mpi_markers = PyccelKernel(eval_3d.eval_spline_mpi_markers) -eval_3d.eval_spline_mpi_matrix = PyccelKernel(eval_3d.eval_spline_mpi_matrix, outputs=(10,)) -eval_spline_mpi_tensor_product_fixed = PyccelKernel(eval_spline_mpi_tensor_product_fixed) from struphy.feec.linear_operators import BoundaryOperator from struphy.feec.local_projectors_kernels import get_local_problem_size, select_quasi_points from struphy.feec.projectors import CommutingProjector, CommutingProjectorLocal @@ -72,16 +58,11 @@ def _to_numpy_for_kernel(value): - """Convert CuPy arrays to NumPy for compiled kernel calls. - - xp.is_gpu, not xp.to_numpy: some callers pass plain Python scalars (e.g. a - degree or index) that must reach the compiled kernel unchanged, and - xp.to_numpy would wrap those into 0-d NumPy arrays via np.asarray -- a type - the kernel signature does not expect. xp.is_gpu leaves anything that isn't - actually a CuPy array untouched, matching the original hasattr(value, "get") - passthrough behaviour exactly. - """ - return value.get() if xp.is_gpu(value) else value + """Convert CuPy arrays to NumPy for compiled kernel calls.""" + if hasattr(value, "get"): + # This is a CuPy array + return value.get() + return value class DiscreteDerham: @@ -1944,12 +1925,11 @@ def _get_domain_array(self): else: nproc = 1 - # send buffer (host-resident: passed directly to mpi4py's Allgather, and - # consumed by struphy.pic.base.Particles, which expects a host domain_array) - dom_arr_loc = np.zeros(9, dtype=float) + # send buffer + dom_arr_loc = xp.zeros(9, dtype=float) # main array (receive buffers) - dom_arr = np.zeros(nproc * 9, dtype=float) + dom_arr = xp.zeros(nproc * 9, dtype=float) # Get global starts and ends of domain decomposition gl_s = self.domain_decomposition.starts @@ -1992,14 +1972,11 @@ def _get_index_array(self, decomposition): else: nproc = 1 - # send/receive buffers for Allgather -- rank/topology bookkeeping - # (nproc * 6 ints), always host regardless of backend: mpi4py's - # uppercase buffer-protocol Allgather needs host-readable buffers, - # and there is no benefit to a device round trip for data this small. - ind_arr_loc = np.zeros(6, dtype=int) + # send buffer + ind_arr_loc = xp.zeros(6, dtype=int) # main array (receive buffers) - ind_arr = np.zeros(nproc * 6, dtype=int) + ind_arr = xp.zeros(nproc * 6, dtype=int) # Get global starts and ends of cart OR domain decomposition gl_s = decomposition.starts @@ -2048,9 +2025,7 @@ def _get_neighbours(self): neighbours along the edges only have one 1, neighbours along the edges have no 1 in the index. """ - # rank/topology bookkeeping (27 neighbour ranks), always host, see - # _get_index_array. - neighs = np.empty((3, 3, 3), dtype=int) + neighs = xp.empty((3, 3, 3), dtype=int) for i in range(3): for j in range(3): @@ -2095,12 +2070,8 @@ def _get_neighbour_one_component(self, comp): if comp == [1, 1, 1]: return neigh_id - # rank/index bookkeeping, always host: neigh_inds below holds a mix - # of ints and None (compared against None further down), which is a - # NumPy object-dtype array and has no CuPy equivalent -- see - # _get_index_array. - comp = np.array(comp) - kinds = np.array(kinds) + comp = xp.array(comp) + kinds = xp.array(kinds) # if only one process: check if comp is neighbour in non-peridic directions, if this is not the case then return the rank as neighbour id if size == 1: @@ -2135,15 +2106,15 @@ def _get_neighbour_one_component(self, comp): "Wrong value for component; must be 0 or 1 or 2 !", ) - neigh_inds = np.array(neigh_inds) + neigh_inds = xp.array(neigh_inds) # only use indices where information is present to find the neighbours rank - inds = np.where(np.not_equal(neigh_inds, None)) + inds = xp.where(xp.not_equal(neigh_inds, None)) # find ranks (row index of domain_array) which agree in start/end indices - index_temp = np.squeeze(self.index_array[:, inds]) - unique_ranks = np.where( - np.equal(index_temp, neigh_inds[inds]).all(1), + index_temp = xp.squeeze(self.index_array[:, inds]) + unique_ranks = xp.where( + xp.equal(index_temp, neigh_inds[inds]).all(1), )[0] # if any row satisfies condition, return its index (=rank of neighbour) @@ -2184,26 +2155,21 @@ def _get_span_and_basis_for_eval_mpi(self, etas, Nspace, end): 2d array of pn values of D-splines indexed by (eta, spline value). """ - from cunumpy.xp import to_cunumpy, to_numpy - from struphy.bsplines import bsplines_kernels - # bsplines_kernels.find_span/b_d_splines_slim are Pyccel-compiled and - # only understand NumPy; this is a per-point Python loop, so convert - # once up front rather than wrapping every individual kernel call. - Tn = to_numpy(Nspace.knots) + # Extract knot vectors, degree and kind of basis + Tn = Nspace.knots pn = Nspace.degree - etas_np = to_numpy(etas) - spans = np.zeros(etas_np.size, dtype=int) - bns = np.zeros((etas_np.size, pn + 1), dtype=float) - bds = np.zeros((etas_np.size, pn), dtype=float) - bn = np.zeros(pn + 1, dtype=float) - bd = np.zeros(pn, dtype=float) + spans = xp.zeros(etas.size, dtype=int) + bns = xp.zeros((etas.size, pn + 1), dtype=float) + bds = xp.zeros((etas.size, pn), dtype=float) + bn = xp.zeros(pn + 1, dtype=float) + bd = xp.zeros(pn, dtype=float) - for n in range(etas_np.size): + for n in range(etas.size): # avoid 1. --> 0. for clamped interpolation - eta = etas_np[n] % (1.0 + 1e-14) + eta = etas[n] % (1.0 + 1e-14) span = bsplines_kernels.find_span(Tn, pn, eta) bsplines_kernels.b_d_splines_slim(Tn, pn, eta, span, bn, bd) # correct span for mpi spline eval @@ -2213,7 +2179,7 @@ def _get_span_and_basis_for_eval_mpi(self, etas, Nspace, end): bns[n] = bn bds[n] = bd - return to_cunumpy(spans), to_cunumpy(bns), to_cunumpy(bds) + return spans, bns, bds class SplineFunction: @@ -2667,18 +2633,11 @@ def initialize_coeffs_from_restart_file(self, file, key): """ TODO """ - # h5py always returns plain host numpy arrays; under the cupy - # backend a bare `cupy_array[:] = numpy_array` full-slice - # assignment raises ("non-scalar numpy.ndarray cannot be used for - # fill" -- cupy's `[:] =` fast path doesn't do the host->device - # transfer implicitly), so route through xp.asarray first, which is - # a no-op under the numpy backend and a safe host->device copy - # under cupy. if isinstance(self.vector, StencilVector): - self.vector._data[:] = xp.asarray(file[key][-1]) + self.vector._data[:] = file[key][-1] else: for n in range(3): - self.vector[n]._data[:] = xp.asarray(file[key + "/" + str(n + 1)][-1]) + self.vector[n]._data[:] = file[key + "/" + str(n + 1)][-1] self._vector.update_ghost_regions() @@ -3483,7 +3442,7 @@ def get_pts_and_wts(space_1d, start, end, n_quad=None, polar_shift=False): # make sure that greville points used for interpolation are in [0, 1] # Use numpy for comparison since greville points are NumPy arrays - greville_loc_np = xp.to_numpy(greville_loc) + greville_loc_np = greville_loc.get() if hasattr(greville_loc, "get") else greville_loc assert np.all(np.logical_and(greville_loc_np >= 0.0, greville_loc_np <= 1.0)) # interpolation @@ -3531,23 +3490,14 @@ def get_pts_and_wts(space_1d, start, end, n_quad=None, polar_shift=False): ) ] - # determine subinterval index (= 0 or 1): 1 unless the left end of - # the cell coincides with a histopolation grid point. - # - # This used to be a Python double loop over the two grids. On the CuPy - # backend every `abs(x_h - x_g) < 1e-14` in it was a kernel launch - # whose result then had to come back to the host for the `if`, so the - # call cost grew as O(N^2) device round trips -- the second-largest - # entry in a 64^2 setup profile. - # - # The vectorized form runs on the host: these are two tiny 1-D grids, - # and they do not reliably live on the same backend (`x_grid` is - # rebuilt through a Python set above, `histopolation_grid` stays - # NumPy). - x_left_np = xp.to_numpy(x_grid[:-1]) - histopol_loc_np = xp.to_numpy(histopol_loc) - matches = np.abs(x_left_np[:, None] - histopol_loc_np[None, :]) < 1e-14 - subs = xp.array((~np.any(matches, axis=1)).astype(int)) + # determine subinterval index (= 0 or 1): + subs = xp.zeros(x_grid[:-1].size, dtype=int) + for n, x_h in enumerate(x_grid[:-1]): + add = 1 + for x_g in histopol_loc: + if abs(x_h - x_g) < 1e-14: + add = 0 + subs[n] += add # Gauss - Legendre quadrature points and weights if n_quad is None: @@ -3556,11 +3506,7 @@ def get_pts_and_wts(space_1d, start, end, n_quad=None, polar_shift=False): pts_loc, wts_loc = np.polynomial.legendre.leggauss(n_quad) - # xp.cupy_backend, not "cupy" in xp.__name__: xp is `cunumpy` here, so - # xp.__name__ is always the literal string "cunumpy" -- which does not - # contain "cupy" as a substring -- so that check was dead code, never - # true even when the active backend actually is CuPy. - if xp.cupy_backend: + if "cupy" in xp.__name__: import cupy as cp pts_loc = cp.array(pts_loc) @@ -3650,10 +3596,7 @@ def get_pts_and_wts_quasi( n_quad = degree + 1 # Gauss - Legendre quadrature points and weights # products of basis functions are integrated exactly - # cupy has no polynomial.legendre; this is tiny host-scale math, - # and quadrature_grid below converts to the active backend via - # xp.asarray, which (unlike the reverse direction) is always safe. - pts_loc, wts_loc = np.polynomial.legendre.leggauss(n_quad) + pts_loc, wts_loc = xp.polynomial.legendre.leggauss(n_quad) x, wts = bsp.quadrature_grid(x_grid, pts_loc, wts_loc) pts = x % 1.0 diff --git a/src/struphy/feec/tests/test_l2_projectors.py b/src/struphy/feec/tests/test_l2_projectors.py index 92b461869..7495c86d6 100644 --- a/src/struphy/feec/tests/test_l2_projectors.py +++ b/src/struphy/feec/tests/test_l2_projectors.py @@ -49,7 +49,7 @@ def test_l2_projectors_mappings( # evaluation points e1 = xp.linspace(0.0, 1.0, 30) e2 = xp.linspace(0.0, 1.0, 40) - e3 = xp.array([0.0]) + e3 = 0.0 ee1, ee2, ee3 = xp.meshgrid(e1, e2, e3, indexing="ij") @@ -112,9 +112,7 @@ def test_l2_projectors_mappings( err = xp.max(xp.abs(f_analytic(ee1, ee2, ee3) - field_vals)) f_plot = field_vals else: - err = xp.array( - [xp.max(xp.abs(exact(ee1, ee2, ee3) - field_v)) for exact, field_v in zip(f_analytic, field_vals)] - ) + err = [xp.max(xp.abs(exact(ee1, ee2, ee3) - field_v)) for exact, field_v in zip(f_analytic, field_vals)] f_plot = field_vals[0] logger.info(f"{sp_id =}, {xp.max(err) =}") @@ -237,9 +235,7 @@ def f(x, y, z): err = xp.max(xp.abs(f_analytic(e1, e2, e3) - field_vals)) f_plot = field_vals else: - err = xp.array( - [xp.max(xp.abs(exact(e1, e2, e3) - field_v)) for exact, field_v in zip(f_analytic, field_vals)] - ) + err = [xp.max(xp.abs(exact(e1, e2, e3) - field_v)) for exact, field_v in zip(f_analytic, field_vals)] f_plot = field_vals[0] errors[sp_id] += [xp.max(err)] @@ -263,7 +259,7 @@ def f(x, y, z): line_for_rate_p1 = [Ne ** (-rate_p1) * errors[sp_id][0] / Nels[0] ** (-rate_p1) for Ne in Nels] line_for_rate_p0 = [Ne ** (-rate_p0) * errors[sp_id][0] / Nels[0] ** (-rate_p0) for Ne in Nels] - m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors[sp_id])), deg=1) + m, _ = xp.polyfit(xp.log(Nels), xp.log(errors[sp_id]), deg=1) logger.info(f"{sp_id =}, fitted convergence rate = {-m}, degree = {pi}") if sp_id in ("H1", "H1vec"): assert -m > (pi + 1 - 0.05) diff --git a/src/struphy/feec/variational_kernels_cuda.py b/src/struphy/feec/variational_kernels_cuda.py deleted file mode 100644 index e601d4566..000000000 --- a/src/struphy/feec/variational_kernels_cuda.py +++ /dev/null @@ -1,65 +0,0 @@ -"""CUDA kernels for fused variational grid evaluations.""" - -from struphy.cuda import CudaKernel, launch_1d, load_cuda_source - -_KINETIC_ENERGY_SOURCE = load_cuda_source(__file__, "variational_kernels_cuda/kinetic_energy_grid.cu") - -_kinetic_energy_kernel = CudaKernel(_KINETIC_ENERGY_SOURCE, "kinetic_energy_grid_cuda") - - -def prepare_kinetic_energy_kernel(): - """Force the fused kinetic-energy CUDA kernel to compile now, during - model setup, rather than lazily on the first timed propagation step. - - Idempotent (see :meth:`~struphy.cuda.CudaKernel.compile`): every actual - invocation still goes through the normal - ``launch_1d(_kinetic_energy_kernel, ...)`` call in - :func:`kinetic_energy_grid_gpu` below, which is a no-op past compilation - once this has run. - """ - _kinetic_energy_kernel.compile() - - -def kinetic_energy_grid_gpu( - spans, - bases, - degree, - starts, - coefficients, - coefficients1, - metric, - out, - values, - values1, -): - """Evaluate both H1-vector splines and their metric product in one launch.""" - import cupy as cp - import numpy as np - - prepare_kinetic_energy_kernel() - spans = tuple(cp.ascontiguousarray(cp.asarray(value, dtype=cp.int64)) for value in spans) - bases = tuple(cp.ascontiguousarray(cp.asarray(value, dtype=cp.float64)) for value in bases) - coefficients = tuple(cp.ascontiguousarray(value) for value in coefficients) - coefficients1 = tuple(cp.ascontiguousarray(value) for value in coefficients1) - metric = cp.ascontiguousarray(metric) - total = out.size - launch_1d( - _kinetic_energy_kernel, - total, - ( - *spans, - *bases, - *(np.int32(value.size) for value in spans), - *(np.int32(value) for value in degree), - *(np.int32(value) for value in starts), - *coefficients, - *coefficients1, - np.int32(coefficients[0].shape[1]), - np.int32(coefficients[0].shape[2]), - metric, - out, - *values, - *values1, - ), - ) - return out diff --git a/src/struphy/feec/variational_utilities.py b/src/struphy/feec/variational_utilities.py index c2af5de6e..dab2a9e28 100644 --- a/src/struphy/feec/variational_utilities.py +++ b/src/struphy/feec/variational_utilities.py @@ -5,7 +5,6 @@ from feectools.linalg.basic import IdentityOperator, Vector from feectools.linalg.block import BlockVector from feectools.linalg.solvers import inverse -from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.feec.basis_projection_ops import ( @@ -258,49 +257,49 @@ def dot(self, v, out=None): self.vf.vector = v - with ProfileManager.profile_region("momentum bracket: gradients"): - grad_1_v = self.gp1.dot(v, out=self.gp1v) - grad_2_v = self.gp2.dot(v, out=self.gp2v) - grad_3_v = self.gp3.dot(v, out=self.gp3v) + grad_1_v = self.gp1.dot(v, out=self.gp1v) + grad_2_v = self.gp2.dot(v, out=self.gp2v) + grad_3_v = self.gp3.dot(v, out=self.gp3v) # To avoid tmp we need to update the fields we created. self.gv1f.vector = grad_1_v self.gv2f.vector = grad_2_v self.gv3f.vector = grad_3_v - with ProfileManager.profile_region("momentum bracket: spline evaluation"): - vf_values = self.vf.eval_tp_fixed_loc( - self.interpolation_grid_spans, - [self.interpolation_grid_bn] * 3, - out=self._vf_values, - ) - gvf1_values = self.gv1f.eval_tp_fixed_loc( - self.interpolation_grid_spans, - self.interpolation_grid_gradient, - out=self._gvf1_values, - ) - gvf2_values = self.gv2f.eval_tp_fixed_loc( - self.interpolation_grid_spans, - self.interpolation_grid_gradient, - out=self._gvf2_values, - ) - gvf3_values = self.gv3f.eval_tp_fixed_loc( - self.interpolation_grid_spans, - self.interpolation_grid_gradient, - out=self._gvf3_values, - ) + vf_values = self.vf.eval_tp_fixed_loc( + self.interpolation_grid_spans, + [self.interpolation_grid_bn] * 3, + out=self._vf_values, + ) + + gvf1_values = self.gv1f.eval_tp_fixed_loc( + self.interpolation_grid_spans, + self.interpolation_grid_gradient, + out=self._gvf1_values, + ) - with ProfileManager.profile_region("momentum bracket: projector weights"): - self.PiuT.update_weights([[vf_values[0], vf_values[1], vf_values[2]]]) - self.PigvT_1.update_weights([[gvf1_values[0], gvf1_values[1], gvf1_values[2]]]) - self.PigvT_2.update_weights([[gvf2_values[0], gvf2_values[1], gvf2_values[2]]]) - self.PigvT_3.update_weights([[gvf3_values[0], gvf3_values[1], gvf3_values[2]]]) + gvf2_values = self.gv2f.eval_tp_fixed_loc( + self.interpolation_grid_spans, + self.interpolation_grid_gradient, + out=self._gvf2_values, + ) + + gvf3_values = self.gv3f.eval_tp_fixed_loc( + self.interpolation_grid_spans, + self.interpolation_grid_gradient, + out=self._gvf3_values, + ) + + self.PiuT.update_weights([[vf_values[0], vf_values[1], vf_values[2]]]) - with ProfileManager.profile_region("momentum bracket: operator application"): - if out is not None: - self.mbrackvw.dot(self._u, out=out) - else: - out = self.mbrackvw.dot(self._u) + self.PigvT_1.update_weights([[gvf1_values[0], gvf1_values[1], gvf1_values[2]]]) + self.PigvT_2.update_weights([[gvf2_values[0], gvf2_values[1], gvf2_values[2]]]) + self.PigvT_3.update_weights([[gvf3_values[0], gvf3_values[1], gvf3_values[2]]]) + + if out is not None: + self.mbrackvw.dot(self._u, out=out) + else: + out = self.mbrackvw.dot(self._u) return out @@ -372,7 +371,6 @@ def __init__(self, derham, transposed=False, weights=None): self._op = self.Proj @ self.div.T else: self._op = self.div @ self.Proj - self._dot_tmp = self._op.tmp_vectors[0] hist_grid = self._derham.V2splines.proj_grid_pts @@ -423,17 +421,7 @@ def transpose(self, conjugate=False): return L2_transport_operator(self._derham, not self._transposed, weights=self._weights) def dot(self, v, out=None): - direction = "transpose" if self._transposed else "forward" - if self._transposed: - with ProfileManager.profile_region(f"L2 transport {direction}: divergence"): - self.div.T.dot(v, out=self._dot_tmp) - with ProfileManager.profile_region(f"L2 transport {direction}: projection"): - out = self.Proj.dot(self._dot_tmp, out=out) - else: - with ProfileManager.profile_region(f"L2 transport {direction}: projection"): - self.Proj.dot(v, out=self._dot_tmp) - with ProfileManager.profile_region(f"L2 transport {direction}: divergence"): - out = self.div.dot(self._dot_tmp, out=out) + out = self._op.dot(v, out=out) return out def update_coeffs(self, coeff): @@ -1452,11 +1440,6 @@ class KineticEnergyEvaluator: """ def __init__(self, derham, domain, mass_ops): - # Kept for get_u2_grid's GPU branch, which needs derham.degree. The NumPy - # branch never touches it, so a missing assignment here failed only under - # ARRAY_BACKEND=cupy -- and there for every model that builds this evaluator. - self._derham = derham - integration_grid = [grid_1d.flatten() for grid_1d in derham.V0splines.quad_grid_pts[0]] self.integration_grid_spans, self.integration_grid_bn, self.integration_grid_bd = derham.prepare_eval_tp_fixed( @@ -1506,27 +1489,6 @@ def get_u2_grid(self, un, un1, out): self.uf.vector = un self.uf1.vector = un1 - tensor_u = self.uf.vector.tp if hasattr(self.uf.vector, "tp") else self.uf.vector - tensor_u1 = self.uf1.vector.tp if hasattr(self.uf1.vector, "tp") else self.uf1.vector - first_data = tensor_u.blocks[0]._data if hasattr(tensor_u, "blocks") else tensor_u._data - if xp.is_gpu(first_data): - from struphy.feec.variational_kernels_cuda import kinetic_energy_grid_gpu - - coefficients = tuple(block._data for block in tensor_u.blocks) - coefficients1 = tuple(block._data for block in tensor_u1.blocks) - return kinetic_energy_grid_gpu( - self.integration_grid_spans, - self.integration_grid_bn, - self._derham.degree, - self.uf.starts[0], - coefficients, - coefficients1, - self._proj_u2_metric_term, - out, - self._uf_values, - self._uf1_values, - ) - uf_values = self.uf.eval_tp_fixed_loc( self.integration_grid_spans, [ @@ -1560,7 +1522,7 @@ def assemble_M_un(self, un): """Update the weights of the matrix M_un with the vector fields given by the coeficient un""" self.uf.vector = un - self.uf.eval_tp_fixed_loc( + uf_values = self.uf.eval_tp_fixed_loc( self.integration_grid_spans, [ self.integration_grid_bn, @@ -1569,16 +1531,12 @@ def assemble_M_un(self, un): out=self._uf_values, ) - self.assemble_M_un_cached() - - def assemble_M_un_cached(self): - """Assemble ``M_un`` from velocity values cached by ``get_u2_grid``.""" for i in range(3): self._Guf_values[i] *= 0.0 for j in range(3): self._tmp_int_grid *= 0.0 self._tmp_int_grid += self._mass_u_metric_term[i, j] - self._tmp_int_grid *= self._uf_values[j] + self._tmp_int_grid *= uf_values[j] self._Guf_values[i] += self._tmp_int_grid self._M_un.assemble( @@ -1589,7 +1547,7 @@ def assemble_M_un1(self, un1): """Update the weights of the matrix M_un1 with the vector fields given by the coeficient un1""" self.uf1.vector = un1 - self.uf1.eval_tp_fixed_loc( + uf1_values = self.uf1.eval_tp_fixed_loc( self.integration_grid_spans, [ self.integration_grid_bn, @@ -1598,16 +1556,12 @@ def assemble_M_un1(self, un1): out=self._uf1_values, ) - self.assemble_M_un1_cached() - - def assemble_M_un1_cached(self): - """Assemble ``M_un1`` from velocity values cached by ``get_u2_grid``.""" for i in range(3): self._Guf_values[i] *= 0.0 for j in range(3): self._tmp_int_grid *= 0.0 self._tmp_int_grid += self._mass_u_metric_term[i, j] - self._tmp_int_grid *= self._uf1_values[j] + self._tmp_int_grid *= uf1_values[j] self._Guf_values[i] += self._tmp_int_grid self._M_un1.assemble( diff --git a/src/struphy/fields_background/equils.py b/src/struphy/fields_background/equils.py index d532b067c..e1b4bf3b1 100644 --- a/src/struphy/fields_background/equils.py +++ b/src/struphy/fields_background/equils.py @@ -3,7 +3,6 @@ import copy import importlib.util import logging -import math import os import sys import warnings @@ -11,7 +10,6 @@ from typing import TYPE_CHECKING import cunumpy as xp -import numpy as np from line_profiler import profile from scipy.integrate import odeint, quad from scipy.interpolate import RectBivariateSpline, UnivariateSpline @@ -980,20 +978,12 @@ def __init__( self._p_i = None elif self.params["q_kind"] == 1 or self.params["q_kind"] == 2: - # Built entirely with plain NumPy/math, never xp: this is a ~200-point, - # one-off host-side interpolation setup feeding scipy.integrate.quad and - # scipy.interpolate.UnivariateSpline, both host-only. Under the CuPy backend - # xp.sqrt() on a plain Python float returns a 0-d CuPy array rather than a - # float (see the comment in psi_r()), which quad's integrand cannot return - # and UnivariateSpline cannot accept -- using np/math here instead of xp - # sidesteps that entirely, at no cost since this never runs on the device - # regardless of backend. - r_i = np.linspace(0.0, self.params["a"], self.params["psi_nel"] + 1) + r_i = xp.linspace(0.0, self.params["a"], self.params["psi_nel"] + 1) def dpsi_dr(r): - return self.params["B0"] * r / (self.q_r(r) * math.sqrt(1 - r**2 / self.params["R0"] ** 2)) + return self.params["B0"] * r / (self.q_r(r) * xp.sqrt(1 - r**2 / self.params["R0"] ** 2)) - psis = np.zeros_like(r_i) + psis = xp.zeros_like(r_i) for i, rr in enumerate(r_i): psis[i] = quad(dpsi_dr, 0.0, rr)[0] @@ -1016,7 +1006,7 @@ def dp_dr(r): * (2 * self.q_r(r) - r * self.q_r(r, der=1)) ) - ps = np.zeros_like(r_i) + ps = xp.zeros_like(r_i) for i, rr in enumerate(r_i): ps[i] = quad(dp_dr, 0.0, rr)[0] @@ -1093,21 +1083,12 @@ def psi_r(self, r, der=0): # alternative profile (interpolated) elif self.params["q_kind"] == 1 or self.params["q_kind"] == 2: - # UnivariateSpline is a host-only scipy object: a plain float still works - # directly, but under the CuPy backend even a "scalar" r may already be a - # 0-d CuPy array (e.g. from xp.sqrt() on a Python float in psi()), and a - # genuine array r may live on the device -- both need an explicit host copy - # first (scipy raises on an implicit CuPy->NumPy conversion), converted back - # afterwards so device callers still get a device array back. - was_gpu = xp.is_gpu(r) - r_np = r if isinstance(r, (int, float)) else xp.to_numpy(r) - out = self._psi_i(r_np, nu=der) + out = self._psi_i(r, nu=der) # remove all "dimensions" for point-wise evaluation - if isinstance(r, (int, float)) or (hasattr(r, "ndim") and r.ndim == 0): + if isinstance(r, (int, float)): + assert out.ndim == 0 out = out.item() - elif was_gpu: - out = xp.asarray(out) return out @@ -1231,16 +1212,12 @@ def p_r(self, r): # alternative profile elif self.params["q_kind"] == 1: - # see the matching comment in psi_r() for why r needs a host copy here. - was_gpu = xp.is_gpu(r) - r_np = r if isinstance(r, (int, float)) else xp.to_numpy(r) - pout = self._p_i(r_np) + pout = self._p_i(r) # remove all "dimensions" for point-wise evaluation - if isinstance(r, (int, float)) or (hasattr(r, "ndim") and r.ndim == 0): + if isinstance(r, (int, float)): + assert pout.ndim == 0 pout = pout.item() - elif was_gpu: - pout = xp.asarray(pout) # ad-hoc profile elif self.params["p_kind"] == 1: diff --git a/src/struphy/geometry/base.py b/src/struphy/geometry/base.py index 6011fa1a1..6add7e504 100644 --- a/src/struphy/geometry/base.py +++ b/src/struphy/geometry/base.py @@ -24,15 +24,10 @@ def _to_numpy_for_kernel(value): - """Convert CuPy arrays to NumPy for passing to compiled kernels. - - xp.is_gpu, not xp.to_numpy: some callers pass plain Python scalars that must - reach the compiled kernel unchanged, and xp.to_numpy would wrap those into - 0-d NumPy arrays via np.asarray -- a type the kernel signature does not - expect. xp.is_gpu leaves anything that isn't actually a CuPy array - untouched, matching the original hasattr(value, "get") passthrough exactly. - """ - return value.get() if xp.is_gpu(value) else value + """Convert CuPy arrays to NumPy for passing to compiled kernels.""" + if hasattr(value, "get"): # CuPy array + return value.get() + return value class DomainMeta(ABCMeta): @@ -240,7 +235,7 @@ def _build_args_domain(self): """Build runtime mapping arguments used by compiled evaluation kernels.""" return DomainArguments( self.kind_map, - _to_numpy_for_kernel(self.params_numpy), + self.params_numpy, _to_numpy_for_kernel(xp.array(self.degree)), _to_numpy_for_kernel(self.T[0]), _to_numpy_for_kernel(self.T[1]), diff --git a/src/struphy/io/output_handling.py b/src/struphy/io/output_handling.py index 507a89852..d906ad100 100644 --- a/src/struphy/io/output_handling.py +++ b/src/struphy/io/output_handling.py @@ -2,7 +2,6 @@ import logging import os -import cunumpy as xp import h5py import numpy as np @@ -80,7 +79,14 @@ def dset_dict(self): @staticmethod def _as_numpy_array(val): """Return a NumPy view/copy suitable for h5py writes.""" - return xp.to_numpy(val) + if isinstance(val, np.ndarray): + return val + + get = getattr(val, "get", None) + if callable(get) and "cupy" in val.__class__.__module__: + return get() + + return np.asarray(val) def add_data(self, data_dict): """ diff --git a/src/struphy/linear_algebra/schur_solver.py b/src/struphy/linear_algebra/schur_solver.py index 560c68c76..36c0c7956 100644 --- a/src/struphy/linear_algebra/schur_solver.py +++ b/src/struphy/linear_algebra/schur_solver.py @@ -1,4 +1,4 @@ -from feectools.linalg.basic import IdentityOperator, LinearOperator, MatrixFreeLinearOperator, Vector +from feectools.linalg.basic import IdentityOperator, LinearOperator, Vector from feectools.linalg.block import BlockLinearOperator, BlockVector from feectools.linalg.solvers import inverse from line_profiler import profile @@ -222,35 +222,13 @@ def __init__(self, M, solver_name, **solver_params): self._C = M[1, 0] assert isinstance(M[1, 1], IdentityOperator) - # Avoid the generic composed/block operator for the Schur product. - # Its nested ``dot`` calls allocate intermediate vectors on every - # Krylov iteration. Reuse two work vectors instead. - self._schur_tmp_y = self._C.codomain.zeros() - self._schur_tmp_a = self._A.codomain.zeros() - self._S = MatrixFreeLinearOperator( - domain=self._A.domain, - codomain=self._A.codomain, - dot=lambda v, *, out=None: self._dot_schur(v, out=out), - ) + self._S = self._A - self._B @ self._C self._solver = inverse(self._S, solver_name, **solver_params) # right-hand side vector (avoids temporary memory allocation!) self._rhs = self._A.codomain.zeros() - def _dot_schur(self, v, out=None): - """Apply ``A - B C`` using preallocated work vectors.""" - with ProfileManager.profile_region("density schur apply: C"): - self._C.dot(v, out=self._schur_tmp_y) - with ProfileManager.profile_region("density schur apply: B"): - self._B.dot(self._schur_tmp_y, out=out) - with ProfileManager.profile_region("density schur apply: A"): - self._A.dot(v, out=self._schur_tmp_a) - with ProfileManager.profile_region("density schur apply: combine"): - out *= -1.0 - out += self._schur_tmp_a - return out - @profile @ProfileManager.profile("solve: SchurSolverFull") def dot(self, v, out=None): @@ -285,18 +263,15 @@ def dot(self, v, out=None): by = v[1] # right-hand side vector rhs bx - B by - with ProfileManager.profile_region("density schur: rhs"): - rhs = self._B.dot(by, out=self._rhs) - rhs *= -1 - rhs += bx + rhs = self._B.dot(by, out=self._rhs) + rhs *= -1 + rhs += bx # solve linear system (in-place if out is not None) - with ProfileManager.profile_region("density schur: Krylov"): - x = self._solver.dot(rhs, out=out[0]) - with ProfileManager.profile_region("density schur: back substitution"): - y = self._C.dot(x, out=out[1]) - y *= -1 - y += by + x = self._solver.dot(rhs, out=out[0]) + y = self._C.dot(x, out=out[1]) + y *= -1 + y += by return out diff --git a/src/struphy/models/base.py b/src/struphy/models/base.py index e4785ec93..3cf928503 100644 --- a/src/struphy/models/base.py +++ b/src/struphy/models/base.py @@ -4,7 +4,6 @@ from textwrap import indent import cunumpy as xp -import numpy as np from feectools.ddm.mpi import MockMPI from feectools.ddm.mpi import mpi as MPI @@ -425,17 +424,13 @@ def update_markers_to_be_saved(self): assert isinstance(obj, Particles) if var.n_to_save > 0: - # The selection runs on whichever backend the markers live - # on (device under CuPy); var.saved_markers is the host - # buffer that gets written to HDF5, so the selected rows are - # brought across explicitly here. markers_on_proc = xp.logical_and( obj.markers[:, -1] >= 0.0, obj.markers[:, -1] < var.n_to_save, ) - n_markers_on_proc = int(xp.count_nonzero(markers_on_proc)) + n_markers_on_proc = xp.count_nonzero(markers_on_proc) var.saved_markers[:] = -1.0 - var.saved_markers[:n_markers_on_proc] = xp.to_numpy(obj.markers[markers_on_proc]) + var.saved_markers[:n_markers_on_proc] = obj.markers[markers_on_proc] @profile def update_distr_functions(self): @@ -472,13 +467,8 @@ def update_distr_functions(self): components, edges, output_quantity=binning_quantity, divide_by_jac=divide_by_jac ) - # obj.binning() computes on host (markers are always - # host-resident regardless of backend, see - # ISSUE_cupy_particles_never_pushed.md), but bin_plot.f/df - # follow the active backend -- xp.asarray is a no-op - # under numpy and a safe host->device copy under cupy. - bin_plot.f[:] = xp.asarray(f_slice) - bin_plot.df[:] = xp.asarray(df_slice) + bin_plot.f[:] = f_slice + bin_plot.df[:] = df_slice for kd_plot in species.saving_params.kernel_density_plots: h1 = 1 / obj.boxes_per_dim[0] diff --git a/src/struphy/models/linear_vlasov_ampere_one_species.py b/src/struphy/models/linear_vlasov_ampere_one_species.py index e41ddae7a..9acaa41dd 100644 --- a/src/struphy/models/linear_vlasov_ampere_one_species.py +++ b/src/struphy/models/linear_vlasov_ampere_one_species.py @@ -204,30 +204,19 @@ def _compute_en_w(self): ) assert isinstance(self._f0, Maxwellian3D) - # particles.phasespace_coords is always host-resident (see - # ISSUE_cupy_particles_never_pushed.md), but self._f0 follows the - # active backend, so its coordinate args need converting first. - coords = tuple(xp.to_cunumpy(c) for c in particles.phasespace_coords.T) - self._f0_values[particles.valid_mks] = self._f0(*coords) + self._f0_values[particles.valid_mks] = self._f0(*particles.phasespace_coords.T) # alpha^2 * v_th^2 / (2*N) * sum_p s_0 * w_p^2 / f_{0,p} alpha = self.kinetic_ions.equation_params.alpha vth = self._f0.params["vth1"][0] - # particles.weights/sampling_density_values are host-resident too - # (same reason as phasespace_coords above); self._f0_values follows - # the active backend, so this division/dot needs both sides on the - # same backend. - weights = xp.to_cunumpy(particles.weights) - sampling_density_values = xp.to_cunumpy(particles.sampling_density_values) - self._tmp[0] = ( alpha**2 * vth**2 / (2 * particles.Np) * xp.dot( - weights**2, # w_p^2 - sampling_density_values / self._f0_values[particles.valid_mks], # s_{0,p} / f_{0,p} + particles.weights**2, # w_p^2 + particles.sampling_density_values / self._f0_values[particles.valid_mks], # s_{0,p} / f_{0,p} ) ) return self._tmp[0] diff --git a/src/struphy/models/scalars.py b/src/struphy/models/scalars.py index d81eedff7..35acedb34 100644 --- a/src/struphy/models/scalars.py +++ b/src/struphy/models/scalars.py @@ -285,18 +285,14 @@ class KineticEnergyPIC(PICScalar): """ def _local_update(self): - if not hasattr(self, "Np"): + if not hasattr(self, "velocities"): + self.velocities = self.variables[ + 0 + ].particles.velocities # TODO: velocities need to redefined for Particles5d? Put magnetic moment as COM. + self.weights = self.variables[0].particles.weights self.Np = self.variables[0].particles.Np - # velocities/weights must be re-read every call: they are fresh - # copies of the (evolving) marker array, not persistent views, so - # caching them here would freeze this scalar at its initial value. - velocities = self.variables[ - 0 - ].particles.velocities # TODO: velocities need to redefined for Particles5d? Put magnetic moment as COM. - weights = self.variables[0].particles.weights - - energy = self.normalization * 0.5 / self.Np * xp.sum(weights * xp.sum(velocities**2, axis=1)) + energy = self.normalization * 0.5 / self.Np * xp.sum(self.weights * xp.sum(self.velocities**2, axis=1)) self.local_value[0] = energy @@ -335,14 +331,12 @@ class KineticEnergySPH(SPHScalar): """ def _local_update(self): - if not hasattr(self, "Np"): + if not hasattr(self, "velocities"): + self.velocities = self.variables[0].particles.velocities + self.weights = self.variables[0].particles.weights self.Np = self.variables[0].particles.Np - # velocities/weights must be re-read every call, see KineticEnergyPIC. - velocities = self.variables[0].particles.velocities - weights = self.variables[0].particles.weights - - energy = self.normalization * 0.5 / self.Np * xp.sum(weights * xp.sum(velocities**2, axis=1)) + energy = self.normalization * 0.5 / self.Np * xp.sum(self.weights * xp.sum(self.velocities**2, axis=1)) self.local_value[0] = energy diff --git a/src/struphy/models/species.py b/src/struphy/models/species.py index 714c01640..17daabe84 100644 --- a/src/struphy/models/species.py +++ b/src/struphy/models/species.py @@ -178,15 +178,8 @@ def __init__( con = ConstantsOfNature() - # relevant frequencies (scalar physics constants -- xp.sqrt - # returns a 0-d backend array under cupy, not a Python float; - # left as such it propagates into alpha/epsilon/kappa below and - # eventually into things like `sigma_3 * coeff * StencilVector` - # in ImplicitDiffusion, which numpy tolerates but cupy's - # stricter __array_ufunc__ protocol rejects with a "NotImplemented" - # TypeError. There's no vectorization to gain here, so force - # back to a plain float immediately.) - om_p = float(xp.sqrt(units.n * (Z * con.e) ** 2 / (con.eps0 * A * con.mH))) + # relevant frequencies + om_p = xp.sqrt(units.n * (Z * con.e) ** 2 / (con.eps0 * A * con.mH)) om_c = Z * con.e * units.B / (A * con.mH) # compute equation parameters diff --git a/src/struphy/models/variables.py b/src/struphy/models/variables.py index 1bf44bca2..9aa86a2e0 100644 --- a/src/struphy/models/variables.py +++ b/src/struphy/models/variables.py @@ -6,7 +6,7 @@ from abc import ABCMeta, abstractmethod from typing import TYPE_CHECKING -import numpy as np +import cunumpy as xp from feectools.ddm.mpi import mpi as MPI from struphy.feec.linear_operators import BoundaryOperator @@ -617,7 +617,7 @@ def allocate( f"The number of markers for which data should be stored (={self._n_to_save}) must be <= than the total number of markers (={self.particles.Np})" ) if self._n_to_save > 0: - self._saved_markers = np.zeros( + self._saved_markers = xp.zeros( (self._n_to_save, self.particles.markers.shape[1]), dtype=float, ) @@ -698,7 +698,7 @@ def n_to_save(self) -> int: return self._n_to_save @property - def saved_markers(self) -> np.ndarray: + def saved_markers(self) -> xp.ndarray: return self._saved_markers @@ -909,7 +909,7 @@ def allocate( f"The number of markers for which data should be stored (={self._n_to_save}) must be <= than the total number of markers (={self.particles.Np})" ) if self._n_to_save > 0: - self._saved_markers = np.zeros( + self._saved_markers = xp.zeros( (self._n_to_save, self.particles.markers.shape[1]), dtype=float, ) @@ -986,5 +986,5 @@ def n_to_save(self) -> int: return self._n_to_save @property - def saved_markers(self) -> np.ndarray: + def saved_markers(self) -> xp.ndarray: return self._saved_markers diff --git a/src/struphy/ode/solvers.py b/src/struphy/ode/solvers.py index 0249ebe44..ed89bd098 100644 --- a/src/struphy/ode/solvers.py +++ b/src/struphy/ode/solvers.py @@ -62,17 +62,9 @@ def __init__( @ProfileManager.profile("solve: ODEsolverFEEC") def __call__(self, tn, h): - # These coefficients scale FEEC vectors (StencilVector/BlockVector), which do - # not implement __array_ufunc__. Under CuPy `a[i, j]` is a 0-d *device* array, - # so `h * a[i, j] * vec` dispatches to CuPy's __mul__ and fails with - # NotImplemented; under NumPy the same expression yields a np.float64 scalar - # and falls back to the vector's __rmul__, which is why this only ever broke on - # the GPU. The tableau itself must stay on the active backend -- the particle - # pushers hand `a_stage`/`b`/`c` straight to CUDA kernels -- so only the scalars - # used here are brought to the host, once per call and s^2 values at most. - a = xp.to_numpy(self.butcher.a) - b = xp.to_numpy(self.butcher.b) - c = xp.to_numpy(self.butcher.c) + a = self.butcher.a + b = self.butcher.b + c = self.butcher.c # keep initial condition for v, vn in zip(self.y, self.yn): diff --git a/src/struphy/physics/physics.py b/src/struphy/physics/physics.py index c0a5ee07d..190b5fdca 100644 --- a/src/struphy/physics/physics.py +++ b/src/struphy/physics/physics.py @@ -112,7 +112,7 @@ def derive_units(self, velocity_scale: str = "light", A_bulk: int = None, Z_bulk self._v = xp.sqrt(self.kBT * 1000 * con.e / (con.mH * A_bulk)) # time (s) - self._t = float(self.x / self.v) + self._t = self.x / self.v # return if no bulk is present if A_bulk is None: diff --git a/src/struphy/pic/accumulation/accum_kernels_cuda.py b/src/struphy/pic/accumulation/accum_kernels_cuda.py deleted file mode 100644 index e8b78a414..000000000 --- a/src/struphy/pic/accumulation/accum_kernels_cuda.py +++ /dev/null @@ -1,778 +0,0 @@ -"""Hand-written CUDA replacements for select accumulation (particle-to-grid -deposition) kernels, used only under ``ARRAY_BACKEND=cupy``. - -Unlike the pusher kernels in :mod:`~struphy.pic.pushing.pusher_kernels_cuda` -(each marker only ever writes to its own row -- embarrassingly parallel, no -cross-thread interaction), accumulation kernels *scatter* every marker's -contribution into a shared grid array (:func:`~struphy.pic.accumulation.filler_kernels.fill_vec`'s -``vec[i1, i2, i3] += ...``): many markers whose (p+1)^3 local basis-function -support overlaps the same grid cell write to the same memory location. The -CPU kernel handles this by running the marker loop strictly sequentially (its -OpenMP ``reduction`` pragma is commented out in the source specifically -because of this race). The GPU port instead uses ``atomicAdd`` -- one thread -per marker, same as the pushers, but the grid write goes through an atomic -rather than a plain store. Double-precision ``atomicAdd`` is natively -supported on every CUDA compute capability this codebase targets (>= 6.0), -so no software fallback is needed. - -Currently covered: :func:`~struphy.pic.accumulation.accum_kernels.charge_density_0form`, -used by :class:`~struphy.propagators.push_deterministic_diffusion.PushDeterministicDiffusion` -every step to build the (H^1) density field consumed by -:func:`~struphy.pic.pushing.pusher_kernels_cuda.push_deterministic_diffusion_stage_general_gpu`. -This one needs no domain-mapping Jacobian at all (the H^1 filling weight is -just the marker weight), so it reuses only the B-spline evaluation device -functions, not the geometry-mapping ones. -""" -from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source - -_CHARGE_DENSITY_0FORM_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_charge_density_0form_src.cu") -_charge_density_0form_kernel = CudaKernel(_CHARGE_DENSITY_0FORM_SRC, "charge_density_0form_cuda") - - -def charge_density_0form_gpu( - markers, - weight_idx: int, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - vec_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels.charge_density_0form`. - - ``markers`` is the host marker array, transferred to the device once per - call (matching the pusher kernels' round-trip pattern). ``vec_dev`` is - the target :class:`~feectools.linalg.stencil.StencilVector`'s ``._data`` - -- already device-resident under CuPy and already zeroed by the caller - (:meth:`~struphy.pic.accumulation.particles_to_grid.AccumulatorVector._accumulate` - always does ``dat[:] = 0.0`` before invoking the kernel), so this - function only needs to add to it, not read markers back afterward: the - caller reads ``vec_dev`` directly since it was written in place. - """ - import numpy as np - - n_markers = markers.shape[0] - dev_markers = markers - launch_1d( - _charge_density_0form_kernel, - n_markers, - ( - dev_markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(weight_idx), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - vec_dev, - np.int32(vec_dev.shape[1]), - np.int32(vec_dev.shape[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# linear_vlasov_ampere: accumulates into a symmetric V1 -> V1 block matrix -# (mat11, mat12, mat13, mat22, mat23, mat33) plus a V1 vector (vec1, vec2, -# vec3), using DF^-1(eta_p) @ v_p at each marker -- unlike -# charge_density_0form this needs the full domain-mapping Jacobian, so the -# kernel source below is prefixed with pusher_kernels_cuda's -# _GENERAL_GEOMETRY_SRC (df_dispatch_dev and friends) rather than -# duplicating it. -# -# The row/column basis combinations for the 6 matrix blocks and the fill -# formulas mirror struphy.pic.accumulation.particle_to_mat_kernels.m_v_fill_b_v1_symm -# exactly (which itself calls filler_kernels.fill_mat_vec/fill_mat) -- -# fill_mat_vec_dev/fill_mat_dev below are direct ports of those two. -# --------------------------------------------------------------------------- - -_LINEAR_VLASOV_AMPERE_EXTRA_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu") - - -def _linear_vlasov_ampere_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC - - -# --------------------------------------------------------------------------- -# vlasov_maxwell: same symmetric V1 -> V1 6-block-matrix-plus-vector fill as -# linear_vlasov_ampere (reuses fill_mat_vec_dev/fill_mat_dev from -# _LINEAR_VLASOV_AMPERE_EXTRA_SRC above), but with a different filling: -# A_p = w_p * G^-1(eta_p) (the metric inverse, not an outer product of -# velocity) and B_p = w_p * DF^-1(eta_p) v_p -- no f0_values/s0 involved, so -# unlike linear_vlasov_ampere this one can't hit the inf/nan-from-div-by-s0 -# path. Also note: the CPU reference only skips markers[ip,0]==-1.0 (no -# markers[ip,-1]==-2.0 check), unlike linear_vlasov_ampere -- ported as-is. -# --------------------------------------------------------------------------- - -_VLASOV_MAXWELL_EXTRA_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_vlasov_maxwell_extra_src.cu") - - -def _vlasov_maxwell_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _VLASOV_MAXWELL_EXTRA_SRC - - -_vlasov_maxwell_kernels = CudaKernelSet(_vlasov_maxwell_source) - - -def vlasov_maxwell_gpu( - markers, - kind_map: int, - params_dev, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - mat11_dev, - mat12_dev, - mat13_dev, - mat22_dev, - mat23_dev, - mat33_dev, - vec1_dev, - vec2_dev, - vec3_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels.vlasov_maxwell`. Same - calling convention as :func:`linear_vlasov_ampere_gpu`, minus - ``f0_values`` (this kernel doesn't need a background distribution). - """ - import numpy as np - - n_markers = markers.shape[0] - dev_markers = markers - - def dims(a): - return ( - np.int32(a.shape[1]), - np.int32(a.shape[2]), - np.int32(a.shape[3]), - np.int32(a.shape[4]), - np.int32(a.shape[5]), - ) - - launch_1d( - _vlasov_maxwell_kernels["vlasov_maxwell_cuda"], - n_markers, - ( - dev_markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(kind_map), - params_dev, - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - mat11_dev, - mat12_dev, - mat13_dev, - mat22_dev, - mat23_dev, - mat33_dev, - vec1_dev, - vec2_dev, - vec3_dev, - *dims(mat11_dev), - *dims(mat12_dev), - *dims(mat13_dev), - *dims(mat22_dev), - *dims(mat23_dev), - *dims(mat33_dev), - np.int32(vec1_dev.shape[1]), - np.int32(vec1_dev.shape[2]), - np.int32(vec2_dev.shape[1]), - np.int32(vec2_dev.shape[2]), - np.int32(vec3_dev.shape[1]), - np.int32(vec3_dev.shape[2]), - ), - ) - - -_linear_vlasov_ampere_kernels = CudaKernelSet(_linear_vlasov_ampere_source) - - -def linear_vlasov_ampere_gpu( - markers, - kind_map: int, - params_dev, - f0_values_dev, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - mat11_dev, - mat12_dev, - mat13_dev, - mat22_dev, - mat23_dev, - mat33_dev, - vec1_dev, - vec2_dev, - vec3_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels.linear_vlasov_ampere`. - - ``markers`` is the host marker array, round-tripped through the device - once per call (this kernel only reads markers, never writes them back). - ``params_dev``/``f0_values_dev`` and all ``mat*_dev``/``vec*_dev`` arrays - are expected to already be device-resident (cached once by the caller); - the ``mat*_dev``/``vec*_dev`` arrays must already be zeroed, matching - :meth:`~struphy.pic.accumulation.particles_to_grid.Accumulator._accumulate`'s - ``dat[:] = 0.0`` reset before the kernel call. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - dev_markers = markers - f0_values_dev = cp.ascontiguousarray(f0_values_dev) - - def dims(a): - return ( - np.int32(a.shape[1]), - np.int32(a.shape[2]), - np.int32(a.shape[3]), - np.int32(a.shape[4]), - np.int32(a.shape[5]), - ) - - launch_1d( - _linear_vlasov_ampere_kernels["linear_vlasov_ampere_cuda"], - n_markers, - ( - dev_markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(kind_map), - params_dev, - f0_values_dev, - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - mat11_dev, - mat12_dev, - mat13_dev, - mat22_dev, - mat23_dev, - mat33_dev, - vec1_dev, - vec2_dev, - vec3_dev, - *dims(mat11_dev), - *dims(mat12_dev), - *dims(mat13_dev), - *dims(mat22_dev), - *dims(mat23_dev), - *dims(mat33_dev), - np.int32(vec1_dev.shape[1]), - np.int32(vec1_dev.shape[2]), - np.int32(vec2_dev.shape[1]), - np.int32(vec2_dev.shape[2]), - np.int32(vec3_dev.shape[1]), - np.int32(vec3_dev.shape[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# cc_lin_mhd_6d_1: accumulates into the 3 antisymmetric off-diagonal blocks -# (mat12, mat13, mat23) of a V_u -> V_u matrix, no vector, where V_u is -# whichever of H1vec/Hcurl/Hdiv the propagator's ``u_space`` option selects -# (runtime int ``basis_u`` in {0, 1, 2}). All 3 branches ultimately do a -# 3-block antisymmetric fill using fill_mat_dev (from -# _LINEAR_VLASOV_AMPERE_EXTRA_SRC above) with the row/col basis-degree -# combination matching struphy's mat_fill_v0vec_asym (basis_u=0, N-N-N both -# sides), mat_fill_v1_asym (basis_u=1, D-N-N/N-D-N/N-N-D -- same combination -# already used for linear_vlasov_ampere/vlasov_maxwell's off-diagonal -# blocks) and mat_fill_v2_asym (basis_u=2, Hdiv's N-D-D/D-N-D/D-D-N). Since -# basis_u is one value per kernel LAUNCH (not per marker), the branch is -# warp-coherent -- every thread takes the same path, no divergence cost. -# basis_u=0 needs no domain Jacobian at all (see the CPU reference: dfm is -# computed unconditionally there but only actually used by basis_u 1/2), so -# df_dispatch_dev is only called inside the basis_u==1/2 branches here. -# --------------------------------------------------------------------------- - -_CC_LIN_MHD_6D_1_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu") - - -def _cc_lin_mhd_6d_1_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_6D_1_SRC - - -_cc_lin_mhd_6d_1_kernels = CudaKernelSet(_cc_lin_mhd_6d_1_source) - - -def cc_lin_mhd_6d_1_gpu( - markers, - kind_map: int, - params_dev, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - b2_1_dev, - b2_2_dev, - b2_3_dev, - basis_u: int, - scale_mat: float, - boundary_cut: float, - mat12_dev, - mat13_dev, - mat23_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels.cc_lin_mhd_6d_1`. - ``b2_*_dev`` are the Hdiv (2-form) magnetic field FE coefficients, - already device-resident. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - dev_markers = markers - b2_1_dev = cp.ascontiguousarray(b2_1_dev) - b2_2_dev = cp.ascontiguousarray(b2_2_dev) - b2_3_dev = cp.ascontiguousarray(b2_3_dev) - - def dims(a): - return ( - np.int32(a.shape[1]), - np.int32(a.shape[2]), - np.int32(a.shape[3]), - np.int32(a.shape[4]), - np.int32(a.shape[5]), - ) - - launch_1d( - _cc_lin_mhd_6d_1_kernels["cc_lin_mhd_6d_1_cuda"], - n_markers, - ( - dev_markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(kind_map), - params_dev, - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - b2_1_dev, - np.int32(b2_1_dev.shape[1]), - np.int32(b2_1_dev.shape[2]), - b2_2_dev, - np.int32(b2_2_dev.shape[1]), - np.int32(b2_2_dev.shape[2]), - b2_3_dev, - np.int32(b2_3_dev.shape[1]), - np.int32(b2_3_dev.shape[2]), - np.int32(basis_u), - np.float64(scale_mat), - np.float64(boundary_cut), - mat12_dev, - mat13_dev, - mat23_dev, - *dims(mat12_dev), - *dims(mat13_dev), - *dims(mat23_dev), - ), - ) - - -# --------------------------------------------------------------------------- -# cc_lin_mhd_6d_2: like cc_lin_mhd_6d_1 (B2 field evaluation, bx() matrix, -# runtime basis_u in {0, 1, 2} selecting H1vec/Hcurl/Hdiv), but fills the -# full symmetric 6-block matrix plus a vector (like linear_vlasov_ampere / -# vlasov_maxwell), not just the 3 antisymmetric off-diagonal blocks. Basis -# combinations per branch (matching struphy's m_v_fill_v0vec_symm / -# m_v_fill_v1_symm / m_v_fill_v2_symm): basis_u=0 uses N-N-N everywhere -# (all 6 matrix blocks AND the vector); basis_u=1 is the same D-N-N/N-D-N/ -# N-N-D combination already used for linear_vlasov_ampere/vlasov_maxwell; -# basis_u=2 is Hdiv's N-D-D/D-N-D/D-D-N (same as cc_lin_mhd_6d_1's -# basis_u=2). Per the CPU reference, basis_u=0 and 2 only ever need df_inv -# (g_inv is computed there but never actually used in those two branches -- -# not replicated here); only basis_u=1 needs the full g_inv = DF^-1 DF^-T. -# --------------------------------------------------------------------------- - -_CC_LIN_MHD_6D_2_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu") - - -def _cc_lin_mhd_6d_2_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_6D_2_SRC - - -_cc_lin_mhd_6d_2_kernels = CudaKernelSet(_cc_lin_mhd_6d_2_source) - - -def cc_lin_mhd_6d_2_gpu( - markers, - kind_map: int, - params_dev, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - b2_1_dev, - b2_2_dev, - b2_3_dev, - basis_u: int, - scale_mat: float, - scale_vec: float, - boundary_cut: float, - mat11_dev, - mat12_dev, - mat13_dev, - mat22_dev, - mat23_dev, - mat33_dev, - vec1_dev, - vec2_dev, - vec3_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels.cc_lin_mhd_6d_2`. - ``b2_*_dev`` are the Hdiv (2-form) magnetic field FE coefficients, - already device-resident. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - dev_markers = markers - b2_1_dev = cp.ascontiguousarray(b2_1_dev) - b2_2_dev = cp.ascontiguousarray(b2_2_dev) - b2_3_dev = cp.ascontiguousarray(b2_3_dev) - - def dims(a): - return ( - np.int32(a.shape[1]), - np.int32(a.shape[2]), - np.int32(a.shape[3]), - np.int32(a.shape[4]), - np.int32(a.shape[5]), - ) - - launch_1d( - _cc_lin_mhd_6d_2_kernels["cc_lin_mhd_6d_2_cuda"], - n_markers, - ( - dev_markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(kind_map), - params_dev, - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - b2_1_dev, - np.int32(b2_1_dev.shape[1]), - np.int32(b2_1_dev.shape[2]), - b2_2_dev, - np.int32(b2_2_dev.shape[1]), - np.int32(b2_2_dev.shape[2]), - b2_3_dev, - np.int32(b2_3_dev.shape[1]), - np.int32(b2_3_dev.shape[2]), - np.int32(basis_u), - np.float64(scale_mat), - np.float64(scale_vec), - np.float64(boundary_cut), - mat11_dev, - mat12_dev, - mat13_dev, - mat22_dev, - mat23_dev, - mat33_dev, - vec1_dev, - vec2_dev, - vec3_dev, - *dims(mat11_dev), - *dims(mat12_dev), - *dims(mat13_dev), - *dims(mat22_dev), - *dims(mat23_dev), - *dims(mat33_dev), - np.int32(vec1_dev.shape[1]), - np.int32(vec1_dev.shape[2]), - np.int32(vec2_dev.shape[1]), - np.int32(vec2_dev.shape[2]), - np.int32(vec3_dev.shape[1]), - np.int32(vec3_dev.shape[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# pc_lin_mhd_6d_full / pc_lin_mhd_6d: accumulate a "pressure tensor" -- the -# same DF^-1(eta_p) DF^-T(eta_p) V1 -> V1 filling as vlasov_maxwell, but -# additionally scaled by every v_a*v_b product (a,b in x,y,z) of the marker -# velocity, giving one full symmetric 6-block matrix PER velocity-pair (6 -# pairs: xx, xy, xz, yy, yz, zz -> 6*6=36 matrix arrays) plus one vector PER -# velocity-component (3*3=9 vector arrays) -- see -# particle_to_mat_kernels.m_v_fill_v1_pressure_full and -# filler_kernels.fill_mat_vec_pressure_full/fill_mat_pressure_full, which -# this is a direct port of (fill_mat_vec_pressure_full_dev/ -# fill_mat_pressure_full_dev below are the CUDA equivalents, generalizing -# fill_mat_vec_dev/fill_mat_dev from a single mat/vec output to six/three). -# -# pc_lin_mhd_6d (no "_full") is the same accumulation restricted to the -# (x, y) "perpendicular" velocity plane only: 3 velocity-pairs (xx, xy, yy) -# and 2 velocity-components (x, y), i.e. 6*3=18 matrix arrays and 3*2=6 -# vector arrays -- see m_v_fill_v1_pressure/fill_mat_vec_pressure/ -# fill_mat_pressure. Both variants are called with the SAME 36+9=45 output -# arrays (the propagators share one call signature for _full and non-_full) -# but pc_lin_mhd_6d only ever writes the 24 "perp" ones -- the CPU reference -# leaves the other 21 untouched (at whatever the caller zeroed them to), and -# so does this port: pc_lin_mhd_6d_gpu accepts all 45 positionally (to match -# Accumulator._accumulate's ``*self._args_data`` unpacking) but only passes -# the 24 it needs into the CUDA launch. -# -# Both variants only differ from vlasov_maxwell's filling in the v_a*v_b -# scaling and in which marker column holds the weight: pc_lin_mhd_6d_full -# uses markers[ip, 8], pc_lin_mhd_6d uses markers[ip, 6] (matching the CPU -# reference exactly). -# --------------------------------------------------------------------------- - -_SPATIAL_BLOCKS = ("11", "12", "13", "22", "23", "33") -_PC_PRESSURE_FILLERS_SRC = load_cuda_source(__file__, "accum_kernels_cuda/_pc_pressure_fillers_src.cu") -_PC_LIN_MHD_6D_FULL_SRC = load_cuda_source(__file__, "accum_kernels_cuda/pc_lin_mhd_6d_full.cu") -_PC_LIN_MHD_6D_SRC = load_cuda_source(__file__, "accum_kernels_cuda/pc_lin_mhd_6d.cu") - - -def _pc_lin_mhd_6d_full_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _PC_PRESSURE_FILLERS_SRC + _PC_LIN_MHD_6D_FULL_SRC - - -def _pc_lin_mhd_6d_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _PC_PRESSURE_FILLERS_SRC + _PC_LIN_MHD_6D_SRC - - -_pc_lin_mhd_6d_full_kernels = CudaKernelSet(_pc_lin_mhd_6d_full_source) -_pc_lin_mhd_6d_kernels = CudaKernelSet(_pc_lin_mhd_6d_source) - - -def _pc_lin_mhd_6d_launch( - kernel, - markers, - kind_map: int, - params_dev, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - ep_scale: float, - mat_args_45: dict, - vec_args_45: dict, - vel_pairs, - vec_is, -): - """Shared launch logic for pc_lin_mhd_6d_full_gpu/pc_lin_mhd_6d_gpu. - ``mat_args_45``/``vec_args_45`` map every full-45-array name - (``mat{sp}_{vel}`` / ``vec{mu}_{i}``) to its device array; only the - subset named in ``vel_pairs``/``vec_is`` is actually passed to the - kernel launch. - """ - import numpy as np - - n_markers = markers.shape[0] - dev_markers = markers - - def dims(a): - return ( - np.int32(a.shape[1]), - np.int32(a.shape[2]), - np.int32(a.shape[3]), - np.int32(a.shape[4]), - np.int32(a.shape[5]), - ) - - args = [ - dev_markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(kind_map), - params_dev, - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - np.float64(ep_scale), - ] - for vel in vel_pairs: - for sp in _SPATIAL_BLOCKS: - args.append(mat_args_45[f"mat{sp}_{vel}"]) - for i in vec_is: - for mu in ("1", "2", "3"): - args.append(vec_args_45[f"vec{mu}_{i}"]) - for sp in _SPATIAL_BLOCKS: - args.extend(dims(mat_args_45[f"mat{sp}_11"])) - for mu in ("1", "2", "3"): - v = vec_args_45[f"vec{mu}_1"] - args.extend((np.int32(v.shape[1]), np.int32(v.shape[2]))) - - launch_1d(kernel, n_markers, args) - - -def pc_lin_mhd_6d_full_gpu( - markers, - kind_map: int, - params_dev, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - ep_scale: float, - *mat_and_vec_args, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels.pc_lin_mhd_6d_full`. - ``mat_and_vec_args`` are the 45 output arrays in the exact positional - order of the CPU kernel's signature (36 matrix blocks: velocity-pair - outer -- xx, xy, xz, yy, yz, zz -- spatial-block inner -- 11, 12, 13, - 22, 23, 33; then 9 vector blocks: velocity-component outer -- x, y, z - -- spatial-component inner -- 1, 2, 3), matching - ``Accumulator._args_data``'s construction for ``symmetry="pressure"``. - """ - mat_args_45 = { - f"mat{sp}_{vel}": mat_and_vec_args[k] - for k, (vel, sp) in enumerate((vel, sp) for vel in _SPATIAL_BLOCKS for sp in _SPATIAL_BLOCKS) - } - vec_args_45 = { - f"vec{mu}_{i}": mat_and_vec_args[36 + k] - for k, (i, mu) in enumerate((i, mu) for i in ("1", "2", "3") for mu in ("1", "2", "3")) - } - _pc_lin_mhd_6d_launch( - _pc_lin_mhd_6d_full_kernels["pc_lin_mhd_6d_full_cuda"], - markers, - kind_map, - params_dev, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - ep_scale, - mat_args_45, - vec_args_45, - _SPATIAL_BLOCKS, - ("1", "2", "3"), - ) - - -def pc_lin_mhd_6d_gpu( - markers, - kind_map: int, - params_dev, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - ep_scale: float, - *mat_and_vec_args, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels.pc_lin_mhd_6d`. Same - 45-array positional convention as :func:`pc_lin_mhd_6d_full_gpu` - (the propagator passes the identical 45-array signature for both), but - -- matching the CPU reference exactly -- only the "perp" (x, y) subset - (18 of the 36 matrix arrays, 6 of the 9 vector arrays) is ever written; - the rest are left untouched (they stay at whatever - ``Accumulator._accumulate``'s ``dat[:] = 0.0`` reset left them at). - """ - mat_args_45 = { - f"mat{sp}_{vel}": mat_and_vec_args[k] - for k, (vel, sp) in enumerate((vel, sp) for vel in _SPATIAL_BLOCKS for sp in _SPATIAL_BLOCKS) - } - vec_args_45 = { - f"vec{mu}_{i}": mat_and_vec_args[36 + k] - for k, (i, mu) in enumerate((i, mu) for i in ("1", "2", "3") for mu in ("1", "2", "3")) - } - _pc_lin_mhd_6d_launch( - _pc_lin_mhd_6d_kernels["pc_lin_mhd_6d_cuda"], - markers, - kind_map, - params_dev, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - ep_scale, - mat_args_45, - vec_args_45, - ("11", "12", "22"), - ("1", "2"), - ) diff --git a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py b/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py deleted file mode 100644 index 6b18c6de5..000000000 --- a/src/struphy/pic/accumulation/accum_kernels_gc_cuda.py +++ /dev/null @@ -1,569 +0,0 @@ -"""Hand-written CUDA replacement for -:func:`~struphy.pic.accumulation.accum_kernels_gc.gc_mag_density_0form`, -used only under ``ARRAY_BACKEND=cupy``. See -:mod:`~struphy.pic.pushing.pusher_kernels_gc_cuda` for the scope of this -branch's 5D guiding-center porting (2 explicit pushers + this one -accumulator, out of 21 real kernels in the gc family). - -Same atomicAdd-scatter approach as -:func:`~struphy.pic.accumulation.accum_kernels_cuda.charge_density_0form_gpu` --- this kernel is nearly identical (an H^1/0-form vec_fill_b_v0 scatter), -just with a ``mu * weight * scale`` filling instead of a plain weight, and -``mu`` read from the marker's ``mu_idx`` column instead of a fixed offset. -""" -from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source - -_GC_MAG_DENSITY_0FORM_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu") -_gc_mag_density_0form_kernel = CudaKernel(_GC_MAG_DENSITY_0FORM_SRC, "gc_mag_density_0form_cuda") - - -def gc_mag_density_0form_gpu( - markers, - mu_idx: int, - scale: float, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - vec_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels_gc.gc_mag_density_0form`. - ``vec_dev`` is already device-resident and already zeroed by the caller. - """ - import numpy as np - - n_markers = markers.shape[0] - dev_markers = markers - launch_1d( - _gc_mag_density_0form_kernel, - n_markers, - ( - dev_markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(mu_idx), - np.float64(scale), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - vec_dev, - np.int32(vec_dev.shape[1]), - np.int32(vec_dev.shape[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# gc_density_0form is byte-for-byte the same computation as -# accum_kernels.charge_density_0form (an H^1/0-form vec_fill_b_v0 scatter with -# the marker weight as filling); only the docstring differs. Rather than -# duplicating the CUDA source, reuse the already-validated kernel. -# --------------------------------------------------------------------------- - -from struphy.pic.accumulation.accum_kernels_cuda import charge_density_0form_gpu as _charge_density_0form_gpu - - -def gc_density_0form_gpu(markers, weight_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, vec_dev): - """GPU replacement for - :func:`~struphy.pic.accumulation.accum_kernels_gc.gc_density_0form`. - - Identical to - :func:`~struphy.pic.accumulation.accum_kernels_cuda.charge_density_0form_gpu` - (same filling, same 0-form scatter), so it simply delegates. - """ - _charge_density_0form_gpu(markers, weight_idx, pn, tn1_dev, tn2_dev, tn3_dev, starts, vec_dev) - - -# --------------------------------------------------------------------------- -# cc_lin_mhd_5d_D: same 3-block antisymmetric V_u -> V_u fill as -# accum_kernels.cc_lin_mhd_6d_1 (runtime basis_u in {0,1,2} selecting -# H1vec/Hcurl/Hdiv, fill_mat_dev for each block), but the scalar prefactor is -# the guiding-centre density factor -# -# -w_p * (1 - b_para/b*_para) * ep_scale / epsilon -# -# with b*_para = norm_b1 . (b2 + epsilon*v_par*curl_norm_b). It therefore needs -# a 1-form (norm_b1) and a second 2-form (curl_norm_b) evaluation on top of -# the B-field, but reuses fill_mat_dev from accum_kernels_cuda's -# _LINEAR_VLASOV_AMPERE_EXTRA_SRC unchanged. -# --------------------------------------------------------------------------- - -_CC_LIN_MHD_5D_D_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu") - - -def _cc_lin_mhd_5d_D_source(): - from struphy.pic.accumulation.accum_kernels_cuda import _LINEAR_VLASOV_AMPERE_EXTRA_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_5D_D_SRC - - -_cc_lin_mhd_5d_D_kernels = CudaKernelSet(_cc_lin_mhd_5d_D_source) - - -def cc_lin_mhd_5d_D_gpu( - markers, - kind_map, - params_dev, - epsilon, - ep_scale, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2, - norm_b1, - curl_norm_b, - basis_u, - mat12_dev, - mat13_dev, - mat23_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_D`. - - ``b2``/``norm_b1``/``curl_norm_b`` are 3-tuples of device-resident FE - coefficient arrays; the ``mat*_dev`` are already zeroed by the caller. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - def dims(a): - return ( - np.int32(a.shape[1]), - np.int32(a.shape[2]), - np.int32(a.shape[3]), - np.int32(a.shape[4]), - np.int32(a.shape[5]), - ) - - launch_1d( - _cc_lin_mhd_5d_D_kernels["cc_lin_mhd_5d_D_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.float64(ep_scale), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(norm_b1[0]), - *d(norm_b1[1]), - *d(norm_b1[2]), - *d(curl_norm_b[0]), - *d(curl_norm_b[1]), - *d(curl_norm_b[2]), - np.int32(basis_u), - mat12_dev, - mat13_dev, - mat23_dev, - *dims(mat12_dev), - *dims(mat13_dev), - *dims(mat23_dev), - ), - ) - - -# --------------------------------------------------------------------------- -# cc_lin_mhd_5d_gradB: vector-only accumulation (no matrix) into V_u, with -# filling w_p * mu * [B2_x . norm_b_x . grad(PB)] / |B*_para| (times -# 1/det(DF) for basis_u=2, which additionally adds grad_PBeq to grad_PB). -# Uses fill_vec_dev below -- the vector half of fill_mat_vec_dev, needed on -# its own here since no matrix block is filled. -# --------------------------------------------------------------------------- - -# Port of filler_kernels.fill_vec; shared by all vector-filling accumulators -# in this module. -_FILL_VEC_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_fill_vec_src.cu") - -_CC_LIN_MHD_5D_GRADB_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu") - -def _cc_lin_mhd_5d_gradB_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _FILL_VEC_SRC + _CC_LIN_MHD_5D_GRADB_SRC - - -_cc_gradB_kernels = CudaKernelSet(_cc_lin_mhd_5d_gradB_source) - - -def cc_lin_mhd_5d_gradB_gpu( - markers, - first_init_idx, - mu_idx, - kind_map, - params_dev, - epsilon, - ep_scale, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2, - norm_b1, - curl_norm_b, - grad_PB, - grad_PBeq, - basis_u, - vec1_dev, - vec2_dev, - vec3_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_gradB`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _cc_gradB_kernels["cc_lin_mhd_5d_gradB_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(mu_idx), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.float64(ep_scale), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(norm_b1[0]), - *d(norm_b1[1]), - *d(norm_b1[2]), - *d(curl_norm_b[0]), - *d(curl_norm_b[1]), - *d(curl_norm_b[2]), - *d(grad_PB[0]), - *d(grad_PB[1]), - *d(grad_PB[2]), - *d(grad_PBeq[0]), - *d(grad_PBeq[1]), - *d(grad_PBeq[2]), - np.int32(basis_u), - vec1_dev, - np.int32(vec1_dev.shape[1]), - np.int32(vec1_dev.shape[2]), - vec2_dev, - np.int32(vec2_dev.shape[1]), - np.int32(vec2_dev.shape[2]), - vec3_dev, - np.int32(vec3_dev.shape[1]), - np.int32(vec3_dev.shape[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# cc_lin_mhd_5d_curlb: full symmetric 6-block matrix + vector fill, with the -# curvature filling -# -# M = w_p * v^2 * [B2_x (curl_b (x) curl_b) (-B2_x)] / |B*_para|^2 -# V = w_p * v^2 * [B2_x curl_b] / |B*_para| -# -# (times 1/det^2 resp. 1/det for basis_u=2). Only basis_u 0 and 2 exist here. -# -# NOTE: the basis_u == 0 branch of the CPU kernel used to accumulate into -# filling_m/filling_v with `+=` across markers, which made it order-dependent -# and inherently sequential; that was a typo and is fixed on this branch -- -# see ISSUE_cc_lin_mhd_5d_curlb_order_dependent.md. This port assumes the -# fixed (per-marker) semantics. -# --------------------------------------------------------------------------- - -_CC_LIN_MHD_5D_CURLB_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu") - -def _cc_lin_mhd_5d_curlb_source(): - from struphy.pic.accumulation.accum_kernels_cuda import _LINEAR_VLASOV_AMPERE_EXTRA_SRC - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _LINEAR_VLASOV_AMPERE_EXTRA_SRC + _CC_LIN_MHD_5D_CURLB_SRC - - -_cc_curlb_kernels = CudaKernelSet(_cc_lin_mhd_5d_curlb_source) - - -def cc_lin_mhd_5d_curlb_gpu( - markers, - kind_map, - params_dev, - epsilon, - ep_scale, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2, - norm_b1, - curl_norm_b, - basis_u, - mat11_dev, - mat12_dev, - mat13_dev, - mat22_dev, - mat23_dev, - mat33_dev, - vec1_dev, - vec2_dev, - vec3_dev, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_curlb`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - def dims(a): - return ( - np.int32(a.shape[1]), - np.int32(a.shape[2]), - np.int32(a.shape[3]), - np.int32(a.shape[4]), - np.int32(a.shape[5]), - ) - - launch_1d( - _cc_curlb_kernels["cc_lin_mhd_5d_curlb_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.float64(ep_scale), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(norm_b1[0]), - *d(norm_b1[1]), - *d(norm_b1[2]), - *d(curl_norm_b[0]), - *d(curl_norm_b[1]), - *d(curl_norm_b[2]), - np.int32(basis_u), - mat11_dev, - mat12_dev, - mat13_dev, - mat22_dev, - mat23_dev, - mat33_dev, - vec1_dev, - vec2_dev, - vec3_dev, - *dims(mat11_dev), - *dims(mat12_dev), - *dims(mat13_dev), - *dims(mat22_dev), - *dims(mat23_dev), - *dims(mat33_dev), - np.int32(vec1_dev.shape[1]), - np.int32(vec1_dev.shape[2]), - np.int32(vec2_dev.shape[1]), - np.int32(vec2_dev.shape[2]), - np.int32(vec3_dev.shape[1]), - np.int32(vec3_dev.shape[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# cc_lin_mhd_5d_gradB_dg_init / cc_lin_mhd_5d_gradB_dg -# -# Both are vector-only accumulators of the same shape; they differ only in -# -# * where they evaluate: `dg_init` at the current position eta, `dg` at the -# midpoint eta_mid = mod((eta + eta^n) / 2, 1), -# * `dg` adds a discrete-gradient correction term proportional to -# eta_diff = eta - eta^n, scaled by `const`. -# -# They are therefore compiled from one source with an `is_dg` switch, so the -# per-marker geometry/spline work is written once. -# -# V = sum over {Beq, B} of w_p mu [X_x b_x] grad(PB_.) / |B*_para| -# (+ const [X_x b_x] eta_diff / |B*_para| for `dg`) -# -# (times 1/det for basis_u=2). Only basis_u 0 and 2 exist here. -# --------------------------------------------------------------------------- - -_CC_LIN_MHD_5D_GRADB_DG_SRC = load_cuda_source(__file__, "accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu") - -def _cc_lin_mhd_5d_gradB_dg_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _FILL_VEC_SRC + _CC_LIN_MHD_5D_GRADB_DG_SRC - - -_cc_gradB_dg_kernels = CudaKernelSet(_cc_lin_mhd_5d_gradB_dg_source) - - -def cc_lin_mhd_5d_gradB_dg_gpu( - markers, - first_init_idx, - mu_idx, - kind_map, - params_dev, - epsilon, - ep_scale, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2, - beq2, - norm_b1, - curl_norm_b, - grad_PB, - grad_PBeq, - basis_u, - vec1_dev, - vec2_dev, - vec3_dev, - const=0.0, - is_dg=False, -): - """GPU replacement for one call of - :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_gradB_dg` - (``is_dg=True``, using ``const``) or of - :func:`~struphy.pic.accumulation.accum_kernels_gc.cc_lin_mhd_5d_gradB_dg_init` - (``is_dg=False``, where ``const`` is unused).""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _cc_gradB_dg_kernels["cc_lin_mhd_5d_gradB_dg_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(mu_idx), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.float64(ep_scale), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(beq2[0]), - *d(beq2[1]), - *d(beq2[2]), - *d(norm_b1[0]), - *d(norm_b1[1]), - *d(norm_b1[2]), - *d(curl_norm_b[0]), - *d(curl_norm_b[1]), - *d(curl_norm_b[2]), - *d(grad_PB[0]), - *d(grad_PB[1]), - *d(grad_PB[2]), - *d(grad_PBeq[0]), - *d(grad_PBeq[1]), - *d(grad_PBeq[2]), - np.int32(basis_u), - np.float64(const), - np.int32(bool(is_dg)), - vec1_dev, - np.int32(vec1_dev.shape[1]), - np.int32(vec1_dev.shape[2]), - vec2_dev, - np.int32(vec2_dev.shape[1]), - np.int32(vec2_dev.shape[2]), - vec3_dev, - np.int32(vec3_dev.shape[1]), - np.int32(vec3_dev.shape[2]), - ), - ) diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu deleted file mode 100644 index c84809bd1..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_1_src.cu +++ /dev/null @@ -1,118 +0,0 @@ -extern "C" __global__ -void cc_lin_mhd_6d_1_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int b2_1_n2, const int b2_1_n3, - const double* b2_2, const int b2_2_n2, const int b2_2_n3, - const double* b2_3, const int b2_3_n2, const int b2_3_n3, - const int basis_u, const double scale_mat, const double boundary_cut, - double* mat12, double* mat13, double* mat23, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double weight = row[6]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - b2_1, b2_1_n2, b2_1_n3, b2_2, b2_2_n2, b2_2_n3, b2_3, b2_3_n2, b2_3_n3, b); - - // b_prod = bx() as a row-major 3x3 matrix - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - double fill12, fill13, fill23; - - if (basis_u == 0) { - fill12 = -weight * b_prod[1] * scale_mat; - fill13 = -weight * b_prod[2] * scale_mat; - fill23 = -weight * b_prod[5] * scale_mat; - - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - - } else if (basis_u == 1) { - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - double df_inv[9], g_inv[9]; - matrix_inv_dev(dfm, df_inv); - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; - g_inv[3*i+j] = s; - } - double tmp1[9], tmp2[9]; - matmat_dev(g_inv, b_prod, tmp1); - matmat_dev(tmp1, g_inv, tmp2); - - fill12 = -weight * tmp2[1] * scale_mat; - fill13 = -weight * tmp2[2] * scale_mat; - fill23 = -weight * tmp2[5] * scale_mat; - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - - } else if (basis_u == 2) { - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - const double det2 = det_df * det_df; - - fill12 = -weight * b_prod[1] * scale_mat / det2; - fill13 = -weight * b_prod[2] * scale_mat / det2; - fill23 = -weight * b_prod[5] * scale_mat / det2; - - // Hdiv component shapes: comp1 = N-D-D, comp2 = D-N-D, comp3 = D-D-N - fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu deleted file mode 100644 index 65593c8ce..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_cc_lin_mhd_6d_2_src.cu +++ /dev/null @@ -1,172 +0,0 @@ -extern "C" __global__ -void cc_lin_mhd_6d_2_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int b2_1_n2, const int b2_1_n3, - const double* b2_2, const int b2_2_n2, const int b2_2_n3, - const double* b2_3, const int b2_3_n2, const int b2_3_n3, - const int basis_u, const double scale_mat, const double scale_vec, const double boundary_cut, - double* mat11, double* mat12, double* mat13, - double* mat22, double* mat23, double* mat33, - double* vec1, double* vec2, double* vec3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, - const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, - const int v2_n2, const int v2_n3, - const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - const double weight = row[6]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - b2_1, b2_1_n2, b2_1_n3, b2_2, b2_2_n2, b2_2_n3, b2_3, b2_3_n2, b2_3_n3, b); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - double df_inv[9]; - matrix_inv_dev(dfm, df_inv); - - double tmp1[9], tmp_m[9], tmp_v[3]; - - if (basis_u == 1) { - double g_inv[9]; - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; - g_inv[3*i+j] = s; - } - double tmp0[9]; - matmat_dev(g_inv, b_prod, tmp0); - matmat_dev(tmp0, df_inv, tmp1); - } else { - // basis_u == 0 or 2: tmp1 = b_prod @ df_inv (g_inv computed but - // unused in the CPU reference for these two branches) - matmat_dev(b_prod, df_inv, tmp1); - } - - // tmp_m = tmp1 @ tmp1^T ; tmp_v = tmp1 @ v - for (int i = 0; i < 3; i++) { - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += tmp1[3*i+k] * tmp1[3*j+k]; - tmp_m[3*i+j] = s; - } - } - matvec_dev(tmp1, v, tmp_v); - - double mat_scale = weight * scale_mat; - double vec_scale = weight * scale_vec; - if (basis_u == 2) { - mat_scale /= det_df * det_df; - vec_scale /= det_df; - } - - double filling_m[9], filling_v[3]; - for (int k = 0; k < 9; k++) filling_m[k] = tmp_m[k] * mat_scale; - for (int k = 0; k < 3; k++) filling_v[k] = tmp_v[k] * vec_scale; - - const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; - const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - if (basis_u == 0) { - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - - } else if (basis_u == 1) { - fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); - fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); - fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - - } else if (basis_u == 2) { - // Hdiv component shapes: comp1 = N-D-D, comp2 = D-N-D, comp3 = D-D-N - fill_mat_vec_dev(p1,pd2,pd3, p1,pd2,pd3, bn1,bd2,bd3, bn1,bd2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, vec1, v1_n2,v1_n3, filling_v[0]); - fill_mat_vec_dev(pd1,p2,pd3, pd1,p2,pd3, bd1,bn2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, vec2, v2_n2,v2_n3, filling_v[1]); - fill_mat_vec_dev(pd1,pd2,p3, pd1,pd2,p3, bd1,bd2,bn3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_charge_density_0form_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_charge_density_0form_src.cu deleted file mode 100644 index 0a578638c..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_charge_density_0form_src.cu +++ /dev/null @@ -1,88 +0,0 @@ -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -// Only the N-spline values (bn) are needed for an H^1/0-form fill; D-spline -// values are computed alongside (same recursion as -// pusher_kernels_cuda.py's b_d_splines_dev) and simply unused. -__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) -{ - double left[MAXP]; - double right[MAXP]; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -extern "C" __global__ -void charge_density_0form_cuda( - const double* markers, - const int n_cols, - const int n_markers, - const int weight_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - double* vec, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double filling = row[weight_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bn1[il1] * filling; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bn2[il2]; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bn3[il3]; - atomicAdd(&vec[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3], b3); - } - } - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu deleted file mode 100644 index fb3c5133f..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_linear_vlasov_ampere_extra_src.cu +++ /dev/null @@ -1,187 +0,0 @@ -__device__ void outer_dev(const double* a, const double* b, double* c) -{ - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) - c[3*i+j] = a[i] * b[j]; -} - -// Port of filler_kernels.fill_mat_vec: fills one matrix block (banded -// storage, j = pad + jl - il) and, along the shared (i1,i2,i3) row loop, -// also fills the corresponding vector block. -__device__ void fill_mat_vec_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat, int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double* vec, int vn2, int vn3, - double filling_vec) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - - atomicAdd(&vec[(size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3], b3 * filling_vec); - - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat[idx], b6); - } - } - } - } - } - } -} - -// Port of filler_kernels.fill_mat: matrix-only block fill (off-diagonal -// blocks, no associated vector). -__device__ void fill_mat_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat, int d2, int d3, int d4, int d5, int d6, - double filling_mat) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1] * filling_mat; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1]; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat[idx], b6); - } - } - } - } - } - } -} - -extern "C" __global__ -void linear_vlasov_ampere_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const double* f0_values, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - double* mat11, double* mat12, double* mat13, - double* mat22, double* mat23, double* mat33, - double* vec1, double* vec2, double* vec3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, - const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, - const int v2_n2, const int v2_n3, - const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - const double weight = row[6]; - const double s0 = row[7]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - double df_inv[9], df_inv_v[3]; - matrix_inv_dev(dfm, df_inv); - matvec_dev(df_inv, v, df_inv_v); - - double filling_m[9]; - outer_dev(df_inv_v, df_inv_v, filling_m); - const double fm_scale = f0_values[ip] / s0; - for (int k = 0; k < 9; k++) filling_m[k] *= fm_scale; - - double filling_v[3]; - filling_v[0] = weight * df_inv_v[0]; - filling_v[1] = weight * df_inv_v[1]; - filling_v[2] = weight * df_inv_v[2]; - - const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; - const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, - vec1, v1_n2,v1_n3, filling_v[0]); - - fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, - vec2, v2_n2,v2_n3, filling_v[1]); - - fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, - vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_pc_pressure_fillers_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_pc_pressure_fillers_src.cu deleted file mode 100644 index 2943db3d8..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_pc_pressure_fillers_src.cu +++ /dev/null @@ -1,198 +0,0 @@ -// Port of filler_kernels.fill_mat_vec_pressure_full: like fill_mat_vec_dev -// but scatters into 6 matrix blocks (scaled by vx*vx, vx*vy, vx*vz, vy*vy, -// vy*vz, vz*vz) and 3 vector blocks (scaled by vx, vy, vz) in one pass. -__device__ void fill_mat_vec_pressure_full_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat_11, double* mat_12, double* mat_13, double* mat_22, double* mat_23, double* mat_33, - int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double* vec_1, double* vec_2, double* vec_3, int vn2, int vn3, - double filling_vec, - double vx, double vy, double vz) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - size_t vidx = (size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3; - double bv = b3 * filling_vec; - atomicAdd(&vec_1[vidx], bv * vx); - atomicAdd(&vec_2[vidx], bv * vy); - atomicAdd(&vec_3[vidx], bv * vz); - - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat_11[idx], b6 * vx * vx); - atomicAdd(&mat_12[idx], b6 * vx * vy); - atomicAdd(&mat_13[idx], b6 * vx * vz); - atomicAdd(&mat_22[idx], b6 * vy * vy); - atomicAdd(&mat_23[idx], b6 * vy * vz); - atomicAdd(&mat_33[idx], b6 * vz * vz); - } - } - } - } - } - } -} - -// Port of filler_kernels.fill_mat_pressure_full: same as above minus the -// vector part (off-diagonal spatial blocks have no associated vector). -__device__ void fill_mat_pressure_full_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat_11, double* mat_12, double* mat_13, double* mat_22, double* mat_23, double* mat_33, - int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double vx, double vy, double vz) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat_11[idx], b6 * vx * vx); - atomicAdd(&mat_12[idx], b6 * vx * vy); - atomicAdd(&mat_13[idx], b6 * vx * vz); - atomicAdd(&mat_22[idx], b6 * vy * vy); - atomicAdd(&mat_23[idx], b6 * vy * vz); - atomicAdd(&mat_33[idx], b6 * vz * vz); - } - } - } - } - } - } -} - -// Port of filler_kernels.fill_mat_vec_pressure: the "perp" (xy-plane only) -// variant -- 3 matrix blocks (vx*vx, vx*vy, vy*vy) and 2 vector blocks -// (vx, vy). -__device__ void fill_mat_vec_pressure_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat_11, double* mat_12, double* mat_22, - int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double* vec_1, double* vec_2, int vn2, int vn3, - double filling_vec, - double vx, double vy) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - size_t vidx = (size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3; - double bv = b3 * filling_vec; - atomicAdd(&vec_1[vidx], bv * vx); - atomicAdd(&vec_2[vidx], bv * vy); - - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat_11[idx], b6 * vx * vx); - atomicAdd(&mat_12[idx], b6 * vx * vy); - atomicAdd(&mat_22[idx], b6 * vy * vy); - } - } - } - } - } - } -} - -// Port of filler_kernels.fill_mat_pressure: "perp" matrix-only variant. -__device__ void fill_mat_pressure_dev( - int pi1, int pi2, int pi3, int pj1, int pj2, int pj3, - const double* bi1, const double* bi2, const double* bi3, - const double* bj1, const double* bj2, const double* bj3, - int span1, int span2, int span3, - int start0, int start1, int start2, - int pad0, int pad1, int pad2, - double* mat_11, double* mat_12, double* mat_22, - int d2, int d3, int d4, int d5, int d6, - double filling_mat, - double vx, double vy) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1]; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - for (int jl1 = 0; jl1 <= pj1; jl1++) { - int j1 = pad0 + jl1 - il1; - double b4 = b3 * bj1[jl1] * filling_mat; - for (int jl2 = 0; jl2 <= pj2; jl2++) { - int j2 = pad1 + jl2 - il2; - double b5 = b4 * bj2[jl2]; - for (int jl3 = 0; jl3 <= pj3; jl3++) { - int j3 = pad2 + jl3 - il3; - double b6 = b5 * bj3[jl3]; - size_t idx = (((((size_t)i1*d2+i2)*d3+i3)*d4+j1)*d5+j2)*d6+j3; - atomicAdd(&mat_11[idx], b6 * vx * vx); - atomicAdd(&mat_12[idx], b6 * vx * vy); - atomicAdd(&mat_22[idx], b6 * vy * vy); - } - } - } - } - } - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_vlasov_maxwell_extra_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_vlasov_maxwell_extra_src.cu deleted file mode 100644 index 41fc2f777..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/_vlasov_maxwell_extra_src.cu +++ /dev/null @@ -1,97 +0,0 @@ -extern "C" __global__ -void vlasov_maxwell_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - double* mat11, double* mat12, double* mat13, - double* mat22, double* mat23, double* mat33, - double* vec1, double* vec2, double* vec3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, - const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, - const int v2_n2, const int v2_n3, - const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - const double weight = row[6]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - double df_inv[9], df_inv_v[3]; - matrix_inv_dev(dfm, df_inv); - matvec_dev(df_inv, v, df_inv_v); - - // g_inv = DF^-1 @ DF^-T ; g_inv[i,j] = sum_k df_inv[i,k]*df_inv[j,k] - double filling_m[9]; - for (int i = 0; i < 3; i++) { - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; - filling_m[3*i+j] = weight * s; - } - } - - double filling_v[3]; - filling_v[0] = weight * df_inv_v[0]; - filling_v[1] = weight * df_inv_v[1]; - filling_v[2] = weight * df_inv_v[2]; - - const double fill11 = filling_m[0], fill12 = filling_m[1], fill13 = filling_m[2]; - const double fill22 = filling_m[4], fill23 = filling_m[5], fill33 = filling_m[8]; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - fill_mat_vec_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, - vec1, v1_n2,v1_n3, filling_v[0]); - - fill_mat_vec_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, - vec2, v2_n2,v2_n3, filling_v[1]); - - fill_mat_vec_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, - vec3, v3_n2,v3_n3, filling_v[2]); - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12); - - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13); - - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23); -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu deleted file mode 100644 index f245f4ba5..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d.cu +++ /dev/null @@ -1,83 +0,0 @@ -extern "C" __global__ -void pc_lin_mhd_6d_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double ep_scale, - double* mat11_11, double* mat12_11, double* mat13_11, double* mat22_11, double* mat23_11, double* mat33_11, double* mat11_12, double* mat12_12, double* mat13_12, double* mat22_12, double* mat23_12, double* mat33_12, double* mat11_22, double* mat12_22, double* mat13_22, double* mat22_22, double* mat23_22, double* mat33_22, - double* vec1_1, double* vec2_1, double* vec3_1, double* vec1_2, double* vec2_2, double* vec3_2, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, const int v2_n2, const int v2_n3, const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v[3] = {row[3], row[4], row[5]}; - const double weight = row[6]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - double df_inv[9]; - matrix_inv_dev(dfm, df_inv); - double g_inv[9]; - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; - g_inv[3*i+j] = s; - } - double tmp_v[3]; - matvec_dev(df_inv, v, tmp_v); - - const double fill11 = weight * g_inv[0] * ep_scale; - const double fill12 = weight * g_inv[1] * ep_scale; - const double fill13 = weight * g_inv[2] * ep_scale; - const double fill22 = weight * g_inv[4] * ep_scale; - const double fill23 = weight * g_inv[5] * ep_scale; - const double fill33 = weight * g_inv[8] * ep_scale; - const double fill1 = weight * tmp_v[0] * ep_scale; - const double fill2 = weight * tmp_v[1] * ep_scale; - const double fill3 = weight * tmp_v[2] * ep_scale; - const double vx = v[0], vy = v[1]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - fill_mat_vec_pressure_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11_11, mat11_12, mat11_22, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, - vec1_1, vec1_2, v1_n2,v1_n3, fill1, vx,vy); - - fill_mat_pressure_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12_11, mat12_12, mat12_22, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12, vx,vy); - - fill_mat_pressure_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13_11, mat13_12, mat13_22, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13, vx,vy); - - fill_mat_vec_pressure_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22_11, mat22_12, mat22_22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, - vec2_1, vec2_2, v2_n2,v2_n3, fill2, vx,vy); - - fill_mat_pressure_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23_11, mat23_12, mat23_22, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23, vx,vy); - - fill_mat_vec_pressure_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33_11, mat33_12, mat33_22, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, - vec3_1, vec3_2, v3_n2,v3_n3, fill3, vx,vy); - -} diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d_full.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d_full.cu deleted file mode 100644 index 373752396..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_cuda/pc_lin_mhd_6d_full.cu +++ /dev/null @@ -1,83 +0,0 @@ -extern "C" __global__ -void pc_lin_mhd_6d_full_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double ep_scale, - double* mat11_11, double* mat12_11, double* mat13_11, double* mat22_11, double* mat23_11, double* mat33_11, double* mat11_12, double* mat12_12, double* mat13_12, double* mat22_12, double* mat23_12, double* mat33_12, double* mat11_13, double* mat12_13, double* mat13_13, double* mat22_13, double* mat23_13, double* mat33_13, double* mat11_22, double* mat12_22, double* mat13_22, double* mat22_22, double* mat23_22, double* mat33_22, double* mat11_23, double* mat12_23, double* mat13_23, double* mat22_23, double* mat23_23, double* mat33_23, double* mat11_33, double* mat12_33, double* mat13_33, double* mat22_33, double* mat23_33, double* mat33_33, - double* vec1_1, double* vec2_1, double* vec3_1, double* vec1_2, double* vec2_2, double* vec3_2, double* vec1_3, double* vec2_3, double* vec3_3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, const int v2_n2, const int v2_n3, const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v[3] = {row[3], row[4], row[5]}; - const double weight = row[8]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - double df_inv[9]; - matrix_inv_dev(dfm, df_inv); - double g_inv[9]; - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) { - double s = 0.0; - for (int k = 0; k < 3; k++) s += df_inv[3*i+k] * df_inv[3*j+k]; - g_inv[3*i+j] = s; - } - double tmp_v[3]; - matvec_dev(df_inv, v, tmp_v); - - const double fill11 = weight * g_inv[0] * ep_scale; - const double fill12 = weight * g_inv[1] * ep_scale; - const double fill13 = weight * g_inv[2] * ep_scale; - const double fill22 = weight * g_inv[4] * ep_scale; - const double fill23 = weight * g_inv[5] * ep_scale; - const double fill33 = weight * g_inv[8] * ep_scale; - const double fill1 = weight * tmp_v[0] * ep_scale; - const double fill2 = weight * tmp_v[1] * ep_scale; - const double fill3 = weight * tmp_v[2] * ep_scale; - const double vx = v[0], vy = v[1], vz = v[2]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - fill_mat_vec_pressure_full_dev(pd1,p2,p3, pd1,p2,p3, bd1,bn2,bn3, bd1,bn2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11_11, mat11_12, mat11_13, mat11_22, mat11_23, mat11_33, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, fill11, - vec1_1, vec1_2, vec1_3, v1_n2,v1_n3, fill1, vx,vy,vz); - - fill_mat_pressure_full_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12_11, mat12_12, mat12_13, mat12_22, mat12_23, mat12_33, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, fill12, vx,vy,vz); - - fill_mat_pressure_full_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13_11, mat13_12, mat13_13, mat13_22, mat13_23, mat13_33, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, fill13, vx,vy,vz); - - fill_mat_vec_pressure_full_dev(p1,pd2,p3, p1,pd2,p3, bn1,bd2,bn3, bn1,bd2,bn3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22_11, mat22_12, mat22_13, mat22_22, mat22_23, mat22_33, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, fill22, - vec2_1, vec2_2, vec2_3, v2_n2,v2_n3, fill2, vx,vy,vz); - - fill_mat_pressure_full_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23_11, mat23_12, mat23_13, mat23_22, mat23_23, mat23_33, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, fill23, vx,vy,vz); - - fill_mat_vec_pressure_full_dev(p1,p2,pd3, p1,p2,pd3, bn1,bn2,bd3, bn1,bn2,bd3, span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33_11, mat33_12, mat33_13, mat33_22, mat33_23, mat33_33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, fill33, - vec3_1, vec3_2, vec3_3, v3_n2,v3_n3, fill3, vx,vy,vz); - -} diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu deleted file mode 100644 index 1f8aabf22..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_curlb_src.cu +++ /dev/null @@ -1,144 +0,0 @@ -extern "C" __global__ -void cc_lin_mhd_5d_curlb_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const double epsilon, const double ep_scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const int basis_u, - double* mat11, double* mat12, double* mat13, - double* mat22, double* mat23, double* mat33, - double* vec1, double* vec2, double* vec3, - const int m11_d2, const int m11_d3, const int m11_d4, const int m11_d5, const int m11_d6, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m22_d2, const int m22_d3, const int m22_d4, const int m22_d5, const int m22_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6, - const int m33_d2, const int m33_d3, const int m33_d4, const int m33_d5, const int m33_d6, - const int v1_n2, const int v1_n3, - const int v2_n2, const int v2_n3, - const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double weight = row[5]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double bfull_star[3]; - for (int k = 0; k < 3; k++) bfull_star[k] = b[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, bfull_star); - - // tmp = curl_norm_b (x) curl_norm_b - double tmp[9]; - outer_dev(curl_norm_b, curl_norm_b, tmp); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - double b_prod_neg[9]; - for (int k = 0; k < 9; k++) b_prod_neg[k] = -b_prod[k]; - - double tmp1[9], tmp_m[9], tmp_v[3]; - matmat_dev(b_prod, tmp, tmp1); - matmat_dev(tmp1, b_prod_neg, tmp_m); - matvec_dev(b_prod, curl_norm_b, tmp_v); - - double fm[9], fv[3]; - if (basis_u == 0) { - const double sm = weight * v * v / (abs_b_star_para * abs_b_star_para) * ep_scale; - const double sv = weight * v * v / abs_b_star_para * ep_scale; - for (int k = 0; k < 9; k++) fm[k] = tmp_m[k] * sm; - for (int k = 0; k < 3; k++) fv[k] = tmp_v[k] * sv; - } else { - const double sm = weight * v * v / (abs_b_star_para * abs_b_star_para) - / (det_df * det_df) * ep_scale; - const double sv = weight * v * v / abs_b_star_para / det_df * ep_scale; - for (int k = 0; k < 9; k++) fm[k] = tmp_m[k] * sm; - for (int k = 0; k < 3; k++) fv[k] = tmp_v[k] * sv; - } - - const double f11 = fm[0], f12 = fm[1], f13 = fm[2]; - const double f22 = fm[4], f23 = fm[5], f33 = fm[8]; - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - if (basis_u == 0) { - // V0vec: every block N-N-N - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, f11, vec1, v1_n2,v1_n3, fv[0]); - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, f22, vec2, v2_n2,v2_n3, fv[1]); - fill_mat_vec_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, f33, vec3, v3_n2,v3_n3, fv[2]); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - - } else if (basis_u == 2) { - // V2 (Hdiv): comp1 N-D-D, comp2 D-N-D, comp3 D-D-N - fill_mat_vec_dev(p1,pd2,pd3, p1,pd2,pd3, bn1,bd2,bd3, bn1,bd2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat11, m11_d2,m11_d3,m11_d4,m11_d5,m11_d6, f11, vec1, v1_n2,v1_n3, fv[0]); - fill_mat_vec_dev(pd1,p2,pd3, pd1,p2,pd3, bd1,bn2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat22, m22_d2,m22_d3,m22_d4,m22_d5,m22_d6, f22, vec2, v2_n2,v2_n3, fv[1]); - fill_mat_vec_dev(pd1,pd2,p3, pd1,pd2,p3, bd1,bd2,bn3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat33, m33_d2,m33_d3,m33_d4,m33_d5,m33_d6, f33, vec3, v3_n2,v3_n3, fv[2]); - fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu deleted file mode 100644 index ddea85479..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_d_src.cu +++ /dev/null @@ -1,131 +0,0 @@ -extern "C" __global__ -void cc_lin_mhd_5d_D_cuda( - const double* markers, const int n_cols, const int n_markers, - const int kind_map, const double* params, - const double epsilon, const double ep_scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int bb1_n2, const int bb1_n3, - const double* b2_2, const int bb2_n2, const int bb2_n3, - const double* b2_3, const int bb3_n2, const int bb3_n3, - const double* nb11, const int nb1_n2, const int nb1_n3, - const double* nb12, const int nb2_n2, const int nb2_n3, - const double* nb13, const int nb3_n2, const int nb3_n3, - const double* cnb1, const int cb1_n2, const int cb1_n3, - const double* cnb2, const int cb2_n2, const int cb2_n3, - const double* cnb3, const int cb3_n2, const int cb3_n3, - const int basis_u, - double* mat12, double* mat13, double* mat23, - const int m12_d2, const int m12_d3, const int m12_d4, const int m12_d5, const int m12_d6, - const int m13_d2, const int m13_d3, const int m13_d4, const int m13_d5, const int m13_d6, - const int m23_d2, const int m23_d3, const int m23_d4, const int m23_d5, const int m23_d6) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - const double weight = row[5]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b2_1,bb1_n2,bb1_n3, b2_2,bb2_n2,bb2_n3, b2_3,bb3_n2,bb3_n3, b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb11,nb1_n2,nb1_n3, nb12,nb2_n2,nb2_n3, nb13,nb3_n2,nb3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,cb1_n2,cb1_n3, cnb2,cb2_n2,cb2_n3, cnb3,cb3_n2,cb3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = b[k] + epsilon * v * curl_norm_b[k]; - - const double b_para = dot3_dev(norm_b1, b); - const double b_star_para = dot3_dev(norm_b1, b_star); - const double density_const = 1.0 - b_para / b_star_para; - - const double pref = -weight * density_const * ep_scale / epsilon; - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - - double f12, f13, f23; - - if (basis_u == 0) { - f12 = pref * b_prod[1]; - f13 = pref * b_prod[2]; - f23 = pref * b_prod[5]; - - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(p1,p2,p3, p1,p2,p3, bn1,bn2,bn3, bn1,bn2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - - } else if (basis_u == 1) { - double df_inv[9], g_inv[9]; - matrix_inv_dev(dfm, df_inv); - for (int i = 0; i < 3; i++) - for (int j = 0; j < 3; j++) { - double sacc = 0.0; - for (int k = 0; k < 3; k++) sacc += df_inv[3*i+k] * df_inv[3*j+k]; - g_inv[3*i+j] = sacc; - } - double tmp1[9], tmp2[9]; - matmat_dev(g_inv, b_prod, tmp1); - matmat_dev(tmp1, g_inv, tmp2); - - f12 = pref * tmp2[1]; - f13 = pref * tmp2[2]; - f23 = pref * tmp2[5]; - - fill_mat_dev(pd1,p2,p3, p1,pd2,p3, bd1,bn2,bn3, bn1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(pd1,p2,p3, p1,p2,pd3, bd1,bn2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(p1,pd2,p3, p1,p2,pd3, bn1,bd2,bn3, bn1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - - } else if (basis_u == 2) { - const double det2 = det_df * det_df; - f12 = pref * b_prod[1] / det2; - f13 = pref * b_prod[2] / det2; - f23 = pref * b_prod[5] / det2; - - fill_mat_dev(p1,pd2,pd3, pd1,p2,pd3, bn1,bd2,bd3, bd1,bn2,bd3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat12, m12_d2,m12_d3,m12_d4,m12_d5,m12_d6, f12); - fill_mat_dev(p1,pd2,pd3, pd1,pd2,p3, bn1,bd2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat13, m13_d2,m13_d3,m13_d4,m13_d5,m13_d6, f13); - fill_mat_dev(pd1,p2,pd3, pd1,pd2,p3, bd1,bn2,bd3, bd1,bd2,bn3, - span1,span2,span3, start0,start1,start2, p1,p2,p3, - mat23, m23_d2,m23_d3,m23_d4,m23_d5,m23_d6, f23); - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu deleted file mode 100644 index 64aa28596..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_dg_src.cu +++ /dev/null @@ -1,144 +0,0 @@ -__device__ double dg_mod1_dev(double x) -{ - double r = fmod(x, 1.0); - if (r < 0.0) r += 1.0; - return r; -} - -extern "C" __global__ -void cc_lin_mhd_5d_gradB_dg_cuda( - const double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, const double ep_scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* beq_1, const int e1_n2, const int e1_n3, - const double* beq_2, const int e2_n2, const int e2_n3, - const double* beq_3, const int e3_n2, const int e3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* gpb1, const int g1_n2, const int g1_n3, - const double* gpb2, const int g2_n2, const int g2_n3, - const double* gpb3, const int g3_n2, const int g3_n3, - const double* gpq1, const int q1_n2, const int q1_n3, - const double* gpq2, const int q2_n2, const int q2_n3, - const double* gpq3, const int q3_n2, const int q3_n3, - const int basis_u, const double konst, const int is_dg, - double* vec1, const int v1_n2, const int v1_n3, - double* vec2, const int v2_n2, const int v2_n3, - double* vec3, const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3], eta_diff[3] = {0.0, 0.0, 0.0}; - if (is_dg) { - for (int k = 0; k < 3; k++) { - eta[k] = dg_mod1_dev((row[k] + row[first_init_idx + k]) / 2.0); - eta_diff[k] = row[k] - row[first_init_idx + k]; - } - } else { - for (int k = 0; k < 3; k++) eta[k] = row[k]; - } - - const double weight = row[5]; - const double v = row[3]; - const double mu = row[mu_idx]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta[0], eta[1], eta[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], beq[3], norm_b1[3], curl_norm_b[3], grad_PB[3], grad_PBeq[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - beq_1,e1_n2,e1_n3, beq_2,e2_n2,e2_n3, beq_3,e3_n2,e3_n3, beq); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gpb1,g1_n2,g1_n3, gpb2,g2_n2,g2_n3, gpb3,g3_n2,g3_n3, grad_PB); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gpq1,q1_n2,q1_n3, gpq2,q2_n2,q2_n3, gpq3,q3_n2,q3_n3, grad_PBeq); - - // NOTE: unlike cc_lin_mhd_5d_gradB, B* here includes the equilibrium field. - double bfull_star[3]; - for (int k = 0; k < 3; k++) bfull_star[k] = b[k] + beq[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, bfull_star); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - double beq_prod[9] = {0.0, -beq[2], beq[1], beq[2], 0.0, -beq[0], -beq[1], beq[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - // basis_u == 0 has no 1/det; basis_u == 2 carries one. - const double inv_det = (basis_u == 2) ? (1.0 / det_df) : 1.0; - const double w_fac = weight * mu / abs_b_star_para * inv_det * ep_scale; - const double d_fac = konst / abs_b_star_para * inv_det; - - double tmp[9], tmp_v[3], fv[3] = {0.0, 0.0, 0.0}; - - // the two field blocks, Beq first then B, each contributing - // grad_PBeq, grad_PB and (for `dg`) the eta_diff correction - for (int blk = 0; blk < 2; blk++) { - matmat_dev(blk == 0 ? beq_prod : b_prod, norm_b_prod, tmp); - - matvec_dev(tmp, grad_PBeq, tmp_v); - for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * w_fac; - - matvec_dev(tmp, grad_PB, tmp_v); - for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * w_fac; - - if (is_dg) { - matvec_dev(tmp, eta_diff, tmp_v); - for (int k = 0; k < 3; k++) fv[k] += tmp_v[k] * d_fac; - } - } - - if (basis_u == 0) { - // H1vec: N-N-N in all three components - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec1, v1_n2,v1_n3, fv[0]); - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec2, v2_n2,v2_n3, fv[1]); - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec3, v3_n2,v3_n3, fv[2]); - } else if (basis_u == 2) { - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - fill_vec_dev(p1,pd2,pd3, bn1,bd2,bd3, span1,span2,span3, start0,start1,start2, - vec1, v1_n2,v1_n3, fv[0]); - fill_vec_dev(pd1,p2,pd3, bd1,bn2,bd3, span1,span2,span3, start0,start1,start2, - vec2, v2_n2,v2_n3, fv[1]); - fill_vec_dev(pd1,pd2,p3, bd1,bd2,bn3, span1,span2,span3, start0,start1,start2, - vec3, v3_n2,v3_n3, fv[2]); - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu deleted file mode 100644 index 324680361..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_cc_lin_mhd_5d_gradb_src.cu +++ /dev/null @@ -1,111 +0,0 @@ -extern "C" __global__ -void cc_lin_mhd_5d_gradB_cuda( - const double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, const double ep_scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* gpb1, const int g1_n2, const int g1_n3, - const double* gpb2, const int g2_n2, const int g2_n3, - const double* gpb3, const int g3_n2, const int g3_n3, - const double* gpq1, const int q1_n2, const int q1_n3, - const double* gpq2, const int q2_n2, const int q2_n3, - const double* gpq3, const int q3_n2, const int q3_n3, - const int basis_u, - double* vec1, const int v1_n2, const int v1_n3, - double* vec2, const int v2_n2, const int v2_n3, - double* vec3, const int v3_n2, const int v3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double weight = row[5]; - const double v = row[3]; - const double mu = row[mu_idx]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], norm_b1[3], curl_norm_b[3], grad_PB[3], grad_PBeq[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gpb1,g1_n2,g1_n3, gpb2,g2_n2,g2_n3, gpb3,g3_n2,g3_n3, grad_PB); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gpq1,q1_n2,q1_n3, gpq2,q2_n2,q2_n3, gpq3,q3_n2,q3_n3, grad_PBeq); - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double b_prod[9] = {0.0, -b[2], b[1], b[2], 0.0, -b[0], -b[1], b[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - const int pd1 = p1 - 1, pd2 = p2 - 1, pd3 = p3 - 1; - double tmp[9], tmp_v[3], fv[3]; - - if (basis_u == 0) { - matmat_dev(b_prod, norm_b_prod, tmp); - matvec_dev(tmp, grad_PB, tmp_v); - for (int k = 0; k < 3; k++) fv[k] = weight * tmp_v[k] * mu / abs_b_star_para * ep_scale; - - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec1, v1_n2,v1_n3, fv[0]); - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec2, v2_n2,v2_n3, fv[1]); - fill_vec_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - vec3, v3_n2,v3_n3, fv[2]); - - } else if (basis_u == 2) { - for (int k = 0; k < 3; k++) grad_PB[k] += grad_PBeq[k]; - matmat_dev(b_prod, norm_b_prod, tmp); - matvec_dev(tmp, grad_PB, tmp_v); - for (int k = 0; k < 3; k++) - fv[k] = weight * tmp_v[k] * mu / abs_b_star_para / det_df * ep_scale; - - // Hdiv components: N-D-D, D-N-D, D-D-N - fill_vec_dev(p1,pd2,pd3, bn1,bd2,bd3, span1,span2,span3, start0,start1,start2, - vec1, v1_n2,v1_n3, fv[0]); - fill_vec_dev(pd1,p2,pd3, bd1,bn2,bd3, span1,span2,span3, start0,start1,start2, - vec2, v2_n2,v2_n3, fv[1]); - fill_vec_dev(pd1,pd2,p3, bd1,bd2,bn3, span1,span2,span3, start0,start1,start2, - vec3, v3_n2,v3_n3, fv[2]); - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_fill_vec_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_fill_vec_src.cu deleted file mode 100644 index d00c9a0a3..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_fill_vec_src.cu +++ /dev/null @@ -1,23 +0,0 @@ -__device__ void fill_vec_dev( - int pi1, int pi2, int pi3, - const double* bi1, const double* bi2, const double* bi3, - int span1, int span2, int span3, - int start0, int start1, int start2, - double* vec, int vn2, int vn3, - double filling) -{ - for (int il1 = 0; il1 <= pi1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bi1[il1] * filling; - for (int il2 = 0; il2 <= pi2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bi2[il2]; - for (int il3 = 0; il3 <= pi3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bi3[il3]; - atomicAdd(&vec[(size_t)i1*vn2*vn3 + (size_t)i2*vn3 + i3], b3); - } - } - } -} - diff --git a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu b/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu deleted file mode 100644 index dd9e0a142..000000000 --- a/src/struphy/pic/accumulation/cuda/accum_kernels_gc_cuda/_gc_mag_density_0form_src.cu +++ /dev/null @@ -1,88 +0,0 @@ -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) -{ - double left[MAXP]; - double right[MAXP]; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -extern "C" __global__ -void gc_mag_density_0form_cuda( - const double* markers, - const int n_cols, - const int n_markers, - const int mu_idx, - const double scale, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - double* vec, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - const double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double weight = row[5]; - const double mu = row[mu_idx]; - const double filling = mu * weight * scale; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - double b1 = bn1[il1] * filling; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - double b2 = b1 * bn2[il2]; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - double b3 = b2 * bn3[il3]; - atomicAdd(&vec[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3], b3); - } - } - } -} - diff --git a/src/struphy/pic/accumulation/filter.py b/src/struphy/pic/accumulation/filter.py index 2c8b0dd67..82430abd7 100644 --- a/src/struphy/pic/accumulation/filter.py +++ b/src/struphy/pic/accumulation/filter.py @@ -1,7 +1,6 @@ from dataclasses import dataclass import cunumpy as xp -import numpy as np from scipy.fft import irfft, rfft from struphy.feec.psydac_derham import Derham @@ -150,33 +149,24 @@ def _apply_toroidal_fourier_filter(self, vec, modes: tuple[int, ...]): Mode numbers which are not filtered out. """ - # Host-only throughout: this is a per-(i,j)-line loop over scipy.fft calls (no - # batched-2D API used here), never vectorized even under NumPy, so `modes`/`pn`/ - # `ir` are plain NumPy/Python (used only as Python loop bounds and fancy-index - # arrays -- under the CuPy backend `xp.empty(...)` would make `ir` a CuPy array, - # and `range(ir[0])` on a device scalar raises TypeError). tor_num_elements = self.derham.num_elements[2] - modes = np.asarray(modes, dtype=int) + modes = xp.asarray(modes, dtype=int) - assert tor_num_elements >= 2 * int(modes.max()), "num_elements[2] must be at least 2*max(modes)" + assert tor_num_elements >= 2 * int(xp.max(modes)), "num_elements[2] must be at least 2*max(modes)" assert self.derham.domain_decomposition.nprocs[2] == 1, "No domain decomposition along toroidal direction" - pn = np.asarray(self.derham.degree, dtype=int) + pn = xp.asarray(self.derham.degree, dtype=int) + ir = xp.empty(3, dtype=int) # rfft output length if (tor_num_elements % 2) == 0: - vec_temp = np.zeros(int(tor_num_elements / 2) + 1, dtype=complex) + vec_temp = xp.zeros(int(tor_num_elements / 2) + 1, dtype=complex) else: - vec_temp = np.zeros(int((tor_num_elements - 1) / 2) + 1, dtype=complex) + vec_temp = xp.zeros(int((tor_num_elements - 1) / 2) + 1, dtype=complex) for _, comp, starts, ends in self._yield_dir_components(vec): - ir = [int(ends[i] + 1 - starts[i]) for i in range(3)] - - # Under the CuPy backend, comp._data lives on the device; pulled to host once - # per component and pushed back once, rather than paying a device round-trip - # on every one of the ir[0]*ir[1] lines below (also correct on the NumPy - # backend, where to_numpy/asarray are no-ops). - data = xp.to_numpy(comp._data) + for i in range(3): + ir[i] = int(ends[i] + 1 - starts[i]) # filter along toroidal index (k direction) for i in range(ir[0]): @@ -185,13 +175,11 @@ def _apply_toroidal_fourier_filter(self, vec, modes: tuple[int, ...]): jj = pn[1] + j # forward FFT along toroidal line - line = rfft(data[ii, jj, pn[2] : pn[2] + ir[2]]) + line = rfft(comp._data[ii, jj, pn[2] : pn[2] + ir[2]]) vec_temp[:] = 0 vec_temp[modes] = line[modes] # keep selected modes only # inverse FFT back to real space, write in-place - data[ii, jj, pn[2] : pn[2] + ir[2]] = irfft(vec_temp, n=tor_num_elements) - - comp._data[...] = xp.asarray(data) + comp._data[ii, jj, pn[2] : pn[2] + ir[2]] = irfft(vec_temp, n=tor_num_elements) comp.update_ghost_regions() diff --git a/src/struphy/pic/accumulation/particles_to_grid.py b/src/struphy/pic/accumulation/particles_to_grid.py index 6fe3502f7..0deedc6d4 100644 --- a/src/struphy/pic/accumulation/particles_to_grid.py +++ b/src/struphy/pic/accumulation/particles_to_grid.py @@ -16,22 +16,6 @@ from struphy.io.options import LiteralOptions from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.models.variables import PICVariable, SPHVariable -from struphy.pic.accumulation.accum_kernels_cuda import ( - cc_lin_mhd_6d_1_gpu, - cc_lin_mhd_6d_2_gpu, - charge_density_0form_gpu, - linear_vlasov_ampere_gpu, - pc_lin_mhd_6d_full_gpu, - pc_lin_mhd_6d_gpu, - vlasov_maxwell_gpu, -) -from struphy.pic.accumulation.accum_kernels_gc_cuda import ( - cc_lin_mhd_5d_curlb_gpu, - cc_lin_mhd_5d_D_gpu, - cc_lin_mhd_5d_gradB_dg_gpu, - cc_lin_mhd_5d_gradB_gpu, - gc_mag_density_0form_gpu, -) from struphy.pic.accumulation.filter import AccumFilter, FilterParameters from struphy.pic.base import Particles from struphy.utils.utils import __dataclass_repr_no_defaults__, check_option @@ -212,167 +196,6 @@ def __init__( # initialize filter self._accfilter = AccumFilter(filter_params, self._derham, self._space_id) - # GPU replacement for linear_vlasov_ampere: evaluates DF^-1(eta_p) per - # marker and atomically scatters into the 6 symmetric V1 -> V1 matrix - # blocks plus the V1 vector, instead of the CPU's strictly-sequential - # marker loop. See pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS - # for the domains this covers. - from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS - - self._gpu_linear_vlasov_ampere = ( - xp.cupy_backend - and kernel.name == "linear_vlasov_ampere" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_linear_vlasov_ampere: - import cupy as cp - import numpy as np - - self._gpu_lva_kind_map = int(args_domain.kind_map) - self._gpu_lva_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - args_derham = self.derham.args_derham - self._gpu_lva_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_lva_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_lva_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) - self._gpu_lva_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) - self._gpu_lva_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) - - # GPU replacement for vlasov_maxwell: same 6-block symmetric V1 -> V1 - # matrix-plus-vector fill as linear_vlasov_ampere above, but with a - # G^-1(eta_p)-based filling (no f0_values/optional_args needed). - self._gpu_vlasov_maxwell = ( - xp.cupy_backend and kernel.name == "vlasov_maxwell" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_vlasov_maxwell: - import cupy as cp - import numpy as np - - self._gpu_vm_kind_map = int(args_domain.kind_map) - self._gpu_vm_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - args_derham = self.derham.args_derham - self._gpu_vm_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_vm_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_vm_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) - self._gpu_vm_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) - self._gpu_vm_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) - - # GPU replacement for cc_lin_mhd_6d_1: 3-block antisymmetric fill - # (mat12, mat13, mat23 only, no vector) into whichever of - # H1vec/Hcurl/Hdiv the propagator's basis_u optional_arg selects. - # b2_*/basis_u/scale_mat/boundary_cut arrive fresh via optional_args - # each call (only the spline/domain info below is cached). - self._gpu_cc_lin_mhd_6d_1 = ( - xp.cupy_backend and kernel.name == "cc_lin_mhd_6d_1" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_cc_lin_mhd_6d_1: - import cupy as cp - import numpy as np - - self._gpu_cc1_kind_map = int(args_domain.kind_map) - self._gpu_cc1_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - args_derham = self.derham.args_derham - self._gpu_cc1_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_cc1_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_cc1_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) - self._gpu_cc1_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) - self._gpu_cc1_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) - - # GPU replacement for cc_lin_mhd_6d_2: same runtime basis_u - # dispatch as cc_lin_mhd_6d_1, but a full symmetric 6-block - # matrix-plus-vector fill (like linear_vlasov_ampere/vlasov_maxwell) - # instead of the 3 antisymmetric off-diagonal blocks only. - self._gpu_cc_lin_mhd_6d_2 = ( - xp.cupy_backend and kernel.name == "cc_lin_mhd_6d_2" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_cc_lin_mhd_6d_2: - import cupy as cp - import numpy as np - - self._gpu_cc2_kind_map = int(args_domain.kind_map) - self._gpu_cc2_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - args_derham = self.derham.args_derham - self._gpu_cc2_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_cc2_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_cc2_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) - self._gpu_cc2_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) - self._gpu_cc2_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) - - # GPU replacement for cc_lin_mhd_5d_D: 3-block antisymmetric fill like - # cc_lin_mhd_6d_1, with the guiding-centre density prefactor - # (1 - b_para/b*_para) / epsilon. - self._gpu_cc_lin_mhd_5d_D = ( - xp.cupy_backend and kernel.name == "cc_lin_mhd_5d_D" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_cc_lin_mhd_5d_D: - import cupy as cp - import numpy as np - - self._gpu_cc5d_kind_map = int(args_domain.kind_map) - self._gpu_cc5d_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - args_derham = self.derham.args_derham - self._gpu_cc5d_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_cc5d_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_cc5d_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) - self._gpu_cc5d_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) - self._gpu_cc5d_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) - - # GPU replacements for the two remaining 5D current-coupling - # accumulators. cc_lin_mhd_5d_curlb is a full symmetric 6-block - # matrix-plus-vector curvature fill; cc_lin_mhd_5d_gradB is - # vector-only (its 6 matrix args are unused by the CPU body, so the - # matrices stay zero on both backends). They need the same cached - # spline/domain data, so one block covers both. - self._gpu_cc_lin_mhd_5d_curlb = ( - xp.cupy_backend - and kernel.name == "cc_lin_mhd_5d_curlb" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - self._gpu_cc_lin_mhd_5d_gradB = ( - xp.cupy_backend - and kernel.name == "cc_lin_mhd_5d_gradB" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_cc_lin_mhd_5d_curlb or self._gpu_cc_lin_mhd_5d_gradB: - import cupy as cp - import numpy as np - - self._gpu_cg_kind_map = int(args_domain.kind_map) - self._gpu_cg_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - args_derham = self.derham.args_derham - self._gpu_cg_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_cg_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_cg_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) - self._gpu_cg_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) - self._gpu_cg_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) - self._gpu_cg_first_init_idx = int(self.particles.args_markers.first_init_idx) - self._gpu_cg_mu_idx = int(self.particles.mu_idx) - - # GPU replacement for pc_lin_mhd_6d_full / pc_lin_mhd_6d: the - # symmetry="pressure" case -- 45-array (36 matrix + 9 vector) - # velocity-moment "pressure tensor" fill. See accum_kernels_cuda.py - # for why both variants share one call convention (pc_lin_mhd_6d - # only ever writes 24 of the 45 arrays, matching the CPU reference). - self._gpu_pc_lin_mhd_6d_full = ( - xp.cupy_backend - and kernel.name == "pc_lin_mhd_6d_full" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - self._gpu_pc_lin_mhd_6d = ( - xp.cupy_backend and kernel.name == "pc_lin_mhd_6d" and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_pc_lin_mhd_6d_full or self._gpu_pc_lin_mhd_6d: - import cupy as cp - import numpy as np - - self._gpu_pc_kind_map = int(args_domain.kind_map) - self._gpu_pc_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - args_derham = self.derham.args_derham - self._gpu_pc_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_pc_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_pc_tn1 = cp.asarray(np.asarray(args_derham.tn1, dtype=float), dtype=cp.float64) - self._gpu_pc_tn2 = cp.asarray(np.asarray(args_derham.tn2, dtype=float), dtype=cp.float64) - self._gpu_pc_tn3 = cp.asarray(np.asarray(args_derham.tn3, dtype=float), dtype=cp.float64) - def __call__(self, *optional_args, **args_control): """ Performs the accumulation into the matrix/vector by calling the chosen accumulation kernel and additional analytical contributions (control variate, optional). @@ -406,218 +229,14 @@ def _accumulate(self, *optional_args, **args_control): dat[:] = 0.0 # accumulate into matrix (and vector) with markers - if self._gpu_linear_vlasov_ampere and len(optional_args) == 1: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - (f0_values,) = optional_args - linear_vlasov_ampere_gpu( - self.particles.markers, - self._gpu_lva_kind_map, - self._gpu_lva_params, - f0_values, - self._gpu_lva_pn, - self._gpu_lva_tn1, - self._gpu_lva_tn2, - self._gpu_lva_tn3, - self._gpu_lva_starts, - *self._args_data, - ) - elif self._gpu_vlasov_maxwell and not optional_args: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - vlasov_maxwell_gpu( - self.particles.markers, - self._gpu_vm_kind_map, - self._gpu_vm_params, - self._gpu_vm_pn, - self._gpu_vm_tn1, - self._gpu_vm_tn2, - self._gpu_vm_tn3, - self._gpu_vm_starts, - *self._args_data, - ) - elif self._gpu_cc_lin_mhd_6d_1 and len(optional_args) == 6: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - b2_1, b2_2, b2_3, basis_u, scale_mat, boundary_cut = optional_args - cc_lin_mhd_6d_1_gpu( - self.particles.markers, - self._gpu_cc1_kind_map, - self._gpu_cc1_params, - self._gpu_cc1_pn, - self._gpu_cc1_tn1, - self._gpu_cc1_tn2, - self._gpu_cc1_tn3, - self._gpu_cc1_starts, - b2_1, - b2_2, - b2_3, - basis_u, - scale_mat, - boundary_cut, - *self._args_data, - ) - elif self._gpu_cc_lin_mhd_6d_2 and len(optional_args) == 7: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - b2_1, b2_2, b2_3, basis_u, scale_mat, scale_vec, boundary_cut = optional_args - cc_lin_mhd_6d_2_gpu( - self.particles.markers, - self._gpu_cc2_kind_map, - self._gpu_cc2_params, - self._gpu_cc2_pn, - self._gpu_cc2_tn1, - self._gpu_cc2_tn2, - self._gpu_cc2_tn3, - self._gpu_cc2_starts, - b2_1, - b2_2, - b2_3, - basis_u, - scale_mat, - scale_vec, - boundary_cut, - *self._args_data, - ) - elif self._gpu_cc_lin_mhd_5d_D and len(optional_args) == 12: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - ( - epsilon, - ep_scale, - b2_1, - b2_2, - b2_3, - nb1_1, - nb1_2, - nb1_3, - cnb_1, - cnb_2, - cnb_3, - basis_u, - ) = optional_args - cc_lin_mhd_5d_D_gpu( - self.particles.markers, - self._gpu_cc5d_kind_map, - self._gpu_cc5d_params, - epsilon, - ep_scale, - self._gpu_cc5d_pn, - self._gpu_cc5d_tn1, - self._gpu_cc5d_tn2, - self._gpu_cc5d_tn3, - self._gpu_cc5d_starts, - (b2_1, b2_2, b2_3), - (nb1_1, nb1_2, nb1_3), - (cnb_1, cnb_2, cnb_3), - basis_u, - *self._args_data, - ) - elif self._gpu_cc_lin_mhd_5d_curlb and len(optional_args) == 12: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - ( - epsilon, - ep_scale, - b2_1, - b2_2, - b2_3, - nb1_1, - nb1_2, - nb1_3, - cnb_1, - cnb_2, - cnb_3, - basis_u, - ) = optional_args - cc_lin_mhd_5d_curlb_gpu( - self.particles.markers, - self._gpu_cg_kind_map, - self._gpu_cg_params, - epsilon, - ep_scale, - self._gpu_cg_pn, - self._gpu_cg_tn1, - self._gpu_cg_tn2, - self._gpu_cg_tn3, - self._gpu_cg_starts, - (b2_1, b2_2, b2_3), - (nb1_1, nb1_2, nb1_3), - (cnb_1, cnb_2, cnb_3), - basis_u, - *self._args_data, - ) - elif self._gpu_cc_lin_mhd_5d_gradB and len(optional_args) == 17: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - ( - epsilon, - ep_scale, - b2_1, - b2_2, - b2_3, - nb1_1, - nb1_2, - nb1_3, - cnb_1, - cnb_2, - cnb_3, - gpb_1, - gpb_2, - gpb_3, - gpq_1, - gpq_2, - gpq_3, - basis_u, - ) = optional_args - # the kernel's own 6 matrix args + vector are already in - # self._args_data; only the vector blocks are ever written. - vec_data = self._args_data[6:] - cc_lin_mhd_5d_gradB_gpu( - self.particles.markers, - self._gpu_cg_first_init_idx, - self._gpu_cg_mu_idx, - self._gpu_cg_kind_map, - self._gpu_cg_params, - epsilon, - ep_scale, - self._gpu_cg_pn, - self._gpu_cg_tn1, - self._gpu_cg_tn2, - self._gpu_cg_tn3, - self._gpu_cg_starts, - (b2_1, b2_2, b2_3), - (nb1_1, nb1_2, nb1_3), - (cnb_1, cnb_2, cnb_3), - (gpb_1, gpb_2, gpb_3), - (gpq_1, gpq_2, gpq_3), - basis_u, - *vec_data, - ) - elif (self._gpu_pc_lin_mhd_6d_full or self._gpu_pc_lin_mhd_6d) and len(optional_args) == 1: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - (ep_scale,) = optional_args - pc_fn = pc_lin_mhd_6d_full_gpu if self._gpu_pc_lin_mhd_6d_full else pc_lin_mhd_6d_gpu - pc_fn( - self.particles.markers, - self._gpu_pc_kind_map, - self._gpu_pc_params, - self._gpu_pc_pn, - self._gpu_pc_tn1, - self._gpu_pc_tn2, - self._gpu_pc_tn3, - self._gpu_pc_starts, - ep_scale, - *self._args_data, - ) - else: - # no CUDA port for this kernel: fall back to the compiled - # host-only one. Accumulation kernels only read markers (they - # write into the grid arrays), so no write-back is needed. - with ( - ProfileManager.profile_region("kernel: " + self.kernel.name), - self.particles.host_markers(write=False) as args_markers, - ): - self.kernel( - args_markers, - self.derham.args_derham, - self.args_domain, - *self._args_data, - *optional_args, - ) + with ProfileManager.profile_region("kernel: " + self.kernel.name): + self.kernel( + self.particles.args_markers, + self.derham.args_derham, + self.args_domain, + *self._args_data, + *optional_args, + ) # apply filter if self.accfilter.params.use_filter is not None: @@ -938,41 +557,6 @@ def __init__( # initialize filter self._accfilter = AccumFilter(filter_params, self._derham, self._space_id) - # hand-written CUDA replacement for charge_density_0form (the only - # AccumulatorVector kernel ported so far -- see accum_kernels_cuda.py). - # No optional_args/domain-mapping support needed for this one. - self._gpu_charge_density_0form = xp.cupy_backend and kernel.name in ( - "charge_density_0form", - # gc_density_0form is the same 0-form weight scatter (see - # accum_kernels_gc_cuda.gc_density_0form_gpu) - "gc_density_0form", - ) - if self._gpu_charge_density_0form: - import cupy as cp - - args_derham = self.derham.args_derham - self._gpu_cd0_weight_idx = self.particles.index["weights"] - self._gpu_cd0_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_cd0_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_cd0_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_cd0_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_cd0_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - - # hand-written CUDA replacement for gc_mag_density_0form (5D - # guiding-center analog of charge_density_0form -- see - # accum_kernels_gc_cuda.py). optional_args = (ep_scale,). - self._gpu_gc_mag_density_0form = xp.cupy_backend and kernel.name == "gc_mag_density_0form" - if self._gpu_gc_mag_density_0form: - import cupy as cp - - args_derham = self.derham.args_derham - self._gpu_gcmd_mu_idx = int(self.particles.mu_idx) - self._gpu_gcmd_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_gcmd_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_gcmd_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_gcmd_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_gcmd_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - def __call__(self, *optional_args, **args_control): """ Performs the accumulation into the vector by calling the chosen accumulation kernel @@ -1005,47 +589,14 @@ def _accumulate(self, *optional_args, **args_control): dat[:] = 0.0 # accumulate into matrix (and vector) with markers - if self._gpu_charge_density_0form and not optional_args: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - charge_density_0form_gpu( - self.particles.markers, - self._gpu_cd0_weight_idx, - self._gpu_cd0_pn, - self._gpu_cd0_tn1, - self._gpu_cd0_tn2, - self._gpu_cd0_tn3, - self._gpu_cd0_starts, - self._args_data[0], - ) - elif self._gpu_gc_mag_density_0form and len(optional_args) == 1: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - (scale,) = optional_args - gc_mag_density_0form_gpu( - self.particles.markers, - self._gpu_gcmd_mu_idx, - scale, - self._gpu_gcmd_pn, - self._gpu_gcmd_tn1, - self._gpu_gcmd_tn2, - self._gpu_gcmd_tn3, - self._gpu_gcmd_starts, - self._args_data[0], - ) - else: - # no CUDA port for this kernel: fall back to the compiled - # host-only one. Accumulation kernels only read markers (they - # write into the grid arrays), so no write-back is needed. - with ( - ProfileManager.profile_region("kernel: " + self.kernel.name), - self.particles.host_markers(write=False) as args_markers, - ): - self.kernel( - args_markers, - self.derham.args_derham, - self.args_domain, - *self._args_data, - *optional_args, - ) + with ProfileManager.profile_region("kernel: " + self.kernel.name): + self.kernel( + self.particles.args_markers, + self.derham.args_derham, + self.args_domain, + *self._args_data, + *optional_args, + ) # apply filter if self.accfilter.params.use_filter is not None: diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 02929d8c8..2421c3a3d 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -52,17 +52,12 @@ class Intracomm: from struphy.pic import sampling_kernels, sobol_seq from struphy.pic.pushing import eval_kernels_sph from struphy.pic.pushing.pusher_utilities_kernels import reflect -from struphy.pic.pushing.pusher_utilities_kernels_cuda import reflect_gpu from struphy.pic.sorting import SortingBoxes from struphy.pic.sorting_kernels import ( assign_box_to_each_particle, assign_particles_to_boxes, sort_boxed_particles, ) -from struphy.pic.sorting_kernels_cuda import ( - assign_box_to_each_particle_gpu, - assign_particles_to_boxes_gpu, -) from struphy.pic.sph_eval_kernels import ( box_based_evaluation_flat, box_based_evaluation_meshgrid, @@ -70,12 +65,6 @@ class Intracomm: naive_evaluation_flat, naive_evaluation_meshgrid, ) -from struphy.pic.sph_eval_kernels_cuda import ( - box_based_evaluation_flat_gpu, - box_based_evaluation_meshgrid_gpu, - naive_evaluation_flat_gpu, - naive_evaluation_meshgrid_gpu, -) from struphy.utils import utils from struphy.utils.clone_config import CloneConfig @@ -815,15 +804,6 @@ def domain_array(self): """ return self._domain_array - @property - def _reflect_params_dev(self): - """Domain mapping parameters on the device, cached for :func:`reflect_gpu`.""" - if getattr(self, "_reflect_params_dev_cache", None) is None: - self._reflect_params_dev_cache = xp.asarray( - np.asarray(self.domain.args_domain.params, dtype=float), - ) - return self._reflect_params_dev_cache - @property def domain_array_dev(self): """:attr:`domain_array` on the active backend. @@ -2104,27 +2084,15 @@ def apply_kinetic_bc(self, newton=False): for axis in self._reflect_axes: if len(outside_inds_per_axis[axis]) == 0: continue - # flip velocity - from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS - - if xp.cupy_backend and self.domain.args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS: - reflect_gpu( - self.markers, - int(self.domain.args_domain.kind_map), - self._reflect_params_dev, - outside_inds_per_axis[axis], + # flip velocity via the compiled host-only kernel, through the + # marker host mirror. + with self.host_markers(write=True) as args_markers: + reflect( + args_markers.markers, + self.domain.args_domain, + _to_numpy_for_kernel(outside_inds_per_axis[axis]), axis, ) - else: - # no CUDA port for this domain kind_map: fall back to the - # compiled host-only kernel via the marker host mirror. - with self.host_markers(write=True) as args_markers: - reflect( - args_markers.markers, - self.domain.args_domain, - _to_numpy_for_kernel(outside_inds_per_axis[axis]), - axis, - ) def update_holes(self, update_valid_mks: bool = True): """Recompute the :attr:`~struphy.pic.base.Particles.holes` mask (rows with ``markers[:, 0] == -1``) @@ -2169,24 +2137,14 @@ def put_particles_in_boxes(self): neighbouring boxes of neighbouring processes are also communicated (as ghost particles).""" self._remove_ghost_particles() - if xp.cupy_backend: - assign_box_to_each_particle_gpu( - self.markers, - self.holes, - self._sorting_boxes.nx, - self._sorting_boxes.ny, - self._sorting_boxes.nz, - self.domain_array[self.mpi_rank], - ) - else: - assign_box_to_each_particle( - self.markers, - self.holes, - self._sorting_boxes.nx, - self._sorting_boxes.ny, - self._sorting_boxes.nz, - self.domain_array[self.mpi_rank], - ) + assign_box_to_each_particle( + self.markers, + self.holes, + self._sorting_boxes.nx, + self._sorting_boxes.ny, + self._sorting_boxes.nz, + self.domain_array[self.mpi_rank], + ) self._check_and_assign_particles_to_boxes() @@ -3394,20 +3352,12 @@ def _check_and_assign_particles_to_boxes(self): ) self.mpi_comm.Abort() - if xp.cupy_backend: - assign_particles_to_boxes_gpu( - self.markers, - self.holes, - self._sorting_boxes._boxes, - self._sorting_boxes._next_index, - ) - else: - assign_particles_to_boxes( - self.markers, - self.holes, - self._sorting_boxes._boxes, - self._sorting_boxes._next_index, - ) + assign_particles_to_boxes( + self.markers, + self.holes, + self._sorting_boxes._boxes, + self._sorting_boxes._next_index, + ) def _update_ghost_particles(self): """Refresh :attr:`~struphy.pic.base.Particles.ghost_particles`: a marker is flagged @@ -4675,40 +4625,6 @@ def _eval_sph( self.put_particles_in_boxes() if fast: - if xp.cupy_backend and len(_shp) in (1, 3): - # CUDA replacement for box_based_evaluation_flat/_meshgrid: - # one thread per evaluation point, see sph_eval_kernels_cuda. - if len(_shp) == 3: - if _shp[0] > 1: - assert eta1[0, 0, 0] != eta1[1, 0, 0], "Meshgrids must be obtained with indexing='ij'!" - if _shp[1] > 1: - assert eta2[0, 0, 0] != eta2[0, 1, 0], "Meshgrids must be obtained with indexing='ij'!" - if _shp[2] > 1: - assert eta3[0, 0, 0] != eta3[0, 0, 1], "Meshgrids must be obtained with indexing='ij'!" - gpu_func = box_based_evaluation_flat_gpu if len(_shp) == 1 else box_based_evaluation_meshgrid_gpu - gpu_func( - self.markers, - eta1, - eta2, - eta3, - self.sorting_boxes.nx, - self.sorting_boxes.ny, - self.sorting_boxes.nz, - self.domain_array[self.mpi_rank], - self.sorting_boxes.boxes, - self.sorting_boxes.neighbours, - self.holes, - periodic1, - periodic2, - periodic3, - index, - ker_id, - h1, - h2, - h3, - out, - ) - return out if len(_shp) == 1: func = PyccelKernel(box_based_evaluation_flat) elif len(_shp) == 3: @@ -4742,27 +4658,6 @@ def _eval_sph( h3, out, ) - elif xp.cupy_backend and len(_shp) in (1, 3): - # CUDA replacement for naive_evaluation_flat/_meshgrid: one - # thread per evaluation point, see sph_eval_kernels_cuda. - gpu_func = naive_evaluation_flat_gpu if len(_shp) == 1 else naive_evaluation_meshgrid_gpu - gpu_func( - self.markers, - float(self.Np), - eta1, - eta2, - eta3, - self.holes, - periodic1, - periodic2, - periodic3, - index, - ker_id, - h1, - h2, - h3, - out, - ) else: if len(_shp) == 1: func = PyccelKernel(naive_evaluation_flat) diff --git a/src/struphy/pic/cuda/sorting_kernels_cuda/_sort_src.cu b/src/struphy/pic/cuda/sorting_kernels_cuda/_sort_src.cu deleted file mode 100644 index 97ca0070a..000000000 --- a/src/struphy/pic/cuda/sorting_kernels_cuda/_sort_src.cu +++ /dev/null @@ -1,84 +0,0 @@ -extern "C" __device__ long long flatten_index_dev( - long long n1, long long n2, long long n3, - long long nx, long long ny, long long nz) -{ - // fortran_ordering (the struphy default) - return n1 + n2 * (nx + 2) + n3 * (nx + 2) * (ny + 2); -} - -extern "C" __device__ long long find_box_dev( - double eta1, double eta2, double eta3, - long long nx, long long ny, long long nz, - const double* domain_array) -{ - if (eta1 == domain_array[0]) eta1 += 1e-8; - if (eta2 == domain_array[3]) eta2 += 1e-8; - if (eta3 == domain_array[6]) eta3 += 1e-8; - if (eta1 == domain_array[1]) eta1 -= 1e-8; - if (eta2 == domain_array[4]) eta2 -= 1e-8; - if (eta3 == domain_array[7]) eta3 -= 1e-8; - - double x_l = domain_array[0] - (domain_array[1] - domain_array[0]) / nx; - double x_r = domain_array[1] + (domain_array[1] - domain_array[0]) / nx; - double y_l = domain_array[3] - (domain_array[4] - domain_array[3]) / ny; - double y_r = domain_array[4] + (domain_array[4] - domain_array[3]) / ny; - double z_l = domain_array[6] - (domain_array[7] - domain_array[6]) / nz; - double z_r = domain_array[7] + (domain_array[7] - domain_array[6]) / nz; - - if (eta1 < x_l || eta1 > x_r || eta2 < y_l || eta2 > y_r || eta3 < z_l || eta3 > z_r) - return -1; - - long long n1 = (long long)floor((eta1 - x_l) / (x_r - x_l) * (nx + 2)); - long long n2 = (long long)floor((eta2 - y_l) / (y_r - y_l) * (ny + 2)); - long long n3 = (long long)floor((eta3 - z_l) / (z_r - z_l) * (nz + 2)); - - return flatten_index_dev(n1, n2, n3, nx, ny, nz); -} - -extern "C" __global__ -void assign_box_to_each_particle_cuda( - const double* eta, // AoS, row p at eta[3*p : 3*p+3] - const int* holes, - const long long n_mks, - const long long nx, - const long long ny, - const long long nz, - const double* domain_array, - double* box_out) -{ - long long p = (long long)blockIdx.x * blockDim.x + threadIdx.x; - if (p >= n_mks) return; - - long long n_boxes_total = (nx + 2) * (ny + 2) * (nz + 2); - long long n_box; - - if (holes[p]) { - n_box = n_boxes_total; - } else { - long long a = find_box_dev(eta[3 * p], eta[3 * p + 1], eta[3 * p + 2], nx, ny, nz, domain_array); - n_box = (a >= n_boxes_total || a < 0) ? n_boxes_total : a; - } - - box_out[p] = (double) n_box; -} - -extern "C" __global__ -void assign_particles_to_boxes_cuda( - const double* box_id, - const int* holes, - const long long n_mks, - int* boxes, - int* next_index, - const long long box_cols) -{ - long long p = (long long)blockIdx.x * blockDim.x + threadIdx.x; - if (p >= n_mks) return; - if (holes[p]) return; - - int a = (int) box_id[p]; - int slot = atomicAdd(&next_index[a], 1); - if (slot < box_cols) { - boxes[(long long) a * box_cols + slot] = (int) p; - } -} - diff --git a/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_flat_src.cu b/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_flat_src.cu deleted file mode 100644 index 90e836ad4..000000000 --- a/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_flat_src.cu +++ /dev/null @@ -1,291 +0,0 @@ -#define PI 3.14159265358979323846 - -__device__ double distance_dev(double x, double y, bool periodic) -{ - double d = x - y; - if (periodic) { - while (d > 0.5) d -= 1.0; - while (d < -0.5) d += 1.0; - } - return d; -} - -// --- uni-variate kernels (struphy.pic.sph_smoothing_kernels) --- - -__device__ double trigonometric_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return 0.785398163397448 / h * cos(x / h * PI / 2.0); - return 0.0; -} - -__device__ double grad_trigonometric_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return -(1.2337005501361697 / (h * h)) * sin(x / h * PI / 2.0); - return 0.0; -} - -__device__ double gaussian_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return 1.0 / (sqrt(PI) * h / 3.0) * exp(-(x * x) / ((h / 3.0) * (h / 3.0))); - return 0.0; -} - -__device__ double grad_gaussian_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return -54.0 * x / (h * h * h * sqrt(PI)) * exp(-(x * x) / ((h / 3.0) * (h / 3.0))); - return 0.0; -} - -__device__ double linear_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return (1.0 - fabs(x / h)) / h; - return 0.0; -} - -__device__ double grad_linear_uni(double x, double h) -{ - if (fabs(x / h) <= 1.0) return (x > 0.0) ? -(1.0 / (h * h)) : (1.0 / (h * h)); - return 0.0; -} - -// --- kernel_type dispatch (struphy.pic.sph_smoothing_kernels.smoothing_kernel) --- - -__device__ double smoothing_kernel_dev( - int kernel_type, - double r1, double r2, double r3, - double h1, double h2, double h3) -{ - switch (kernel_type) { - // 1d - case 100: return trigonometric_uni(r1, h1); - case 101: return grad_trigonometric_uni(r1, h1); - case 110: return gaussian_uni(r1, h1); - case 111: return grad_gaussian_uni(r1, h1); - case 120: return linear_uni(r1, h1); - case 121: return grad_linear_uni(r1, h1); - - // 2d (tensor products) - case 340: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2); - case 341: return grad_trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2); - case 342: return trigonometric_uni(r1, h1) * grad_trigonometric_uni(r2, h2); - case 350: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2); - case 351: return grad_gaussian_uni(r1, h1) * gaussian_uni(r2, h2); - case 352: return gaussian_uni(r1, h1) * grad_gaussian_uni(r2, h2); - case 360: return linear_uni(r1, h1) * linear_uni(r2, h2); - case 361: return grad_linear_uni(r1, h1) * linear_uni(r2, h2); - case 362: return linear_uni(r1, h1) * grad_linear_uni(r2, h2); - - // 3d (tensor products) - case 670: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); - case 671: return grad_trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); - case 672: return trigonometric_uni(r1, h1) * grad_trigonometric_uni(r2, h2) * trigonometric_uni(r3, h3); - case 673: return trigonometric_uni(r1, h1) * trigonometric_uni(r2, h2) * grad_trigonometric_uni(r3, h3); - case 680: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * gaussian_uni(r3, h3); - case 681: return grad_gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * gaussian_uni(r3, h3); - case 682: return gaussian_uni(r1, h1) * grad_gaussian_uni(r2, h2) * gaussian_uni(r3, h3); - case 683: return gaussian_uni(r1, h1) * gaussian_uni(r2, h2) * grad_gaussian_uni(r3, h3); - case 700: return linear_uni(r1, h1) * linear_uni(r2, h2) * linear_uni(r3, h3); - case 701: return grad_linear_uni(r1, h1) * linear_uni(r2, h2) * linear_uni(r3, h3); - case 702: return linear_uni(r1, h1) * grad_linear_uni(r2, h2) * linear_uni(r3, h3); - case 703: return linear_uni(r1, h1) * linear_uni(r2, h2) * grad_linear_uni(r3, h3); - - // 3d, radially symmetric (linear_isotropic_3d and its gradient) - case 690: { - double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); - double h = h1; - if (r / h > 1.0) return 0.0; - return (1.0 - r / h) / (1.0471975512 * h * h * h); - } - case 691: { - double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); - double h = h1; - if (r / h > 1.0) return 0.0; - if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); - return -r1 / (r * h) / (1.0471975512 * h * h * h); - } - case 692: { - double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); - double h = h1; - if (r / h > 1.0) return 0.0; - if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); - return -r2 / (r * h) / (1.0471975512 * h * h * h); - } - case 693: { - double r = sqrt(r1 * r1 + r2 * r2 + r3 * r3); - double h = h1; - if (r / h > 1.0) return 0.0; - if (r == 0.0) return -1.0 / h / (1.0471975512 * h * h * h); - return -r3 / (r * h) / (1.0471975512 * h * h * h); - } - } - return 0.0; -} - -// --- box lookup (struphy.pic.sorting_kernels.find_box / flatten_index) --- - -__device__ int find_box_dev( - double eta1, double eta2, double eta3, - int nx, int ny, int nz, - const double* domain_array) -{ - if (eta1 == domain_array[0]) eta1 += 1e-8; - if (eta2 == domain_array[3]) eta2 += 1e-8; - if (eta3 == domain_array[6]) eta3 += 1e-8; - if (eta1 == domain_array[1]) eta1 -= 1e-8; - if (eta2 == domain_array[4]) eta2 -= 1e-8; - if (eta3 == domain_array[7]) eta3 -= 1e-8; - - double x_l = domain_array[0] - (domain_array[1] - domain_array[0]) / nx; - double x_r = domain_array[1] + (domain_array[1] - domain_array[0]) / nx; - double y_l = domain_array[3] - (domain_array[4] - domain_array[3]) / ny; - double y_r = domain_array[4] + (domain_array[4] - domain_array[3]) / ny; - double z_l = domain_array[6] - (domain_array[7] - domain_array[6]) / nz; - double z_r = domain_array[7] + (domain_array[7] - domain_array[6]) / nz; - - if (eta1 < x_l || eta1 > x_r || eta2 < y_l || eta2 > y_r || eta3 < z_l || eta3 > z_r) - return -1; - - int n1 = (int)floor((eta1 - x_l) / (x_r - x_l) * (nx + 2)); - int n2 = (int)floor((eta2 - y_l) / (y_r - y_l) * (ny + 2)); - int n3 = (int)floor((eta3 - z_l) / (z_r - z_l) * (nz + 2)); - - // flatten_index, fortran_ordering (the struphy default) - return n1 + n2 * (nx + 2) + n3 * (nx + 2) * (ny + 2); -} - -// --- entry point (struphy.pic.sph_eval_kernels.box_based_evaluation_flat) --- - -extern "C" __global__ -void box_based_evaluation_flat_cuda( - const double* markers, - const int n_cols, - const double* eta1, - const double* eta2, - const double* eta3, - const int n_eval, - const int nx, - const int ny, - const int nz, - const double* domain_array, - const int* boxes, - const int n_box_cols, - const int* neighbours, - const int* holes, - const int periodic1, - const int periodic2, - const int periodic3, - const int index, - const int kernel_type, - const double h1, - const double h2, - const double h3, - double* out) -{ - int i = blockIdx.x * blockDim.x + threadIdx.x; - if (i >= n_eval) return; - - double e1 = eta1[i], e2 = eta2[i], e3 = eta3[i]; - - int loc_box = find_box_dev(e1, e2, e3, nx, ny, nz, domain_array); - if (loc_box == -1) { - out[i] = 0.0; - return; - } - - double acc = 0.0; - for (int neigh = 0; neigh < 27; neigh++) { - int box_to_search = neighbours[loc_box * 27 + neigh]; - int c = 0; - while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { - int p = boxes[(size_t)box_to_search * n_box_cols + c]; - c++; - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - } - out[i] = acc; -} - -// --- entry point (struphy.pic.sph_eval_kernels.box_based_evaluation_meshgrid) --- -// -// eta1/eta2/eta3 are the 3 distinct 1-D axis vectors of the meshgrid (the -// Pyccel kernel this ports only ever reads eta1[i,0,0]/eta2[0,j,0]/eta3[0,0,k], -// never the broadcast values, so the Python wrapper passes just the axes -- -// no reason to transfer the O(n1*n2*n3) redundant meshgrid). One CUDA thread -// per (i, j, k) evaluation point, flattened to match out's C-order layout. - -extern "C" __global__ -void box_based_evaluation_meshgrid_cuda( - const double* markers, - const int n_cols, - const double* eta1, - const double* eta2, - const double* eta3, - const int n1_eval, - const int n2_eval, - const int n3_eval, - const int nx, - const int ny, - const int nz, - const double* domain_array, - const int* boxes, - const int n_box_cols, - const int* neighbours, - const int* holes, - const int periodic1, - const int periodic2, - const int periodic3, - const int index, - const int kernel_type, - const double h1, - const double h2, - const double h3, - double* out) -{ - size_t idx = (size_t)blockIdx.x * blockDim.x + threadIdx.x; - size_t n_total = (size_t)n1_eval * n2_eval * n3_eval; - if (idx >= n_total) return; - - int i = idx / ((size_t)n2_eval * n3_eval); - int rem = idx % ((size_t)n2_eval * n3_eval); - int j = rem / n3_eval; - int k = rem % n3_eval; - - out[idx] = 0.0; - - double e1 = eta1[i]; - if (e1 < domain_array[0] || (e1 >= domain_array[1] && e1 != 1.0)) return; - - double e2 = eta2[j]; - if (e2 < domain_array[3] || (e2 >= domain_array[4] && e2 != 1.0)) return; - - double e3 = eta3[k]; - if (e3 < domain_array[6] || (e3 >= domain_array[7] && e3 != 1.0)) return; - - int loc_box = find_box_dev(e1, e2, e3, nx, ny, nz, domain_array); - if (loc_box == -1) return; - - double acc = 0.0; - for (int neigh = 0; neigh < 27; neigh++) { - int box_to_search = neighbours[loc_box * 27 + neigh]; - int c = 0; - while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { - int p = boxes[(size_t)box_to_search * n_box_cols + c]; - c++; - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - } - out[idx] = acc; -} - diff --git a/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_naive_src.cu b/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_naive_src.cu deleted file mode 100644 index db6e99e06..000000000 --- a/src/struphy/pic/cuda/sph_eval_kernels_cuda/_sph_eval_naive_src.cu +++ /dev/null @@ -1,86 +0,0 @@ -extern "C" __global__ -void naive_evaluation_flat_cuda( - const double* markers, - const int n_cols, - const int n_markers, - const double Np, - const double* eta1, - const double* eta2, - const double* eta3, - const int n_eval, - const int* holes, - const int periodic1, - const int periodic2, - const int periodic3, - const int index, - const int kernel_type, - const double h1, - const double h2, - const double h3, - double* out) -{ - int i = blockIdx.x * blockDim.x + threadIdx.x; - if (i >= n_eval) return; - - double e1 = eta1[i], e2 = eta2[i], e3 = eta3[i]; - - double acc = 0.0; - for (int p = 0; p < n_markers; p++) { - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - out[i] = acc / Np; -} - -extern "C" __global__ -void naive_evaluation_meshgrid_cuda( - const double* markers, - const int n_cols, - const int n_markers, - const double Np, - const double* eta1, - const double* eta2, - const double* eta3, - const int n1_eval, - const int n2_eval, - const int n3_eval, - const int* holes, - const int periodic1, - const int periodic2, - const int periodic3, - const int index, - const int kernel_type, - const double h1, - const double h2, - const double h3, - double* out) -{ - size_t idx = (size_t)blockIdx.x * blockDim.x + threadIdx.x; - size_t n_total = (size_t)n1_eval * n2_eval * n3_eval; - if (idx >= n_total) return; - - int i = idx / ((size_t)n2_eval * n3_eval); - int rem = idx % ((size_t)n2_eval * n3_eval); - int j = rem / n3_eval; - int k = rem % n3_eval; - - double e1 = eta1[i], e2 = eta2[j], e3 = eta3[k]; - - double acc = 0.0; - for (int p = 0; p < n_markers; p++) { - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - out[idx] = acc / Np; -} - diff --git a/src/struphy/pic/cuda/utilities_kernels_cuda/_gc_from_6d_src.cu b/src/struphy/pic/cuda/utilities_kernels_cuda/_gc_from_6d_src.cu deleted file mode 100644 index 8075806a4..000000000 --- a/src/struphy/pic/cuda/utilities_kernels_cuda/_gc_from_6d_src.cu +++ /dev/null @@ -1,75 +0,0 @@ -extern "C" __global__ -void eval_guiding_center_from_6d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b21, const int b1_n2, const int b1_n3, - const double* b22, const int b2_n2, const int b2_n3, - const double* b23, const int b3_n2, const int b3_n3, - const double* absB, const int a_n2, const int a_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double x = row[first_diagnostics_idx]; - const double y = row[first_diagnostics_idx + 1]; - const double z = row[first_diagnostics_idx + 2]; - double v[3] = {row[3], row[4], row[5]}; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b2[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - b21, b1_n2, b1_n3, b22, b2_n2, b2_n3, b23, b3_n2, b3_n3, b2); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, a_n2, a_n3); - - // normalized magnetic field, cartesian - b2[0] /= abs_B; b2[1] /= abs_B; b2[2] /= abs_B; - double norm_b_cart[3]; - matvec_dev(dfm, b2, norm_b_cart); - norm_b_cart[0] /= det_df; norm_b_cart[1] /= det_df; norm_b_cart[2] /= det_df; - - const double v_parallel = dot3_dev(norm_b_cart, v); - - double temp[3], v_perp[3]; - cross_dev(v, norm_b_cart, temp); - cross_dev(norm_b_cart, temp, v_perp); - const double v_perp_square = v_perp[0]*v_perp[0] + v_perp[1]*v_perp[1] + v_perp[2]*v_perp[2]; - - row[first_diagnostics_idx + 6] = v_parallel; - row[first_diagnostics_idx + 4] = 0.5 * v_perp_square / abs_B; - - double Larmor_r[3]; - cross_dev(norm_b_cart, v_perp, Larmor_r); - for (int k = 0; k < 3; k++) Larmor_r[k] = Larmor_r[k] / abs_B * epsilon; - - row[first_diagnostics_idx + 0] = x - Larmor_r[0]; - row[first_diagnostics_idx + 1] = y - Larmor_r[1]; - row[first_diagnostics_idx + 2] = z - Larmor_r[2]; -} - diff --git a/src/struphy/pic/cuda/utilities_kernels_cuda/_gradb_ediff_src.cu b/src/struphy/pic/cuda/utilities_kernels_cuda/_gradb_ediff_src.cu deleted file mode 100644 index 47509643e..000000000 --- a/src/struphy/pic/cuda/utilities_kernels_cuda/_gradb_ediff_src.cu +++ /dev/null @@ -1,58 +0,0 @@ -__device__ double gradb_ediff_mod1_dev(double x) -{ - double r = fmod(x, 1.0); - if (r < 0.0) r += 1.0; - return r; -} - -extern "C" __global__ -void eval_gradB_ediff_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int mu_idx, const int idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* pb1, const int p1_n2, const int p1_n3, - const double* pb2, const int p2_n2, const int p2_n3, - const double* pb3, const int p3_n2, const int p3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta_mid[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - eta_mid[k] = gradb_ediff_mod1_dev((row[k] + row[first_init_idx + k]) / 2.0); - eta_diff[k] = row[k] - row[first_init_idx + k]; - } - const double mu = row[mu_idx]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double gradB[3], grad_PB_b[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, gradB); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - pb1,p1_n2,p1_n3, pb2,p2_n2,p2_n3, pb3,p3_n2,p3_n3, grad_PB_b); - - double tmp[3]; - for (int k = 0; k < 3; k++) tmp[k] = gradB[k] + grad_PB_b[k]; - - row[idx] = mu * dot3_dev(eta_diff, tmp); -} - diff --git a/src/struphy/pic/cuda/utilities_kernels_cuda/_utilities_src.cu b/src/struphy/pic/cuda/utilities_kernels_cuda/_utilities_src.cu deleted file mode 100644 index 18be443fe..000000000 --- a/src/struphy/pic/cuda/utilities_kernels_cuda/_utilities_src.cu +++ /dev/null @@ -1,303 +0,0 @@ -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) -{ - double left[MAXP]; - double right[MAXP]; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -__device__ double eval_0form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bn2, const double* bn3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c, int n2x, int n3x) -{ - double out = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] * bn1[il1] * bn2[il2] * bn3[il3]; - } - } - } - return out; -} - -// markers[ip, first_diagnostics_idx] = mu_p * |B_0(eta_p)| -extern "C" __global__ -void eval_magnetic_background_energy_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, const int mu_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* abs_B0, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double mu = row[mu_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, abs_B0, n2x, n3x); - - row[first_diagnostics_idx] = mu * abs_B; -} - -// markers[ip, first_diagnostics_idx] = v_par^2 / 2 + mu_p * |B(eta_p)| -extern "C" __global__ -void eval_energy_5d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, const int mu_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* absB, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v_parallel = row[3]; - const double mu = row[mu_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, n2x, n3x); - - row[first_diagnostics_idx] = 0.5 * v_parallel * v_parallel + mu * abs_B; -} - -// markers[ip, idx_can_momentum] = shifted canonical toroidal momentum (5D) -extern "C" __global__ -void eval_canonical_toroidal_moment_5d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, const int mu_idx, const int idx_can_momentum, - const double epsilon, const double B0, const double R0, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* absB, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v_para = row[3]; - const double mu = row[mu_idx]; - const double energy = row[first_diagnostics_idx]; - const double psi = row[idx_can_momentum]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, n2x, n3x); - - double out = psi - epsilon * B0 * R0 / abs_B * v_para; - if (energy - mu * B0 > 0.0) { - // sign(v_para) matches numpy.sign: 0 for exactly 0 - const double sgn = (v_para > 0.0) ? 1.0 : ((v_para < 0.0) ? -1.0 : 0.0); - out += epsilon * sgn * sqrt(2.0 * (energy - mu * B0)) * R0; - } - row[idx_can_momentum] = out; -} - -// markers[ip, first_diagnostics_idx + 5] = shifted canonical toroidal momentum (6D) -extern "C" __global__ -void eval_canonical_toroidal_moment_6d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, - const double epsilon, const double B0, const double R0, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* absB, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double energy = row[first_diagnostics_idx + 3]; - const double mu = row[first_diagnostics_idx + 4]; - const double psi = row[first_diagnostics_idx + 5]; - const double v_para = row[first_diagnostics_idx + 6]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, n2x, n3x); - - double out = psi - epsilon * B0 * R0 / abs_B * v_para; - if (energy - mu * B0 > 0.0) { - const double sgn = (v_para > 0.0) ? 1.0 : ((v_para < 0.0) ? -1.0 : 0.0); - out += epsilon * sgn * sqrt(2.0 * (energy - mu * B0)) * R0; - } - row[first_diagnostics_idx + 5] = out; -} - -// markers[ip, first_diagnostics_idx + 1] = v_perp^2 / (2 |B(eta_p)|) -extern "C" __global__ -void eval_magnetic_moment_5d_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* absB, const int n2x, const int n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v_perp = row[4]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_splines_dev(tn1, p1, eta1, span1, bn1); - b_splines_dev(tn2, p2, eta2, span2, bn2); - b_splines_dev(tn3, p3, eta3, span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, absB, n2x, n3x); - - row[first_diagnostics_idx + 1] = 0.5 * v_perp * v_perp / abs_B; -} - -// markers[ip, first_diagnostics_idx] = mu_p * (|B_0| + PBb)(eta_p) -// NOTE: the CPU reference also evaluates the Jacobian DF(eta) here, but never -// uses the result, so it is not replicated. -extern "C" __global__ -void eval_magnetic_energy_PBb_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_diagnostics_idx, const int mu_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* abs_B0, const int a_n2x, const int a_n3x, - const double* PBb, const int b_n2x, const int b_n3x) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - // eta = mod(markers[0:3], 1.0); fmod can return negative, match numpy mod - double eta[3]; - for (int k = 0; k < 3; k++) { - double e = fmod(row[k], 1.0); - if (e < 0.0) e += 1.0; - eta[k] = e; - } - - const double mu = row[mu_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - b_splines_dev(tn1, p1, eta[0], span1, bn1); - b_splines_dev(tn2, p2, eta[1], span2, bn2); - b_splines_dev(tn3, p3, eta[2], span3, bn3); - - const double abs_B = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, abs_B0, a_n2x, a_n3x); - const double PB_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, PBb, b_n2x, b_n3x); - - row[first_diagnostics_idx] = mu * (abs_B + PB_b); -} - diff --git a/src/struphy/pic/particles.py b/src/struphy/pic/particles.py index c273f1bfe..19d5965a7 100644 --- a/src/struphy/pic/particles.py +++ b/src/struphy/pic/particles.py @@ -11,16 +11,7 @@ from struphy.kinetic_background import maxwellians from struphy.kinetic_background.base import Maxwellian, SumKineticBackground from struphy.pic import utilities_kernels -from struphy.pic.base import Particles, _to_numpy_for_kernel -from struphy.pic.utilities_kernels_cuda import ( - eval_canonical_toroidal_moment_5d_gpu, - eval_canonical_toroidal_moment_6d_gpu, - eval_energy_5d_gpu, - eval_guiding_center_from_6d_gpu, - eval_magnetic_background_energy_gpu, - eval_magnetic_energy_PBb_gpu, - eval_magnetic_moment_5d_gpu, -) +from struphy.pic.base import Particles class Particles6D(Particles): @@ -146,41 +137,17 @@ def save_constants_of_motion(self): ) # eval guiding center phase space - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS - - if xp.cupy_backend and self.domain.args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS: - import cupy as cp - import numpy as np - - eval_guiding_center_from_6d_gpu( - self.markers, - self._derham.args_derham, - int(self.domain.args_domain.kind_map), - cp.asarray(np.asarray(self.domain.args_domain.params, dtype=float), dtype=cp.float64), - self.first_diagnostics_idx, - self.equation_params.epsilon, - self._b2_h[0]._data, - self._b2_h[1]._data, - self._b2_h[2]._data, - self._absB0_h._data, - ) - else: - # no CUDA port for this domain kind_map: fall back to the - # compiled host-only kernel via the marker host mirror. - with self.host_markers(write=True) as _args_markers: - utilities_kernels.eval_guiding_center_from_6d( - _args_markers.markers, - self._derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.equation_params.epsilon, - _to_numpy_for_kernel(self._b2_h[0]._data), - _to_numpy_for_kernel(self._b2_h[1]._data), - _to_numpy_for_kernel(self._b2_h[2]._data), - _to_numpy_for_kernel(self._absB0_h._data), - ) + utilities_kernels.eval_guiding_center_from_6d( + self.markers, + self._derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.equation_params.epsilon, + self._b2_h[0]._data, + self._b2_h[1]._data, + self._b2_h[2]._data, + self._absB0_h._data, + ) # apply domain inverse map to get logical guiding center positions # TODO: currently only possible with the geometry where its inverse map is defined. @@ -212,26 +179,15 @@ def save_constants_of_motion(self): if self.mpi_comm is not None: self.mpi_sort_markers(alpha=1) - if xp.cupy_backend: - eval_canonical_toroidal_moment_6d_gpu( - self.markers, - self._derham.args_derham, - self.first_diagnostics_idx, - self.equation_params.epsilon, - B0, - R0, - self._absB0_h._data, - ) - else: - utilities_kernels.eval_canonical_toroidal_moment_6d( - self.markers, - self._derham.args_derham, - self.first_diagnostics_idx, - self.equation_params.epsilon, - B0, - R0, - self._absB0_h._data, - ) + utilities_kernels.eval_canonical_toroidal_moment_6d( + self.markers, + self._derham.args_derham, + self.first_diagnostics_idx, + self.equation_params.epsilon, + B0, + R0, + self._absB0_h._data, + ) # send back and clear buffer if self.mpi_comm is not None: @@ -462,23 +418,13 @@ def save_constants_of_motion(self): # idx and slice idx_can_momentum = self.first_diagnostics_idx + 1 - if xp.cupy_backend: - # CUDA port: operates on the device-resident markers directly. - eval_energy_5d_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) - else: - utilities_kernels.eval_energy_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) + utilities_kernels.eval_energy_5d( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + ) # eval psi at etas a1 = self.equil.domain.params["a1"] @@ -488,30 +434,17 @@ def save_constants_of_motion(self): r = self.markers[~self.holes, 0] * (1 - a1) + a1 self.markers[~self.holes, idx_can_momentum] = self.equil.psi_r(r) - if xp.cupy_backend: - eval_canonical_toroidal_moment_5d_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - idx_can_momentum, - self.equation_params.epsilon, - B0, - R0, - self.absB0_h._data, - ) - else: - utilities_kernels.eval_canonical_toroidal_moment_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - idx_can_momentum, - self.equation_params.epsilon, - B0, - R0, - self.absB0_h._data, - ) + utilities_kernels.eval_canonical_toroidal_moment_5d( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + idx_can_momentum, + self.equation_params.epsilon, + B0, + R0, + self.absB0_h._data, + ) def save_magnetic_energy(self, PBb): r""" @@ -528,28 +461,15 @@ def save_magnetic_energy(self, PBb): PBbt = E0T.dot(PBb, out=self._tmp0) PBbt.update_ghost_regions() - # utilities_kernels is a Pyccel-compiled extension that requires - # real numpy buffers; absB0_h/PBbt follow the active backend, so - # under cupy their ._data needs converting first. - if xp.cupy_backend: - eval_magnetic_energy_PBb_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - PBbt._data, - ) - else: - utilities_kernels.eval_magnetic_energy_PBb( - self.markers, - self.derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - PBbt._data, - ) + utilities_kernels.eval_magnetic_energy_PBb( + self.markers, + self.derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + PBbt._data, + ) def save_magnetic_background_energy(self): r""" @@ -557,24 +477,14 @@ def save_magnetic_background_energy(self): The result is stored in the energy diagnostics column (``self.first_diagnostics_idx``). """ - if xp.cupy_backend: - # CUDA port: operates on the device-resident markers directly. - eval_magnetic_background_energy_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) - else: - utilities_kernels.eval_magnetic_background_energy( - self.markers, - self.derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) + utilities_kernels.eval_magnetic_background_energy( + self.markers, + self.derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + ) class Particles5Dvperp(Particles): @@ -759,22 +669,12 @@ def draw_markers(self, sort: bool = True): super().draw_markers(sort=sort) # magnetic moment is an adiabatic invariant: evaluate once at draw time (diagnostics column 1) - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - if xp.cupy_backend: - eval_magnetic_moment_5d_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self._absB0_h._data, - ) - else: - utilities_kernels.eval_magnetic_moment_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self._absB0_h._data, - ) + utilities_kernels.eval_magnetic_moment_5d( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self._absB0_h._data, + ) def save_constants_of_motion(self): """ @@ -793,23 +693,13 @@ def save_constants_of_motion(self): # idx and slice idx_can_momentum = self.first_diagnostics_idx + 2 - if xp.cupy_backend: - # CUDA port: operates on the device-resident markers directly. - eval_energy_5d_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) - else: - utilities_kernels.eval_energy_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) + utilities_kernels.eval_energy_5d( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + ) # eval psi at etas a1 = self.equil.domain.params["a1"] @@ -819,30 +709,17 @@ def save_constants_of_motion(self): r = self.markers[~self.holes, 0] * (1 - a1) + a1 self.markers[~self.holes, idx_can_momentum] = self.equil.psi_r(r) - if xp.cupy_backend: - eval_canonical_toroidal_moment_5d_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - idx_can_momentum, - self.equation_params.epsilon, - B0, - R0, - self.absB0_h._data, - ) - else: - utilities_kernels.eval_canonical_toroidal_moment_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - idx_can_momentum, - self.equation_params.epsilon, - B0, - R0, - self.absB0_h._data, - ) + utilities_kernels.eval_canonical_toroidal_moment_5d( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.mu_idx, + idx_can_momentum, + self.equation_params.epsilon, + B0, + R0, + self.absB0_h._data, + ) def save_magnetic_energy(self, PBb): r""" @@ -859,27 +736,15 @@ def save_magnetic_energy(self, PBb): PBbt = E0T.dot(PBb, out=self._tmp0) PBbt.update_ghost_regions() - # compiled host-only Pyccel kernel: writes a marker diagnostics - # column in place, so it needs the host mirror of the markers. - if xp.cupy_backend: - eval_magnetic_energy_PBb_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - PBbt._data, - ) - else: - utilities_kernels.eval_magnetic_energy_PBb( - self.markers, - self.derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - PBbt._data, - ) + utilities_kernels.eval_magnetic_energy_PBb( + self.markers, + self.derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + PBbt._data, + ) def save_magnetic_background_energy(self): r""" @@ -887,24 +752,14 @@ def save_magnetic_background_energy(self): The result is stored in the energy diagnostics column (``self.first_diagnostics_idx``). """ - if xp.cupy_backend: - # CUDA port: operates on the device-resident markers directly. - eval_magnetic_background_energy_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) - else: - utilities_kernels.eval_magnetic_background_energy( - self.markers, - self.derham.args_derham, - self.domain.args_domain, - self.first_diagnostics_idx, - self.mu_idx, - self.absB0_h._data, - ) + utilities_kernels.eval_magnetic_background_energy( + self.markers, + self.derham.args_derham, + self.domain.args_domain, + self.first_diagnostics_idx, + self.mu_idx, + self.absB0_h._data, + ) def save_magnetic_moment(self): r""" @@ -912,20 +767,12 @@ def save_magnetic_moment(self): diagnostics column (``self.first_diagnostics_idx + 1``). """ - if xp.cupy_backend: - eval_magnetic_moment_5d_gpu( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.absB0_h._data, - ) - else: - utilities_kernels.eval_magnetic_moment_5d( - self.markers, - self.derham.args_derham, - self.first_diagnostics_idx, - self.absB0_h._data, - ) + utilities_kernels.eval_magnetic_moment_5d( + self.markers, + self.derham.args_derham, + self.first_diagnostics_idx, + self.absB0_h._data, + ) class Particles3D(Particles): diff --git a/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_dk_hamiltonian_src.cu b/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_dk_hamiltonian_src.cu deleted file mode 100644 index 4c800ad94..000000000 --- a/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_dk_hamiltonian_src.cu +++ /dev/null @@ -1,124 +0,0 @@ -#define MAXP 8 - -__device__ int find_span_dev(const double* t, int p, int len_t, double eta) -{ - int low = p; - int high = len_t - 1 - p; - - if (eta <= t[low]) return low; - if (eta >= t[high]) return high - 1; - - int span = (low + high) / 2; - while (eta < t[span] || eta >= t[span + 1]) { - if (eta < t[span]) high = span; - else low = span; - span = (low + high) / 2; - } - return span; -} - -__device__ void b_splines_dev(const double* t, int p, double eta, int span, double* bn) -{ - double left[MAXP]; - double right[MAXP]; - - for (int i = 0; i <= p; i++) bn[i] = 0.0; - bn[0] = 1.0; - - for (int j = 0; j < p; j++) { - left[j] = eta - t[span - j]; - right[j] = t[span + 1 + j] - eta; - double saved = 0.0; - for (int r = 0; r <= j; r++) { - double temp = bn[r] / (right[r] + left[j - r]); - bn[r] = saved + right[r] * temp; - saved = left[j - r] * temp; - } - bn[j + 1] = saved; - } -} - -__device__ double eval_0form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bn2, const double* bn3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c, int n2x, int n3x) -{ - double out = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] - * bn1[il1] * bn2[il2] * bn3[il3]; - } - } - } - return out; -} - -__device__ double mod1_dev(double x) -{ - double r = fmod(x, 1.0); - if (r < 0.0) r += 1.0; - return r; -} - -extern "C" __global__ -void driftkinetic_hamiltonian_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, - const int first_init_idx, const int first_shift_idx, const int mu_idx, - const double a0, const double a1, const double a2, const double a3, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* B_dot_b, const int b_n2, const int b_n3, - const double* phi_c, const int p_n2, const int p_n3, - const int evaluate_e_field) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double alpha[3] = {a0, a1, a2}; - double eta[3]; - for (int i = 0; i < 3; i++) { - const double eta_k = row[i] + row[first_shift_idx + i]; - const double eta_n = row[first_init_idx + i]; - eta[i] = mod1_dev(alpha[i] * eta_k + (1.0 - alpha[i]) * eta_n); - } - - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - const double v = a3 * v_k + (1.0 - a3) * v_n; - const double mu = row[mu_idx]; - - double bn1[MAXP + 1], bn2[MAXP + 1], bn3[MAXP + 1]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - b_splines_dev(tn1, p1, eta[0], span1, bn1); - b_splines_dev(tn2, p2, eta[1], span2, bn2); - b_splines_dev(tn3, p3, eta[2], span3, bn3); - - double phi = 0.0; - if (evaluate_e_field) { - phi = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, phi_c, p_n2, p_n3); - } - - const double bdb = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, B_dot_b, b_n2, b_n3); - - row[column_nr] = epsilon * v * v / 2.0 + epsilon * mu * bdb + phi; -} - diff --git a/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_gc_marker_column_src.cu b/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_gc_marker_column_src.cu deleted file mode 100644 index 7d739ed5f..000000000 --- a/src/struphy/pic/pushing/cuda/eval_kernels_gc_cuda/_gc_marker_column_src.cu +++ /dev/null @@ -1,212 +0,0 @@ -__device__ void weighted_eta_v_dev( - const double* row, int first_init_idx, int first_shift_idx, - const double* alpha, double* eta, double* v_out) -{ - for (int k = 0; k < 3; k++) { - const double eta_k = row[k] + row[first_shift_idx + k]; - const double eta_n = row[first_init_idx + k]; - double e = alpha[k] * eta_k + (1.0 - alpha[k]) * eta_n; - double r = fmod(e, 1.0); - if (r < 0.0) r += 1.0; - eta[k] = r; - } - if (v_out) { - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - *v_out = alpha[3] * v_k + (1.0 - alpha[3]) * v_n; - } -} - -extern "C" __global__ -void grad_driftkinetic_hamiltonian_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int n_comps, const int* comps, - const int first_init_idx, const int first_shift_idx, const int mu_idx, - const double* alpha, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const int evaluate_e_field) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3]; - weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, 0); - const double mu = row[mu_idx]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - for (int j = 0; j < n_comps; j++) row[column_nr + j] = grad_H[comps[j]]; -} - -extern "C" __global__ -void bstar_parallel_3form_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, - const int first_init_idx, const int first_shift_idx, - const double* alpha, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* cub, const int cub_n2, const int cub_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3], v; - weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, &v); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta[0], eta[1], eta[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bn2[MAXP+1], bn3[MAXP+1]; - double bd1[MAXP], bd2[MAXP], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, cub, cub_n2, cub_n3); - - b_star_parallel = (b_star_parallel * epsilon * v + B_dot_b) * det_df; - - row[column_nr] = b_star_parallel; -} - -extern "C" __global__ -void bstar_2form_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int n_comps, const int* comps, - const int first_init_idx, const int first_shift_idx, - const double* alpha, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b1, const int b1_n2, const int b1_n3, - const double* b2, const int b2_n2, const int b2_n3, - const double* b3, const int b3_n2, const int b3_n3, - const double* cb1, const int c1_n2, const int c1_n3, - const double* cb2, const int c2_n2, const int c2_n3, - const double* cb3, const int c3_n2, const int c3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3], v; - weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, &v); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - double bb[3], b_star[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b1,b1_n2,b1_n3, b2,b2_n2,b2_n3, b3,b3_n2,b3_n3, bb); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cb1,c1_n2,c1_n3, cb2,c2_n2,c2_n3, cb3,c3_n2,c3_n3, b_star); - - for (int k = 0; k < 3; k++) b_star[k] = b_star[k] * epsilon * v + bb[k]; - - for (int j = 0; j < n_comps; j++) row[column_nr + j] = b_star[comps[j]]; -} - -extern "C" __global__ -void unit_b_1form_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int n_comps, const int* comps, - const int first_init_idx, const int first_shift_idx, - const double* alpha, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* ub1, const int u1_n2, const int u1_n3, - const double* ub2, const int u2_n2, const int u2_n3, - const double* ub3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - double eta[3]; - weighted_eta_v_dev(row, first_init_idx, first_shift_idx, alpha, eta, 0); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta[2], span3, bn3, bd3); - - double unit_b1[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ub1,u1_n2,u1_n3, ub2,u2_n2,u2_n3, ub3,u3_n2,u3_n3, unit_b1); - - for (int j = 0; j < n_comps; j++) row[column_nr + j] = unit_b1[comps[j]]; -} - diff --git a/src/struphy/pic/pushing/cuda/eval_kernels_sph_cuda/_sph_marker_column_src.cu b/src/struphy/pic/pushing/cuda/eval_kernels_sph_cuda/_sph_marker_column_src.cu deleted file mode 100644 index 23e19ef76..000000000 --- a/src/struphy/pic/pushing/cuda/eval_kernels_sph_cuda/_sph_marker_column_src.cu +++ /dev/null @@ -1,111 +0,0 @@ -extern "C" __global__ -void sph_pressure_coeffs_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int weight_idx, - const int* valid_mks, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const int loc_box = (int)row[n_cols - 2]; - - const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); - - const double weight = row[weight_idx]; - const double gamma = 5.0 / 3.0; - - row[column_nr] = n_at_eta; - row[column_nr + 1] = weight / n_at_eta; - row[column_nr + 2] = weight * pow(n_at_eta, gamma - 2.0); -} - -extern "C" __global__ -void sph_mean_velocity_coeffs_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int weight_idx, - const int* valid_mks, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const int loc_box = (int)row[n_cols - 2]; - - const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); - - const double weight = row[weight_idx]; - const double scale = weight / n_at_eta; - - row[column_nr + 0] = scale * row[3]; - row[column_nr + 1] = scale * row[4]; - row[column_nr + 2] = scale * row[5]; -} - -extern "C" __global__ -void sph_viscosity_tensor_cuda( - double* markers, const int n_cols, const int n_markers, - const int column_nr, const int weight_idx, const int first_free_idx, - const int* valid_mks, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3, - const double mu) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const int loc_box = (int)row[n_cols - 2]; - - const double n_at_eta = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type, h1, h2, h3); - const double weight = row[weight_idx]; - - double grad_v[3][3]; - for (int j = 0; j < 3; j++) { - for (int k = 0; k < 3; k++) { - grad_v[j][k] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, - first_free_idx + j, kernel_type + 1 + k, h1, h2, h3); - } - } - - double d_dev[3][3]; - for (int j = 0; j < 3; j++) - for (int k = 0; k < 3; k++) - d_dev[j][k] = 0.5 * (grad_v[j][k] + grad_v[k][j]); - - const double mean_trace = (d_dev[0][0] + d_dev[1][1] + d_dev[2][2]) / 3.0; - d_dev[0][0] -= mean_trace; - d_dev[1][1] -= mean_trace; - d_dev[2][2] -= mean_trace; - - const double scale = -2.0 * mu * (weight / n_at_eta); - for (int j = 0; j < 3; j++) { - for (int k = 0; k < 3; k++) { - row[column_nr + 3 * j + k] = d_dev[j][k] * scale; - } - } -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_general_geometry_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_general_geometry_src.cu index 0d77a1311..aedd4e1be 100644 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_general_geometry_src.cu +++ b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_general_geometry_src.cu @@ -17,27 +17,7 @@ __device__ void matrix_inv_dev(const double* a, double* b) b[8] = (a[0]*a[4] - a[3]*a[1]) / det_a; } -// c = a^T @ b (used for both DF^-1 @ v and DF^-T @ e_form: pass dfinv or -// its transpose accordingly -- here we need dfinv @ v (not transposed) for -// push_eta_stage, and dfinvT @ e_form for push_v_with_efield, so both a -// plain and a transposed matvec are provided). -__device__ void matvec_dev(const double* a, const double* v, double* out) -{ - out[0] = a[0]*v[0] + a[1]*v[1] + a[2]*v[2]; - out[1] = a[3]*v[0] + a[4]*v[1] + a[5]*v[2]; - out[2] = a[6]*v[0] + a[7]*v[1] + a[8]*v[2]; -} - -// c = a @ b, 3x3 row-major matrices. -__device__ void matmat_dev(const double* a, const double* b, double* c) -{ - for (int i = 0; i < 3; i++) { - for (int j = 0; j < 3; j++) { - c[3*i+j] = a[3*i+0]*b[0*3+j] + a[3*i+1]*b[1*3+j] + a[3*i+2]*b[2*3+j]; - } - } -} - +// c = a^T @ v (used for DF^-T @ e_form in push_v_with_efield_general below). __device__ void matvecT_dev(const double* a, const double* v, double* out) { out[0] = a[0]*v[0] + a[3]*v[1] + a[6]*v[2]; @@ -313,110 +293,6 @@ __device__ void b_d_splines_dev(const double* t, int p, double eta, int span, do } } -__device__ double det3_dev(const double* a) -{ - return a[0]*(a[4]*a[8] - a[5]*a[7]) - - a[1]*(a[3]*a[8] - a[5]*a[6]) - + a[2]*(a[3]*a[7] - a[4]*a[6]); -} - -__device__ void cross_dev(const double* a, const double* b, double* out) -{ - out[0] = a[1]*b[2] - a[2]*b[1]; - out[1] = a[2]*b[0] - a[0]*b[2]; - out[2] = a[0]*b[1] - a[1]*b[0]; -} - -__device__ double dot3_dev(const double* a, const double* b) -{ - return a[0]*b[0] + a[1]*b[1] + a[2]*b[2]; -} - -// Single-point evaluation of a Derham 2-form spline, matching -// struphy.bsplines.evaluation_kernels_3d.eval_2form_spline_mpi (N-D-D / -// D-N-D / D-D-N tensor-product sums, the dual basis combination of the -// 1-form evaluation in push_v_with_efield_general above). -__device__ void eval_2form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bd1, - const double* bn2, const double* bd2, - const double* bn3, const double* bd3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c1, int n2x1, int n3x1, - const double* c2, int n2x2, int n3x2, - const double* c3, int n2x3, int n3x3, - double* out) -{ - out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; - - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 < p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 < p3; il3++) { - int i3 = span3 + il3 - start2; - out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bn1[il1] * bd2[il2] * bd3[il3]; - } - } - } - for (int il1 = 0; il1 < p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 < p3; il3++) { - int i3 = span3 + il3 - start2; - out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bd1[il1] * bn2[il2] * bd3[il3]; - } - } - } - for (int il1 = 0; il1 < p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 < p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bd1[il1] * bd2[il2] * bn3[il3]; - } - } - } -} - -extern "C" __global__ -void push_eta_stage_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; - - double dfm[9], dfinv[9], v[3], k[3]; - v[0] = row[3]; v[1] = row[4]; v[2] = row[5]; - - df_dispatch_dev(kind_map, row[0], row[1], row[2], params, dfm); - matrix_inv_dev(dfm, dfinv); - matvec_dev(dfinv, v, k); - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - extern "C" __global__ void push_v_with_efield_general( double* markers, @@ -495,958 +371,3 @@ void push_v_with_efield_general( row[4] += dt_const * dfinvT_e[1]; row[5] += dt_const * dfinvT_e[2]; } - -// Shared setup for push_vxb_analytic_general / push_vxb_implicit_general: -// evaluate DF(eta), its determinant, and the Cartesian B-field at the -// marker's position. Returns 0 (and leaves b_cart untouched) if the marker -// is a hole/ghost, matching both CPU kernels' skip check. -__device__ int eval_b_cart_dev( - const double* row, const int n_cols, const int first_init_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const int kind_map, const double* params, - double* b_cart) -{ - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - - double bn1[MAXP + 1], bd1[MAXP]; - double bn2[MAXP + 1], bd2[MAXP]; - double bn3[MAXP + 1], bd3[MAXP]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b_form[3]; - eval_2form_dev( - p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, - span1, span2, span3, start0, start1, start2, - b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - b_form - ); - - double dfm[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - const double det_df = det3_dev(dfm); - - matvec_dev(dfm, b_form, b_cart); - b_cart[0] /= det_df; - b_cart[1] /= det_df; - b_cart[2] /= det_df; - return 1; -} - -extern "C" __global__ -void push_vxb_analytic_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; - - double b_cart[3]; - eval_b_cart_dev( - row, n_cols, first_init_idx, p1, p2, p3, - tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, - b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, b_cart - ); - - const double b_abs = sqrt(b_cart[0]*b_cart[0] + b_cart[1]*b_cart[1] + b_cart[2]*b_cart[2]); - if (b_abs == 0.0) return; - - double b_norm[3] = {b_cart[0]/b_abs, b_cart[1]/b_abs, b_cart[2]/b_abs}; - double v[3] = {row[3], row[4], row[5]}; - - const double vpar = dot3_dev(v, b_norm); - - double vxb_norm[3], vperp[3], b_normxvperp[3]; - cross_dev(v, b_norm, vxb_norm); - cross_dev(b_norm, vxb_norm, vperp); - cross_dev(b_norm, vperp, b_normxvperp); - - const double cbt = cos(b_abs * dt), sbt = sin(b_abs * dt); - row[3] = vpar * b_norm[0] + cbt * vperp[0] - sbt * b_normxvperp[0]; - row[4] = vpar * b_norm[1] + cbt * vperp[1] - sbt * b_normxvperp[1]; - row[5] = vpar * b_norm[2] + cbt * vperp[2] - sbt * b_normxvperp[2]; -} - -extern "C" __global__ -void push_vxb_implicit_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - // NOTE: the CPU push_vxb_implicit only checks the hole flag, not the - // ghost flag (unlike push_vxb_analytic) -- faithfully reproduced here. - if (row[first_init_idx] == -1.0) return; - - double b_cart[3]; - eval_b_cart_dev( - row, n_cols, first_init_idx, p1, p2, p3, - tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, - b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, b_cart - ); - - // b_prod = [[0, bz, -by], [-bz, 0, bx], [by, -bx, 0]] (row-major), such - // that b_prod @ v == v x b_cart (matches the CPU kernel's b_prod, which - // solves v x B via a matrix product rather than a cross product). - double b_prod[9] = { - 0.0, b_cart[2], -b_cart[1], - -b_cart[2], 0.0, b_cart[0], - b_cart[1], -b_cart[0], 0.0, - }; - - double rhs[9], lhs[9]; - for (int k = 0; k < 9; k++) { - const double id = (k == 0 || k == 4 || k == 8) ? 1.0 : 0.0; - rhs[k] = id + 0.5 * dt * b_prod[k]; - lhs[k] = id - 0.5 * dt * b_prod[k]; - } - - double lhs_inv[9]; - matrix_inv_dev(lhs, lhs_inv); - - double v[3] = {row[3], row[4], row[5]}; - double vec[3], res[3]; - matvec_dev(rhs, v, vec); - matvec_dev(lhs_inv, vec, res); - - row[3] = res[0]; - row[4] = res[1]; - row[5] = res[2]; -} - -// Single-point evaluation of a Derham 1-form spline (D-N-N / N-D-N / N-N-D), -// matching struphy.bsplines.evaluation_kernels_3d.eval_1form_spline_mpi. -// A standalone copy of the same math already inlined in -// push_v_with_efield_general above -- kept separate (not factored out and -// reused there) to avoid touching that already-validated kernel. -__device__ void eval_1form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bd1, - const double* bn2, const double* bd2, - const double* bn3, const double* bd3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c1, int n2x1, int n3x1, - const double* c2, int n2x2, int n3x2, - const double* c3, int n2x3, int n3x3, - double* out) -{ - out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; - - for (int il1 = 0; il1 < p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * bd1[il1] * bn2[il2] * bn3[il3]; - } - } - } - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 < p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * bn1[il1] * bd2[il2] * bn3[il3]; - } - } - } - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 < p3; il3++) { - int i3 = span3 + il3 - start2; - out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * bn1[il1] * bn2[il2] * bd3[il3]; - } - } - } -} - -// Single-point evaluation of a vector-field spline (H^1)^3 (N-N-N for every -// component), matching -// struphy.bsplines.evaluation_kernels_3d.eval_vectorfield_spline_mpi. -__device__ void eval_vectorfield_dev( - int p1, int p2, int p3, - const double* bn1, const double* bn2, const double* bn3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c1, int n2x1, int n3x1, - const double* c2, int n2x2, int n3x2, - const double* c3, int n2x3, int n3x3, - double* out) -{ - out[0] = 0.0; out[1] = 0.0; out[2] = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - double b123 = bn1[il1] * bn2[il2] * bn3[il3]; - out[0] += c1[(size_t)i1 * n2x1 * n3x1 + (size_t)i2 * n3x1 + i3] * b123; - out[1] += c2[(size_t)i1 * n2x2 * n3x2 + (size_t)i2 * n3x2 + i3] * b123; - out[2] += c3[(size_t)i1 * n2x3 * n3x3 + (size_t)i2 * n3x3 + i3] * b123; - } - } - } -} - -// Shared setup for push_bxu_{Hdiv,Hcurl,H1vec}_general: evaluate DF(eta), -// its determinant, and the Cartesian B-field (always a 2-form) at the -// marker's position. Also computes and caches the local N-/D-spline basis -// values and span indices, reused by the caller for its own U-field -// evaluation (which differs per FEEC space). -__device__ void eval_b_cart_and_basis_dev( - double eta1, double eta2, double eta3, - int p1, int p2, int p3, - const double* tn1, int len_tn1, - const double* tn2, int len_tn2, - const double* tn3, int len_tn3, - int start0, int start1, int start2, - const double* b2_1, int n2x1, int n3x1, - const double* b2_2, int n2x2, int n3x2, - const double* b2_3, int n2x3, int n3x3, - int kind_map, const double* params, - double* bn1, double* bd1, double* bn2, double* bd2, double* bn3, double* bd3, - int* span1_out, int* span2_out, int* span3_out, - double* dfm, double* b_cart) -{ - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - *span1_out = span1; *span2_out = span2; *span3_out = span3; - - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double b_form[3]; - eval_2form_dev( - p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, - span1, span2, span3, start0, start1, start2, - b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - b_form - ); - - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - const double det_df = det3_dev(dfm); - matvec_dev(dfm, b_form, b_cart); - b_cart[0] /= det_df; - b_cart[1] /= det_df; - b_cart[2] /= det_df; -} - -extern "C" __global__ -void push_bxu_Hdiv_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const double* u2_1, const int m2x1, const int m3x1, - const double* u2_2, const int m2x2, const int m3x2, - const double* u2_3, const int m2x3, const int m3x3, - const int kind_map, - const double* params, - const double boundary_cut, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfm[9], b_cart[3]; - eval_b_cart_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart - ); - - double u_form[3]; - eval_2form_dev( - p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, - span1, span2, span3, start0, start1, start2, - u2_1, m2x1, m3x1, u2_2, m2x2, m3x2, u2_3, m2x3, m3x3, - u_form - ); - const double det_df = det3_dev(dfm); - double u_cart[3]; - matvec_dev(dfm, u_form, u_cart); - u_cart[0] /= det_df; u_cart[1] /= det_df; u_cart[2] /= det_df; - - double e_cart[3]; - cross_dev(b_cart, u_cart, e_cart); - row[3] += dt * e_cart[0]; - row[4] += dt * e_cart[1]; - row[5] += dt * e_cart[2]; -} - -extern "C" __global__ -void push_bxu_Hcurl_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const double* u1_1, const int m2x1, const int m3x1, - const double* u1_2, const int m2x2, const int m3x2, - const double* u1_3, const int m2x3, const int m3x3, - const int kind_map, - const double* params, - const double boundary_cut, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfm[9], b_cart[3]; - eval_b_cart_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart - ); - - double u_form[3]; - eval_1form_dev( - p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, - span1, span2, span3, start0, start1, start2, - u1_1, m2x1, m3x1, u1_2, m2x2, m3x2, u1_3, m2x3, m3x3, - u_form - ); - double dfinv[9], dfinvT[9], u_cart[3]; - matrix_inv_dev(dfm, dfinv); - matvecT_dev(dfinv, u_form, u_cart); - - double e_cart[3]; - cross_dev(b_cart, u_cart, e_cart); - row[3] += dt * e_cart[0]; - row[4] += dt * e_cart[1]; - row[5] += dt * e_cart[2]; -} - -extern "C" __global__ -void push_bxu_H1vec_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b2_1, const int n2x1, const int n3x1, - const double* b2_2, const int n2x2, const int n3x2, - const double* b2_3, const int n2x3, const int n3x3, - const double* uv_1, const int m2x1, const int m3x1, - const double* uv_2, const int m2x2, const int m3x2, - const double* uv_3, const int m2x3, const int m3x3, - const int kind_map, - const double* params, - const double boundary_cut, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[0] < boundary_cut || row[0] > 1.0 - boundary_cut) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfm[9], b_cart[3]; - eval_b_cart_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - start0, start1, start2, b2_1, n2x1, n3x1, b2_2, n2x2, n3x2, b2_3, n2x3, n3x3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfm, b_cart - ); - - double u_form[3]; - eval_vectorfield_dev( - p1, p2, p3, bn1, bn2, bn3, - span1, span2, span3, start0, start1, start2, - uv_1, m2x1, m3x1, uv_2, m2x2, m3x2, uv_3, m2x3, m3x3, - u_form - ); - double u_cart[3]; - matvec_dev(dfm, u_form, u_cart); - - double e_cart[3]; - cross_dev(b_cart, u_cart, e_cart); - row[3] += dt * e_cart[0]; - row[4] += dt * e_cart[1]; - row[5] += dt * e_cart[2]; -} - -// Shared setup for push_pc_GXu{_full,}_general: DF(eta)/dfinv/dfinvT plus -// span/basis values, reused by the caller to evaluate the 3 (or 2) rows of -// the GXu matrix via eval_1form_dev. -__device__ void eval_dfinvt_and_basis_dev( - double eta1, double eta2, double eta3, - int p1, int p2, int p3, - const double* tn1, int len_tn1, - const double* tn2, int len_tn2, - const double* tn3, int len_tn3, - int kind_map, const double* params, - double* bn1, double* bd1, double* bn2, double* bd2, double* bn3, double* bd3, - int* span1_out, int* span2_out, int* span3_out, - double* dfinvt) -{ - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - *span1_out = span1; *span2_out = span2; *span3_out = span3; - - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - // dfinvt = dfinv^T, stored explicitly (row-major) since the caller needs - // it as a plain matrix for matvec_dev, not just for a single matvecT_dev - // application. - dfinvt[0] = dfinv[0]; dfinvt[1] = dfinv[3]; dfinvt[2] = dfinv[6]; - dfinvt[3] = dfinv[1]; dfinvt[4] = dfinv[4]; dfinvt[5] = dfinv[7]; - dfinvt[6] = dfinv[2]; dfinvt[7] = dfinv[5]; dfinvt[8] = dfinv[8]; -} - -extern "C" __global__ -void push_pc_GXu_full_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* g11, const double* g12, const double* g13, - const double* g21, const double* g22, const double* g23, - const double* g31, const double* g32, const double* g33, - const int n2xc1, const int n3xc1, - const int n2xc2, const int n3xc2, - const int n2xc3, const int n3xc3, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfinvt[9]; - eval_dfinvt_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfinvt - ); - - // components 1/2/3 of a 1-form generally have different shapes - // (D-N-N / N-D-N / N-N-D), but the shape only depends on the component - // index, not on which "row" of GXu is being evaluated -- so the same - // (n2xc1,n3xc1)/(n2xc2,n3xc2)/(n2xc3,n3xc3) apply to all three rows. - double gxu_row0[3], gxu_row1[3], gxu_row2[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g11, n2xc1, n3xc1, g12, n2xc2, n3xc2, g13, n2xc3, n3xc3, gxu_row0); - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g21, n2xc1, n3xc1, g22, n2xc2, n3xc2, g23, n2xc3, n3xc3, gxu_row1); - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g31, n2xc1, n3xc1, g32, n2xc2, n3xc2, g33, n2xc3, n3xc3, gxu_row2); - - // GXu[i][j] = gxu_row_i[j]; e[j] = sum_i GXu[i][j] * v[i] - double v[3] = {row[3], row[4], row[5]}; - double e[3]; - e[0] = gxu_row0[0]*v[0] + gxu_row1[0]*v[1] + gxu_row2[0]*v[2]; - e[1] = gxu_row0[1]*v[0] + gxu_row1[1]*v[1] + gxu_row2[1]*v[2]; - e[2] = gxu_row0[2]*v[0] + gxu_row1[2]*v[1] + gxu_row2[2]*v[2]; - - double e_cart[3]; - matvec_dev(dfinvt, e, e_cart); - - row[3] -= dt * e_cart[0] / 2.0; - row[4] -= dt * e_cart[1] / 2.0; - row[5] -= dt * e_cart[2] / 2.0; -} - -extern "C" __global__ -void push_pc_GXu_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* g11, const double* g12, const double* g13, - const double* g21, const double* g22, const double* g23, - const int n2xc1, const int n3xc1, - const int n2xc2, const int n3xc2, - const int n2xc3, const int n3xc3, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - int span1, span2, span3; - double dfinvt[9]; - eval_dfinvt_and_basis_dev( - eta1, eta2, eta3, p1, p2, p3, tn1, len_tn1, tn2, len_tn2, tn3, len_tn3, - kind_map, params, bn1, bd1, bn2, bd2, bn3, bd3, &span1, &span2, &span3, dfinvt - ); - - double gxu_row0[3], gxu_row1[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g11, n2xc1, n3xc1, g12, n2xc2, n3xc2, g13, n2xc3, n3xc3, gxu_row0); - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - g21, n2xc1, n3xc1, g22, n2xc2, n3xc2, g23, n2xc3, n3xc3, gxu_row1); - - double v[3] = {row[3], row[4], row[5]}; - double e[3]; - e[0] = gxu_row0[0]*v[0] + gxu_row1[0]*v[1]; - e[1] = gxu_row0[1]*v[0] + gxu_row1[1]*v[1]; - e[2] = gxu_row0[2]*v[0] + gxu_row1[2]*v[1]; - - double e_cart[3]; - matvec_dev(dfinvt, e, e_cart); - - row[3] -= dt * e_cart[0] / 2.0; - row[4] -= dt * e_cart[1] / 2.0; - row[5] -= dt * e_cart[2] / 2.0; -} - -extern "C" __global__ -void push_pc_eta_stage_Hcurl_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* u_1, const int n2x1, const int n3x1, - const double* u_2, const int n2x2, const int n3x2, - const double* u_3, const int n2x3, const int n3x3, - const int use_perp_model, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9], dfinvt[9], ginv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - dfinvt[0]=dfinv[0]; dfinvt[1]=dfinv[3]; dfinvt[2]=dfinv[6]; - dfinvt[3]=dfinv[1]; dfinvt[4]=dfinv[4]; dfinvt[5]=dfinv[7]; - dfinvt[6]=dfinv[2]; dfinvt[7]=dfinv[5]; dfinvt[8]=dfinv[8]; - matmat_dev(dfinv, dfinvt, ginv); - - double k_v[3]; - matvec_dev(dfinv, v, k_v); - - double u[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); - if (use_perp_model) u[2] = 0.0; - - double k_u[3]; - matvec_dev(ginv, u, k_u); - - double k[3] = {k_v[0]+k_u[0], k_v[1]+k_u[1], k_v[2]+k_u[2]}; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_pc_eta_stage_Hdiv_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* u_1, const int n2x1, const int n3x1, - const double* u_2, const int n2x2, const int n3x2, - const double* u_3, const int n2x3, const int n3x3, - const int use_perp_model, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - const double det_df = det3_dev(dfm); - matrix_inv_dev(dfm, dfinv); - - double k_v[3]; - matvec_dev(dfinv, v, k_v); - - double u[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); - if (use_perp_model) u[2] = 0.0; - - double k_u[3] = {u[0]/det_df, u[1]/det_df, u[2]/det_df}; - double k[3] = {k_v[0]+k_u[0], k_v[1]+k_u[1], k_v[2]+k_u[2]}; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_pc_eta_stage_H1vec_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* u_1, const int n2x1, const int n3x1, - const double* u_2, const int n2x2, const int n3x2, - const double* u_3, const int n2x3, const int n3x3, - const int use_perp_model, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - - double k_v[3]; - matvec_dev(dfinv, v, k_v); - - double u[3]; - eval_vectorfield_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, start0, start1, start2, - u_1, n2x1, n3x1, u_2, n2x2, n3x2, u_3, n2x3, n3x3, u); - if (use_perp_model) u[2] = 0.0; - - double k[3] = {k_v[0]+u[0], k_v[1]+u[1], k_v[2]+u[2]}; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_weights_with_efield_lin_va_general( - double* markers, - const int n_cols, - const int n_markers, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* e1_1, const int n2x1, const int n3x1, - const double* e1_2, const int n2x2, const int n3x2, - const double* e1_3, const int n2x3, const int n3x3, - const double* f0_values, - const double kappa, - const double vth, - const int kind_map, - const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9], dfinv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - - double dfinv_v[3]; - matvec_dev(dfinv, v, dfinv_v); - - double e_vec[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - e1_1, n2x1, n3x1, e1_2, n2x2, n3x2, e1_3, n2x3, n3x3, e_vec); - - const double update = (dfinv_v[0]*e_vec[0] + dfinv_v[1]*e_vec[1] + dfinv_v[2]*e_vec[2]) - * f0_values[ip] * kappa * dt / (2.0 * row[7] * vth * vth); - row[6] += update; -} - -// Single-point evaluation of a Derham 0-form spline (N-N-N), matching -// struphy.bsplines.evaluation_kernels_3d.eval_0form_spline_mpi. -__device__ double eval_0form_dev( - int p1, int p2, int p3, - const double* bn1, const double* bn2, const double* bn3, - int span1, int span2, int span3, - int start0, int start1, int start2, - const double* c, int n2x, int n3x) -{ - double out = 0.0; - for (int il1 = 0; il1 <= p1; il1++) { - int i1 = span1 + il1 - start0; - for (int il2 = 0; il2 <= p2; il2++) { - int i2 = span2 + il2 - start1; - for (int il3 = 0; il3 <= p3; il3++) { - int i3 = span3 + il3 - start2; - out += c[(size_t)i1 * n2x * n3x + (size_t)i2 * n3x + i3] * bn1[il1] * bn2[il2] * bn3[il3]; - } - } - } - return out; -} - -extern "C" __global__ -void push_deterministic_diffusion_stage_general( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* pi_u, const int n2xu, const int n3xu, - const double* pi_grad_u1, const int n2x1, const int n3x1, - const double* pi_grad_u2, const int n2x2, const int n3x2, - const double* pi_grad_u3, const int n2x3, const int n3x3, - const double diffusion_coeff, - const int kind_map, - const double* params, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - - double bn1[MAXP + 1], bd1[MAXP], bn2[MAXP + 1], bd2[MAXP], bn3[MAXP + 1], bd3[MAXP]; - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - const double pi_u_value = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, pi_u, n2xu, n3xu); - - double pi_du_value[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, start0, start1, start2, - pi_grad_u1, n2x1, n3x1, pi_grad_u2, n2x2, n3x2, pi_grad_u3, n2x3, n3x3, pi_du_value); - - // ginv = G^-1 = DF^-1 @ DF^-T, matching struphy.geometry.evaluation_kernels.g_inv - // (computed there as (DF^T @ DF)^-1 instead -- same result, different - // intermediate path, reusing the dfinv this file already needs elsewhere). - double dfm[9], dfinv[9], dfinvt[9], ginv[9]; - df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm); - matrix_inv_dev(dfm, dfinv); - dfinvt[0]=dfinv[0]; dfinvt[1]=dfinv[3]; dfinvt[2]=dfinv[6]; - dfinvt[3]=dfinv[1]; dfinvt[4]=dfinv[4]; dfinvt[5]=dfinv[7]; - dfinvt[6]=dfinv[2]; dfinvt[7]=dfinv[5]; dfinvt[8]=dfinv[8]; - matmat_dev(dfinv, dfinvt, ginv); - - double tmp[3] = { - -diffusion_coeff * pi_du_value[0] / pi_u_value, - -diffusion_coeff * pi_du_value[1] / pi_u_value, - -diffusion_coeff * pi_du_value[2] / pi_u_value, - }; - double k[3]; - matvec_dev(ginv, tmp, k); - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_cuboid_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_cuboid_src.cu deleted file mode 100644 index 25c3adb1b..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_cuboid_src.cu +++ /dev/null @@ -1,37 +0,0 @@ -extern "C" __global__ -void push_eta_stage_cuboid( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const double sx, - const double sy, - const double sz, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - - // skip holes and ghost/boundary particles, matching push_eta_stage - if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double kx = sx * row[3]; - const double ky = sy * row[4]; - const double kz = sz * row[5]; - - // accumulate for the last stage (must happen before the position update, - // which reads the just-updated accumulator) - row[first_free_idx + 0] += dt_b * kx; - row[first_free_idx + 1] += dt_b * ky; - row[first_free_idx + 2] += dt_b * kz; - - row[0] = row[first_init_idx + 0] + dt_a * kx + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * ky + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * kz + last * row[first_free_idx + 2]; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_rk_periodic_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_rk_periodic_src.cu deleted file mode 100644 index 5eb0454ef..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_push_eta_rk_periodic_src.cu +++ /dev/null @@ -1,55 +0,0 @@ -extern "C" __global__ -void push_eta_rk_periodic( - double* markers, - const int n_cols, - const int n_markers, - const int first_init_idx, - const int first_free_idx, - const int first_shift_idx, - const double sx, - const double sy, - const double sz, - const double dt_a, - const double dt_b, - const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - - if (row[first_init_idx] == -1.0 || row[n_cols - 1] == -2.0) return; - - const double kx = sx * row[3]; - const double ky = sy * row[4]; - const double kz = sz * row[5]; - - row[first_free_idx + 0] += dt_b * kx; - row[first_free_idx + 1] += dt_b * ky; - row[first_free_idx + 2] += dt_b * kz; - - double e0 = row[first_init_idx + 0] + dt_a * kx + last * row[first_free_idx + 0]; - double e1 = row[first_init_idx + 1] + dt_a * ky + last * row[first_free_idx + 1]; - double e2 = row[first_init_idx + 2] + dt_a * kz + last * row[first_free_idx + 2]; - - // periodic wrap + shift bookkeeping, matching the periodic branch of - // Particles.apply_kinetic_bc (Python's a % 1.0 is always in [0, 1)) - double shift0 = 0.0, shift1 = 0.0, shift2 = 0.0; - - if (e0 > 1.0) { e0 = fmod(e0, 1.0); shift0 = 1.0; } - else if (e0 < 0.0) { e0 = fmod(e0, 1.0); if (e0 < 0.0) e0 += 1.0; shift0 = -1.0; } - - if (e1 > 1.0) { e1 = fmod(e1, 1.0); shift1 = 1.0; } - else if (e1 < 0.0) { e1 = fmod(e1, 1.0); if (e1 < 0.0) e1 += 1.0; shift1 = -1.0; } - - if (e2 > 1.0) { e2 = fmod(e2, 1.0); shift2 = 1.0; } - else if (e2 < 0.0) { e2 = fmod(e2, 1.0); if (e2 < 0.0) e2 += 1.0; shift2 = -1.0; } - - row[0] = e0; - row[1] = e1; - row[2] = e2; - row[first_shift_idx + 0] = shift0; - row[first_shift_idx + 1] = shift1; - row[first_shift_idx + 2] = shift2; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_random_diffusion_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_random_diffusion_src.cu deleted file mode 100644 index da406865f..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_cuda/_random_diffusion_src.cu +++ /dev/null @@ -1,19 +0,0 @@ -extern "C" __global__ -void push_random_diffusion_stage( - double* markers, - const int n_cols, - const int n_markers, - const double* noise, - const double scale) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - row[0] += scale * noise[3*ip + 0]; - row[1] += scale * noise[3*ip + 1]; - row[2] += scale * noise[3*ip + 2]; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_1st_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_1st_src.cu deleted file mode 100644 index 59e6df71c..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_1st_src.cu +++ /dev/null @@ -1,195 +0,0 @@ -// mod(x, 1.0) matching numpy (result in [0, 1)) -__device__ double mod1_dev(double x) -{ - double r = fmod(x, 1.0); - if (r < 0.0) r += 1.0; - return r; -} - -extern "C" __global__ -void push_gc_bxEstar_discrete_gradient_1st_order_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int gb1_n2, const int gb1_n3, - const double* gb2, const int gb2_n2, const int gb2_n3, - const double* gb3, const int gb3_n2, const int gb3_n3, - const double* e1c, const int e1_n2, const int e1_n3, - const double* e2c, const int e2_n2, const int e2_n3, - const double* e3c, const int e3_n2, const int e3_n3, - const int evaluate_e_field, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; - for (int i = 0; i < 3; i++) { - eta_k[i] = row[i] + row[first_shift_idx + i]; - eta_n[i] = row[first_init_idx + i]; - eta_mid[i] = mod1_dev((eta_k[i] + eta_n[i]) / 2.0); - eta_diff[i] = eta_k[i] - eta_n[i]; - } - - const double mu = row[mu_idx]; - const double H_n = row[first_free_idx]; - const double b_star_parallel = row[first_free_idx + 1]; - double unit_b1[3] = { - row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; - const double H_k = row[first_free_idx + 5]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,gb1_n2,gb1_n3, gb2,gb2_n2,gb2_n3, gb3,gb3_n2,gb3_n3, grad_H); - for (int i = 0; i < 3; i++) grad_H[i] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - e1c,e1_n2,e1_n3, e2c,e2_n2,e2_n3, e3c,e3_n2,e3_n3, e_field); - for (int i = 0; i < 3; i++) grad_H[i] += -e_field[i]; - } - - const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H); - const double dZ_squared = dot3_dev(eta_diff, eta_diff); - - double grad_I[3]; - if (dZ_squared == 0.0) { - for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i]; - } else { - const double c = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; - for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i] + eta_diff[i] * c; - } - - double Exb[3]; - cross_dev(unit_b1, grad_I, Exb); - - double k[3]; - for (int i = 0; i < 3; i++) k[i] = Exb[i] / b_star_parallel; - - for (int i = 0; i < 3; i++) row[i] = eta_n[i] + dt * k[i]; - - row[residual_idx] = sqrt( - (row[0] - eta_k[0]) * (row[0] - eta_k[0]) - + (row[1] - eta_k[1]) * (row[1] - eta_k[1]) - + (row[2] - eta_k[2]) * (row[2] - eta_k[2])); -} - -extern "C" __global__ -void push_gc_Bstar_discrete_gradient_1st_order_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int gb1_n2, const int gb1_n3, - const double* gb2, const int gb2_n2, const int gb2_n3, - const double* gb3, const int gb3_n2, const int gb3_n3, - const double* e1c, const int e1_n2, const int e1_n3, - const double* e2c, const int e2_n2, const int e2_n3, - const double* e3c, const int e3_n2, const int e3_n3, - const int evaluate_e_field, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; - for (int i = 0; i < 3; i++) { - eta_k[i] = row[i] + row[first_shift_idx + i]; - eta_n[i] = row[first_init_idx + i]; - eta_mid[i] = mod1_dev((eta_k[i] + eta_n[i]) / 2.0); - eta_diff[i] = eta_k[i] - eta_n[i]; - } - - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - const double v_mid = (v_k + v_n) / 2.0; - const double v_diff = v_k - v_n; - - const double mu = row[mu_idx]; - const double H_n = row[first_free_idx]; - const double b_star_parallel = epsilon * row[first_free_idx + 1]; - double b_star[3] = { - row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; - const double H_k = row[first_free_idx + 5]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,gb1_n2,gb1_n3, gb2,gb2_n2,gb2_n3, gb3,gb3_n2,gb3_n3, grad_H); - for (int i = 0; i < 3; i++) grad_H[i] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - e1c,e1_n2,e1_n3, e2c,e2_n2,e2_n3, e3c,e3_n2,e3_n3, e_field); - for (int i = 0; i < 3; i++) grad_H[i] += -e_field[i]; - } - - const double grad_H_v = epsilon * v_mid; - const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H) + v_diff * grad_H_v; - const double dZ_squared = dot3_dev(eta_diff, eta_diff) + v_diff * v_diff; - - double grad_I[3]; - double grad_I_v; - if (dZ_squared == 0.0) { - for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i]; - grad_I_v = grad_H_v; - } else { - const double c = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; - for (int i = 0; i < 3; i++) grad_I[i] = grad_H[i] + eta_diff[i] * c; - grad_I_v = grad_H_v + v_diff * c; - } - - double k[3]; - for (int i = 0; i < 3; i++) k[i] = b_star[i] / b_star_parallel * grad_I_v; - - double k_v = dot3_dev(b_star, grad_I); - k_v /= -b_star_parallel; - - for (int i = 0; i < 3; i++) row[i] = eta_n[i] + dt * k[i]; - row[3] = v_n + dt * k_v; - - row[residual_idx] = sqrt( - (row[0] - eta_k[0]) * (row[0] - eta_k[0]) - + (row[1] - eta_k[1]) * (row[1] - eta_k[1]) - + (row[2] - eta_k[2]) * (row[2] - eta_k[2]) - + ((row[3] - v_k) / v_k) * ((row[3] - v_k) / v_k)); -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_2nd_order_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_2nd_order_src.cu deleted file mode 100644 index 9a6210f42..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_2nd_order_src.cu +++ /dev/null @@ -1,227 +0,0 @@ -extern "C" __global__ -void push_gc_bxEstar_discrete_gradient_2nd_order_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* ub1, const int u1_n2, const int u1_n3, - const double* ub2, const int u2_n2, const int u2_n3, - const double* ub3, const int u3_n2, const int u3_n3, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* cub, const int cub_n2, const int cub_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const int evaluate_e_field, const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - eta_k[k] = row[k] + row[first_shift_idx + k]; - eta_n[k] = row[first_init_idx + k]; - double m = fmod((eta_k[k] + eta_n[k]) / 2.0, 1.0); - if (m < 0.0) m += 1.0; - eta_mid[k] = m; - eta_diff[k] = eta_k[k] - eta_n[k]; - } - const double v = row[3]; - const double mu = row[mu_idx]; - - const double H_n = row[first_free_idx]; - const double H_k = row[first_free_idx + 1]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double unit_b1[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ub1,u1_n2,u1_n3, ub2,u2_n2,u2_n3, ub3,u3_n2,u3_n3, unit_b1); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H); - const double dZ_squared = dot3_dev(eta_diff, eta_diff); - - double grad_I[3]; - if (dZ_squared == 0.0) { - for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k]; - } else { - const double s = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; - for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k] + eta_diff[k] * s; - } - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, cub, cub_n2, cub_n3); - b_star_parallel = (b_star_parallel * epsilon * v + B_dot_b) * det_df; - - double Exb[3]; - cross_dev(unit_b1, grad_I, Exb); - - double k_vec[3]; - for (int k = 0; k < 3; k++) k_vec[k] = Exb[k] / b_star_parallel; - - row[0] = eta_n[0] + dt * k_vec[0]; - row[1] = eta_n[1] + dt * k_vec[1]; - row[2] = eta_n[2] + dt * k_vec[2]; - - const double r0 = row[0] - eta_k[0], r1 = row[1] - eta_k[1], r2 = row[2] - eta_k[2]; - row[residual_idx] = sqrt(r0*r0 + r1*r1 + r2*r2); -} - -extern "C" __global__ -void push_gc_Bstar_discrete_gradient_2nd_order_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* b2_1, const int b1_n2, const int b1_n3, - const double* b2_2, const int b2_n2, const int b2_n3, - const double* b2_3, const int b3_n2, const int b3_n3, - const double* cb1, const int c1_n2, const int c1_n3, - const double* cb2, const int c2_n2, const int c2_n3, - const double* cb3, const int c3_n2, const int c3_n3, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* cub, const int cub_n2, const int cub_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const int evaluate_e_field, const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_n[3], eta_mid[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - eta_k[k] = row[k] + row[first_shift_idx + k]; - eta_n[k] = row[first_init_idx + k]; - double m = fmod((eta_k[k] + eta_n[k]) / 2.0, 1.0); - if (m < 0.0) m += 1.0; - eta_mid[k] = m; - eta_diff[k] = eta_k[k] - eta_n[k]; - } - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - const double v_mid = (v_k + v_n) / 2.0; - const double v_diff = v_k - v_n; - const double mu = row[mu_idx]; - - const double H_n = row[first_free_idx]; - const double H_k = row[first_free_idx + 1]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - const double grad_H_v = epsilon * v_mid; - const double dZ_dot_grad_H = dot3_dev(eta_diff, grad_H) + v_diff * grad_H_v; - const double dZ_squared = dot3_dev(eta_diff, eta_diff) + v_diff * v_diff; - - double grad_I[3]; - double grad_I_v; - if (dZ_squared == 0.0) { - for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k]; - grad_I_v = grad_H_v; - } else { - const double s = (H_k - H_n - dZ_dot_grad_H) / dZ_squared; - for (int k = 0; k < 3; k++) grad_I[k] = grad_H[k] + eta_diff[k] * s; - grad_I_v = grad_H_v + v_diff * s; - } - - double b2[3], b_star[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b2_1,b1_n2,b1_n3, b2_2,b2_n2,b2_n3, b2_3,b3_n2,b3_n3, b2); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cb1,c1_n2,c1_n3, cb2,c2_n2,c2_n3, cb3,c3_n2,c3_n3, b_star); - for (int k = 0; k < 3; k++) b_star[k] = b_star[k] * epsilon * v_mid + b2[k]; - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, cub, cub_n2, cub_n3); - b_star_parallel = (b_star_parallel * epsilon * v_mid + B_dot_b) * epsilon * det_df; - - double k_vec[3]; - for (int k = 0; k < 3; k++) k_vec[k] = b_star[k] / b_star_parallel * grad_I_v; - const double k_v = -dot3_dev(b_star, grad_I) / b_star_parallel; - - row[0] = eta_n[0] + dt * k_vec[0]; - row[1] = eta_n[1] + dt * k_vec[1]; - row[2] = eta_n[2] + dt * k_vec[2]; - row[3] = v_n + dt * k_v; - - const double r0 = row[0] - eta_k[0], r1 = row[1] - eta_k[1], r2 = row[2] - eta_k[2]; - const double rv = (row[3] - v_k) / v_k; - row[residual_idx] = sqrt(r0*r0 + r1*r1 + r2*r2 + rv*rv); -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_newton_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_newton_src.cu deleted file mode 100644 index 29bcb624a..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_dg_newton_src.cu +++ /dev/null @@ -1,256 +0,0 @@ -extern "C" __global__ -void push_gc_bxEstar_discrete_gradient_1st_order_newton_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const double* phi, const int p_n2, const int p_n3, - const int evaluate_e_field, const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - const double eta_k_shifted = row[k] + row[first_shift_idx + k]; - eta_k[k] = row[k]; - eta_diff[k] = eta_k_shifted - row[first_init_idx + k]; - } - const double v = row[3]; - const double mu = row[mu_idx]; - - const double H_n = row[first_free_idx]; - const double b_star_parallel = row[first_free_idx + 1]; - const double unit_b1[3] = {row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; - const double H_k1 = row[first_free_idx + 5]; - const double H_k12 = row[first_free_idx + 6]; - const double grad_H_1 = row[first_free_idx + 7]; - const double grad_H_12[2] = {row[first_free_idx + 8], row[first_free_idx + 9]}; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_k[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_k[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_k[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_k[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_k[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_k[2], span3, bn3, bd3); - - double phi_val = 0.0; - if (evaluate_e_field) { - phi_val = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, phi, p_n2, p_n3); - } - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - const double H_k = epsilon * v * v / 2.0 + epsilon * mu * B_dot_b + phi_val; - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - double grad_I[3]; - grad_I[0] = (eta_diff[0] == 0.0) ? grad_H[0] : (H_k1 - H_n) / eta_diff[0]; - grad_I[1] = (eta_diff[1] == 0.0) ? grad_H[1] : (H_k12 - H_k1) / eta_diff[1]; - grad_I[2] = (eta_diff[2] == 0.0) ? grad_H[2] : (H_k - H_k12) / eta_diff[2]; - - double bcross_mat[9] = { - 0.0, -unit_b1[2], unit_b1[1], - unit_b1[2], 0.0, -unit_b1[0], - -unit_b1[1], unit_b1[0], 0.0}; - for (int k = 0; k < 9; k++) bcross_mat[k] /= b_star_parallel; - - double func[3]; - matvec_dev(bcross_mat, grad_I, func); - for (int k = 0; k < 3; k++) func[k] = eta_diff[k] - dt * func[k]; - - double Ddg[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; - if (eta_diff[0] != 0.0) Ddg[0] = (grad_H_1 * eta_diff[0] - (H_k1 - H_n)) / (eta_diff[0] * eta_diff[0]); - if (eta_diff[1] != 0.0) { - Ddg[4] = (grad_H_12[1] * eta_diff[1] - (H_k12 - H_k1)) / (eta_diff[1] * eta_diff[1]); - Ddg[3] = (grad_H_12[0] - grad_H_1) / eta_diff[1]; - } - if (eta_diff[2] != 0.0) { - Ddg[8] = (grad_H[2] * eta_diff[2] - (H_k - H_k12)) / (eta_diff[2] * eta_diff[2]); - Ddg[6] = (grad_H[0] - grad_H_12[0]) / eta_diff[2]; - Ddg[7] = (grad_H[1] - grad_H_12[1]) / eta_diff[2]; - } - - double Dfunc[9]; - matmat_dev(bcross_mat, Ddg, Dfunc); - for (int k = 0; k < 9; k++) Dfunc[k] *= -dt; - Dfunc[0] += 1.0; Dfunc[4] += 1.0; Dfunc[8] += 1.0; - - double Dfunc_inv[9], k_vec[3]; - matrix_inv_dev(Dfunc, Dfunc_inv); - matvec_dev(Dfunc_inv, func, k_vec); - - row[0] -= k_vec[0]; - row[1] -= k_vec[1]; - row[2] -= k_vec[2]; - - row[residual_idx] = sqrt(k_vec[0]*k_vec[0] + k_vec[1]*k_vec[1] + k_vec[2]*k_vec[2]); -} - -extern "C" __global__ -void push_gc_Bstar_discrete_gradient_1st_order_newton_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_shift_idx, - const int residual_idx, const int first_free_idx, const int mu_idx, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* gb1, const int g1_n2, const int g1_n3, - const double* gb2, const int g2_n2, const int g2_n3, - const double* gb3, const int g3_n2, const int g3_n3, - const double* bdb, const int bdb_n2, const int bdb_n3, - const double* ef1, const int e1_n2, const int e1_n3, - const double* ef2, const int e2_n2, const int e2_n3, - const double* ef3, const int e3_n2, const int e3_n3, - const double* phi, const int p_n2, const int p_n3, - const int evaluate_e_field, const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - double eta_k[3], eta_diff[3]; - for (int k = 0; k < 3; k++) { - const double eta_k_shifted = row[k] + row[first_shift_idx + k]; - eta_k[k] = row[k]; - eta_diff[k] = eta_k_shifted - row[first_init_idx + k]; - } - const double v_k = row[3]; - const double v_n = row[first_init_idx + 3]; - const double v_diff = v_k - v_n; - const double mu = row[mu_idx]; - - const double H_n = row[first_free_idx]; - const double b_star_parallel = epsilon * row[first_free_idx + 1]; - const double b_star[3] = {row[first_free_idx + 2], row[first_free_idx + 3], row[first_free_idx + 4]}; - const double H_k1 = row[first_free_idx + 5]; - const double H_k12 = row[first_free_idx + 6]; - const double grad_H_1 = row[first_free_idx + 7]; - const double grad_H_12[2] = {row[first_free_idx + 8], row[first_free_idx + 9]}; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_k[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_k[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_k[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_k[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_k[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_k[2], span3, bn3, bd3); - - double phi_val = 0.0; - if (evaluate_e_field) { - phi_val = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, phi, p_n2, p_n3); - } - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, bdb, bdb_n2, bdb_n3); - const double H_k = epsilon * v_k * v_k / 2.0 + epsilon * mu * B_dot_b + phi_val; - const double H_k123 = epsilon * v_n * v_n / 2.0 + epsilon * mu * B_dot_b + phi_val; - - double grad_H[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - gb1,g1_n2,g1_n3, gb2,g2_n2,g2_n3, gb3,g3_n2,g3_n3, grad_H); - for (int k = 0; k < 3; k++) grad_H[k] *= epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ef1,e1_n2,e1_n3, ef2,e2_n2,e2_n3, ef3,e3_n2,e3_n3, e_field); - for (int k = 0; k < 3; k++) grad_H[k] -= e_field[k]; - } - - const double grad_H_v = epsilon * v_k; - - double grad_I[3]; - grad_I[0] = (eta_diff[0] == 0.0) ? grad_H[0] : (H_k1 - H_n) / eta_diff[0]; - grad_I[1] = (eta_diff[1] == 0.0) ? grad_H[1] : (H_k12 - H_k1) / eta_diff[1]; - grad_I[2] = (eta_diff[2] == 0.0) ? grad_H[2] : (H_k123 - H_k12) / eta_diff[2]; - const double grad_I_v = (v_diff == 0.0) ? grad_H_v : (H_k - H_k123) / v_diff; - - double J_vec[3]; - for (int k = 0; k < 3; k++) J_vec[k] = b_star[k] / b_star_parallel; - - double func[3]; - for (int k = 0; k < 3; k++) func[k] = eta_diff[k] - dt * (J_vec[k] * grad_I_v); - double func_v = v_diff + dt * dot3_dev(J_vec, grad_I); - - double Ddg[9] = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; - if (eta_diff[0] != 0.0) Ddg[0] = (grad_H_1 * eta_diff[0] - (H_k1 - H_n)) / (eta_diff[0] * eta_diff[0]); - if (eta_diff[1] != 0.0) { - Ddg[4] = (grad_H_12[1] * eta_diff[1] - (H_k12 - H_k1)) / (eta_diff[1] * eta_diff[1]); - Ddg[3] = (grad_H_12[0] - grad_H_1) / eta_diff[1]; - } - if (eta_diff[2] != 0.0) { - Ddg[8] = (grad_H[2] * eta_diff[2] - (H_k123 - H_k12)) / (eta_diff[2] * eta_diff[2]); - Ddg[6] = (grad_H[0] - grad_H_12[0]) / eta_diff[2]; - Ddg[7] = (grad_H[1] - grad_H_12[1]) / eta_diff[2]; - } - const double Ddg_v = (v_diff == 0.0) ? 0.0 : (grad_H_v * v_diff - (H_k - H_k123)) / (v_diff * v_diff); - - // DF = [[I, B], [C^T, 1]], B = -dt*Ddg_v*J_vec, C = dt*Ddg^T @ J_vec - double Bv[3], Cv[3]; - for (int k = 0; k < 3; k++) Bv[k] = -dt * Ddg_v * J_vec[k]; - double DdgT[9] = {Ddg[0], Ddg[3], Ddg[6], Ddg[1], Ddg[4], Ddg[7], Ddg[2], Ddg[5], Ddg[8]}; - matvec_dev(DdgT, J_vec, Cv); - for (int k = 0; k < 3; k++) Cv[k] *= dt; - - const double schur = 1.0 - dot3_dev(Cv, Bv); - - double A_inv[9]; - A_inv[0] = Bv[0]*Cv[0]; A_inv[1] = Bv[0]*Cv[1]; A_inv[2] = Bv[0]*Cv[2]; - A_inv[3] = Bv[1]*Cv[0]; A_inv[4] = Bv[1]*Cv[1]; A_inv[5] = Bv[1]*Cv[2]; - A_inv[6] = Bv[2]*Cv[0]; A_inv[7] = Bv[2]*Cv[1]; A_inv[8] = Bv[2]*Cv[2]; - for (int k = 0; k < 9; k++) A_inv[k] /= schur; - A_inv[0] += 1.0; A_inv[4] += 1.0; A_inv[8] += 1.0; - - double Binv[3], Cinv[3]; - for (int k = 0; k < 3; k++) { Binv[k] = -Bv[k] / schur; Cinv[k] = -Cv[k] / schur; } - - double k_vec[3]; - matvec_dev(A_inv, func, k_vec); - for (int k = 0; k < 3; k++) k_vec[k] += Binv[k] * func_v; - double k_v = dot3_dev(Cinv, func) + func_v / schur; - - row[0] -= k_vec[0]; - row[1] -= k_vec[1]; - row[2] -= k_vec[2]; - row[3] -= k_v; - - row[residual_idx] = sqrt(k_vec[0]*k_vec[0] + k_vec[1]*k_vec[1] + k_vec[2]*k_vec[2] + (k_v/v_k)*(k_v/v_k)); -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bstar_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bstar_src.cu deleted file mode 100644 index 4a79eb487..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bstar_src.cu +++ /dev/null @@ -1,109 +0,0 @@ -extern "C" __global__ -void push_gc_Bstar_explicit_multistage_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_free_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* grad_b_full_1, const int gb1_n2, const int gb1_n3, - const double* grad_b_full_2, const int gb2_n2, const int gb2_n3, - const double* grad_b_full_3, const int gb3_n2, const int gb3_n3, - const double* b2_1, const int b1_n2, const int b1_n3, - const double* b2_2, const int b2n2, const int b2n3, - const double* b2_3, const int b3_n2, const int b3_n3, - const double* curl_unit_b2_1, const int cb1_n2, const int cb1_n3, - const double* curl_unit_b2_2, const int cb2_n2, const int cb2_n3, - const double* curl_unit_b2_3, const int cb3_n2, const int cb3_n3, - const double* B_dot_b_coeffs, const int bdb_n2, const int bdb_n3, - const double* curl_unit_b_dot_b0, const int cub_n2, const int cub_n3, - const double* e_field_1, const int e1_n2, const int e1_n3, - const double* e_field_2, const int e2_n2, const int e2_n3, - const double* e_field_3, const int e3_n2, const int e3_n3, - const int evaluate_e_field, - const double dt_a, const double dt_b, const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - const double mu = row[mu_idx]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double e_star[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - grad_b_full_1, gb1_n2, gb1_n3, grad_b_full_2, gb2_n2, gb2_n3, grad_b_full_3, gb3_n2, gb3_n3, e_star); - e_star[0] *= -epsilon * mu; - e_star[1] *= -epsilon * mu; - e_star[2] *= -epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - e_field_1, e1_n2, e1_n3, e_field_2, e2_n2, e2_n3, e_field_3, e3_n2, e3_n3, e_field); - e_star[0] += e_field[0]; - e_star[1] += e_field[1]; - e_star[2] += e_field[2]; - } - - double b2[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - b2_1, b1_n2, b1_n3, b2_2, b2n2, b2n3, b2_3, b3_n2, b3_n3, b2); - - double b_star[3]; - eval_2form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - curl_unit_b2_1, cb1_n2, cb1_n3, curl_unit_b2_2, cb2_n2, cb2_n3, curl_unit_b2_3, cb3_n2, cb3_n3, b_star); - b_star[0] = b_star[0] * epsilon * v + b2[0]; - b_star[1] = b_star[1] * epsilon * v + b2[1]; - b_star[2] = b_star[2] * epsilon * v + b2[2]; - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, B_dot_b_coeffs, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, curl_unit_b_dot_b0, cub_n2, cub_n3); - b_star_parallel = b_star_parallel * epsilon * v + B_dot_b; - b_star_parallel *= det_df; - - double k[3]; - k[0] = b_star[0] / b_star_parallel * v; - k[1] = b_star[1] / b_star_parallel * v; - k[2] = b_star[2] / b_star_parallel * v; - - double k_v = dot3_dev(b_star, e_star); - k_v /= b_star_parallel * epsilon; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - row[first_free_idx + 3] += dt_b * k_v; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; - row[3] = row[first_init_idx + 3] + dt_a * k_v + last * row[first_free_idx + 3]; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu deleted file mode 100644 index f731e1268..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu +++ /dev/null @@ -1,96 +0,0 @@ -extern "C" __global__ -void push_gc_bxEstar_explicit_multistage_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_free_idx, const int mu_idx, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* unit_b1_1, const int ub1_n2, const int ub1_n3, - const double* unit_b1_2, const int ub2_n2, const int ub2_n3, - const double* unit_b1_3, const int ub3_n2, const int ub3_n3, - const double* grad_b_full_1, const int gb1_n2, const int gb1_n3, - const double* grad_b_full_2, const int gb2_n2, const int gb2_n3, - const double* grad_b_full_3, const int gb3_n2, const int gb3_n3, - const double* B_dot_b_coeffs, const int bdb_n2, const int bdb_n3, - const double* curl_unit_b_dot_b0, const int cub_n2, const int cub_n3, - const double* e_field_1, const int e1_n2, const int e1_n3, - const double* e_field_2, const int e2_n2, const int e2_n3, - const double* e_field_3, const int e3_n2, const int e3_n3, - const int evaluate_e_field, - const double dt_a, const double dt_b, const double last) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - const double mu = row[mu_idx]; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double unit_b1[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - unit_b1_1, ub1_n2, ub1_n3, unit_b1_2, ub2_n2, ub2_n3, unit_b1_3, ub3_n2, ub3_n3, unit_b1); - - double e_star[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - grad_b_full_1, gb1_n2, gb1_n3, grad_b_full_2, gb2_n2, gb2_n3, grad_b_full_3, gb3_n2, gb3_n3, e_star); - e_star[0] *= -epsilon * mu; - e_star[1] *= -epsilon * mu; - e_star[2] *= -epsilon * mu; - - if (evaluate_e_field) { - double e_field[3]; - eval_1form_dev(p1, p2, p3, bn1, bd1, bn2, bd2, bn3, bd3, span1, span2, span3, - start0, start1, start2, - e_field_1, e1_n2, e1_n3, e_field_2, e2_n2, e2_n3, e_field_3, e3_n2, e3_n3, e_field); - e_star[0] += e_field[0]; - e_star[1] += e_field[1]; - e_star[2] += e_field[2]; - } - - const double B_dot_b = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, B_dot_b_coeffs, bdb_n2, bdb_n3); - double b_star_parallel = eval_0form_dev(p1, p2, p3, bn1, bn2, bn3, span1, span2, span3, - start0, start1, start2, curl_unit_b_dot_b0, cub_n2, cub_n3); - b_star_parallel = b_star_parallel * epsilon * v + B_dot_b; - b_star_parallel *= det_df; - - double Exb[3]; - cross_dev(e_star, unit_b1, Exb); - - double k[3]; - k[0] = Exb[0] / b_star_parallel; - k[1] = Exb[1] / b_star_parallel; - k[2] = Exb[2] / b_star_parallel; - - row[first_free_idx + 0] += dt_b * k[0]; - row[first_free_idx + 1] += dt_b * k[1]; - row[first_free_idx + 2] += dt_b * k[2]; - - row[0] = row[first_init_idx + 0] + dt_a * k[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] + dt_a * k[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] + dt_a * k[2] + last * row[first_free_idx + 2]; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu deleted file mode 100644 index bb74b9e50..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu +++ /dev/null @@ -1,216 +0,0 @@ -extern "C" __global__ -void push_gc_cc_J1_H1vec_cuda( - double* markers, const int n_cols, const int n_markers, - const double dt, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - - double b[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_vectorfield_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double e[3]; - cross_dev(b, u, e); - const double temp = dot3_dev(e, curl_norm_b); - - row[3] += temp / abs_b_star_para * v * dt; -} - -extern "C" __global__ -void push_gc_cc_J1_Hcurl_cuda( - double* markers, const int n_cols, const int n_markers, - const double dt, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], u_form[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u_form); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - // g_inv = (DF^T DF)^-1, transforms the 1-form u into H1vec components - double df_t[9] = { - dfm[0], dfm[3], dfm[6], - dfm[1], dfm[4], dfm[7], - dfm[2], dfm[5], dfm[8], - }; - double g[9], g_inv[9], u0[3]; - matmat_dev(df_t, dfm, g); - matrix_inv_dev(g, g_inv); - matvec_dev(g_inv, u_form, u0); - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = (b[k] + curl_norm_b[k] * v * epsilon) / det_df; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double e[3]; - cross_dev(b, u0, e); - const double temp = dot3_dev(e, curl_norm_b) / det_df; - - row[3] += temp / abs_b_star_para * v * dt; -} - -extern "C" __global__ -void push_gc_cc_J1_Hdiv_cuda( - double* markers, const int n_cols, const int n_markers, - const double dt, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double b[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, b); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - for (int k = 0; k < 3; k++) u[k] /= det_df; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = b[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double e[3]; - cross_dev(b, u, e); - const double temp = dot3_dev(e, curl_norm_b); - - row[3] += temp / abs_b_star_para * v * dt; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu deleted file mode 100644 index b84496689..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu +++ /dev/null @@ -1,171 +0,0 @@ -extern "C" __global__ -void push_gc_cc_J2_dg_init_Hdiv_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, - const double dt, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double bb[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double tmp[9], e[3]; - matmat_dev(norm_b_prod, b_prod, tmp); - matvec_dev(tmp, u, e); - for (int k = 0; k < 3; k++) e[k] /= (abs_b_star_para * det_df); - - row[0] -= dt * e[0]; - row[1] -= dt * e[1]; - row[2] -= dt * e[2]; -} - -extern "C" __global__ -void push_gc_cc_J2_dg_Hdiv_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, - const double dt, const double const_, const double alpha, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3, - const double* ud_1, const int ud1_n2, const int ud1_n3, - const double* ud_2, const int ud2_n2, const int ud2_n3, - const double* ud_3, const int ud3_n2, const int ud3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - - const double eta_old0 = row[0], eta_old1 = row[1], eta_old2 = row[2]; - double eta_mid[3]; - eta_mid[0] = mod1_dev((row[0] + row[first_init_idx + 0]) / 2.0); - eta_mid[1] = mod1_dev((row[1] + row[first_init_idx + 1]) / 2.0); - eta_mid[2] = mod1_dev((row[2] + row[first_init_idx + 2]) / 2.0); - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta_mid[0]); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta_mid[1]); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta_mid[2]); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta_mid[0], span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta_mid[1], span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta_mid[2], span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta_mid[0], eta_mid[1], eta_mid[2], params, dfm)) return; - const double det_df = det3_dev(dfm); - - double bb[3], u[3], ud[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - ud_1,ud1_n2,ud1_n3, ud_2,ud2_n2,ud2_n3, ud_3,ud3_n2,ud3_n3, ud); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double tmp[9], e[3], e2[3]; - matmat_dev(norm_b_prod, b_prod, tmp); - matvec_dev(tmp, u, e); - matvec_dev(tmp, ud, e2); - for (int k = 0; k < 3; k++) e[k] = (e[k] + const_ * e2[k]) / (abs_b_star_para * det_df); - - double eta_new[3]; - eta_new[0] = row[first_init_idx + 0] - dt * e[0]; - eta_new[1] = row[first_init_idx + 1] - dt * e[1]; - eta_new[2] = row[first_init_idx + 2] - dt * e[2]; - - row[0] = alpha * eta_new[0] + (1.0 - alpha) * eta_old0; - row[1] = alpha * eta_new[1] + (1.0 - alpha) * eta_old1; - row[2] = alpha * eta_new[2] + (1.0 - alpha) * eta_old2; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu deleted file mode 100644 index 327e3f647..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu +++ /dev/null @@ -1,161 +0,0 @@ -extern "C" __global__ -void push_gc_cc_J2_stage_H1vec_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_free_idx, - const double dt_a, const double dt_b, const double last, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double bb[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); - eval_vectorfield_dev(p1,p2,p3, bn1,bn2,bn3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double tmp[9], e[3]; - matmat_dev(norm_b_prod, b_prod, tmp); - matvec_dev(tmp, u, e); - for (int k = 0; k < 3; k++) e[k] /= abs_b_star_para; - - row[first_free_idx + 0] -= dt_b * e[0]; - row[first_free_idx + 1] -= dt_b * e[1]; - row[first_free_idx + 2] -= dt_b * e[2]; - - row[0] = row[first_init_idx + 0] - dt_a * e[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] - dt_a * e[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] - dt_a * e[2] + last * row[first_free_idx + 2]; -} - -extern "C" __global__ -void push_gc_cc_J2_stage_Hdiv_cuda( - double* markers, const int n_cols, const int n_markers, - const int first_init_idx, const int first_free_idx, - const double dt_a, const double dt_b, const double last, - const int kind_map, const double* params, - const double epsilon, - const int p1, const int p2, const int p3, - const double* tn1, const int len_tn1, - const double* tn2, const int len_tn2, - const double* tn3, const int len_tn3, - const int start0, const int start1, const int start2, - const double* b_1, const int b1_n2, const int b1_n3, - const double* b_2, const int b2_n2, const int b2_n3, - const double* b_3, const int b3_n2, const int b3_n3, - const double* nb1, const int n1_n2, const int n1_n3, - const double* nb2, const int n2_n2, const int n2_n3, - const double* nb3, const int n3_n2, const int n3_n3, - const double* cnb1, const int c1_n2, const int c1_n3, - const double* cnb2, const int c2_n2, const int c2_n3, - const double* cnb3, const int c3_n2, const int c3_n3, - const double* u_1, const int u1_n2, const int u1_n3, - const double* u_2, const int u2_n2, const int u2_n3, - const double* u_3, const int u3_n2, const int u3_n3) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - - double* row = markers + (size_t)ip * n_cols; - if (row[0] == -1.0) return; - if (row[first_init_idx] == -1.0) return; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - const double v = row[3]; - - const int span1 = find_span_dev(tn1, p1, len_tn1, eta1); - const int span2 = find_span_dev(tn2, p2, len_tn2, eta2); - const int span3 = find_span_dev(tn3, p3, len_tn3, eta3); - double bn1[MAXP+1], bd1[MAXP]; - double bn2[MAXP+1], bd2[MAXP]; - double bn3[MAXP+1], bd3[MAXP]; - b_d_splines_dev(tn1, p1, eta1, span1, bn1, bd1); - b_d_splines_dev(tn2, p2, eta2, span2, bn2, bd2); - b_d_splines_dev(tn3, p3, eta3, span3, bn3, bd3); - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - const double det_df = det3_dev(dfm); - - double bb[3], u[3], norm_b1[3], curl_norm_b[3]; - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - b_1,b1_n2,b1_n3, b_2,b2_n2,b2_n3, b_3,b3_n2,b3_n3, bb); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - u_1,u1_n2,u1_n3, u_2,u2_n2,u2_n3, u_3,u3_n2,u3_n3, u); - eval_1form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - nb1,n1_n2,n1_n3, nb2,n2_n2,n2_n3, nb3,n3_n2,n3_n3, norm_b1); - eval_2form_dev(p1,p2,p3, bn1,bd1,bn2,bd2,bn3,bd3, span1,span2,span3, start0,start1,start2, - cnb1,c1_n2,c1_n3, cnb2,c2_n2,c2_n3, cnb3,c3_n2,c3_n3, curl_norm_b); - - double b_prod[9] = {0.0, -bb[2], bb[1], bb[2], 0.0, -bb[0], -bb[1], bb[0], 0.0}; - double norm_b_prod[9] = { - 0.0, -norm_b1[2], norm_b1[1], - norm_b1[2], 0.0, -norm_b1[0], - -norm_b1[1], norm_b1[0], 0.0}; - - double b_star[3]; - for (int k = 0; k < 3; k++) b_star[k] = bb[k] + curl_norm_b[k] * v * epsilon; - const double abs_b_star_para = dot3_dev(norm_b1, b_star); - - double tmp[9], e[3]; - matmat_dev(norm_b_prod, b_prod, tmp); - matvec_dev(tmp, u, e); - for (int k = 0; k < 3; k++) e[k] /= (abs_b_star_para * det_df); - - row[first_free_idx + 0] -= dt_b * e[0]; - row[first_free_idx + 1] -= dt_b * e[1]; - row[first_free_idx + 2] -= dt_b * e[2]; - - row[0] = row[first_init_idx + 0] - dt_a * e[0] + last * row[first_free_idx + 0]; - row[1] = row[first_init_idx + 1] - dt_a * e[1] + last * row[first_free_idx + 1]; - row[2] = row[first_init_idx + 2] - dt_a * e[2] + last * row[first_free_idx + 2]; -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_kernels_sph_cuda/_sph_pusher_src.cu b/src/struphy/pic/pushing/cuda/pusher_kernels_sph_cuda/_sph_pusher_src.cu deleted file mode 100644 index 8ae9c8ef7..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_kernels_sph_cuda/_sph_pusher_src.cu +++ /dev/null @@ -1,194 +0,0 @@ -// Port of struphy.pic.sph_eval_kernels.box_based_kernel: SPH sum over the 27 -// neighbouring boxes of the marker's own box. -__device__ double box_based_kernel_dev( - const double* markers, const int n_cols, - double e1, double e2, double e3, - int loc_box, - const int* boxes, const int n_box_cols, - const int* neighbours, - const int* holes, - int periodic1, int periodic2, int periodic3, - int index, int kernel_type, - double h1, double h2, double h3) -{ - if (loc_box == -1) return 0.0; - - double acc = 0.0; - for (int neigh = 0; neigh < 27; neigh++) { - int box_to_search = neighbours[loc_box * 27 + neigh]; - int c = 0; - while (boxes[(size_t)box_to_search * n_box_cols + c] != -1) { - int p = boxes[(size_t)box_to_search * n_box_cols + c]; - c++; - if (!holes[p]) { - double r1 = distance_dev(e1, markers[(size_t)p * n_cols + 0], (bool)periodic1); - double r2 = distance_dev(e2, markers[(size_t)p * n_cols + 1], (bool)periodic2); - double r3 = distance_dev(e3, markers[(size_t)p * n_cols + 2], (bool)periodic3); - acc += markers[(size_t)p * n_cols + index] - * smoothing_kernel_dev(kernel_type, r1, r2, r3, h1, h2, h3); - } - } - } - return acc; -} - -// Shared tail of all three pushers: pull the logical-space force back to -// Cartesian with DF^-T and apply it to the marker velocity. -__device__ void apply_force_dev( - double* row, double e1, double e2, double e3, - int kind_map, const double* params, - const double* force_logical, const double* gravity, - double dt) -{ - double dfm[9], dfinv[9], force_cart[3]; - if (!df_dispatch_dev(kind_map, e1, e2, e3, params, dfm)) return; - matrix_inv_dev(dfm, dfinv); - // dfinvT @ force_logical == matvecT(dfinv, force_logical) - matvecT_dev(dfinv, force_logical, force_cart); - - row[3] -= dt * (force_cart[0] - gravity[0]); - row[4] -= dt * (force_cart[1] - gravity[1]); - row[5] -= dt * (force_cart[2] - gravity[2]); -} - -// --- push_v_sph_pressure (isothermal closure) --- -extern "C" __global__ -void push_v_sph_pressure_cuda( - double* markers, const int n_cols, const int n_markers, - const int* valid_mks, - const int weight_idx, const int first_free_idx, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3, - const double* gravity, const double kappa, - const int kind_map, const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const double n_at_eta = row[first_free_idx]; - const int loc_box = (int)row[n_cols - 2]; - - double grad_u[3] = {0.0, 0.0, 0.0}; - - grad_u[0] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 1, h1, h2, h3); - grad_u[0] *= kappa / n_at_eta; - grad_u[0] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 1, h1, h2, h3); - - if (kernel_type >= 340) { - grad_u[1] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 2, h1, h2, h3); - grad_u[1] *= kappa / n_at_eta; - grad_u[1] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 2, h1, h2, h3); - } - - if (kernel_type >= 670) { - grad_u[2] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 3, h1, h2, h3); - grad_u[2] *= kappa / n_at_eta; - grad_u[2] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 1, kernel_type + 3, h1, h2, h3); - } - - apply_force_dev(row, e1, e2, e3, kind_map, params, grad_u, gravity, dt); -} - -// --- push_v_sph_pressure_ideal_gas (polytropic closure, gamma = 5/3) --- -extern "C" __global__ -void push_v_sph_pressure_ideal_gas_cuda( - double* markers, const int n_cols, const int n_markers, - const int* valid_mks, - const int weight_idx, const int first_free_idx, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3, - const double* gravity, const double kappa, - const int kind_map, const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - const double gamma = 5.0 / 3.0; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const double n_at_eta = row[first_free_idx]; - const int loc_box = (int)row[n_cols - 2]; - - const double pref = kappa * pow(n_at_eta, gamma - 2.0); - double grad_u[3] = {0.0, 0.0, 0.0}; - - grad_u[0] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 1, h1, h2, h3); - grad_u[0] *= pref; - grad_u[0] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 1, h1, h2, h3); - - if (kernel_type >= 340) { - grad_u[1] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 2, h1, h2, h3); - grad_u[1] *= pref; - grad_u[1] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 2, h1, h2, h3); - } - - if (kernel_type >= 670) { - grad_u[2] = box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, weight_idx, kernel_type + 3, h1, h2, h3); - grad_u[2] *= pref; - grad_u[2] += kappa * box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, first_free_idx + 2, kernel_type + 3, h1, h2, h3); - } - - apply_force_dev(row, e1, e2, e3, kind_map, params, grad_u, gravity, dt); -} - -// --- push_v_viscosity (deviatoric strain-rate tensor) --- -extern "C" __global__ -void push_v_viscosity_cuda( - double* markers, const int n_cols, const int n_markers, - const int* valid_mks, - const int first_free_idx, - const int* boxes, const int n_box_cols, - const int* neighbours, const int* holes, - const int periodic1, const int periodic2, const int periodic3, - const int kernel_type, - const double h1, const double h2, const double h3, - const int kind_map, const double* params, - const double dt) -{ - int ip = blockIdx.x * blockDim.x + threadIdx.x; - if (ip >= n_markers) return; - if (!valid_mks[ip]) return; - - double* row = markers + (size_t)ip * n_cols; - const double e1 = row[0], e2 = row[1], e3 = row[2]; - const int loc_box = (int)row[n_cols - 2]; - - double f_visc[3] = {0.0, 0.0, 0.0}; - for (int j = 0; j < 3; j++) { - for (int k = 0; k < 3; k++) { - const int coeff_idx = first_free_idx + 3 * (j + 1) + k; - f_visc[j] += box_based_kernel_dev(markers, n_cols, e1, e2, e3, loc_box, boxes, n_box_cols, - neighbours, holes, periodic1, periodic2, periodic3, - coeff_idx, kernel_type + 1 + k, h1, h2, h3); - } - } - - const double no_gravity[3] = {0.0, 0.0, 0.0}; - apply_force_dev(row, e1, e2, e3, kind_map, params, f_visc, no_gravity, dt); -} - diff --git a/src/struphy/pic/pushing/cuda/pusher_utilities_kernels_cuda/_reflect_src.cu b/src/struphy/pic/pushing/cuda/pusher_utilities_kernels_cuda/_reflect_src.cu deleted file mode 100644 index 692e42b60..000000000 --- a/src/struphy/pic/pushing/cuda/pusher_utilities_kernels_cuda/_reflect_src.cu +++ /dev/null @@ -1,36 +0,0 @@ -extern "C" __global__ -void reflect_cuda( - double* markers, const int n_cols, - const long long* outside_inds, const int n_outside, - const int axis, - const int kind_map, const double* params) -{ - int i = blockIdx.x * blockDim.x + threadIdx.x; - if (i >= n_outside) return; - - const long long ip = outside_inds[i]; - double* row = markers + (size_t)ip * n_cols; - - const double eta1 = row[0], eta2 = row[1], eta3 = row[2]; - double v[3] = {row[3], row[4], row[5]}; - - double dfm[9]; - if (!df_dispatch_dev(kind_map, eta1, eta2, eta3, params, dfm)) return; - - double dfinv[9], v_logical[3]; - matrix_inv_dev(dfm, dfinv); - - // pull back of the velocity - matvec_dev(dfinv, v, v_logical); - - // reverse the velocity component along `axis` - v_logical[axis] *= -1.0; - - // push forward of the velocity - matvec_dev(dfm, v_logical, v); - - row[3] = v[0]; - row[4] = v[1]; - row[5] = v[2]; -} - diff --git a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py b/src/struphy/pic/pushing/eval_kernels_gc_cuda.py deleted file mode 100644 index d80fa1a06..000000000 --- a/src/struphy/pic/pushing/eval_kernels_gc_cuda.py +++ /dev/null @@ -1,365 +0,0 @@ -"""Hand-written CUDA replacement for -:func:`~struphy.pic.pushing.eval_kernels_gc.driftkinetic_hamiltonian`, used -only under ``ARRAY_BACKEND=cupy``. - -This is the ``eval_kernel`` of the discrete-gradient guiding-centre -propagators: it is re-run on *every* Picard iteration of every RK stage (89 -calls in a 3-step ``LinearMHDDriftkineticCC`` run), writing the Hamiltonian -at the weighted evaluation point into one marker column. With markers -device-resident, leaving it on the host would cost a full marker round trip -per iteration -- by far the most frequent host crossing left in that model. - -It is a plain per-marker 0-form spline evaluation, so it reuses the shared -``find_span_dev``/``b_splines_dev``/``eval_0form_dev`` device functions. -""" -from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source - -_DK_HAMILTONIAN_SRC = load_cuda_source(__file__, "eval_kernels_gc_cuda/_dk_hamiltonian_src.cu") -_dk_kernel = CudaKernel(_DK_HAMILTONIAN_SRC, "driftkinetic_hamiltonian_cuda") - - -def driftkinetic_hamiltonian_gpu( - markers, - alpha, - column_nr, - first_init_idx, - first_shift_idx, - mu_idx, - args_derham, - epsilon, - B_dot_b_coeffs, - phi_coeffs, - evaluate_e_field, -): - """GPU replacement for - :func:`~struphy.pic.pushing.eval_kernels_gc.driftkinetic_hamiltonian`. - ``markers`` is device-resident and written in place. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - bdb = cp.ascontiguousarray(B_dot_b_coeffs) - phi = cp.ascontiguousarray(phi_coeffs) - a = [float(x) for x in (alpha[0], alpha[1], alpha[2], alpha[3])] - - launch_1d( - _dk_kernel, - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(column_nr), - np.int32(first_init_idx), - np.int32(first_shift_idx), - np.int32(mu_idx), - np.float64(a[0]), - np.float64(a[1]), - np.float64(a[2]), - np.float64(a[3]), - np.float64(epsilon), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - bdb, - np.int32(bdb.shape[1]), - np.int32(bdb.shape[2]), - phi, - np.int32(phi.shape[1]), - np.int32(phi.shape[2]), - np.int32(bool(evaluate_e_field)), - ), - ) - - -# --------------------------------------------------------------------------- -# grad_driftkinetic_hamiltonian / bstar_parallel_3form / bstar_2form / -# unit_b_1form: the remaining marker-column init/eval kernels of the -# discrete-gradient guiding-centre propagators. Unlike driftkinetic_hamiltonian -# above (self-contained 0-form-only source), these also need 1-/2-form -# evaluation and (for bstar_parallel_3form) the domain Jacobian, so they are -# built from pusher_kernels_cuda._GENERAL_GEOMETRY_SRC instead. All four -# share the same alpha-weighted evaluation point -# eta_i = mod(alpha_i * (eta_i + shift_i) + (1 - alpha_i) * eta_i^n, 1) -# (and, for the two that need v_parallel, the same alpha-weighted v), factored -# into one device helper. -# --------------------------------------------------------------------------- - -_GC_MARKER_COLUMN_SRC = load_cuda_source(__file__, "eval_kernels_gc_cuda/_gc_marker_column_src.cu") - - -def _gc_marker_column_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _GC_MARKER_COLUMN_SRC - - -_gc_marker_column_kernels = CudaKernelSet(_gc_marker_column_source) - - -def grad_driftkinetic_hamiltonian_gpu( - markers, - alpha, - column_nr, - comps, - first_init_idx, - first_shift_idx, - mu_idx, - args_derham, - epsilon, - grad_b_full_coeffs, - e_field_coeffs, - evaluate_e_field, -): - """GPU replacement for - :func:`~struphy.pic.pushing.eval_kernels_gc.grad_driftkinetic_hamiltonian`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - alpha_dev = cp.asarray(np.asarray(alpha, dtype=np.float64)) - comps_dev = cp.asarray(np.asarray(comps, dtype=np.int32)) - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - - launch_1d( - _gc_marker_column_kernels["grad_driftkinetic_hamiltonian_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(column_nr), - np.int32(comps_dev.shape[0]), - comps_dev, - np.int32(first_init_idx), - np.int32(first_shift_idx), - np.int32(mu_idx), - alpha_dev, - np.float64(epsilon), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - *d(grad_b_full_coeffs[0]), - *d(grad_b_full_coeffs[1]), - *d(grad_b_full_coeffs[2]), - *d(e_field_coeffs[0]), - *d(e_field_coeffs[1]), - *d(e_field_coeffs[2]), - np.int32(bool(evaluate_e_field)), - ), - ) - - -def bstar_parallel_3form_gpu( - markers, - alpha, - column_nr, - first_init_idx, - first_shift_idx, - kind_map, - params_dev, - args_derham, - epsilon, - B_dot_b_coeffs, - curl_unit_b_dot_b0_coeffs, -): - """GPU replacement for - :func:`~struphy.pic.pushing.eval_kernels_gc.bstar_parallel_3form`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - alpha_dev = cp.asarray(np.asarray(alpha, dtype=np.float64)) - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - - launch_1d( - _gc_marker_column_kernels["bstar_parallel_3form_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(column_nr), - np.int32(first_init_idx), - np.int32(first_shift_idx), - alpha_dev, - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - *d(B_dot_b_coeffs), - *d(curl_unit_b_dot_b0_coeffs), - ), - ) - - -def bstar_2form_gpu( - markers, - alpha, - column_nr, - comps, - first_init_idx, - first_shift_idx, - args_derham, - epsilon, - b2_coeffs, - curl_unit_b2_coeffs, -): - """GPU replacement for - :func:`~struphy.pic.pushing.eval_kernels_gc.bstar_2form`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - alpha_dev = cp.asarray(np.asarray(alpha, dtype=np.float64)) - comps_dev = cp.asarray(np.asarray(comps, dtype=np.int32)) - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - - launch_1d( - _gc_marker_column_kernels["bstar_2form_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(column_nr), - np.int32(comps_dev.shape[0]), - comps_dev, - np.int32(first_init_idx), - np.int32(first_shift_idx), - alpha_dev, - np.float64(epsilon), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - *d(b2_coeffs[0]), - *d(b2_coeffs[1]), - *d(b2_coeffs[2]), - *d(curl_unit_b2_coeffs[0]), - *d(curl_unit_b2_coeffs[1]), - *d(curl_unit_b2_coeffs[2]), - ), - ) - - -def unit_b_1form_gpu( - markers, - alpha, - column_nr, - comps, - first_init_idx, - first_shift_idx, - args_derham, - unit_b1_coeffs, -): - """GPU replacement for - :func:`~struphy.pic.pushing.eval_kernels_gc.unit_b_1form`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - alpha_dev = cp.asarray(np.asarray(alpha, dtype=np.float64)) - comps_dev = cp.asarray(np.asarray(comps, dtype=np.int32)) - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - - launch_1d( - _gc_marker_column_kernels["unit_b_1form_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(column_nr), - np.int32(comps_dev.shape[0]), - comps_dev, - np.int32(first_init_idx), - np.int32(first_shift_idx), - alpha_dev, - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - *d(unit_b1_coeffs[0]), - *d(unit_b1_coeffs[1]), - *d(unit_b1_coeffs[2]), - ), - ) diff --git a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py b/src/struphy/pic/pushing/eval_kernels_sph_cuda.py deleted file mode 100644 index 7a1592aca..000000000 --- a/src/struphy/pic/pushing/eval_kernels_sph_cuda.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Hand-written CUDA replacements for the SPH marker-column kernels in -:mod:`~struphy.pic.pushing.eval_kernels_sph`, used only under -``ARRAY_BACKEND=cupy``. - -Like the SPH velocity pushers in -:mod:`~struphy.pic.pushing.pusher_kernels_sph_cuda`, these are per-marker -loops whose inner work is one or more box-neighbourhood SPH sums, i.e. the -same computation as :func:`~struphy.pic.sph_eval_kernels.box_based_kernel`. -Rather than duplicating that device function, this module reuses -``box_based_kernel_dev`` from :mod:`~struphy.pic.pushing.pusher_kernels_sph_cuda` -(which itself reuses ``distance_dev``/``smoothing_kernel_dev`` from -:mod:`~struphy.pic.sph_eval_kernels_cuda` and ``df_dispatch_dev``/ -``matrix_inv_dev`` from :mod:`~struphy.pic.pushing.pusher_kernels_cuda`, -though the two geometry helpers are unused here since none of these three -kernels touch the domain Jacobian). - -The three ``*_gpu`` entry points share one :class:`~struphy.cuda.CudaKernelSet` -(``_sph_marker_column_kernels``, keyed by CUDA entry-point name). -""" -from struphy.cuda import CudaKernelSet, launch_1d, load_cuda_source - -_SPH_MARKER_COLUMN_SRC = load_cuda_source(__file__, "eval_kernels_sph_cuda/_sph_marker_column_src.cu") - - -def _source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - from struphy.pic.pushing.pusher_kernels_sph_cuda import _SPH_PUSHER_SRC - from struphy.pic.sph_eval_kernels_cuda import _SPH_EVAL_FLAT_SRC - - return _GENERAL_GEOMETRY_SRC + _SPH_EVAL_FLAT_SRC + _SPH_PUSHER_SRC + _SPH_MARKER_COLUMN_SRC - - -_sph_marker_column_kernels = CudaKernelSet(_source) - - -def _sph_marker_column_launch( - name, - markers, - valid_mks, - column_nr, - weight_idx, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - *, - first_free_idx=None, - mu=None, -): - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - dev_valid = cp.ascontiguousarray(cp.asarray(valid_mks).astype(cp.int32, copy=False)) - dev_boxes = cp.ascontiguousarray(cp.asarray(boxes).astype(cp.int32, copy=False)) - dev_neigh = cp.ascontiguousarray(cp.asarray(neighbours).astype(cp.int32, copy=False)) - dev_holes = cp.ascontiguousarray(cp.asarray(holes).astype(cp.int32, copy=False)) - - args = [ - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(column_nr), - np.int32(weight_idx), - ] - if first_free_idx is not None: - args.append(np.int32(first_free_idx)) - args += [ - dev_valid, - dev_boxes, - np.int32(dev_boxes.shape[1]), - dev_neigh, - dev_holes, - np.int32(bool(periodic[0])), - np.int32(bool(periodic[1])), - np.int32(bool(periodic[2])), - np.int32(kernel_type), - np.float64(h[0]), - np.float64(h[1]), - np.float64(h[2]), - ] - if mu is not None: - args.append(np.float64(mu)) - - launch_1d(_sph_marker_column_kernels[name], n_markers, args) - - -def sph_pressure_coeffs_gpu( - markers, valid_mks, column_nr, weight_idx, boxes, neighbours, holes, periodic, kernel_type, h -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.eval_kernels_sph.sph_pressure_coeffs`.""" - _sph_marker_column_launch( - "sph_pressure_coeffs_cuda", - markers, - valid_mks, - column_nr, - weight_idx, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - ) - - -def sph_mean_velocity_coeffs_gpu( - markers, valid_mks, column_nr, weight_idx, boxes, neighbours, holes, periodic, kernel_type, h -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.eval_kernels_sph.sph_mean_velocity_coeffs`.""" - _sph_marker_column_launch( - "sph_mean_velocity_coeffs_cuda", - markers, - valid_mks, - column_nr, - weight_idx, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - ) - - -def sph_viscosity_tensor_gpu( - markers, - valid_mks, - column_nr, - weight_idx, - first_free_idx, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - mu, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.eval_kernels_sph.sph_viscosity_tensor`.""" - _sph_marker_column_launch( - "sph_viscosity_tensor_cuda", - markers, - valid_mks, - column_nr, - weight_idx, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - first_free_idx=first_free_idx, - mu=mu, - ) diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 90b1c7588..11304bf52 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -11,56 +11,10 @@ from struphy.kernel_arguments.pusher_args_kernels import DerhamArguments, DomainArguments from struphy.pic.base import Particles -from struphy.pic.pushing.eval_kernels_gc_cuda import ( - bstar_2form_gpu, - bstar_parallel_3form_gpu, - driftkinetic_hamiltonian_gpu, - grad_driftkinetic_hamiltonian_gpu, - unit_b_1form_gpu, -) -from struphy.pic.pushing.eval_kernels_sph_cuda import ( - sph_mean_velocity_coeffs_gpu, - sph_pressure_coeffs_gpu, - sph_viscosity_tensor_gpu, -) from struphy.pic.pushing.pusher_kernels_cuda import ( SUPPORTED_GENERAL_KIND_MAPS, - push_bxu_H1vec_general_gpu, - push_bxu_Hcurl_general_gpu, - push_bxu_Hdiv_general_gpu, - push_deterministic_diffusion_stage_general_gpu, - push_eta_rk_periodic_gpu, - push_eta_stage_cuboid_gpu, - push_eta_stage_general_gpu, - push_pc_eta_stage_H1vec_general_gpu, - push_pc_eta_stage_Hcurl_general_gpu, - push_pc_eta_stage_Hdiv_general_gpu, - push_pc_GXu_full_general_gpu, - push_pc_GXu_general_gpu, - push_random_diffusion_stage_gpu, push_v_with_efield_cuboid_gpu, push_v_with_efield_general_gpu, - push_vxb_analytic_general_gpu, - push_vxb_implicit_general_gpu, - push_weights_with_efield_lin_va_general_gpu, -) -from struphy.pic.pushing.pusher_kernels_gc_cuda import ( - push_gc_Bstar_discrete_gradient_1st_order_gpu, - push_gc_Bstar_discrete_gradient_1st_order_newton_gpu, - push_gc_Bstar_discrete_gradient_2nd_order_gpu, - push_gc_Bstar_explicit_multistage_general_gpu, - push_gc_bxEstar_discrete_gradient_1st_order_gpu, - push_gc_bxEstar_discrete_gradient_1st_order_newton_gpu, - push_gc_bxEstar_discrete_gradient_2nd_order_gpu, - push_gc_bxEstar_explicit_multistage_general_gpu, - push_gc_cc_J1_H1vec_gpu, - push_gc_cc_J1_Hcurl_gpu, - push_gc_cc_J1_Hdiv_gpu, -) -from struphy.pic.pushing.pusher_kernels_sph_cuda import ( - push_v_sph_pressure_gpu, - push_v_sph_pressure_ideal_gas_gpu, - push_v_viscosity_gpu, ) logger = logging.getLogger("struphy") @@ -176,30 +130,6 @@ def __init__( self._args_kernel = args_kernel self._args_domain = args_domain - # hand-written CUDA replacement for push_eta_stage on a Cuboid domain - # (constant, diagonal Jacobian -> no spline evaluation needed at all) - self._gpu_eta_cuboid = cunumpy.cupy_backend and kernel.name == "push_eta_stage" and args_domain.kind_map == 10 - if self._gpu_eta_cuboid: - l1, r1, l2, r2, l3, r3 = (float(p) for p in args_domain.params[:6]) - self._gpu_eta_cuboid_scale = (1.0 / (r1 - l1), 1.0 / (r2 - l2), 1.0 / (r3 - l3)) - - # general (non-Cuboid) CUDA replacement for push_eta_stage: evaluates - # DF(eta) per marker instead of assuming it's constant, so it covers - # any domain in SUPPORTED_GENERAL_KIND_MAPS -- currently Cuboid and - # Colella, see pusher_kernels_cuda.py. Only used when the more - # specialized _gpu_eta_cuboid path above doesn't already apply. - self._gpu_eta_general = ( - cunumpy.cupy_backend - and kernel.name == "push_eta_stage" - and not self._gpu_eta_cuboid - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_eta_general: - import cupy as cp - - self._gpu_eta_general_kind_map = int(args_domain.kind_map) - self._gpu_eta_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - # determines the evaluation points for kernel self._alpha_in_kernel = alpha_in_kernel self._n_stages = n_stages @@ -249,29 +179,12 @@ def __init__( else: self._box_comm = False - # whole-push GPU-resident fast path: on top of _gpu_eta_cuboid, also - # requires an all-periodic bc (so apply_kinetic_bc reduces to wrap + - # shift bookkeeping, which push_eta_rk_periodic_gpu fuses in) and no - # MPI / iterative-solver / eval-kernel machinery (none of which - # push_eta uses, but other Pusher users might). - self._gpu_eta_cuboid_periodic = ( - self._gpu_eta_cuboid - and all(b == "periodic" for b in self.particles.bc) - and not self._init_kernels - and not self._eval_kernels - and self.particles.mpi_comm is None - and self._maxiter == 1 - and not self._newton - ) - # hand-written CUDA replacement for push_v_with_efield's per-marker # math on a Cuboid domain. Unlike the whole-push fast path below, this # is unconditional on MPI/bc/maxiter -- it only swaps out the inner - # kernel call (see the "push markers" branch in _push(), mirroring - # how _gpu_eta_cuboid is used there), so it stays correct alongside - # unmodified apply_kinetic_bc/mpi_sort_markers/update_holes for - # multi-rank runs, exactly like _gpu_eta_cuboid already does for - # push_eta_stage. + # kernel call (see the "push markers" branch in _push()), so it stays + # correct alongside unmodified apply_kinetic_bc/mpi_sort_markers/ + # update_holes for multi-rank runs. self._gpu_v_efield_cuboid = ( cunumpy.cupy_backend and kernel.name == "push_v_with_efield" and args_domain.kind_map == 10 ) @@ -299,8 +212,7 @@ def __init__( # general (non-Cuboid) CUDA replacement for push_v_with_efield: same # B-spline evaluation as _gpu_v_efield_cuboid, but with DF(eta) - # evaluated per marker instead of assumed constant-diagonal -- see - # _gpu_eta_general above. + # evaluated per marker instead of assumed constant-diagonal. self._gpu_v_efield_general = ( cunumpy.cupy_backend and kernel.name == "push_v_with_efield" @@ -332,8 +244,7 @@ def __init__( # additionally bypasses the per-call reset/apply_kinetic_bc/ # update_holes machinery entirely (this kernel never touches position # or holes/ghost columns, so that machinery is a no-op for it -- but - # only provably so under the same conditions as - # _gpu_eta_cuboid_periodic: no MPI, since mpi_sort_markers does real + # only provably so with no MPI, since mpi_sort_markers does real # host-side communication we can't just skip). self._gpu_v_efield_cuboid_wholepush = ( self._gpu_v_efield_cuboid @@ -346,536 +257,6 @@ def __init__( and n_stages == 1 ) - # general (non-Cuboid) CUDA replacement for push_vxb_analytic / - # push_vxb_implicit, sharing the same B-spline/geometry evaluation as - # _gpu_v_efield_general above (2-form instead of 1-form field). - self._gpu_vxb_general = ( - cunumpy.cupy_backend - and kernel.name - in ( - "push_vxb_analytic", - "push_vxb_implicit", - ) - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_vxb_general: - import cupy as cp - - self._gpu_vxb_general_analytic = kernel.name == "push_vxb_analytic" - self._gpu_vxb_general_kind_map = int(args_domain.kind_map) - self._gpu_vxb_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - - args_derham, b2_1, b2_2, b2_3 = args_kernel - self._gpu_vxb_general_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_vxb_general_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_vxb_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_vxb_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_vxb_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - # FE coefficients are already device-resident under CuPy, see - # _gpu_v_efield_cuboid above. Unlike push_v_with_efield's e1_*, - # b2_* here can be *reassigned* between calls (PushVxB.allocate() - # rebuilds self._b_full = b2_0 (+ b2_var) once per allocate(), but - # __call__ does not touch the underlying StencilVector objects - # again after that), so caching the references once here is - # still valid for the propagator's lifetime. - self._gpu_vxb_general_b2_1 = b2_1 - self._gpu_vxb_general_b2_2 = b2_2 - self._gpu_vxb_general_b2_3 = b2_3 - - # general (non-Cuboid) CUDA replacement for push_bxu_{Hdiv,Hcurl,H1vec}, - # sharing the same B-field (2-form) evaluation as _gpu_vxb_general; - # only the U-field's FEEC space (and therefore its evaluation/metric - # handling) differs between the three. - self._gpu_bxu_general = ( - cunumpy.cupy_backend - and kernel.name - in ( - "push_bxu_Hdiv", - "push_bxu_Hcurl", - "push_bxu_H1vec", - ) - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_bxu_general: - import cupy as cp - - self._gpu_bxu_general_variant = kernel.name - self._gpu_bxu_general_kind_map = int(args_domain.kind_map) - self._gpu_bxu_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - - args_derham, b2_1, b2_2, b2_3, u_1, u_2, u_3, boundary_cut = args_kernel - self._gpu_bxu_general_boundary_cut = float(boundary_cut) - self._gpu_bxu_general_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_bxu_general_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_bxu_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_bxu_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_bxu_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - # FE coefficients are device-resident under CuPy, see - # _gpu_v_efield_cuboid above. - self._gpu_bxu_general_b2_1 = b2_1 - self._gpu_bxu_general_b2_2 = b2_2 - self._gpu_bxu_general_b2_3 = b2_3 - self._gpu_bxu_general_u_1 = u_1 - self._gpu_bxu_general_u_2 = u_2 - self._gpu_bxu_general_u_3 = u_3 - - # general (non-Cuboid) CUDA replacement for push_pc_GXu_full / - # push_pc_GXu: the propagator (PressureCoupling6D) always builds the - # full 9-array args_kernel regardless of which of the two it uses - # (push_pc_GXu's CPU kernel also takes all 9, only 6 are read) -- so - # both branches cache the same 9 g_ij arrays and the *_full variant - # is picked purely by kernel.name. - self._gpu_pc_gxu_general = ( - cunumpy.cupy_backend - and kernel.name - in ( - "push_pc_GXu_full", - "push_pc_GXu", - ) - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_pc_gxu_general: - import cupy as cp - - self._gpu_pc_gxu_general_full = kernel.name == "push_pc_GXu_full" - self._gpu_pc_gxu_general_kind_map = int(args_domain.kind_map) - self._gpu_pc_gxu_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - - args_derham, g11, g12, g13, g21, g22, g23, g31, g32, g33 = args_kernel - self._gpu_pc_gxu_general_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_pc_gxu_general_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_pc_gxu_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_pc_gxu_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_pc_gxu_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_pc_gxu_general_g = (g11, g12, g13, g21, g22, g23, g31, g32, g33) - - # general (non-Cuboid) CUDA replacement for - # push_pc_eta_stage_{Hcurl,Hdiv,H1vec}: a variant of _gpu_eta_general - # with an extra U-field vector contribution added to the eta rate. - self._gpu_pc_eta_general = ( - cunumpy.cupy_backend - and kernel.name - in ( - "push_pc_eta_stage_Hcurl", - "push_pc_eta_stage_Hdiv", - "push_pc_eta_stage_H1vec", - ) - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_pc_eta_general: - import cupy as cp - - self._gpu_pc_eta_general_variant = kernel.name - self._gpu_pc_eta_general_kind_map = int(args_domain.kind_map) - self._gpu_pc_eta_general_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - - args_derham, u_1, u_2, u_3, use_perp_model = args_kernel[:5] - self._gpu_pc_eta_general_use_perp_model = bool(use_perp_model) - self._gpu_pc_eta_general_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_pc_eta_general_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_pc_eta_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_pc_eta_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_pc_eta_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_pc_eta_general_u_1 = u_1 - self._gpu_pc_eta_general_u_2 = u_2 - self._gpu_pc_eta_general_u_3 = u_3 - - # general (non-Cuboid) CUDA replacement for - # push_weights_with_efield_lin_va. Unlike the FE-coefficient - # arguments cached above, f0_values is recomputed by the caller - # every step, in place (self._f0_values[:] = ...) -- so the - # reference can be cached once here like the other FE-coefficient - # arguments (see push_weights_with_efield_lin_va_general_gpu's - # docstring). - self._gpu_weights_efield_general = ( - cunumpy.cupy_backend - and kernel.name == "push_weights_with_efield_lin_va" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_weights_efield_general: - import cupy as cp - - self._gpu_weights_efield_general_kind_map = int(args_domain.kind_map) - self._gpu_weights_efield_general_params = cp.asarray( - np.asarray(args_domain.params, dtype=float), dtype=cp.float64 - ) - - args_derham, e1_1, e1_2, e1_3, f0_values, kappa, vth = args_kernel - self._gpu_weights_efield_general_kappa = float(kappa) - self._gpu_weights_efield_general_vth = float(vth) - self._gpu_weights_efield_general_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_weights_efield_general_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_weights_efield_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_weights_efield_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_weights_efield_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_weights_efield_general_e1_1 = e1_1 - self._gpu_weights_efield_general_e1_2 = e1_2 - self._gpu_weights_efield_general_e1_3 = e1_3 - self._gpu_weights_efield_general_f0_values = f0_values - - # general (non-Cuboid) CUDA replacement for - # push_deterministic_diffusion_stage. - self._gpu_det_diffusion_general = ( - cunumpy.cupy_backend - and kernel.name == "push_deterministic_diffusion_stage" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_det_diffusion_general: - import cupy as cp - - self._gpu_det_diffusion_general_kind_map = int(args_domain.kind_map) - self._gpu_det_diffusion_general_params = cp.asarray( - np.asarray(args_domain.params, dtype=float), dtype=cp.float64 - ) - - args_derham, pi_u, pi_grad_u1, pi_grad_u2, pi_grad_u3, diffusion_coeff = args_kernel[:6] - self._gpu_det_diffusion_general_coeff = float(diffusion_coeff) - self._gpu_det_diffusion_general_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_det_diffusion_general_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_det_diffusion_general_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_det_diffusion_general_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_det_diffusion_general_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_det_diffusion_general_pi_u = pi_u - self._gpu_det_diffusion_general_pi_grad_u1 = pi_grad_u1 - self._gpu_det_diffusion_general_pi_grad_u2 = pi_grad_u2 - self._gpu_det_diffusion_general_pi_grad_u3 = pi_grad_u3 - - # CUDA replacement for push_random_diffusion_stage: domain-independent - # (pure additive noise, no geometry), so no kind_map restriction. - self._gpu_random_diffusion = cunumpy.cupy_backend and kernel.name == "push_random_diffusion_stage" - if self._gpu_random_diffusion: - noise, diffusion_coeff = args_kernel[0], args_kernel[1] - self._gpu_random_diffusion_coeff = float(diffusion_coeff) - self._gpu_random_diffusion_noise = noise - - # general (non-Cuboid) CUDA replacement for - # push_gc_bxEstar_explicit_multistage (5D guiding-center pusher). - self._gpu_gc_bxestar_general = ( - cunumpy.cupy_backend - and kernel.name == "push_gc_bxEstar_explicit_multistage" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_gc_bxestar_general: - import cupy as cp - - self._gpu_gc_bxestar_kind_map = int(args_domain.kind_map) - self._gpu_gc_bxestar_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - ( - args_derham, - epsilon, - unit_b1_1, - unit_b1_2, - unit_b1_3, - grad_b_full_1, - grad_b_full_2, - grad_b_full_3, - B_dot_b_coeffs, - curl_unit_b_dot_b0, - e_field_1, - e_field_2, - e_field_3, - evaluate_e_field, - ) = args_kernel[:14] - self._gpu_gc_bxestar_epsilon = float(epsilon) - self._gpu_gc_bxestar_evaluate_e_field = bool(evaluate_e_field) - self._gpu_gc_bxestar_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_gc_bxestar_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_gc_bxestar_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_gc_bxestar_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_gc_bxestar_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_gc_bxestar_unit_b1 = (unit_b1_1, unit_b1_2, unit_b1_3) - self._gpu_gc_bxestar_grad_b_full = (grad_b_full_1, grad_b_full_2, grad_b_full_3) - self._gpu_gc_bxestar_B_dot_b_coeffs = B_dot_b_coeffs - self._gpu_gc_bxestar_curl_unit_b_dot_b0 = curl_unit_b_dot_b0 - self._gpu_gc_bxestar_e_field = (e_field_1, e_field_2, e_field_3) - self._gpu_gc_bxestar_mu_idx = int(particles.mu_idx) - - # general (non-Cuboid) CUDA replacement for - # push_gc_Bstar_explicit_multistage (5D guiding-center pusher). - self._gpu_gc_bstar_general = ( - cunumpy.cupy_backend - and kernel.name == "push_gc_Bstar_explicit_multistage" - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_gc_bstar_general: - import cupy as cp - - self._gpu_gc_bstar_kind_map = int(args_domain.kind_map) - self._gpu_gc_bstar_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - ( - args_derham, - epsilon, - grad_b_full_1, - grad_b_full_2, - grad_b_full_3, - b2_1, - b2_2, - b2_3, - curl_unit_b2_1, - curl_unit_b2_2, - curl_unit_b2_3, - B_dot_b_coeffs, - curl_unit_b_dot_b0, - e_field_1, - e_field_2, - e_field_3, - evaluate_e_field, - ) = args_kernel[:17] - self._gpu_gc_bstar_epsilon = float(epsilon) - self._gpu_gc_bstar_evaluate_e_field = bool(evaluate_e_field) - self._gpu_gc_bstar_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_gc_bstar_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_gc_bstar_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_gc_bstar_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_gc_bstar_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_gc_bstar_grad_b_full = (grad_b_full_1, grad_b_full_2, grad_b_full_3) - self._gpu_gc_bstar_b2 = (b2_1, b2_2, b2_3) - self._gpu_gc_bstar_curl_unit_b2 = (curl_unit_b2_1, curl_unit_b2_2, curl_unit_b2_3) - self._gpu_gc_bstar_B_dot_b_coeffs = B_dot_b_coeffs - self._gpu_gc_bstar_curl_unit_b_dot_b0 = curl_unit_b_dot_b0 - self._gpu_gc_bstar_e_field = (e_field_1, e_field_2, e_field_3) - self._gpu_gc_bstar_mu_idx = int(particles.mu_idx) - - # CUDA replacements for the three SPH velocity pushers. Their inner - # work is the box-neighbourhood SPH sum (see - # pusher_kernels_sph_cuda.box_based_kernel_dev); boxes/neighbours are - # host-owned by SortingBoxes and uploaded per call, everything else is - # already device-resident. - self._gpu_sph_pusher = ( - cunumpy.cupy_backend - and kernel.name in ("push_v_sph_pressure", "push_v_sph_pressure_ideal_gas", "push_v_viscosity") - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_sph_pusher: - import cupy as cp - - self._gpu_sph_name = kernel.name - self._gpu_sph_kind_map = int(args_domain.kind_map) - self._gpu_sph_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - if kernel.name == "push_v_viscosity": - (boxes, neighbours, holes, per1, per2, per3, kernel_nr, h1, h2, h3) = args_kernel - self._gpu_sph_gravity = None - self._gpu_sph_kappa = None - else: - ( - boxes, - neighbours, - holes, - per1, - per2, - per3, - kernel_nr, - h1, - h2, - h3, - gravity, - kappa, - ) = args_kernel - # `gravity` arrives in the propagator's kernel args and so already lives on - # the active backend: under CuPy np.asarray on it raises rather than - # transferring. cp.asarray takes host and device input alike. - self._gpu_sph_gravity = cp.asarray(gravity, dtype=cp.float64) - self._gpu_sph_kappa = float(kappa) - self._gpu_sph_boxes = boxes - self._gpu_sph_neighbours = neighbours - self._gpu_sph_holes = holes - self._gpu_sph_periodic = (bool(per1), bool(per2), bool(per3)) - self._gpu_sph_kernel_nr = int(kernel_nr) - self._gpu_sph_h = (float(h1), float(h2), float(h3)) - - # CUDA replacements for the 1st-order discrete-gradient guiding-centre - # pushers. Each call is ONE Picard iteration (the fixed-point loop is - # the `while` in _push below), so they are per-marker parallel like the - # explicit pushers; no domain Jacobian is needed, the Poisson-matrix - # pieces come from marker columns filled by the init/eval kernels. - self._gpu_gc_dg1 = cunumpy.cupy_backend and kernel.name in ( - "push_gc_bxEstar_discrete_gradient_1st_order", - "push_gc_Bstar_discrete_gradient_1st_order", - ) - if self._gpu_gc_dg1: - import cupy as cp - - self._gpu_gc_dg1_name = kernel.name - ( - args_derham, - epsilon, - gb1, - gb2, - gb3, - ef1, - ef2, - ef3, - evaluate_e_field, - ) = args_kernel[:9] - self._gpu_gc_dg1_epsilon = float(epsilon) - self._gpu_gc_dg1_eval_e = bool(evaluate_e_field) - self._gpu_gc_dg1_pn = tuple(int(x) for x in args_derham.pn) - self._gpu_gc_dg1_starts = tuple(int(x) for x in args_derham.starts) - self._gpu_gc_dg1_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_gc_dg1_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_gc_dg1_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_gc_dg1_gb = (gb1, gb2, gb3) - self._gpu_gc_dg1_ef = (ef1, ef2, ef3) - self._gpu_gc_dg1_mu_idx = int(particles.mu_idx) - - # CUDA replacements for the discrete-gradient GC Newton pushers. Each - # call is ONE Newton iteration (again per-marker parallel, no domain - # Jacobian), reading marker columns filled by driftkinetic_hamiltonian/ - # grad_driftkinetic_hamiltonian eval_kernels (also CUDA-ported above). - self._gpu_gc_dg1_newton = cunumpy.cupy_backend and kernel.name in ( - "push_gc_bxEstar_discrete_gradient_1st_order_newton", - "push_gc_Bstar_discrete_gradient_1st_order_newton", - ) - if self._gpu_gc_dg1_newton: - import cupy as cp - - self._gpu_gc_dg1n_name = kernel.name - ( - args_derham, - epsilon, - gb1, - gb2, - gb3, - B_dot_b, - ef1, - ef2, - ef3, - phi, - evaluate_e_field, - ) = args_kernel[:11] - self._gpu_gc_dg1n_epsilon = float(epsilon) - self._gpu_gc_dg1n_eval_e = bool(evaluate_e_field) - self._gpu_gc_dg1n_pn = tuple(int(x) for x in args_derham.pn) - self._gpu_gc_dg1n_starts = tuple(int(x) for x in args_derham.starts) - self._gpu_gc_dg1n_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_gc_dg1n_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_gc_dg1n_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_gc_dg1n_gb = (gb1, gb2, gb3) - self._gpu_gc_dg1n_bdb = B_dot_b - self._gpu_gc_dg1n_ef = (ef1, ef2, ef3) - self._gpu_gc_dg1n_phi = phi - self._gpu_gc_dg1n_mu_idx = int(particles.mu_idx) - - # CUDA replacements for the Gonzalez discrete-gradient GC pushers - # (one Picard iteration per call, evaluated at the midpoint -- needs - # DF(eta_mid), so restricted to SUPPORTED_GENERAL_KIND_MAPS like the - # other Jacobian-dependent GC kernels). - self._gpu_gc_dg2 = ( - cunumpy.cupy_backend - and kernel.name - in ( - "push_gc_bxEstar_discrete_gradient_2nd_order", - "push_gc_Bstar_discrete_gradient_2nd_order", - ) - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_gc_dg2: - import cupy as cp - - self._gpu_gc_dg2_name = kernel.name - self._gpu_gc_dg2_kind_map = int(args_domain.kind_map) - self._gpu_gc_dg2_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - if kernel.name == "push_gc_bxEstar_discrete_gradient_2nd_order": - ( - args_derham, - epsilon, - ub1, - ub2, - ub3, - gb1, - gb2, - gb3, - B_dot_b, - curl_unit_b_dot_b0, - ef1, - ef2, - ef3, - evaluate_e_field, - ) = args_kernel[:14] - self._gpu_gc_dg2_unit_b1 = (ub1, ub2, ub3) - else: - ( - args_derham, - epsilon, - gb1, - gb2, - gb3, - b2_1, - b2_2, - b2_3, - cb1, - cb2, - cb3, - B_dot_b, - curl_unit_b_dot_b0, - ef1, - ef2, - ef3, - evaluate_e_field, - ) = args_kernel[:17] - self._gpu_gc_dg2_b2 = (b2_1, b2_2, b2_3) - self._gpu_gc_dg2_curl_unit_b2 = (cb1, cb2, cb3) - self._gpu_gc_dg2_epsilon = float(epsilon) - self._gpu_gc_dg2_eval_e = bool(evaluate_e_field) - self._gpu_gc_dg2_pn = tuple(int(x) for x in args_derham.pn) - self._gpu_gc_dg2_starts = tuple(int(x) for x in args_derham.starts) - self._gpu_gc_dg2_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_gc_dg2_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_gc_dg2_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_gc_dg2_gb = (gb1, gb2, gb3) - self._gpu_gc_dg2_bdb = B_dot_b - self._gpu_gc_dg2_cub = curl_unit_b_dot_b0 - self._gpu_gc_dg2_ef = (ef1, ef2, ef3) - self._gpu_gc_dg2_mu_idx = int(particles.mu_idx) - - # CUDA replacements for push_gc_cc_J1_{H1vec,Hcurl,Hdiv} (velocity - # update of CurrentCoupling5DCurlb). Single-stage (dt only, `stage` - # is accepted but unused by the CPU kernels too), needs DF(eta) so - # restricted like the other "general" paths to - # SUPPORTED_GENERAL_KIND_MAPS. - self._gpu_gc_cc_j1 = ( - cunumpy.cupy_backend - and kernel.name - in ( - "push_gc_cc_J1_H1vec", - "push_gc_cc_J1_Hcurl", - "push_gc_cc_J1_Hdiv", - ) - and args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ) - if self._gpu_gc_cc_j1: - import cupy as cp - - self._gpu_gc_cc_j1_name = kernel.name - ( - args_derham, - epsilon, - b1, - b2, - b3, - nb1, - nb2, - nb3, - cnb1, - cnb2, - cnb3, - u1, - u2, - u3, - ) = args_kernel - self._gpu_gc_cc_j1_kind_map = int(args_domain.kind_map) - self._gpu_gc_cc_j1_params = cp.asarray(np.asarray(args_domain.params, dtype=float), dtype=cp.float64) - self._gpu_gc_cc_j1_epsilon = float(epsilon) - self._gpu_gc_cc_j1_pn = tuple(int(x) for x in args_derham.pn) - self._gpu_gc_cc_j1_starts = tuple(int(x) for x in args_derham.starts) - self._gpu_gc_cc_j1_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_gc_cc_j1_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_gc_cc_j1_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_gc_cc_j1_b2 = (b1, b2, b3) - self._gpu_gc_cc_j1_norm_b1 = (nb1, nb2, nb3) - self._gpu_gc_cc_j1_curl_norm_b = (cnb1, cnb2, cnb3) - self._gpu_gc_cc_j1_u = (u1, u2, u3) - @profile def __call__(self, dt: float): """ @@ -883,31 +264,11 @@ def __call__(self, dt: float): applies kinetic boundary conditions and performs MPI sorting. """ with ProfileManager.profile_region(self._region_name): - if self._gpu_eta_cuboid_periodic: - self._push_eta_cuboid_periodic_gpu(dt) - elif self._gpu_v_efield_cuboid_wholepush: + if self._gpu_v_efield_cuboid_wholepush: self._push_v_efield_cuboid_gpu(dt) else: self._push(dt) - def _push_eta_cuboid_periodic_gpu(self, dt: float): - """Whole-push GPU-resident fast path, see :func:`push_eta_rk_periodic_gpu`.""" - particles = self.particles - a, b, _c = self._args_kernel - push_eta_rk_periodic_gpu( - particles.markers, - particles.n_cols, - particles.vdim, - particles.first_pusher_idx, - particles.first_shift_idx, - particles.first_free_idx, - self._gpu_eta_cuboid_scale, - dt, - a, - b, - self.n_stages, - ) - def _push_v_efield_cuboid_gpu(self, dt: float): """Whole-push GPU-resident fast path, see :func:`~struphy.pic.pushing.pusher_kernels_cuda.push_v_with_efield_cuboid_gpu`. @@ -943,165 +304,7 @@ def _kernel_region(self, kernel) -> str: return name def _run_marker_column_kernel(self, ker, alpha, column_nr, comps, add_args): - """Run one init/eval kernel (they write a marker column in place). - - Dispatches to a CUDA port when one exists for this kernel and the - CuPy backend is active; otherwise falls back to the compiled - host-only kernel via the marker host mirror. - """ - name = _kernel_name(ker) - if cunumpy.cupy_backend and name == "driftkinetic_hamiltonian": - args_derham, epsilon, B_dot_b, phi, evaluate_e_field = add_args[:5] - with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): - driftkinetic_hamiltonian_gpu( - self.particles.markers, - alpha, - column_nr, - self.particles.first_pusher_idx, - self.particles.first_shift_idx, - self.particles.mu_idx, - args_derham, - epsilon, - B_dot_b, - phi, - evaluate_e_field, - ) - return - - if cunumpy.cupy_backend and name == "grad_driftkinetic_hamiltonian": - args_derham, epsilon, gb1, gb2, gb3, ef1, ef2, ef3, evaluate_e_field = add_args[:9] - with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): - grad_driftkinetic_hamiltonian_gpu( - self.particles.markers, - alpha, - column_nr, - comps, - self.particles.first_pusher_idx, - self.particles.first_shift_idx, - self.particles.mu_idx, - args_derham, - epsilon, - (gb1, gb2, gb3), - (ef1, ef2, ef3), - evaluate_e_field, - ) - return - - if ( - cunumpy.cupy_backend - and name == "bstar_parallel_3form" - and self._args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - ): - import cupy as cp - import numpy as np - - args_derham, epsilon, B_dot_b_coeffs, curl_unit_b_dot_b0_coeffs = add_args[:4] - if not hasattr(self, "_gpu_marker_col_params_dev"): - self._gpu_marker_col_kind_map = int(self._args_domain.kind_map) - self._gpu_marker_col_params_dev = cp.asarray( - np.asarray(self._args_domain.params, dtype=float), dtype=cp.float64 - ) - with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): - bstar_parallel_3form_gpu( - self.particles.markers, - alpha, - column_nr, - self.particles.first_pusher_idx, - self.particles.first_shift_idx, - self._gpu_marker_col_kind_map, - self._gpu_marker_col_params_dev, - args_derham, - epsilon, - B_dot_b_coeffs, - curl_unit_b_dot_b0_coeffs, - ) - return - - if cunumpy.cupy_backend and name == "bstar_2form": - args_derham, epsilon, b1, b2, b3, cb1, cb2, cb3 = add_args[:8] - with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): - bstar_2form_gpu( - self.particles.markers, - alpha, - column_nr, - comps, - self.particles.first_pusher_idx, - self.particles.first_shift_idx, - args_derham, - epsilon, - (b1, b2, b3), - (cb1, cb2, cb3), - ) - return - - if cunumpy.cupy_backend and name == "unit_b_1form": - args_derham, ub1, ub2, ub3 = add_args[:4] - with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): - unit_b_1form_gpu( - self.particles.markers, - alpha, - column_nr, - comps, - self.particles.first_pusher_idx, - self.particles.first_shift_idx, - args_derham, - (ub1, ub2, ub3), - ) - return - - if cunumpy.cupy_backend and name == "sph_pressure_coeffs": - boxes, neighbours, holes, p1, p2, p3, kernel_type, h1, h2, h3 = add_args[:10] - with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): - sph_pressure_coeffs_gpu( - self.particles.markers, - self.particles.valid_mks, - column_nr, - self.particles.index["weights"], - boxes, - neighbours, - holes, - (p1, p2, p3), - kernel_type, - (h1, h2, h3), - ) - return - - if cunumpy.cupy_backend and name == "sph_mean_velocity_coeffs": - boxes, neighbours, holes, p1, p2, p3, kernel_type, h1, h2, h3 = add_args[:10] - with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): - sph_mean_velocity_coeffs_gpu( - self.particles.markers, - self.particles.valid_mks, - column_nr, - self.particles.index["weights"], - boxes, - neighbours, - holes, - (p1, p2, p3), - kernel_type, - (h1, h2, h3), - ) - return - - if cunumpy.cupy_backend and name == "sph_viscosity_tensor": - boxes, neighbours, holes, p1, p2, p3, kernel_type, h1, h2, h3, mu = add_args[:11] - with ProfileManager.profile_region(self._kernel_region(ker) + " [cuda]"): - sph_viscosity_tensor_gpu( - self.particles.markers, - self.particles.valid_mks, - column_nr, - self.particles.index["weights"], - self.particles.first_free_idx, - boxes, - neighbours, - holes, - (p1, p2, p3), - kernel_type, - (h1, h2, h3), - mu, - ) - return - + """Run one init/eval kernel (they write a marker column in place).""" with ( ProfileManager.profile_region(self._kernel_region(ker)), self.particles.host_markers(write=True) as args_markers, @@ -1208,21 +411,7 @@ def _push(self, dt: float): ) # push markers - if self._gpu_eta_cuboid: - a, b, _c = self._args_kernel - last = 1.0 if stage == self.n_stages - 1 else 0.0 - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - push_eta_stage_cuboid_gpu( - markers, - self.particles.n_cols, - first_pusher_idx, - self.particles.first_free_idx, - self._gpu_eta_cuboid_scale, - dt * float(a[stage]), - dt * float(b[stage]), - last, - ) - elif self._gpu_v_efield_cuboid: + if self._gpu_v_efield_cuboid: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): push_v_with_efield_cuboid_gpu( markers, @@ -1238,141 +427,6 @@ def _push(self, dt: float): self._gpu_v_efield_scale, dt * self._gpu_v_efield_const, ) - elif self._gpu_eta_general: - a, b, _c = self._args_kernel - last = 1.0 if stage == self.n_stages - 1 else 0.0 - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - push_eta_stage_general_gpu( - markers, - self.particles.n_cols, - first_pusher_idx, - self.particles.first_free_idx, - self._gpu_eta_general_kind_map, - self._gpu_eta_general_params, - dt * float(a[stage]), - dt * float(b[stage]), - last, - ) - elif self._gpu_pc_eta_general: - a, b = self._args_kernel[-3], self._args_kernel[-2] - last = 1.0 if stage == self.n_stages - 1 else 0.0 - gpu_pc_eta_fn = { - "push_pc_eta_stage_Hcurl": push_pc_eta_stage_Hcurl_general_gpu, - "push_pc_eta_stage_Hdiv": push_pc_eta_stage_Hdiv_general_gpu, - "push_pc_eta_stage_H1vec": push_pc_eta_stage_H1vec_general_gpu, - }[self._gpu_pc_eta_general_variant] - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - gpu_pc_eta_fn( - markers, - self.particles.n_cols, - first_pusher_idx, - self.particles.first_free_idx, - self._gpu_pc_eta_general_pn, - self._gpu_pc_eta_general_tn1, - self._gpu_pc_eta_general_tn2, - self._gpu_pc_eta_general_tn3, - self._gpu_pc_eta_general_starts, - self._gpu_pc_eta_general_u_1, - self._gpu_pc_eta_general_u_2, - self._gpu_pc_eta_general_u_3, - self._gpu_pc_eta_general_use_perp_model, - self._gpu_pc_eta_general_kind_map, - self._gpu_pc_eta_general_params, - dt * float(a[stage]), - dt * float(b[stage]), - last, - ) - elif self._gpu_det_diffusion_general: - a, b, _c = self._args_kernel[-3:] - last = 1.0 if stage == self.n_stages - 1 else 0.0 - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - push_deterministic_diffusion_stage_general_gpu( - markers, - self.particles.n_cols, - first_pusher_idx, - self.particles.first_free_idx, - self._gpu_det_diffusion_general_pn, - self._gpu_det_diffusion_general_tn1, - self._gpu_det_diffusion_general_tn2, - self._gpu_det_diffusion_general_tn3, - self._gpu_det_diffusion_general_starts, - self._gpu_det_diffusion_general_pi_u, - self._gpu_det_diffusion_general_pi_grad_u1, - self._gpu_det_diffusion_general_pi_grad_u2, - self._gpu_det_diffusion_general_pi_grad_u3, - self._gpu_det_diffusion_general_coeff, - self._gpu_det_diffusion_general_kind_map, - self._gpu_det_diffusion_general_params, - dt * float(a[stage]), - dt * float(b[stage]), - last, - ) - elif self._gpu_random_diffusion: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - push_random_diffusion_stage_gpu( - markers, - self.particles.n_cols, - self._gpu_random_diffusion_noise, - self._gpu_random_diffusion_coeff, - dt, - ) - elif self._gpu_gc_bxestar_general: - a, b, _c = self._args_kernel[-3:] - last = 1.0 if stage == self.n_stages - 1 else 0.0 - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - push_gc_bxEstar_explicit_multistage_general_gpu( - markers, - self.particles.n_cols, - first_pusher_idx, - self.particles.first_free_idx, - self._gpu_gc_bxestar_mu_idx, - self._gpu_gc_bxestar_kind_map, - self._gpu_gc_bxestar_params, - self._gpu_gc_bxestar_epsilon, - self._gpu_gc_bxestar_pn, - self._gpu_gc_bxestar_tn1, - self._gpu_gc_bxestar_tn2, - self._gpu_gc_bxestar_tn3, - self._gpu_gc_bxestar_starts, - *self._gpu_gc_bxestar_unit_b1, - *self._gpu_gc_bxestar_grad_b_full, - self._gpu_gc_bxestar_B_dot_b_coeffs, - self._gpu_gc_bxestar_curl_unit_b_dot_b0, - *self._gpu_gc_bxestar_e_field, - self._gpu_gc_bxestar_evaluate_e_field, - dt * float(a[stage]), - dt * float(b[stage]), - last, - ) - elif self._gpu_gc_bstar_general: - a, b, _c = self._args_kernel[-3:] - last = 1.0 if stage == self.n_stages - 1 else 0.0 - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - push_gc_Bstar_explicit_multistage_general_gpu( - markers, - self.particles.n_cols, - first_pusher_idx, - self.particles.first_free_idx, - self._gpu_gc_bstar_mu_idx, - self._gpu_gc_bstar_kind_map, - self._gpu_gc_bstar_params, - self._gpu_gc_bstar_epsilon, - self._gpu_gc_bstar_pn, - self._gpu_gc_bstar_tn1, - self._gpu_gc_bstar_tn2, - self._gpu_gc_bstar_tn3, - self._gpu_gc_bstar_starts, - *self._gpu_gc_bstar_grad_b_full, - *self._gpu_gc_bstar_b2, - *self._gpu_gc_bstar_curl_unit_b2, - self._gpu_gc_bstar_B_dot_b_coeffs, - self._gpu_gc_bstar_curl_unit_b_dot_b0, - *self._gpu_gc_bstar_e_field, - self._gpu_gc_bstar_evaluate_e_field, - dt * float(a[stage]), - dt * float(b[stage]), - last, - ) elif self._gpu_v_efield_general: with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): push_v_with_efield_general_gpu( @@ -1390,281 +444,6 @@ def _push(self, dt: float): self._gpu_v_efield_general_params, dt * self._gpu_v_efield_general_const, ) - elif self._gpu_vxb_general: - gpu_vxb_fn = ( - push_vxb_analytic_general_gpu - if self._gpu_vxb_general_analytic - else push_vxb_implicit_general_gpu - ) - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - gpu_vxb_fn( - markers, - self.particles.n_cols, - first_pusher_idx, - self._gpu_vxb_general_pn, - self._gpu_vxb_general_tn1, - self._gpu_vxb_general_tn2, - self._gpu_vxb_general_tn3, - self._gpu_vxb_general_starts, - self._gpu_vxb_general_b2_1, - self._gpu_vxb_general_b2_2, - self._gpu_vxb_general_b2_3, - self._gpu_vxb_general_kind_map, - self._gpu_vxb_general_params, - dt, - ) - elif self._gpu_bxu_general: - gpu_bxu_fn = { - "push_bxu_Hdiv": push_bxu_Hdiv_general_gpu, - "push_bxu_Hcurl": push_bxu_Hcurl_general_gpu, - "push_bxu_H1vec": push_bxu_H1vec_general_gpu, - }[self._gpu_bxu_general_variant] - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - gpu_bxu_fn( - markers, - self.particles.n_cols, - self._gpu_bxu_general_pn, - self._gpu_bxu_general_tn1, - self._gpu_bxu_general_tn2, - self._gpu_bxu_general_tn3, - self._gpu_bxu_general_starts, - self._gpu_bxu_general_b2_1, - self._gpu_bxu_general_b2_2, - self._gpu_bxu_general_b2_3, - self._gpu_bxu_general_u_1, - self._gpu_bxu_general_u_2, - self._gpu_bxu_general_u_3, - self._gpu_bxu_general_kind_map, - self._gpu_bxu_general_params, - self._gpu_bxu_general_boundary_cut, - dt, - ) - elif self._gpu_pc_gxu_general: - g11, g12, g13, g21, g22, g23, g31, g32, g33 = self._gpu_pc_gxu_general_g - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - if self._gpu_pc_gxu_general_full: - push_pc_GXu_full_general_gpu( - markers, - self.particles.n_cols, - self._gpu_pc_gxu_general_pn, - self._gpu_pc_gxu_general_tn1, - self._gpu_pc_gxu_general_tn2, - self._gpu_pc_gxu_general_tn3, - self._gpu_pc_gxu_general_starts, - g11, - g12, - g13, - g21, - g22, - g23, - g31, - g32, - g33, - self._gpu_pc_gxu_general_kind_map, - self._gpu_pc_gxu_general_params, - dt, - ) - else: - push_pc_GXu_general_gpu( - markers, - self.particles.n_cols, - self._gpu_pc_gxu_general_pn, - self._gpu_pc_gxu_general_tn1, - self._gpu_pc_gxu_general_tn2, - self._gpu_pc_gxu_general_tn3, - self._gpu_pc_gxu_general_starts, - g11, - g12, - g13, - g21, - g22, - g23, - self._gpu_pc_gxu_general_kind_map, - self._gpu_pc_gxu_general_params, - dt, - ) - elif self._gpu_weights_efield_general: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda general]"): - push_weights_with_efield_lin_va_general_gpu( - markers, - self.particles.n_cols, - self._gpu_weights_efield_general_pn, - self._gpu_weights_efield_general_tn1, - self._gpu_weights_efield_general_tn2, - self._gpu_weights_efield_general_tn3, - self._gpu_weights_efield_general_starts, - self._gpu_weights_efield_general_e1_1, - self._gpu_weights_efield_general_e1_2, - self._gpu_weights_efield_general_e1_3, - self._gpu_weights_efield_general_f0_values, - self._gpu_weights_efield_general_kappa, - self._gpu_weights_efield_general_vth, - self._gpu_weights_efield_general_kind_map, - self._gpu_weights_efield_general_params, - dt, - ) - elif self._gpu_gc_dg1: - fn = ( - push_gc_bxEstar_discrete_gradient_1st_order_gpu - if self._gpu_gc_dg1_name == "push_gc_bxEstar_discrete_gradient_1st_order" - else push_gc_Bstar_discrete_gradient_1st_order_gpu - ) - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - fn( - markers, - self.particles.n_cols, - first_pusher_idx, - self.particles.first_shift_idx, - self.particles.residual_idx, - self.particles.first_free_idx, - self._gpu_gc_dg1_mu_idx, - self._gpu_gc_dg1_epsilon, - self._gpu_gc_dg1_pn, - self._gpu_gc_dg1_tn1, - self._gpu_gc_dg1_tn2, - self._gpu_gc_dg1_tn3, - self._gpu_gc_dg1_starts, - self._gpu_gc_dg1_gb, - self._gpu_gc_dg1_ef, - self._gpu_gc_dg1_eval_e, - dt, - ) - elif self._gpu_gc_dg1_newton: - fn = ( - push_gc_bxEstar_discrete_gradient_1st_order_newton_gpu - if self._gpu_gc_dg1n_name == "push_gc_bxEstar_discrete_gradient_1st_order_newton" - else push_gc_Bstar_discrete_gradient_1st_order_newton_gpu - ) - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - fn( - markers, - first_pusher_idx, - self.particles.first_shift_idx, - self.particles.residual_idx, - self.particles.first_free_idx, - self._gpu_gc_dg1n_mu_idx, - self._gpu_gc_dg1n_epsilon, - self._gpu_gc_dg1n_pn, - self._gpu_gc_dg1n_tn1, - self._gpu_gc_dg1n_tn2, - self._gpu_gc_dg1n_tn3, - self._gpu_gc_dg1n_starts, - self._gpu_gc_dg1n_gb, - self._gpu_gc_dg1n_bdb, - self._gpu_gc_dg1n_ef, - self._gpu_gc_dg1n_phi, - self._gpu_gc_dg1n_eval_e, - dt, - ) - elif self._gpu_gc_dg2: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - if self._gpu_gc_dg2_name == "push_gc_bxEstar_discrete_gradient_2nd_order": - push_gc_bxEstar_discrete_gradient_2nd_order_gpu( - markers, - first_pusher_idx, - self.particles.first_shift_idx, - self.particles.residual_idx, - self.particles.first_free_idx, - self._gpu_gc_dg2_mu_idx, - self._gpu_gc_dg2_kind_map, - self._gpu_gc_dg2_params, - self._gpu_gc_dg2_epsilon, - self._gpu_gc_dg2_pn, - self._gpu_gc_dg2_tn1, - self._gpu_gc_dg2_tn2, - self._gpu_gc_dg2_tn3, - self._gpu_gc_dg2_starts, - self._gpu_gc_dg2_unit_b1, - self._gpu_gc_dg2_gb, - self._gpu_gc_dg2_bdb, - self._gpu_gc_dg2_cub, - self._gpu_gc_dg2_ef, - self._gpu_gc_dg2_eval_e, - dt, - ) - else: - push_gc_Bstar_discrete_gradient_2nd_order_gpu( - markers, - first_pusher_idx, - self.particles.first_shift_idx, - self.particles.residual_idx, - self.particles.first_free_idx, - self._gpu_gc_dg2_mu_idx, - self._gpu_gc_dg2_kind_map, - self._gpu_gc_dg2_params, - self._gpu_gc_dg2_epsilon, - self._gpu_gc_dg2_pn, - self._gpu_gc_dg2_tn1, - self._gpu_gc_dg2_tn2, - self._gpu_gc_dg2_tn3, - self._gpu_gc_dg2_starts, - self._gpu_gc_dg2_gb, - self._gpu_gc_dg2_b2, - self._gpu_gc_dg2_curl_unit_b2, - self._gpu_gc_dg2_bdb, - self._gpu_gc_dg2_cub, - self._gpu_gc_dg2_ef, - self._gpu_gc_dg2_eval_e, - dt, - ) - elif self._gpu_gc_cc_j1: - fn = { - "push_gc_cc_J1_H1vec": push_gc_cc_J1_H1vec_gpu, - "push_gc_cc_J1_Hcurl": push_gc_cc_J1_Hcurl_gpu, - "push_gc_cc_J1_Hdiv": push_gc_cc_J1_Hdiv_gpu, - }[self._gpu_gc_cc_j1_name] - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - fn( - markers, - self._gpu_gc_cc_j1_kind_map, - self._gpu_gc_cc_j1_params, - self._gpu_gc_cc_j1_epsilon, - self._gpu_gc_cc_j1_pn, - self._gpu_gc_cc_j1_tn1, - self._gpu_gc_cc_j1_tn2, - self._gpu_gc_cc_j1_tn3, - self._gpu_gc_cc_j1_starts, - self._gpu_gc_cc_j1_b2, - self._gpu_gc_cc_j1_norm_b1, - self._gpu_gc_cc_j1_curl_norm_b, - self._gpu_gc_cc_j1_u, - dt, - ) - elif self._gpu_sph_pusher: - with ProfileManager.profile_region("kernel: " + self.kernel.name + " [cuda]"): - common = dict( - boxes=self.particles.sorting_boxes.boxes, - neighbours=self.particles.sorting_boxes.neighbours, - holes=self.particles.holes, - periodic=self._gpu_sph_periodic, - kernel_type=self._gpu_sph_kernel_nr, - h=self._gpu_sph_h, - kind_map=self._gpu_sph_kind_map, - params_dev=self._gpu_sph_params, - dt=dt, - ) - if self._gpu_sph_name == "push_v_viscosity": - push_v_viscosity_gpu( - markers, - self.particles.valid_mks, - self.particles.first_free_idx, - **common, - ) - else: - fn = ( - push_v_sph_pressure_gpu - if self._gpu_sph_name == "push_v_sph_pressure" - else push_v_sph_pressure_ideal_gas_gpu - ) - fn( - markers, - self.particles.valid_mks, - self.particles.index["weights"], - self.particles.first_free_idx, - gravity=self._gpu_sph_gravity, - kappa=self._gpu_sph_kappa, - **common, - ) else: # no CUDA port for this kernel: fall back to the compiled # host-only one, which pushes markers in place diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index 60aee6328..fb00d03db 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -8,34 +8,13 @@ Pyccel kernel, specialized for one :class:`~struphy.geometry.domains.Domain` whose Jacobian is cheap enough that hand-specializing pays off. -Currently covered: :func:`~struphy.pic.pushing.pusher_kernels.push_eta_stage` -for the :class:`~struphy.geometry.domains.Cuboid` domain (``kind_map == 10``), -whose Jacobian ``DF = diag(r1 - l1, r2 - l2, r3 - l3)`` is constant, so the -whole stage update collapses to an elementwise scale-and-accumulate per marker -row with no spline evaluation at all. - -Two entry points are provided: - -* :func:`push_eta_stage_cuboid_gpu` replaces a single ``push_eta_stage`` call. - It round-trips the full marker array through the device on every call, which - is fine at the marker counts used in early testing but becomes the dominant - cost at a few hundred thousand markers and up (H2D/D2H bandwidth, not - compute, ends up dominating the run). - -* :func:`push_eta_rk_periodic_gpu` additionally fuses in the boundary-condition - bookkeeping that :meth:`~struphy.pic.base.Particles.apply_kinetic_bc` would - otherwise do on the host between stages (periodic wrap + shift-column - bookkeeping), restricted to an all-periodic ``bc``. That lets the whole - multi-stage RK push run with the marker array resident on the device the - entire time, doing exactly one H2D and one D2H transfer per - :meth:`~struphy.pic.pushing.pusher.Pusher.__call__`, instead of one round - trip per stage per kernel. - -Also covered: :func:`~struphy.pic.pushing.pusher_kernels.push_v_with_efield`, -again for the Cuboid domain. Unlike ``push_eta_stage``, this one does need a -real (small-degree) tensor-product B-spline evaluation -- the electric field -is a 1-form FEEC spline, not a constant -- so :func:`push_v_with_efield_cuboid_gpu` -ports ``find_span`` and the combined N-/D-spline basis recursion +Currently covered: :func:`~struphy.pic.pushing.pusher_kernels.push_v_with_efield`, +for the Cuboid domain (:func:`push_v_with_efield_cuboid_gpu`) and, more +generally, for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS` +(:func:`push_v_with_efield_general_gpu`). This one does need a real +(small-degree) tensor-product B-spline evaluation -- the electric field is a +1-form FEEC spline, not a constant -- so these functions port ``find_span`` +and the combined N-/D-spline basis recursion (:func:`~struphy.bsplines.bsplines_kernels.b_d_splines_slim`) to device code alongside the local stencil sum (:func:`~struphy.bsplines.evaluation_kernels_3d.eval_spline_mpi_kernel`). @@ -58,113 +37,6 @@ """ from struphy.cuda import CudaKernel, launch_1d, load_cuda_source -_PUSH_ETA_CUBOID_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_eta_cuboid_src.cu") -_push_eta_cuboid_kernel = CudaKernel(_PUSH_ETA_CUBOID_SRC, "push_eta_stage_cuboid") - - -def push_eta_stage_cuboid_gpu( - markers, - n_cols: int, - first_init_idx: int, - first_free_idx: int, - scale: tuple[float, float, float], - dt_a: float, - dt_b: float, - last: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_eta_stage`, restricted to - the :class:`~struphy.geometry.domains.Cuboid` domain. - - ``markers`` is the host (pinned-memory) marker array; it is round-tripped - through the device in full, matching the pattern used by - :meth:`~struphy.pic.pushing.pusher.Pusher._reset_marker_buffers_gpu`. - """ - import numpy as np - - n_markers = markers.shape[0] - launch_1d( - _push_eta_cuboid_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_free_idx), - np.float64(scale[0]), - np.float64(scale[1]), - np.float64(scale[2]), - np.float64(dt_a), - np.float64(dt_b), - np.float64(last), - ), - ) - - -_PUSH_ETA_RK_PERIODIC_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_eta_rk_periodic_src.cu") -_push_eta_rk_periodic_kernel = CudaKernel(_PUSH_ETA_RK_PERIODIC_SRC, "push_eta_rk_periodic") - - -def push_eta_rk_periodic_gpu( - markers, - n_cols: int, - vdim: int, - first_init_idx: int, - first_shift_idx: int, - first_free_idx: int, - scale: tuple[float, float, float], - dt: float, - a, - b, - n_stages: int, -): - """Run a full multi-stage RK push of :func:`~struphy.pic.pushing.pusher_kernels.push_eta_stage` - plus periodic boundary handling, entirely on the device. - - Restricted to the :class:`~struphy.geometry.domains.Cuboid` domain and an - all-``"periodic"`` ``bc``. Equivalent to calling - :func:`push_eta_stage_cuboid_gpu` once per stage followed by the periodic - branch of :meth:`~struphy.pic.base.Particles.apply_kinetic_bc`, but with a - single H2D transfer at the start and a single D2H transfer at the end - instead of one round trip per stage. Holes and ghost particles are - invariant under a periodic-only push (positions are wrapped mod 1, never - set to the -1.0 hole sentinel), so :meth:`~struphy.pic.base.Particles.update_holes` - does not need to be called. - """ - import numpy as np - - n_markers = markers.shape[0] - - dev = markers - - # reset: save initial phase-space coords, zero shift/free/residual columns - # (matches Pusher._reset_marker_buffers_gpu, done once instead of per stage) - dev[:, first_init_idx : first_init_idx + 3 + vdim] = dev[:, : 3 + vdim] - dev[:, first_shift_idx:-2] = 0.0 - - for stage in range(n_stages): - last = 1.0 if stage == n_stages - 1 else 0.0 - launch_1d( - _push_eta_rk_periodic_kernel, - n_markers, - ( - dev, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_free_idx), - np.int32(first_shift_idx), - np.float64(scale[0]), - np.float64(scale[1]), - np.float64(scale[2]), - np.float64(dt * float(a[stage])), - np.float64(dt * float(b[stage])), - np.float64(last), - ), - ) - - _PUSH_V_EFIELD_CUBOID_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_v_efield_cuboid_src.cu") _push_v_efield_cuboid_kernel = CudaKernel(_PUSH_V_EFIELD_CUBOID_SRC, "push_v_with_efield_cuboid") @@ -188,11 +60,10 @@ def push_v_with_efield_cuboid_gpu( to the :class:`~struphy.geometry.domains.Cuboid` domain. ``markers`` is the host marker array and is round-tripped through the - device once (matching :func:`push_eta_stage_cuboid_gpu`). ``tn1_dev``, - ``tn2_dev``, ``tn3_dev`` (knot vectors) and ``e1_1_dev``, ``e1_2_dev``, - ``e1_3_dev`` (FE coefficients of the 1-form E-field) are expected to - already be CuPy arrays resident on the device -- callers should cache - them once rather than converting on every call, see + device once. ``tn1_dev``, ``tn2_dev``, ``tn3_dev`` (knot vectors) and + ``e1_1_dev``, ``e1_2_dev``, ``e1_3_dev`` (FE coefficients of the 1-form + E-field) are expected to already be CuPy arrays resident on the device -- + callers should cache them once rather than converting on every call, see :class:`~struphy.pic.pushing.pusher.Pusher`. """ import numpy as np @@ -238,21 +109,21 @@ def push_v_with_efield_cuboid_gpu( # General (non-Cuboid-restricted) domain support # ============================================================================ # -# The two kernels above hardcode Cuboid's Jacobian (a constant diagonal -# matrix, precomputed on the host as `scale`) directly into the marker -# update, which is what makes them fast but restricts them to `kind_map == -# 10`. Everything else about them -- the B-spline evaluation in -# push_v_with_efield_cuboid_gpu -- is already fully general (arbitrary -# degree, arbitrary non-uniform knot vector; nothing there assumes Cuboid). +# push_v_with_efield_cuboid_gpu above hardcodes Cuboid's Jacobian (a constant +# diagonal matrix, precomputed on the host as `scale`) directly into the +# marker update, which is what makes it fast but restricts it to +# `kind_map == 10`. Everything else about it -- the B-spline evaluation -- is +# already fully general (arbitrary degree, arbitrary non-uniform knot vector; +# nothing there assumes Cuboid). # -# push_eta_stage_general_gpu / push_v_with_efield_general_gpu below drop the -# constant-Jacobian assumption: they evaluate DF(eta) (and its inverse) per -# marker, per call, on the device, matching the general -# struphy.geometry.evaluation_kernels.df / struphy.linear_algebra.linalg_kernels -# dispatch that struphy.pic.pushing.pusher_kernels.push_eta_stage / -# push_v_with_efield use on the CPU. This is genuinely more per-marker work -# (a Jacobian evaluation instead of a lookup), but still embarrassingly -# parallel across markers, so it remains a good GPU fit. +# push_v_with_efield_general_gpu below drops the constant-Jacobian +# assumption: it evaluates DF(eta) (and its inverse) per marker, per call, on +# the device, matching the general struphy.geometry.evaluation_kernels.df / +# struphy.linear_algebra.linalg_kernels dispatch that +# struphy.pic.pushing.pusher_kernels.push_v_with_efield uses on the CPU. This +# is genuinely more per-marker work (a Jacobian evaluation instead of a +# lookup), but still embarrassingly parallel across markers, so it remains a +# good GPU fit. # # All analytic (closed-form) mappings in struphy.geometry.mappings_kernels # are implemented: Cuboid (10), Orthogonal (11), Colella (12), @@ -267,65 +138,20 @@ def push_v_with_efield_cuboid_gpu( # evaluating DF there means differentiating that spline (basis_funs_1st_der / # a derivative-spline evaluation, not just the tensor-product sum this file # already has for FEEC fields), which is a separate, larger piece of work. -# Callers must check kind_map themselves (see Pusher._gpu_eta_general / -# _gpu_v_efield_general in pusher.py) and fall back to the host Pyccel kernel -# for anything else -- these functions do not raise on an unsupported -# kind_map, they are simply -# not wired up for one. +# Callers must check kind_map themselves (see Pusher._gpu_v_efield_general in +# pusher.py) and fall back to the host Pyccel kernel for anything else -- +# this function does not raise on an unsupported kind_map, it is simply not +# wired up for one. _GENERAL_GEOMETRY_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_general_geometry_src.cu") -_push_eta_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_eta_stage_general") _push_v_efield_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_v_with_efield_general") -#: kind_map values df_dispatch_dev supports (Cuboid, Colella). Callers should -#: check membership before dispatching to the *_general_gpu functions below. +#: kind_map values df_dispatch_dev supports. Callers should check membership +#: before dispatching to push_v_with_efield_general_gpu. SUPPORTED_GENERAL_KIND_MAPS = (10, 11, 12, 20, 21, 22, 30, 31, 32) -def push_eta_stage_general_gpu( - markers, - n_cols: int, - first_init_idx: int, - first_free_idx: int, - kind_map: int, - params_dev, - dt_a: float, - dt_b: float, - last: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_eta_stage`, for any - domain in :data:`SUPPORTED_GENERAL_KIND_MAPS` (evaluates DF(eta) per - marker instead of assuming a constant Jacobian, unlike - :func:`push_eta_stage_cuboid_gpu`). - - ``markers`` is the host marker array, round-tripped through the device - once per call. ``params_dev`` is the domain's mapping-parameter array - (``args_domain.params``), expected to already be a small CuPy array - (cheap to keep device-resident; callers should cache it once). - """ - import numpy as np - - n_markers = markers.shape[0] - launch_1d( - _push_eta_general_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_free_idx), - np.int32(kind_map), - params_dev, - np.float64(dt_a), - np.float64(dt_b), - np.float64(last), - ), - ) - - def push_v_with_efield_general_gpu( markers, n_cols: int, @@ -346,7 +172,9 @@ def push_v_with_efield_general_gpu( domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. See :func:`push_v_with_efield_cuboid_gpu` for the argument conventions (``tn*_dev``/``e1_*_dev`` are expected to already be device-resident); - ``params_dev`` follows :func:`push_eta_stage_general_gpu`. + ``params_dev`` is the domain's mapping-parameter array + (``args_domain.params``), expected to already be a small CuPy array + (cheap to keep device-resident; callers should cache it once). """ import numpy as np @@ -384,877 +212,3 @@ def push_v_with_efield_general_gpu( np.float64(dt_const), ), ) - - -_push_vxb_analytic_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_vxb_analytic_general") -_push_vxb_implicit_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_vxb_implicit_general") - - -def _launch_vxb_general( - kernel, - markers, - n_cols, - first_init_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - kind_map, - params_dev, - dt, -): - import numpy as np - - n_markers = markers.shape[0] - launch_1d( - kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - b2_1_dev, - np.int32(b2_1_dev.shape[1]), - np.int32(b2_1_dev.shape[2]), - b2_2_dev, - np.int32(b2_2_dev.shape[1]), - np.int32(b2_2_dev.shape[2]), - b2_3_dev, - np.int32(b2_3_dev.shape[1]), - np.int32(b2_3_dev.shape[2]), - np.int32(kind_map), - params_dev, - np.float64(dt), - ), - ) - - -def push_vxb_analytic_general_gpu( - markers, - n_cols: int, - first_init_idx: int, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - b2_1_dev, - b2_2_dev, - b2_3_dev, - kind_map: int, - params_dev, - dt: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_vxb_analytic`, for any - domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. Argument conventions match - :func:`push_v_with_efield_general_gpu` (``tn*_dev``/``b2_*_dev`` already - device-resident, ``params_dev`` the domain's mapping-parameter array). - """ - _launch_vxb_general( - _push_vxb_analytic_general_kernel, - markers, - n_cols, - first_init_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - kind_map, - params_dev, - dt, - ) - - -def push_vxb_implicit_general_gpu( - markers, - n_cols: int, - first_init_idx: int, - pn: tuple[int, int, int], - tn1_dev, - tn2_dev, - tn3_dev, - starts: tuple[int, int, int], - b2_1_dev, - b2_2_dev, - b2_3_dev, - kind_map: int, - params_dev, - dt: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_vxb_implicit` (Crank- - Nicolson rotation), for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. - See :func:`push_vxb_analytic_general_gpu` for argument conventions.""" - _launch_vxb_general( - _push_vxb_implicit_general_kernel, - markers, - n_cols, - first_init_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - kind_map, - params_dev, - dt, - ) - - -_push_bxu_hdiv_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_Hdiv_general") -_push_bxu_hcurl_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_Hcurl_general") -_push_bxu_h1vec_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_bxu_H1vec_general") - - -def _launch_bxu_general( - kernel, - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - u_1_dev, - u_2_dev, - u_3_dev, - kind_map, - params_dev, - boundary_cut, - dt, -): - import numpy as np - - n_markers = markers.shape[0] - launch_1d( - kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - b2_1_dev, - np.int32(b2_1_dev.shape[1]), - np.int32(b2_1_dev.shape[2]), - b2_2_dev, - np.int32(b2_2_dev.shape[1]), - np.int32(b2_2_dev.shape[2]), - b2_3_dev, - np.int32(b2_3_dev.shape[1]), - np.int32(b2_3_dev.shape[2]), - u_1_dev, - np.int32(u_1_dev.shape[1]), - np.int32(u_1_dev.shape[2]), - u_2_dev, - np.int32(u_2_dev.shape[1]), - np.int32(u_2_dev.shape[2]), - u_3_dev, - np.int32(u_3_dev.shape[1]), - np.int32(u_3_dev.shape[2]), - np.int32(kind_map), - params_dev, - np.float64(boundary_cut), - np.float64(dt), - ), - ) - - -def push_bxu_Hdiv_general_gpu( - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - u2_1_dev, - u2_2_dev, - u2_3_dev, - kind_map: int, - params_dev, - boundary_cut: float, - dt: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_Hdiv`, for any domain - in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u2_*_dev`` is the U-field's - 2-form FE coefficients (same evaluation as ``b2_*_dev``).""" - _launch_bxu_general( - _push_bxu_hdiv_general_kernel, - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - u2_1_dev, - u2_2_dev, - u2_3_dev, - kind_map, - params_dev, - boundary_cut, - dt, - ) - - -def push_bxu_Hcurl_general_gpu( - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - u1_1_dev, - u1_2_dev, - u1_3_dev, - kind_map: int, - params_dev, - boundary_cut: float, - dt: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_Hcurl`, for any - domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u1_*_dev`` is the - U-field's 1-form FE coefficients.""" - _launch_bxu_general( - _push_bxu_hcurl_general_kernel, - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - u1_1_dev, - u1_2_dev, - u1_3_dev, - kind_map, - params_dev, - boundary_cut, - dt, - ) - - -def push_bxu_H1vec_general_gpu( - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - uv_1_dev, - uv_2_dev, - uv_3_dev, - kind_map: int, - params_dev, - boundary_cut: float, - dt: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_bxu_H1vec`, for any - domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``uv_*_dev`` is the - U-field's (H^1)^3 vector-field FE coefficients.""" - _launch_bxu_general( - _push_bxu_h1vec_general_kernel, - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2_1_dev, - b2_2_dev, - b2_3_dev, - uv_1_dev, - uv_2_dev, - uv_3_dev, - kind_map, - params_dev, - boundary_cut, - dt, - ) - - -_push_pc_gxu_full_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_GXu_full_general") -_push_pc_gxu_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_GXu_general") - - -def push_pc_GXu_full_general_gpu( - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - g11_dev, - g12_dev, - g13_dev, - g21_dev, - g22_dev, - g23_dev, - g31_dev, - g32_dev, - g33_dev, - kind_map: int, - params_dev, - dt: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_pc_GXu_full`, for any - domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``g{i}{j}_dev`` is the FE - coefficients of :math:`\\nabla_j(\\mathcal X \\cdot u)_i`, each row - ``i`` a 1-form (same evaluation as ``push_v_with_efield_general_gpu``'s - ``e1_*``).""" - import numpy as np - - n_markers = markers.shape[0] - g = (g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev, g31_dev, g32_dev, g33_dev) - launch_1d( - _push_pc_gxu_full_general_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *g, - np.int32(g11_dev.shape[1]), - np.int32(g11_dev.shape[2]), - np.int32(g12_dev.shape[1]), - np.int32(g12_dev.shape[2]), - np.int32(g13_dev.shape[1]), - np.int32(g13_dev.shape[2]), - np.int32(kind_map), - params_dev, - np.float64(dt), - ), - ) - - -def push_pc_GXu_general_gpu( - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - g11_dev, - g12_dev, - g13_dev, - g21_dev, - g22_dev, - g23_dev, - kind_map: int, - params_dev, - dt: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_pc_GXu` (the 2-row - variant of :func:`push_pc_GXu_full_general_gpu`), for any domain in - :data:`SUPPORTED_GENERAL_KIND_MAPS`.""" - import numpy as np - - n_markers = markers.shape[0] - g = (g11_dev, g12_dev, g13_dev, g21_dev, g22_dev, g23_dev) - launch_1d( - _push_pc_gxu_general_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *g, - np.int32(g11_dev.shape[1]), - np.int32(g11_dev.shape[2]), - np.int32(g12_dev.shape[1]), - np.int32(g12_dev.shape[2]), - np.int32(g13_dev.shape[1]), - np.int32(g13_dev.shape[2]), - np.int32(kind_map), - params_dev, - np.float64(dt), - ), - ) - - -_push_pc_eta_hcurl_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_Hcurl_general") -_push_pc_eta_hdiv_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_Hdiv_general") -_push_pc_eta_h1vec_general_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC, "push_pc_eta_stage_H1vec_general") - - -def _launch_pc_eta_general( - kernel, - markers, - n_cols, - first_init_idx, - first_free_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - u_1_dev, - u_2_dev, - u_3_dev, - use_perp_model, - kind_map, - params_dev, - dt_a, - dt_b, - last, -): - import numpy as np - - n_markers = markers.shape[0] - launch_1d( - kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_free_idx), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - u_1_dev, - np.int32(u_1_dev.shape[1]), - np.int32(u_1_dev.shape[2]), - u_2_dev, - np.int32(u_2_dev.shape[1]), - np.int32(u_2_dev.shape[2]), - u_3_dev, - np.int32(u_3_dev.shape[1]), - np.int32(u_3_dev.shape[2]), - np.int32(1 if use_perp_model else 0), - np.int32(kind_map), - params_dev, - np.float64(dt_a), - np.float64(dt_b), - np.float64(last), - ), - ) - - -def push_pc_eta_stage_Hcurl_general_gpu( - markers, - n_cols, - first_init_idx, - first_free_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - u_1_dev, - u_2_dev, - u_3_dev, - use_perp_model: bool, - kind_map: int, - params_dev, - dt_a: float, - dt_b: float, - last: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_Hcurl`, for - any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the - U-field's 1-form FE coefficients.""" - _launch_pc_eta_general( - _push_pc_eta_hcurl_general_kernel, - markers, - n_cols, - first_init_idx, - first_free_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - u_1_dev, - u_2_dev, - u_3_dev, - use_perp_model, - kind_map, - params_dev, - dt_a, - dt_b, - last, - ) - - -def push_pc_eta_stage_Hdiv_general_gpu( - markers, - n_cols, - first_init_idx, - first_free_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - u_1_dev, - u_2_dev, - u_3_dev, - use_perp_model: bool, - kind_map: int, - params_dev, - dt_a: float, - dt_b: float, - last: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_Hdiv`, for - any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the - U-field's 2-form FE coefficients.""" - _launch_pc_eta_general( - _push_pc_eta_hdiv_general_kernel, - markers, - n_cols, - first_init_idx, - first_free_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - u_1_dev, - u_2_dev, - u_3_dev, - use_perp_model, - kind_map, - params_dev, - dt_a, - dt_b, - last, - ) - - -def push_pc_eta_stage_H1vec_general_gpu( - markers, - n_cols, - first_init_idx, - first_free_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - u_1_dev, - u_2_dev, - u_3_dev, - use_perp_model: bool, - kind_map: int, - params_dev, - dt_a: float, - dt_b: float, - last: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_pc_eta_stage_H1vec`, for - any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``u_*_dev`` is the - U-field's (H^1)^3 vector-field FE coefficients.""" - _launch_pc_eta_general( - _push_pc_eta_h1vec_general_kernel, - markers, - n_cols, - first_init_idx, - first_free_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - u_1_dev, - u_2_dev, - u_3_dev, - use_perp_model, - kind_map, - params_dev, - dt_a, - dt_b, - last, - ) - - -_push_weights_efield_lin_va_general_kernel = CudaKernel( - _GENERAL_GEOMETRY_SRC, "push_weights_with_efield_lin_va_general" -) - - -def push_weights_with_efield_lin_va_general_gpu( - markers, - n_cols, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - e1_1_dev, - e1_2_dev, - e1_3_dev, - f0_values, - kappa: float, - vth: float, - kind_map: int, - params_dev, - dt: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_weights_with_efield_lin_va`, - for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``f0_values`` is - allocated via ``xp.zeros`` by the caller (EfieldWeightsCoupling) and - updated in place every step, so under CuPy it is already device-resident - -- passed straight through here, like ``e1_*_dev``.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - f0_dev = cp.ascontiguousarray(f0_values) - launch_1d( - _push_weights_efield_lin_va_general_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - e1_1_dev, - np.int32(e1_1_dev.shape[1]), - np.int32(e1_1_dev.shape[2]), - e1_2_dev, - np.int32(e1_2_dev.shape[1]), - np.int32(e1_2_dev.shape[2]), - e1_3_dev, - np.int32(e1_3_dev.shape[1]), - np.int32(e1_3_dev.shape[2]), - f0_dev, - np.float64(kappa), - np.float64(vth), - np.int32(kind_map), - params_dev, - np.float64(dt), - ), - ) - - -_push_deterministic_diffusion_general_kernel = CudaKernel( - _GENERAL_GEOMETRY_SRC, "push_deterministic_diffusion_stage_general" -) - - -def push_deterministic_diffusion_stage_general_gpu( - markers, - n_cols, - first_init_idx, - first_free_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - pi_u_dev, - pi_grad_u1_dev, - pi_grad_u2_dev, - pi_grad_u3_dev, - diffusion_coeff: float, - kind_map: int, - params_dev, - dt_a: float, - dt_b: float, - last: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_deterministic_diffusion_stage`, - for any domain in :data:`SUPPORTED_GENERAL_KIND_MAPS`. ``pi_u_dev`` is - the 0-form FE coefficients of the (fixed-in-time) density, ``pi_grad_u{1,2,3}_dev`` - its gradient as a 1-form.""" - import numpy as np - - n_markers = markers.shape[0] - launch_1d( - _push_deterministic_diffusion_general_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_free_idx), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - pi_u_dev, - np.int32(pi_u_dev.shape[1]), - np.int32(pi_u_dev.shape[2]), - pi_grad_u1_dev, - np.int32(pi_grad_u1_dev.shape[1]), - np.int32(pi_grad_u1_dev.shape[2]), - pi_grad_u2_dev, - np.int32(pi_grad_u2_dev.shape[1]), - np.int32(pi_grad_u2_dev.shape[2]), - pi_grad_u3_dev, - np.int32(pi_grad_u3_dev.shape[1]), - np.int32(pi_grad_u3_dev.shape[2]), - np.float64(diffusion_coeff), - np.int32(kind_map), - params_dev, - np.float64(dt_a), - np.float64(dt_b), - np.float64(last), - ), - ) - - -# push_random_diffusion_stage does not touch geometry at all (a pure additive -# noise kick, no Jacobian, no field evaluation), so it gets its own minimal, -# domain-independent RawKernel source instead of living in -# _GENERAL_GEOMETRY_SRC -- it applies to every domain, not just -# SUPPORTED_GENERAL_KIND_MAPS. -_RANDOM_DIFFUSION_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_random_diffusion_src.cu") -_push_random_diffusion_kernel = CudaKernel(_RANDOM_DIFFUSION_SRC, "push_random_diffusion_stage") - - -def push_random_diffusion_stage_gpu(markers, n_cols, noise, diffusion_coeff: float, dt: float): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels.push_random_diffusion_stage`. - Domain-independent (no geometry involved), so unlike the other - ``*_general_gpu`` functions this one has no ``kind_map`` restriction. - ``noise`` may be a host or a device array: - :class:`~struphy.propagators.push_random_diffusion.PushRandomDiffusion` draws it - through ``xp.random``, so under CuPy it is already on the device and no transfer - happens here; a host array is accepted (and copied) so the signature stays - backend-agnostic.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - # cp.asarray takes host or device input; np.ascontiguousarray would raise on a - # device array rather than transferring it. - noise_dev = cp.ascontiguousarray(cp.asarray(noise, dtype=cp.float64)) - scale = float(np.sqrt(2.0 * dt * diffusion_coeff)) - launch_1d( - _push_random_diffusion_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - noise_dev, - np.float64(scale), - ), - ) diff --git a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py b/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py deleted file mode 100644 index 426fa7d6c..000000000 --- a/src/struphy/pic/pushing/pusher_kernels_gc_cuda.py +++ /dev/null @@ -1,1001 +0,0 @@ -"""Hand-written CUDA replacements for select 5D guiding-center pusher -kernels in :mod:`~struphy.pic.pushing.pusher_kernels_gc`, used only under -``ARRAY_BACKEND=cupy``. - -Scope: this branch's 6D (full-orbit) work ported every real (non-dead-code) -kernel in ``pusher_kernels.py``/``accum_kernels.py``. The 5D guiding-center -family (``pusher_kernels_gc.py``/``accum_kernels_gc.py``) is a separate, -much larger body of kernels -- 15 pushers + 8 accumulators, 21 of them with -real propagator callers -- several of which (the ``*_discrete_gradient_*`` -variants) are implicit per-marker Newton solves, not simple explicit RK -stages, and are a substantially bigger porting effort. - -Currently covered here: the two *explicit* multistage GC pushers, -:func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_explicit_multistage` -and :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_explicit_multistage` --- both are plain explicit-RK marker loops (same ``dt*a[stage]``/``dt*b[stage]``/ -``last`` structure as ``push_eta_stage`` in the 6D family), and reuse the -existing 0-/1-/2-form spline evaluation and geometry device functions from -:mod:`~struphy.pic.pushing.pusher_kernels_cuda`'s ``_GENERAL_GEOMETRY_SRC`` -unchanged. The accompanying accumulation kernel -:func:`~struphy.pic.accumulation.accum_kernels_gc.gc_mag_density_0form` is -ported alongside these in -:mod:`~struphy.pic.accumulation.accum_kernels_gc_cuda` (same -``atomicAdd``-scatter approach as ``charge_density_0form``). -""" -from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source - -_PUSH_GC_BXESTAR_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_bxestar_src.cu") - -_PUSH_GC_BSTAR_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_bstar_src.cu") - - -def _push_gc_bxEstar_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _PUSH_GC_BXESTAR_SRC - - -def _push_gc_Bstar_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _PUSH_GC_BSTAR_SRC - - -_push_gc_bxEstar_kernel = CudaKernel(_push_gc_bxEstar_source, "push_gc_bxEstar_explicit_multistage_cuda") -_push_gc_Bstar_kernel = CudaKernel(_push_gc_Bstar_source, "push_gc_Bstar_explicit_multistage_cuda") - - -def push_gc_bxEstar_explicit_multistage_general_gpu( - markers, - n_cols, - first_init_idx, - first_free_idx, - mu_idx, - kind_map, - params_dev, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - unit_b1_1_dev, - unit_b1_2_dev, - unit_b1_3_dev, - grad_b_full_1_dev, - grad_b_full_2_dev, - grad_b_full_3_dev, - B_dot_b_coeffs_dev, - curl_unit_b_dot_b0_dev, - e_field_1_dev, - e_field_2_dev, - e_field_3_dev, - evaluate_e_field: bool, - dt_a: float, - dt_b: float, - last: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_explicit_multistage`, - for any domain in :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. - """ - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _push_gc_bxEstar_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_free_idx), - np.int32(mu_idx), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(unit_b1_1_dev), - *d(unit_b1_2_dev), - *d(unit_b1_3_dev), - *d(grad_b_full_1_dev), - *d(grad_b_full_2_dev), - *d(grad_b_full_3_dev), - *d(B_dot_b_coeffs_dev), - *d(curl_unit_b_dot_b0_dev), - *d(e_field_1_dev), - *d(e_field_2_dev), - *d(e_field_3_dev), - np.int32(bool(evaluate_e_field)), - np.float64(dt_a), - np.float64(dt_b), - np.float64(last), - ), - ) - - -def push_gc_Bstar_explicit_multistage_general_gpu( - markers, - n_cols, - first_init_idx, - first_free_idx, - mu_idx, - kind_map, - params_dev, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - grad_b_full_1_dev, - grad_b_full_2_dev, - grad_b_full_3_dev, - b2_1_dev, - b2_2_dev, - b2_3_dev, - curl_unit_b2_1_dev, - curl_unit_b2_2_dev, - curl_unit_b2_3_dev, - B_dot_b_coeffs_dev, - curl_unit_b_dot_b0_dev, - e_field_1_dev, - e_field_2_dev, - e_field_3_dev, - evaluate_e_field: bool, - dt_a: float, - dt_b: float, - last: float, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_explicit_multistage`, - for any domain in :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. - """ - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _push_gc_Bstar_kernel, - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_free_idx), - np.int32(mu_idx), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(grad_b_full_1_dev), - *d(grad_b_full_2_dev), - *d(grad_b_full_3_dev), - *d(b2_1_dev), - *d(b2_2_dev), - *d(b2_3_dev), - *d(curl_unit_b2_1_dev), - *d(curl_unit_b2_2_dev), - *d(curl_unit_b2_3_dev), - *d(B_dot_b_coeffs_dev), - *d(curl_unit_b_dot_b0_dev), - *d(e_field_1_dev), - *d(e_field_2_dev), - *d(e_field_3_dev), - np.int32(bool(evaluate_e_field)), - np.float64(dt_a), - np.float64(dt_b), - np.float64(last), - ), - ) - - -# --------------------------------------------------------------------------- -# Discrete-gradient (implicit) guiding-centre pushers. -# -# Despite the name these are NOT internally iterative: each call performs one -# Picard iteration, and the outer fixed-point loop lives in -# Pusher._push (the ``while`` over ``maxiter``/``tol``). So they are just as -# per-marker parallel as the explicit multistage pushers, and the residual -# each marker writes to ``residual_idx`` is what drives the outer loop. -# -# They need no domain Jacobian at all: the Poisson-matrix pieces -# (b_star_parallel, unit_b1 / b_star) are precomputed into marker columns by -# the propagator's init/eval kernels, so only a 1-form spline evaluation of -# grad|B| (and optionally E) at the midpoint is done here. -# --------------------------------------------------------------------------- - -_DG_1ST_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_1st_src.cu") - - -def _dg_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _DG_1ST_SRC - - -_dg_kernels = CudaKernelSet(_dg_source) - - -def _dg_launch( - name, - markers, - n_cols, - first_init_idx, - first_shift_idx, - residual_idx, - first_free_idx, - mu_idx, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - grad_b_full, - e_field, - evaluate_e_field, - dt, -): - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _dg_kernels[name], - n_markers, - ( - markers, - np.int32(n_cols), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_shift_idx), - np.int32(residual_idx), - np.int32(first_free_idx), - np.int32(mu_idx), - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(grad_b_full[0]), - *d(grad_b_full[1]), - *d(grad_b_full[2]), - *d(e_field[0]), - *d(e_field[1]), - *d(e_field[2]), - np.int32(bool(evaluate_e_field)), - np.float64(dt), - ), - ) - - -def push_gc_bxEstar_discrete_gradient_1st_order_gpu(*args, **kwargs): - """GPU replacement for one Picard iteration of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_discrete_gradient_1st_order`.""" - _dg_launch("push_gc_bxEstar_discrete_gradient_1st_order_cuda", *args, **kwargs) - - -def push_gc_Bstar_discrete_gradient_1st_order_gpu(*args, **kwargs): - """GPU replacement for one Picard iteration of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_discrete_gradient_1st_order`.""" - _dg_launch("push_gc_Bstar_discrete_gradient_1st_order_cuda", *args, **kwargs) - - -# --------------------------------------------------------------------------- -# push_gc_cc_J1_{H1vec,Hcurl,Hdiv}: single-stage (dt, no Butcher coefficients -# -- `stage` is accepted but unused by the CPU kernels too) velocity update -# for CurrentCoupling5DCurlb. All three read the same fields (b, norm_b1, -# curl_norm_b) and differ only in which FEEC space `u` lives in and how it -# is transformed to Cartesian: -# H1vec: u is already a vector field (eval_vectorfield_dev), no transform -# Hcurl: u is a 1-form; transform via g^-1 = (DF^T DF)^-1 -# Hdiv: u is a 2-form (like b); transform by dividing by det(DF) -# --------------------------------------------------------------------------- - -_PUSH_GC_CC_J1_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j1_src.cu") - - -def _j1_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _PUSH_GC_CC_J1_SRC - - -_j1_kernels = CudaKernelSet(_j1_source) - - -def _j1_launch( - name, - markers, - kind_map, - params_dev, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2, - norm_b1, - curl_norm_b, - u, - dt, -): - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _j1_kernels[name], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.float64(dt), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(norm_b1[0]), - *d(norm_b1[1]), - *d(norm_b1[2]), - *d(curl_norm_b[0]), - *d(curl_norm_b[1]), - *d(curl_norm_b[2]), - *d(u[0]), - *d(u[1]), - *d(u[2]), - ), - ) - - -def push_gc_cc_J1_H1vec_gpu(*args, **kwargs): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J1_H1vec`.""" - _j1_launch("push_gc_cc_J1_H1vec_cuda", *args, **kwargs) - - -def push_gc_cc_J1_Hcurl_gpu(*args, **kwargs): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J1_Hcurl`.""" - _j1_launch("push_gc_cc_J1_Hcurl_cuda", *args, **kwargs) - - -def push_gc_cc_J1_Hdiv_gpu(*args, **kwargs): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J1_Hdiv`.""" - _j1_launch("push_gc_cc_J1_Hdiv_cuda", *args, **kwargs) - - -# --------------------------------------------------------------------------- -# push_gc_cc_J2_stage_{H1vec,Hdiv}: multistage (a[stage]/b[stage]/last, same -# first_init_idx/first_free_idx accumulation scheme as -# push_gc_bxEstar_explicit_multistage above) position update for -# CurrentCoupling5DGradB. Both build the same b_prod/norm_b_prod -# cross-product matrices and e = (norm_b_prod @ b_prod @ u) / |B*_para|; -# H1vec evaluates u as a vector field and stops there (its DF/det(DF) are -# computed by the CPU reference but never used); Hdiv evaluates u as a -# 2-form and divides e by det(DF) as well. -# --------------------------------------------------------------------------- - -_PUSH_GC_CC_J2_STAGE_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j2_stage_src.cu") - - -def _j2_stage_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _PUSH_GC_CC_J2_STAGE_SRC - - -_j2_stage_kernels = CudaKernelSet(_j2_stage_source) - - -def _j2_stage_launch( - name, - markers, - first_init_idx, - first_free_idx, - kind_map, - params_dev, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2, - norm_b1, - curl_norm_b, - u, - dt_a, - dt_b, - last, -): - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _j2_stage_kernels[name], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_free_idx), - np.float64(dt_a), - np.float64(dt_b), - np.float64(last), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(norm_b1[0]), - *d(norm_b1[1]), - *d(norm_b1[2]), - *d(curl_norm_b[0]), - *d(curl_norm_b[1]), - *d(curl_norm_b[2]), - *d(u[0]), - *d(u[1]), - *d(u[2]), - ), - ) - - -def push_gc_cc_J2_stage_H1vec_gpu(*args, **kwargs): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_stage_H1vec`.""" - _j2_stage_launch("push_gc_cc_J2_stage_H1vec_cuda", *args, **kwargs) - - -def push_gc_cc_J2_stage_Hdiv_gpu(*args, **kwargs): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_stage_Hdiv`.""" - _j2_stage_launch("push_gc_cc_J2_stage_Hdiv_cuda", *args, **kwargs) - - -# --------------------------------------------------------------------------- -# push_gc_cc_J2_dg_init_Hdiv / push_gc_cc_J2_dg_Hdiv: the discrete-gradient -# variant of CurrentCoupling5DGradB's position push. Both are single-pass -# marker loops (no per-marker Newton solve -- the outer fixed-point loop in -# CurrentCoupling5DGradB.__call__ is over one global scalar `const` from an -# energy reduction, recomputed and re-applied to all markers each iteration). -# dg_init: like push_gc_cc_J2_stage_Hdiv's single-stage core, evaluated at -# the current position, straight `eta -= dt*e`. -# dg: evaluated at the midpoint eta_mid = mod((eta+eta_init)/2, 1), -# with a second U-field `ud` (discrete-gradient correction term, -# scaled by `const`) added before the same |B*_para|/det(DF) -# division, then `eta = alpha*(eta_init - dt*e) + (1-alpha)*eta_old`. -# --------------------------------------------------------------------------- - -_PUSH_GC_CC_J2_DG_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_push_gc_cc_j2_dg_src.cu") - - -def _j2_dg_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _DG_1ST_SRC + _PUSH_GC_CC_J2_DG_SRC - - -_j2_dg_kernels = CudaKernelSet(_j2_dg_source) - - -def push_gc_cc_J2_dg_init_Hdiv_gpu( - markers, - first_init_idx, - kind_map, - params_dev, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2, - norm_b1, - curl_norm_b, - u, - dt, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_dg_init_Hdiv`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _j2_dg_kernels["push_gc_cc_J2_dg_init_Hdiv_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.float64(dt), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(norm_b1[0]), - *d(norm_b1[1]), - *d(norm_b1[2]), - *d(curl_norm_b[0]), - *d(curl_norm_b[1]), - *d(curl_norm_b[2]), - *d(u[0]), - *d(u[1]), - *d(u[2]), - ), - ) - - -def push_gc_cc_J2_dg_Hdiv_gpu( - markers, - first_init_idx, - kind_map, - params_dev, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - b2, - norm_b1, - curl_norm_b, - u, - ud, - const, - alpha, - dt, -): - """GPU replacement for one call of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_cc_J2_dg_Hdiv`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _j2_dg_kernels["push_gc_cc_J2_dg_Hdiv_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.float64(dt), - np.float64(const), - np.float64(alpha), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(norm_b1[0]), - *d(norm_b1[1]), - *d(norm_b1[2]), - *d(curl_norm_b[0]), - *d(curl_norm_b[1]), - *d(curl_norm_b[2]), - *d(u[0]), - *d(u[1]), - *d(u[2]), - *d(ud[0]), - *d(ud[1]), - *d(ud[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# push_gc_bxEstar_discrete_gradient_1st_order_newton / -# push_gc_Bstar_discrete_gradient_1st_order_newton: one Newton iteration -# (per marker, so per-marker parallel like the non-Newton 1st_order variants -# above) for the Itoh-Abe discrete-gradient guiding-centre pushers. Unlike -# the *_1st_order Picard kernels, these read a richer set of pre-evaluated -# marker columns (the Hamiltonian and its gradient at several points along -# the coordinate axes, written by driftkinetic_hamiltonian/ -# grad_driftkinetic_hamiltonian eval_kernels -- both already CUDA-ported -# above) and solve one 3x3 (bxEstar) or 4x4 (Bstar, via Schur complement of -# its [[I,B],[C,1]] block structure) Newton step in closed form; no domain -# Jacobian is needed. Purely marker-local, no shared per-marker helper beyond -# what's already in _GENERAL_GEOMETRY_SRC. -# --------------------------------------------------------------------------- - -_DG_NEWTON_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_newton_src.cu") - - -def _dg_newton_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _DG_NEWTON_SRC - - -_dg_newton_kernels = CudaKernelSet(_dg_newton_source) - - -def _dg_newton_launch( - name, - markers, - first_init_idx, - first_shift_idx, - residual_idx, - first_free_idx, - mu_idx, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - grad_b_full, - B_dot_b_coeffs, - e_field, - phi_coeffs, - evaluate_e_field, - dt, -): - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _dg_newton_kernels[name], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_shift_idx), - np.int32(residual_idx), - np.int32(first_free_idx), - np.int32(mu_idx), - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(grad_b_full[0]), - *d(grad_b_full[1]), - *d(grad_b_full[2]), - *d(B_dot_b_coeffs), - *d(e_field[0]), - *d(e_field[1]), - *d(e_field[2]), - *d(phi_coeffs), - np.int32(bool(evaluate_e_field)), - np.float64(dt), - ), - ) - - -def push_gc_bxEstar_discrete_gradient_1st_order_newton_gpu(*args, **kwargs): - """GPU replacement for one Newton iteration of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_discrete_gradient_1st_order_newton`.""" - _dg_newton_launch("push_gc_bxEstar_discrete_gradient_1st_order_newton_cuda", *args, **kwargs) - - -def push_gc_Bstar_discrete_gradient_1st_order_newton_gpu(*args, **kwargs): - """GPU replacement for one Newton iteration of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_discrete_gradient_1st_order_newton`.""" - _dg_newton_launch("push_gc_Bstar_discrete_gradient_1st_order_newton_cuda", *args, **kwargs) - - -# --------------------------------------------------------------------------- -# push_gc_bxEstar_discrete_gradient_2nd_order / -# push_gc_Bstar_discrete_gradient_2nd_order: one Picard iteration (per -# marker, so per-marker parallel like the *_1st_order variants) of the -# Gonzalez discrete-gradient guiding-centre pushers -- unlike *_1st_order_newton -# this evaluates fields at the midpoint eta_mid = mod((eta_k+eta_n)/2, 1) and -# needs the domain Jacobian there (df_dispatch_dev/det3_dev), and only reads -# 2 pre-evaluated marker columns (H_n, H_k) instead of the Itoh-Abe set. -# --------------------------------------------------------------------------- - -_DG_2ND_ORDER_SRC = load_cuda_source(__file__, "pusher_kernels_gc_cuda/_dg_2nd_order_src.cu") - - -def _dg_2nd_order_source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _DG_2ND_ORDER_SRC - - -_dg_2nd_order_kernels = CudaKernelSet(_dg_2nd_order_source) - - -def push_gc_bxEstar_discrete_gradient_2nd_order_gpu( - markers, - first_init_idx, - first_shift_idx, - residual_idx, - first_free_idx, - mu_idx, - kind_map, - params_dev, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - unit_b1, - grad_b_full, - B_dot_b_coeffs, - curl_unit_b_dot_b0, - e_field, - evaluate_e_field, - dt, -): - """GPU replacement for one Picard iteration of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_bxEstar_discrete_gradient_2nd_order`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _dg_2nd_order_kernels["push_gc_bxEstar_discrete_gradient_2nd_order_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_shift_idx), - np.int32(residual_idx), - np.int32(first_free_idx), - np.int32(mu_idx), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(unit_b1[0]), - *d(unit_b1[1]), - *d(unit_b1[2]), - *d(grad_b_full[0]), - *d(grad_b_full[1]), - *d(grad_b_full[2]), - *d(B_dot_b_coeffs), - *d(curl_unit_b_dot_b0), - *d(e_field[0]), - *d(e_field[1]), - *d(e_field[2]), - np.int32(bool(evaluate_e_field)), - np.float64(dt), - ), - ) - - -def push_gc_Bstar_discrete_gradient_2nd_order_gpu( - markers, - first_init_idx, - first_shift_idx, - residual_idx, - first_free_idx, - mu_idx, - kind_map, - params_dev, - epsilon, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - grad_b_full, - b2, - curl_unit_b2, - B_dot_b_coeffs, - curl_unit_b_dot_b0, - e_field, - evaluate_e_field, - dt, -): - """GPU replacement for one Picard iteration of - :func:`~struphy.pic.pushing.pusher_kernels_gc.push_gc_Bstar_discrete_gradient_2nd_order`.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _dg_2nd_order_kernels["push_gc_Bstar_discrete_gradient_2nd_order_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(first_shift_idx), - np.int32(residual_idx), - np.int32(first_free_idx), - np.int32(mu_idx), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(grad_b_full[0]), - *d(grad_b_full[1]), - *d(grad_b_full[2]), - *d(b2[0]), - *d(b2[1]), - *d(b2[2]), - *d(curl_unit_b2[0]), - *d(curl_unit_b2[1]), - *d(curl_unit_b2[2]), - *d(B_dot_b_coeffs), - *d(curl_unit_b_dot_b0), - *d(e_field[0]), - *d(e_field[1]), - *d(e_field[2]), - np.int32(bool(evaluate_e_field)), - np.float64(dt), - ), - ) diff --git a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py b/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py deleted file mode 100644 index 45b3bb8e8..000000000 --- a/src/struphy/pic/pushing/pusher_kernels_sph_cuda.py +++ /dev/null @@ -1,223 +0,0 @@ -"""Hand-written CUDA replacements for the SPH velocity pushers in -:mod:`~struphy.pic.pushing.pusher_kernels_sph`, used only under -``ARRAY_BACKEND=cupy``. - -All three are per-marker loops whose inner work is a box-neighbourhood SPH -sum, i.e. the same computation as -:func:`~struphy.pic.sph_eval_kernels.box_based_kernel`. That sum is factored -out here into the ``box_based_kernel_dev`` device function, which mirrors the -already-validated accumulation loop in -:mod:`~struphy.pic.sph_eval_kernels_cuda`'s -``box_based_evaluation_flat_cuda`` -- the only difference is that the -marker's own box index is read from its ``n_cols - 2`` column instead of -being looked up with ``find_box_dev``. - -The smoothing-kernel evaluation (``smoothing_kernel_dev``) and the periodic -distance helper (``distance_dev``) are reused from that module's source -string, and the geometry (``df_dispatch_dev``, ``matrix_inv_dev``) from -:mod:`~struphy.pic.pushing.pusher_kernels_cuda`. - -Note on ``df_inv``: the CPU kernels call -:func:`~struphy.geometry.evaluation_kernels.df_inv` with -``avoid_round_off=False``, which is exactly ``matrix_inv(df(eta))`` -- the -manual zeroing of analytically-zero entries is skipped -- so -``matrix_inv_dev(df_dispatch_dev(...))`` reproduces it exactly. - -The three ``push_v_*_gpu`` entry points share one :class:`~struphy.cuda.CudaKernelSet` -(``_kernels``, keyed by CUDA entry-point name) built from ``_SPH_PUSHER_SRC`` -plus the geometry/SPH device functions it reuses. -""" -from struphy.cuda import CudaKernelSet, load_cuda_source - -_SPH_PUSHER_SRC = load_cuda_source(__file__, "pusher_kernels_sph_cuda/_sph_pusher_src.cu") - - -def _source(): - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - from struphy.pic.sph_eval_kernels_cuda import _SPH_EVAL_FLAT_SRC - - # _SPH_EVAL_FLAT_SRC brings distance_dev/smoothing_kernel_dev (and its own - # __global__ entry points, which are simply unused here); - # _GENERAL_GEOMETRY_SRC brings df_dispatch_dev/matrix_inv_dev/matvecT_dev. - return _GENERAL_GEOMETRY_SRC + _SPH_EVAL_FLAT_SRC + _SPH_PUSHER_SRC - - -_kernels = CudaKernelSet(_source) - - -def _launch( - name, - markers, - valid_mks, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - kind_map, - params_dev, - dt, - *, - weight_idx=None, - first_free_idx=None, - gravity=None, - kappa=None, -): - """Shared launch path for the three SPH velocity pushers.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - threads = 256 - blocks = (n_markers + threads - 1) // threads - - # valid_mks/holes are marker-row-indexed and therefore already device - # arrays; boxes/neighbours belong to SortingBoxes and are host-owned, so - # cp.asarray does the (small, box-sized) upload. - dev_valid = cp.asarray(valid_mks).astype(cp.int32, copy=False) - dev_boxes = cp.asarray(boxes).astype(cp.int32, copy=False) - dev_neigh = cp.asarray(neighbours).astype(cp.int32, copy=False) - dev_holes = cp.asarray(holes).astype(cp.int32, copy=False) - dev_boxes = cp.ascontiguousarray(dev_boxes) - dev_neigh = cp.ascontiguousarray(dev_neigh) - - args = [ - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - dev_valid, - ] - if weight_idx is not None: - args.append(np.int32(weight_idx)) - args.append(np.int32(first_free_idx)) - args += [ - dev_boxes, - np.int32(dev_boxes.shape[1]), - dev_neigh, - dev_holes, - np.int32(bool(periodic[0])), - np.int32(bool(periodic[1])), - np.int32(bool(periodic[2])), - np.int32(kernel_type), - np.float64(h[0]), - np.float64(h[1]), - np.float64(h[2]), - ] - if gravity is not None: - args.append(cp.ascontiguousarray(gravity, dtype=cp.float64)) - args.append(np.float64(kappa)) - args += [np.int32(kind_map), params_dev, np.float64(dt)] - - _kernels[name]((blocks,), (threads,), tuple(args)) - - -def push_v_sph_pressure_gpu( - markers, - valid_mks, - weight_idx, - first_free_idx, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - gravity, - kappa, - kind_map, - params_dev, - dt, -): - """GPU replacement for - :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_sph_pressure`.""" - _launch( - "push_v_sph_pressure_cuda", - markers, - valid_mks, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - kind_map, - params_dev, - dt, - weight_idx=weight_idx, - first_free_idx=first_free_idx, - gravity=gravity, - kappa=kappa, - ) - - -def push_v_sph_pressure_ideal_gas_gpu( - markers, - valid_mks, - weight_idx, - first_free_idx, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - gravity, - kappa, - kind_map, - params_dev, - dt, -): - """GPU replacement for - :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_sph_pressure_ideal_gas`.""" - _launch( - "push_v_sph_pressure_ideal_gas_cuda", - markers, - valid_mks, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - kind_map, - params_dev, - dt, - weight_idx=weight_idx, - first_free_idx=first_free_idx, - gravity=gravity, - kappa=kappa, - ) - - -def push_v_viscosity_gpu( - markers, - valid_mks, - first_free_idx, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - kind_map, - params_dev, - dt, -): - """GPU replacement for - :func:`~struphy.pic.pushing.pusher_kernels_sph.push_v_viscosity`.""" - _launch( - "push_v_viscosity_cuda", - markers, - valid_mks, - boxes, - neighbours, - holes, - periodic, - kernel_type, - h, - kind_map, - params_dev, - dt, - first_free_idx=first_free_idx, - ) diff --git a/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py b/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py deleted file mode 100644 index 2399a7dd6..000000000 --- a/src/struphy/pic/pushing/pusher_utilities_kernels_cuda.py +++ /dev/null @@ -1,63 +0,0 @@ -"""Hand-written CUDA replacement for -:func:`~struphy.pic.pushing.pusher_utilities_kernels.reflect`, used only -under ``ARRAY_BACKEND=cupy``. - -``reflect`` is called from -:meth:`~struphy.pic.base.Particles.apply_kinetic_bc` -- i.e. once per -Runge-Kutta stage, inside the per-step hot path -- whenever a species has a -reflecting boundary. With markers device-resident it was the last thing in -that path still forcing a host<->device round trip of the whole marker -array, so it is ported here. - -It reuses the geometry device functions (``df_dispatch_dev``, -``matrix_inv_dev``, ``matvec_dev``) from -:mod:`~struphy.pic.pushing.pusher_kernels_cuda`'s ``_GENERAL_GEOMETRY_SRC``. -Only the markers listed in ``outside_inds`` are touched, so the kernel is -launched over that index array rather than over all markers. -""" -from struphy.cuda import CudaKernelSet, launch_1d, load_cuda_source - -_REFLECT_SRC = load_cuda_source(__file__, "pusher_utilities_kernels_cuda/_reflect_src.cu") - - -def _reflect_source(): - # Deferred (not a module-level import): pulls in pusher_kernels_cuda only - # once a reflecting boundary is actually hit under CuPy, same as before. - from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - - return _GENERAL_GEOMETRY_SRC + _REFLECT_SRC - - -_reflect_kernels = CudaKernelSet(_reflect_source) - - -def reflect_gpu(markers, kind_map, params_dev, outside_inds, axis): - """GPU replacement for - :func:`~struphy.pic.pushing.pusher_utilities_kernels.reflect`, for any - domain in - :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. - - ``markers`` and ``outside_inds`` are device-resident; markers are updated - in place. - """ - import cupy as cp - import numpy as np - - n_outside = int(outside_inds.shape[0]) - if n_outside == 0: - return - - inds = cp.ascontiguousarray(outside_inds, dtype=cp.int64) - launch_1d( - _reflect_kernels["reflect_cuda"], - n_outside, - ( - markers, - np.int32(markers.shape[1]), - inds, - np.int32(n_outside), - np.int32(axis), - np.int32(kind_map), - params_dev, - ), - ) diff --git a/src/struphy/pic/sobol_seq.py b/src/struphy/pic/sobol_seq.py index dc607ff01..e24193949 100644 --- a/src/struphy/pic/sobol_seq.py +++ b/src/struphy/pic/sobol_seq.py @@ -19,17 +19,7 @@ import logging -# Deliberately plain NumPy, not cunumpy: this Sobol-sequence generator is a -# direct port of a scalar Fortran/MATLAB algorithm (bit shifts, per-element -# int()/bitwise_xor in Python loops, see i4_sobol below) with no vectorized -# hot loop to move to the device -- forcing it onto the CuPy backend only adds -# per-scalar host<->device sync overhead and, worse, breaks outright wherever -# a plain Python int/float is threaded through (xp.transpose on a list, -# xp.bitwise_xor expecting device arrays, etc.). The small (n, dim_num) array -# it produces is consumed via boolean/fancy-index assignment into the marker -# array (see Particles.phasespace_coords's setter in pic/base.py), which -# accepts a NumPy source even when the destination is a CuPy array. -import numpy as xp +import cunumpy as xp from scipy.stats import norm logger = logging.getLogger("struphy") diff --git a/src/struphy/pic/sorting.py b/src/struphy/pic/sorting.py index f2a0b0a7d..940264130 100644 --- a/src/struphy/pic/sorting.py +++ b/src/struphy/pic/sorting.py @@ -1,7 +1,5 @@ import logging -import numpy as np - try: from mpi4py.MPI import Intracomm except ModuleNotFoundError: @@ -231,26 +229,22 @@ def _set_boxes(self): n_particles = self._markers_shape[0] n_mkr = int(n_particles / n_box_in) + 1 - # scalar box-sizing estimate, not physics data; the rest of this box - # structure is host-resident (see below), and round() doesn't accept - # a CuPy 0-d array, so this must stay plain math regardless of backend. n_cols = round( - n_mkr * (1 + 1 / np.sqrt(n_mkr) + self._box_bufsize), + n_mkr * (1 + 1 / xp.sqrt(n_mkr) + self._box_bufsize), ) - # cartesian boxes (extra last row stores holes/outside particles); host-resident, - # read/written directly by the compiled, host-only sorting kernels - self._boxes = np.full((self._n_boxes + 1, n_cols), -1, dtype=int) - self._next_index = np.zeros((self._n_boxes + 1), dtype=int) - self._cumul_next_index = np.zeros((self._n_boxes + 2), dtype=int) - self._neighbours = np.zeros((self._n_boxes, 27), dtype=int) + # cartesian boxes (extra last row stores holes/outside particles) + self._boxes = xp.full((self._n_boxes + 1, n_cols), -1, dtype=int) + self._next_index = xp.zeros((self._n_boxes + 1), dtype=int) + self._cumul_next_index = xp.zeros((self._n_boxes + 2), dtype=int) + self._neighbours = xp.zeros((self._n_boxes, 27), dtype=int) # A particle on box i only sees particles in boxes that belong to neighbours[i] initialize_neighbours(self._neighbours, self.nx, self.ny, self.nz) # logger.info(f"{self._rank = }\n{self._neighbours = }") - self._swap_line_1 = np.zeros(self._markers_shape[1]) - self._swap_line_2 = np.zeros(self._markers_shape[1]) + self._swap_line_1 = xp.zeros(self._markers_shape[1]) + self._swap_line_2 = xp.zeros(self._markers_shape[1]) def _set_boundary_boxes(self): """Collect the (flat) indices of all non-ghost boxes that lie on the outer surface diff --git a/src/struphy/pic/sorting_kernels_cuda.py b/src/struphy/pic/sorting_kernels_cuda.py deleted file mode 100644 index 8fc0c9e57..000000000 --- a/src/struphy/pic/sorting_kernels_cuda.py +++ /dev/null @@ -1,140 +0,0 @@ -"""Hand-written CUDA replacement for the per-particle sorting-box bookkeeping in -:mod:`~struphy.pic.sorting_kernels`, used only under ``ARRAY_BACKEND=cupy``. - -:func:`~struphy.pic.sorting_kernels.assign_box_to_each_particle` and -:func:`~struphy.pic.sorting_kernels.assign_particles_to_boxes` are called every -time :meth:`~struphy.pic.base.Particles.put_particles_in_boxes` runs -- which is -every stage of every SPH pusher call (``Pusher._box_comm`` is true for all SPH -particles) and every :meth:`~struphy.pic.base.Particles.eval_density`/ -:meth:`~struphy.pic.base.Particles.eval_velocity` call (via ``_eval_sph``) -- -so unlike the SPH kernel-density evaluation itself, this is a genuine per-step -hot loop, not just a diagnostics entry point. - -Both operations are per-particle and read-mostly: - -* :func:`assign_box_to_each_particle_gpu` computes, for every marker, the - sorting box it currently sits in (:func:`~struphy.pic.sorting_kernels.find_box`, - identical logic to ``find_box_dev`` in :mod:`~struphy.pic.sph_eval_kernels_cuda`) - and writes the box id into the marker's box column. Embarrassingly parallel, - one thread per marker, no cross-thread interaction. -* :func:`assign_particles_to_boxes_gpu` inverts that: for every non-hole - marker, atomically claims the next free slot in its box's row of the - ``boxes`` array via ``atomicAdd`` (the parallel equivalent of the CPU - version's sequential ``next_index[a] += 1`` counter) and writes the marker's - row index there. The order in which markers land within a box's row is - therefore not deterministic (unlike the CPU kernel, which fills boxes in - marker-row order) -- harmless, since ``boxes`` is read as an unordered - membership list everywhere else (the 27-neighbour SPH sums, ghost-particle - bookkeeping). - -Only ``eta1``/``eta2``/``eta3`` (columns 0:3) and the box column are -transferred for the first kernel, and only the box column for the second -- -not the full ``markers`` array, which at ~350 bytes/row would make the -host<->device round trip far more expensive than the kernel itself for these -two lightweight per-particle operations (unlike -:func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_flat_gpu`, -which genuinely needs every marker column for the density sum). -""" -from struphy.cuda import CudaKernel, launch_1d, load_cuda_source - -import numpy as np - -_SORT_SRC = load_cuda_source(__file__, "sorting_kernels_cuda/_sort_src.cu") - -_assign_box_kernel = CudaKernel(_SORT_SRC, "assign_box_to_each_particle_cuda") -_assign_particles_kernel = CudaKernel(_SORT_SRC, "assign_particles_to_boxes_cuda") - - -def assign_box_to_each_particle_gpu( - markers, - holes, - nx, - ny, - nz, - domain_array, - box_index: int = -2, -): - """GPU port of :func:`~struphy.pic.sorting_kernels.assign_box_to_each_particle`. - - ``markers`` and ``holes`` are host arrays (see module docstring); only the - logical-position columns and the box column are round-tripped through the - device, not the full marker rows. - """ - import cupy as cp - - n_mks, n_cols = markers.shape - box_col = n_cols + box_index - - # markers[:, :3] is a strided view (stride n_cols) of the marker block; - # ascontiguousarray on the host packs it into one AoS (n_mks, 3) buffer - # matching the kernel's eta[3*p:3*p+3] layout, transferred in a single - # H2D copy instead of three (RawKernel reads the raw device pointer - # ignoring strides, so a per-axis strided view can't be passed directly -- - # see sph_eval_kernels_cuda.py's meshgrid contiguity fix for the same - # failure mode). - dev_eta = cp.ascontiguousarray(markers[:, :3], dtype=cp.float64) - dev_holes = cp.ascontiguousarray(holes, dtype=cp.int32) - dev_domain = cp.asarray(domain_array, dtype=cp.float64) - dev_box = cp.empty(n_mks, dtype=cp.float64) - - launch_1d( - _assign_box_kernel, - n_mks, - ( - dev_eta, - dev_holes, - n_mks, - int(nx), - int(ny), - int(nz), - dev_domain, - dev_box, - ), - ) - - # markers is device-resident; write the box column back in place - markers[:, box_col] = dev_box - - -def assign_particles_to_boxes_gpu( - markers, - holes, - boxes, - next_index, - box_index: int = -2, -): - """GPU port of :func:`~struphy.pic.sorting_kernels.assign_particles_to_boxes`. - - Fills ``boxes``/``next_index`` via an atomic scatter instead of the CPU - kernel's sequential counter -- see module docstring for why the resulting - (unordered) box membership is equivalent. - """ - import cupy as cp - - n_mks, n_cols = markers.shape - box_col = n_cols + box_index - n_box_rows, box_cols = boxes.shape - - dev_box_id = cp.ascontiguousarray(markers[:, box_col], dtype=cp.float64) - dev_holes = cp.ascontiguousarray(holes, dtype=cp.int32) - dev_boxes = cp.full((n_box_rows, box_cols), -1, dtype=cp.int32) - dev_next_index = cp.zeros(n_box_rows, dtype=cp.int32) - - launch_1d( - _assign_particles_kernel, - n_mks, - ( - dev_box_id, - dev_holes, - n_mks, - dev_boxes, - dev_next_index, - box_cols, - ), - ) - - # boxes/next_index belong to SortingBoxes and stay host-resident (they - # are also consumed by the host-only SPH kernels), so these two do - # need an explicit device->host copy. - boxes[:, :] = cp.asnumpy(dev_boxes) - next_index[:] = cp.asnumpy(dev_next_index) diff --git a/src/struphy/pic/sph_eval_kernels_cuda.py b/src/struphy/pic/sph_eval_kernels_cuda.py deleted file mode 100644 index 64ee0a4bd..000000000 --- a/src/struphy/pic/sph_eval_kernels_cuda.py +++ /dev/null @@ -1,367 +0,0 @@ -"""Hand-written CUDA replacement for the box-based SPH kernel-density evaluation -in :mod:`~struphy.pic.sph_eval_kernels`, used only under ``ARRAY_BACKEND=cupy``. - -:func:`~struphy.pic.sph_eval_kernels.box_based_evaluation_flat` (called from -:meth:`~struphy.pic.base.Particles._eval_sph`, in turn used by -:meth:`~struphy.pic.base.Particles.eval_density` and -:meth:`~struphy.pic.base.Particles.eval_velocity`) is the actual SPH -kernel-density-estimation sum -- the defining operation of "smoothed particle -hydrodynamics": reconstruct a continuous field at a set of evaluation points -by summing a smoothing kernel over every marker in the 27 sorting boxes -neighbouring each point. It is embarrassingly parallel across evaluation -points (unlike the pusher kernels, there is no per-marker output to race on), -which makes it a clean fit for one CUDA thread per evaluation point. - -:func:`box_based_evaluation_flat_gpu` ports :func:`~struphy.pic.sorting_kernels.find_box`, -the 27-neighbour box loop of :func:`~struphy.pic.sph_eval_kernels.box_based_kernel`, -and every smoothing kernel in :mod:`~struphy.pic.sph_smoothing_kernels` (all of -them: they are cheap closed-form tensor products of one-dimensional -trigonometric/Gaussian/linear kernels, or -- for ``linear_isotropic_3d`` -- a -simple radial one, so there is no reason to port only the default kernel -type). ``markers``/``boxes``/``neighbours``/``holes`` are the same -host-resident arrays used everywhere else in this backend (see -``ISSUE_cupy_particles_never_pushed.md``); this function round-trips them -through the device once per call, matching :func:`push_v_with_efield_cuboid_gpu` -in :mod:`~struphy.pic.pushing.pusher_kernels_cuda` -- ``_eval_sph`` is a -diagnostics/reconstruction entry point, not a per-step hot loop, so there is -no benefit to caching device buffers across calls the way the pushers do. -""" -from struphy.cuda import CudaKernel, launch_1d, load_cuda_source - -_SPH_EVAL_FLAT_SRC = load_cuda_source(__file__, "sph_eval_kernels_cuda/_sph_eval_flat_src.cu") - -_box_based_evaluation_flat_kernel = CudaKernel(_SPH_EVAL_FLAT_SRC, "box_based_evaluation_flat_cuda") -_box_based_evaluation_meshgrid_kernel = CudaKernel(_SPH_EVAL_FLAT_SRC, "box_based_evaluation_meshgrid_cuda") - - -def box_based_evaluation_flat_gpu( - markers, - eta1, - eta2, - eta3, - nx: int, - ny: int, - nz: int, - domain_array, - boxes, - neighbours, - holes, - periodic1: bool, - periodic2: bool, - periodic3: bool, - index: int, - kernel_type: int, - h1: float, - h2: float, - h3: float, - out, -): - """GPU replacement for one call of - :func:`~struphy.pic.sph_eval_kernels.box_based_evaluation_flat`. - - All inputs are host arrays (``markers``, ``domain_array``, ``boxes``, - ``neighbours``, ``holes``, matching the rest of the CuPy backend) except - ``eta1``/``eta2``/``eta3``/``out``, which may already be device-resident - (the caller passes whatever backend it's using for evaluation points). - Everything is round-tripped through the device once for this call. - """ - import cupy as cp - import numpy as np - - n_cols = markers.shape[1] - n_eval = eta1.shape[0] - n_box_cols = boxes.shape[1] - - dev_markers = cp.asarray(markers) - # ascontiguousarray, not asarray: eta1/eta2/eta3 may be arbitrary (e.g. - # strided/sliced) views, and the kernel indexes them as dense 1-D buffers - # -- asarray is a no-op on an already-CuPy, already-float64 view and - # would silently pass the RawKernel a pointer with the wrong stride. - dev_eta1 = cp.ascontiguousarray(eta1, dtype=cp.float64) - dev_eta2 = cp.ascontiguousarray(eta2, dtype=cp.float64) - dev_eta3 = cp.ascontiguousarray(eta3, dtype=cp.float64) - dev_domain = cp.asarray(domain_array, dtype=cp.float64) - dev_boxes = cp.asarray(boxes, dtype=cp.int32) - dev_neighbours = cp.asarray(neighbours, dtype=cp.int32) - dev_holes = cp.asarray(holes, dtype=cp.int32) - dev_out = cp.zeros(n_eval, dtype=cp.float64) - - launch_1d( - _box_based_evaluation_flat_kernel, - n_eval, - ( - dev_markers, - np.int32(n_cols), - dev_eta1, - dev_eta2, - dev_eta3, - np.int32(n_eval), - np.int32(nx), - np.int32(ny), - np.int32(nz), - dev_domain, - dev_boxes, - np.int32(n_box_cols), - dev_neighbours, - dev_holes, - np.int32(1 if periodic1 else 0), - np.int32(1 if periodic2 else 0), - np.int32(1 if periodic3 else 0), - np.int32(index), - np.int32(kernel_type), - np.float64(h1), - np.float64(h2), - np.float64(h3), - dev_out, - ), - ) - if isinstance(out, cp.ndarray): - out[:] = dev_out - else: - dev_out.get(out=out) - - -def box_based_evaluation_meshgrid_gpu( - markers, - eta1, - eta2, - eta3, - nx: int, - ny: int, - nz: int, - domain_array, - boxes, - neighbours, - holes, - periodic1: bool, - periodic2: bool, - periodic3: bool, - index: int, - kernel_type: int, - h1: float, - h2: float, - h3: float, - out, -): - """GPU replacement for one call of - :func:`~struphy.pic.sph_eval_kernels.box_based_evaluation_meshgrid`. - - ``eta1``, ``eta2``, ``eta3`` are the full 3-D meshgrid arrays (as produced - by ``xp.meshgrid(..., indexing="ij")``); only their distinct 1-D axis - vectors are transferred to the device, see the CUDA source. Otherwise - behaves like :func:`box_based_evaluation_flat_gpu`. - """ - import cupy as cp - import numpy as np - - n_cols = markers.shape[1] - n1_eval, n2_eval, n3_eval = eta1.shape[0], eta2.shape[1], eta3.shape[2] - n_box_cols = boxes.shape[1] - - dev_markers = cp.asarray(markers) - # ascontiguousarray, not asarray: eta1[:,0,0] etc. are strided views into - # the full meshgrid (stride = the *other* axes' extents, not 1 element), - # and the kernel indexes them as dense 1-D buffers -- asarray is a no-op - # on an already-CuPy view and would silently pass the RawKernel a pointer - # with the wrong stride (this was a real bug: mismatched against the - # already-validated flat kernel on identical points until fixed). - dev_eta1 = cp.ascontiguousarray(eta1[:, 0, 0], dtype=cp.float64) - dev_eta2 = cp.ascontiguousarray(eta2[0, :, 0], dtype=cp.float64) - dev_eta3 = cp.ascontiguousarray(eta3[0, 0, :], dtype=cp.float64) - dev_domain = cp.asarray(domain_array, dtype=cp.float64) - dev_boxes = cp.asarray(boxes, dtype=cp.int32) - dev_neighbours = cp.asarray(neighbours, dtype=cp.int32) - dev_holes = cp.asarray(holes, dtype=cp.int32) - dev_out = cp.zeros((n1_eval, n2_eval, n3_eval), dtype=cp.float64) - - n_total = n1_eval * n2_eval * n3_eval - launch_1d( - _box_based_evaluation_meshgrid_kernel, - n_total, - ( - dev_markers, - np.int32(n_cols), - dev_eta1, - dev_eta2, - dev_eta3, - np.int32(n1_eval), - np.int32(n2_eval), - np.int32(n3_eval), - np.int32(nx), - np.int32(ny), - np.int32(nz), - dev_domain, - dev_boxes, - np.int32(n_box_cols), - dev_neighbours, - dev_holes, - np.int32(1 if periodic1 else 0), - np.int32(1 if periodic2 else 0), - np.int32(1 if periodic3 else 0), - np.int32(index), - np.int32(kernel_type), - np.float64(h1), - np.float64(h2), - np.float64(h3), - dev_out, - ), - ) - if isinstance(out, cp.ndarray): - out[:] = dev_out - else: - dev_out.get(out=out) - - -# --------------------------------------------------------------------------- -# naive_evaluation_flat / naive_evaluation_meshgrid: the O(N) reference -# implementation of the SPH kernel-density sum above (sums over every marker -# instead of the 27 neighbouring boxes), used only for testing/verification -# per the CPU docstring -- not a hot loop, so like box_based_evaluation_* -# this round-trips its (host-resident) inputs through the device once per -# call rather than caching device buffers. Reuses distance_dev/ -# smoothing_kernel_dev from _SPH_EVAL_FLAT_SRC above; unlike the box-based -# kernels the result is divided by Np, matching -# :func:`~struphy.pic.sph_eval_kernels.naive_evaluation_kernel`. -# --------------------------------------------------------------------------- - -_SPH_EVAL_NAIVE_SRC = load_cuda_source(__file__, "sph_eval_kernels_cuda/_sph_eval_naive_src.cu") - -_naive_evaluation_flat_kernel = CudaKernel(_SPH_EVAL_FLAT_SRC + _SPH_EVAL_NAIVE_SRC, "naive_evaluation_flat_cuda") -_naive_evaluation_meshgrid_kernel = CudaKernel( - _SPH_EVAL_FLAT_SRC + _SPH_EVAL_NAIVE_SRC, "naive_evaluation_meshgrid_cuda" -) - - -def naive_evaluation_flat_gpu( - markers, - Np: float, - eta1, - eta2, - eta3, - holes, - periodic1: bool, - periodic2: bool, - periodic3: bool, - index: int, - kernel_type: int, - h1: float, - h2: float, - h3: float, - out, -): - """GPU replacement for one call of - :func:`~struphy.pic.sph_eval_kernels.naive_evaluation_flat`.""" - import cupy as cp - import numpy as np - - n_cols = markers.shape[1] - n_markers = markers.shape[0] - n_eval = eta1.shape[0] - - dev_markers = cp.asarray(markers) - dev_eta1 = cp.ascontiguousarray(eta1, dtype=cp.float64) - dev_eta2 = cp.ascontiguousarray(eta2, dtype=cp.float64) - dev_eta3 = cp.ascontiguousarray(eta3, dtype=cp.float64) - dev_holes = cp.asarray(holes, dtype=cp.int32) - dev_out = cp.zeros(n_eval, dtype=cp.float64) - - launch_1d( - _naive_evaluation_flat_kernel, - n_eval, - ( - dev_markers, - np.int32(n_cols), - np.int32(n_markers), - np.float64(Np), - dev_eta1, - dev_eta2, - dev_eta3, - np.int32(n_eval), - dev_holes, - np.int32(1 if periodic1 else 0), - np.int32(1 if periodic2 else 0), - np.int32(1 if periodic3 else 0), - np.int32(index), - np.int32(kernel_type), - np.float64(h1), - np.float64(h2), - np.float64(h3), - dev_out, - ), - ) - if isinstance(out, cp.ndarray): - out[:] = dev_out - else: - dev_out.get(out=out) - - -def naive_evaluation_meshgrid_gpu( - markers, - Np: float, - eta1, - eta2, - eta3, - holes, - periodic1: bool, - periodic2: bool, - periodic3: bool, - index: int, - kernel_type: int, - h1: float, - h2: float, - h3: float, - out, -): - """GPU replacement for one call of - :func:`~struphy.pic.sph_eval_kernels.naive_evaluation_meshgrid`. - - Like :func:`box_based_evaluation_meshgrid_gpu`, ``eta1``/``eta2``/``eta3`` - are the 3 distinct 1-D axis vectors of the meshgrid, not the broadcast - arrays -- the CPU kernel this ports only ever reads - ``eta1[i,0,0]``/``eta2[0,j,0]``/``eta3[0,0,k]``. - """ - import cupy as cp - import numpy as np - - n_cols = markers.shape[1] - n_markers = markers.shape[0] - n1_eval, n2_eval, n3_eval = eta1.shape[0], eta2.shape[1], eta3.shape[2] - - dev_markers = cp.asarray(markers) - dev_eta1 = cp.ascontiguousarray(eta1[:, 0, 0], dtype=cp.float64) - dev_eta2 = cp.ascontiguousarray(eta2[0, :, 0], dtype=cp.float64) - dev_eta3 = cp.ascontiguousarray(eta3[0, 0, :], dtype=cp.float64) - dev_holes = cp.asarray(holes, dtype=cp.int32) - dev_out = cp.zeros((n1_eval, n2_eval, n3_eval), dtype=cp.float64) - - n_total = n1_eval * n2_eval * n3_eval - launch_1d( - _naive_evaluation_meshgrid_kernel, - n_total, - ( - dev_markers, - np.int32(n_cols), - np.int32(n_markers), - np.float64(Np), - dev_eta1, - dev_eta2, - dev_eta3, - np.int32(n1_eval), - np.int32(n2_eval), - np.int32(n3_eval), - dev_holes, - np.int32(1 if periodic1 else 0), - np.int32(1 if periodic2 else 0), - np.int32(1 if periodic3 else 0), - np.int32(index), - np.int32(kernel_type), - np.float64(h1), - np.float64(h2), - np.float64(h3), - dev_out, - ), - ) - if isinstance(out, cp.ndarray): - out[:] = dev_out - else: - dev_out.get(out=out) diff --git a/src/struphy/pic/tests/_bench_cuda_kernels_worker.py b/src/struphy/pic/tests/_bench_cuda_kernels_worker.py deleted file mode 100644 index 1dad40731..000000000 --- a/src/struphy/pic/tests/_bench_cuda_kernels_worker.py +++ /dev/null @@ -1,317 +0,0 @@ -""" -Worker script for :mod:`bench_cuda_kernels`. - -Times one of the CUDA-RawKernel-ported operations (see -:mod:`struphy.pic.pushing.pusher_kernels_cuda` and -:mod:`struphy.pic.sph_eval_kernels_cuda`) in isolation, on a single marker -set, and prints the median wall time per call (in seconds) as the last line -on stdout. Runs in a fresh subprocess per (backend, op, Np) combination -because ``ARRAY_BACKEND`` is read once at import time (by ``cunumpy``) and -cannot be changed within a running process. - -Usage:: - - ARRAY_BACKEND= python _bench_cuda_kernels_worker.py - -``op`` is one of: push_eta, push_v, eval_density_flat, eval_density_mesh, sort_boxes, do_sort -""" - -import statistics -import sys -import time - -N_WARMUP = 1 - - -def _timed_calls(call, n_reps: int) -> list[float]: - """Time ``n_reps`` calls to ``call()``, synchronizing the default CUDA - stream after each one under the CuPy backend. - - This matters specifically for :func:`_bench_eval_density`: unlike the - pusher kernels (which end in a synchronizing ``.get(out=markers)``), - ``box_based_evaluation_flat_gpu``/``_meshgrid_gpu`` write their result via - ``out[:] = dev_out`` whenever the caller's ``out`` is already a CuPy - array (as it is here, from ``xp.zeros_like`` on CuPy eval points) -- a - device-to-device copy that's asynchronous, so an unsynchronized - ``time.perf_counter()`` around the call would measure only kernel-launch - overhead, not completion. - """ - import cunumpy - - times = [] - for _ in range(n_reps): - t0 = time.perf_counter() - call() - if cunumpy.cupy_backend: - import cupy as cp - - cp.cuda.Stream.null.synchronize() - times.append(time.perf_counter() - t0) - return times - - -def _bench_pushers(op: str, Np: int, n_reps: int) -> list[float]: - """push_eta_stage / push_v_with_efield on a Cuboid, all-periodic marker - set -- the configuration :func:`~struphy.pic.pushing.pusher.Pusher`'s - device-resident fast paths require, matching ``params_PressureLessSPH.py``. - """ - from cunumpy import PyccelKernel - - from struphy import LoadingParameters, domains - from struphy.feec.psydac_derham import Derham - from struphy.feec.utilities import create_equal_random_arrays - from struphy.io.options import DerhamOptions - from struphy.ode.utils import ButcherTableau - from struphy.pic.particles import ParticlesSPH - from struphy.pic.pushing import pusher_kernels - from struphy.pic.pushing.pusher import Pusher - from struphy.topology.grids import TensorProductGrid - - domain = domains.Cuboid() - dt = 0.01 - - loading_params = LoadingParameters(Np=Np, seed=1234) - particles = ParticlesSPH(loading_params=loading_params, domain=domain) - particles.draw_markers(sort=False) - particles.initialize_weights() - - if op == "push_eta": - butcher = ButcherTableau() - pusher = Pusher( - particles, - PyccelKernel(pusher_kernels.push_eta_stage), - (butcher.a_stage, butcher.b, butcher.c), - domain.args_domain, - alpha_in_kernel=1.0, - n_stages=butcher.n_stages, - mpi_sort="each", - ) - elif op == "push_v": - grid = TensorProductGrid(num_elements=(16, 16, 8)) - derham_opts = DerhamOptions() - derham = Derham(grid, derham_opts, comm=None) - _, e_field = create_equal_random_arrays(derham.V1fem, seed=2345, flattened=True) - pusher = Pusher( - particles, - PyccelKernel(pusher_kernels.push_v_with_efield), - (derham.args_derham, e_field[0]._data, e_field[1]._data, e_field[2]._data, 1.0), - domain.args_domain, - alpha_in_kernel=1.0, - ) - else: - raise ValueError(f"unknown op {op!r}") - - for _ in range(N_WARMUP): - pusher(dt) - - return _timed_calls(lambda: pusher(dt), n_reps) - - -def _bench_eval_density(op: str, Np: int, n_reps: int) -> list[float]: - """box_based_evaluation_flat / _meshgrid, the SPH kernel-density-estimation - sum used by :meth:`~struphy.pic.base.Particles.eval_density`.""" - import cunumpy as xp - - from struphy import BoundaryParameters, LoadingParameters, SortingParameters, domains, perturbations - from struphy.fields_background.equils import ConstantVelocity - from struphy.pic.particles import ParticlesSPH - - domain = domains.Cuboid() - - # A fixed, known-safe box grid (rather than scaling boxes_per_dim with - # Np): the periodic ghost/self-communication bookkeeping in - # put_particles_in_boxes() needs a generous bufsize/box_bufsize margin - # that's easiest to just fix once here (a separate, pre-existing - # bufsize-tuning concern, unrelated to the kernels being benchmarked). - # Pseudo-random loading (the default), not tesselation, so Np is honored - # directly instead of being derived from ppb. - boxes_per_dim = (8, 8, 4) - n_boxes_per_dim = boxes_per_dim[0] - - loading_params = LoadingParameters(Np=Np, seed=1234) - background = ConstantVelocity(n=1.5, density_profile="constant") - background.domain = domain - pert = {"n": perturbations.ModesCosCos(ls=(1,), ms=(1,), amps=(0.3,))} - boundary_params = BoundaryParameters(bc_sph=("periodic", "periodic", "periodic")) - sorting_params = SortingParameters(boxes_per_dim=boxes_per_dim, box_bufsize=10.0) - - particles = ParticlesSPH( - loading_params=loading_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - bufsize=5.0, - domain=domain, - background=background, - perturbations=pert, - n_as_volume_form=True, - ) - particles.draw_markers(sort=False) - particles.initialize_weights() - - # flat: n_eval points total. mesh: n_eval**3 points (a full 3-D grid) -- - # kept an order of magnitude smaller so the *naive* NumPy/Pyccel - # meshgrid path (no vectorization across points) stays tractable up to - # Np=10**6; this doesn't change what's being measured, just how many - # evaluation points are timed per call. - n_eval = 40 if op == "eval_density_flat" else 12 - eta1 = xp.linspace(0.02, 0.98, n_eval) - eta2 = xp.linspace(0.02, 0.98, n_eval) - eta3 = xp.linspace(0.02, 0.98, n_eval) - h1 = h2 = h3 = 1.0 / n_boxes_per_dim - - if op == "eval_density_flat": - e1, e2, e3 = eta1, eta2, eta3 - elif op == "eval_density_mesh": - e1, e2, e3 = xp.meshgrid(eta1, eta2, eta3, indexing="ij") - else: - raise ValueError(f"unknown op {op!r}") - - def call(): - particles.eval_density(e1, e2, e3, h1, h2, h3, kernel_type="gaussian_3d") - - for _ in range(N_WARMUP): - call() - - return _timed_calls(call, n_reps) - - -def _bench_sort_boxes(Np: int, n_reps: int) -> list[float]: - """assign_box_to_each_particle + assign_particles_to_boxes (see - :mod:`~struphy.pic.sorting_kernels_cuda`) via - :meth:`~struphy.pic.base.Particles.put_particles_in_boxes` -- the - per-step box-sorting bookkeeping that both the SPH pushers - (``Pusher._box_comm``) and every ``eval_density``/``eval_velocity`` call - (via ``_eval_sph``) run before touching the box-based marker structure. - Same particle setup as :func:`_bench_eval_density`, minus the evaluation - points, since only the box bookkeeping is being timed here. - - ``sorting_boxes._communicate`` (true by default for SPH particles) is - forced off: it makes ``put_particles_in_boxes`` additionally run - ``_communicate_boxes()``, whose ghost-particle-destination bookkeeping - (``_get_destinations_box``, MPI-send-buffer prep) is pure host/NumPy - Python control flow -- unrelated to, and in single-process runs far more - expensive than, the two CUDA-ported kernels this benchmark targets. With - an actual MPI communicator that bookkeeping is unavoidable and would - dominate real per-step cost regardless of backend; it is out of scope for - this GPU-kernel benchmark specifically. - - Unlike :func:`_bench_eval_density`, ``boxes_per_dim`` is scaled with - ``Np`` here (targeting ~30 particles/box) instead of using a fixed - ``(8, 8, 4)`` grid: box-based SPH only makes sense with a modest, - Np-independent number of particles per box (that's the point of the - 27-neighbour search), and a fixed grid at Np=10**6 would put ~4000 - particles in every box, inflating the ``boxes`` array (and therefore its - host<->device transfer, which -- unlike the in-place CPU kernel -- this - GPU port must do) by two orders of magnitude for no physical reason.""" - from struphy import BoundaryParameters, LoadingParameters, SortingParameters, domains, perturbations - from struphy.fields_background.equils import ConstantVelocity - from struphy.pic.particles import ParticlesSPH - - domain = domains.Cuboid() - n_per_dim = max(2, round((Np / 30.0) ** (1.0 / 3.0))) - boxes_per_dim = (n_per_dim, n_per_dim, n_per_dim) - - loading_params = LoadingParameters(Np=Np, seed=1234) - background = ConstantVelocity(n=1.5, density_profile="constant") - background.domain = domain - pert = {"n": perturbations.ModesCosCos(ls=(1,), ms=(1,), amps=(0.3,))} - boundary_params = BoundaryParameters(bc_sph=("periodic", "periodic", "periodic")) - sorting_params = SortingParameters(boxes_per_dim=boxes_per_dim, box_bufsize=3.0) - - particles = ParticlesSPH( - loading_params=loading_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - bufsize=5.0, - domain=domain, - background=background, - perturbations=pert, - n_as_volume_form=True, - ) - particles.draw_markers(sort=False) - particles.initialize_weights() - particles.sorting_boxes._communicate = False - - def call(): - particles.put_particles_in_boxes() - - for _ in range(N_WARMUP): - call() - - return _timed_calls(call, n_reps) - - -def _bench_do_sort(Np: int, n_reps: int) -> list[float]: - """particles.do_sort(): reorders markers so same-box rows are contiguous - (called periodically, per ``EnvironmentOptions.sort_step``). Times - per-call: unlike the other ops here, this is NOT a CUDA-ported op -- - :func:`~struphy.pic.sorting_kernels.sort_boxed_particles` (the Pyccel - cycle-sort) and its NumPy-argsort alternative (see - ``Particles.do_sort``/``_sort_boxed_particles_numpy`` in - ``struphy.pic.base``) are both host-only; ``do_sort`` under CuPy now - automatically picks the argsort path since it measured faster even on - plain CPU (no GPU kernel can help here: the dominant cost is the - marker-row gather, on the always-host-resident ``markers`` array -- see - ``ISSUE_cupy_particles_never_pushed.md``). This benchmark exists to make - that "no GPU win here, and here's why" result reproducible, not because - a speedup is expected.""" - from struphy import BoundaryParameters, LoadingParameters, SortingParameters, domains, perturbations - from struphy.fields_background.equils import ConstantVelocity - from struphy.pic.particles import ParticlesSPH - - domain = domains.Cuboid() - n_per_dim = max(2, round((Np / 30.0) ** (1.0 / 3.0))) - boxes_per_dim = (n_per_dim, n_per_dim, n_per_dim) - - loading_params = LoadingParameters(Np=Np, seed=1234) - background = ConstantVelocity(n=1.5, density_profile="constant") - background.domain = domain - pert = {"n": perturbations.ModesCosCos(ls=(1,), ms=(1,), amps=(0.3,))} - boundary_params = BoundaryParameters(bc_sph=("periodic", "periodic", "periodic")) - sorting_params = SortingParameters(boxes_per_dim=boxes_per_dim, box_bufsize=3.0) - - particles = ParticlesSPH( - loading_params=loading_params, - boundary_params=boundary_params, - sorting_params=sorting_params, - bufsize=5.0, - domain=domain, - background=background, - perturbations=pert, - n_as_volume_form=True, - ) - particles.draw_markers(sort=False) - particles.initialize_weights() - particles.sorting_boxes._communicate = False - - def call(): - particles.do_sort() - - for _ in range(N_WARMUP): - call() - - return _timed_calls(call, n_reps) - - -def main(op: str, Np: int, n_reps: int) -> float: - if op in ("push_eta", "push_v"): - times = _bench_pushers(op, Np, n_reps) - elif op in ("eval_density_flat", "eval_density_mesh"): - times = _bench_eval_density(op, Np, n_reps) - elif op == "sort_boxes": - times = _bench_sort_boxes(Np, n_reps) - elif op == "do_sort": - times = _bench_do_sort(Np, n_reps) - else: - raise ValueError(f"unknown op {op!r}") - - return statistics.median(times) - - -if __name__ == "__main__": - op = sys.argv[1] - Np = int(sys.argv[2]) - n_reps = int(sys.argv[3]) - - median_time = main(op, Np, n_reps) - print(median_time) diff --git a/src/struphy/pic/tests/bench_cuda_kernels.py b/src/struphy/pic/tests/bench_cuda_kernels.py deleted file mode 100644 index 0c0594758..000000000 --- a/src/struphy/pic/tests/bench_cuda_kernels.py +++ /dev/null @@ -1,79 +0,0 @@ -""" -Standalone benchmark (not a pytest test) that measures the NumPy-vs-CuPy -speedup of the CUDA ``RawKernel``-ported particle operations: - -* ``push_eta`` -- :func:`~struphy.pic.pushing.pusher_kernels_cuda.push_eta_rk_periodic_gpu` -* ``push_v`` -- :func:`~struphy.pic.pushing.pusher_kernels_cuda.push_v_with_efield_cuboid_gpu` -* ``eval_density_flat`` -- :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_flat_gpu` -* ``eval_density_mesh`` -- :func:`~struphy.pic.sph_eval_kernels_cuda.box_based_evaluation_meshgrid_gpu` -* ``sort_boxes`` -- :func:`~struphy.pic.sorting_kernels_cuda.assign_box_to_each_particle_gpu` + - :func:`~struphy.pic.sorting_kernels_cuda.assign_particles_to_boxes_gpu` -* ``do_sort`` -- :meth:`~struphy.pic.base.Particles.do_sort` -- NOT CUDA-ported (see - ``_bench_cuda_kernels_worker.py``'s docstring for why); included for an honest, reproducible - "no GPU win here" comparison alongside the operations that do speed up. - -across a marker-count (``Np``) sweep, one subprocess per (backend, op, Np) -combination (``ARRAY_BACKEND`` is read once at import time by ``cunumpy`` and -can't be changed within a running process -- see the worker script, -``_bench_cuda_kernels_worker.py``). - -Usage:: - - python src/struphy/pic/tests/bench_cuda_kernels.py [--ops push_eta,push_v,eval_density_flat,eval_density_mesh] \\ - [--sizes 2000,20000,200000] [--repeats 5] - -Requires a CUDA-capable GPU and the CuPy backend to be installed; the NumPy -side of each comparison runs regardless. -""" - -import argparse -import os -import subprocess -import sys - -WORKER = os.path.join(os.path.dirname(__file__), "_bench_cuda_kernels_worker.py") - -ALL_OPS = ("push_eta", "push_v", "eval_density_flat", "eval_density_mesh", "sort_boxes", "do_sort") - - -def _median_runtime(backend: str, op: str, Np: int, n_reps: int) -> float: - env = dict(os.environ) - env["ARRAY_BACKEND"] = backend - - cmd = [sys.executable, WORKER, op, str(Np), str(n_reps)] - out = subprocess.run(cmd, env=env, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) - return float(out.stdout.strip().splitlines()[-1]) - - -def main(): - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - parser.add_argument( - "--ops", - type=str, - default=",".join(ALL_OPS), - help=f"comma-separated list of operations to benchmark (default: all of {ALL_OPS})", - ) - parser.add_argument( - "--sizes", - type=str, - default="2000,20000,200000", - help="comma-separated list of Np values to sweep (default: 2000,20000,200000)", - ) - parser.add_argument("--repeats", type=int, default=5, help="repeats per (op, Np) point, median is reported") - args = parser.parse_args() - - ops = args.ops.split(",") - sizes = [int(s) for s in args.sizes.split(",")] - - for op in ops: - print(f"\n=== {op} ===") - print(f"{'Np':>10} {'numpy [ms]':>12} {'cupy [ms]':>12} {'speedup':>10}") - for Np in sizes: - t_numpy = _median_runtime("numpy", op, Np, args.repeats) - t_cupy = _median_runtime("cupy", op, Np, args.repeats) - speedup = t_numpy / t_cupy - print(f"{Np:>10} {t_numpy * 1e3:>12.3f} {t_cupy * 1e3:>12.3f} {speedup:>9.2f}x") - - -if __name__ == "__main__": - main() diff --git a/src/struphy/pic/tests/bench_mpi_sort_markers.py b/src/struphy/pic/tests/bench_mpi_sort_markers.py deleted file mode 100644 index 3e3603fd0..000000000 --- a/src/struphy/pic/tests/bench_mpi_sort_markers.py +++ /dev/null @@ -1,219 +0,0 @@ -"""Standalone MPI benchmark for :meth:`Particles.mpi_sort_markers`. - -The benchmark does not construct or run a Struphy simulation. It creates a -mesh-less :class:`~struphy.pic.particles.Particles6D` instance, redistributes -uniformly placed markers, and measures the complete marker exchange. Marker -IDs are checked after every measured call, so this is useful while changing -the implementation of ``mpi_sort_markers``. - -Run with MPI (one process per rank/GPU), for example:: - - mpiexec -n 4 python src/struphy/pic/tests/bench_mpi_sort_markers.py \ - --sizes 10000,100000,1000000 --repeats 10 - -For the NumPy baseline, set ``ARRAY_BACKEND`` before Python imports -``cunumpy`` (the benchmark does not change the backend at runtime):: - - ARRAY_BACKEND=numpy python src/struphy/pic/tests/bench_mpi_sort_markers.py \ - --sizes 100000,1000000 --repeats 10 - -For CuPy, use instead (one rank per GPU; device binding and the MPI opt-in for the -CuPy backend are handled automatically, see below):: - - ARRAY_BACKEND=cupy mpiexec -n 4 python src/struphy/pic/tests/bench_mpi_sort_markers.py - -The reported time is the maximum wall time over ranks (the useful MPI step -time). GPU streams are synchronized around the timed region. -""" - -import argparse -import os -import time - -# cunumpy does not import feectools.ddm.mpi (verified), so this is safe to import -# first and do CUDA-only, MPI-independent setup (device binding, the MPI opt-in env -# var below) before struphy -- which does transitively import feectools.ddm.mpi as -# part of its own __init__ -- gets imported next. -import cunumpy as xp -import numpy as np - -# Under CuPy with more than one MPI rank per node, every rank must bind to its own -# GPU -- cunumpy defaults to device 0, so without this every rank on a node would -# contend for the same GPU instead of getting one each (same pattern as the -# profiling/examples/*/params_*_scaling.py cases). SLURM_LOCALID (the rank's index -# within its node) is set by srun before this process starts, so it works without -# MPI being initialized yet. Falls back to device 0 outside SLURM. -if xp.cupy_backend: - xp.set_device(int(os.environ.get("SLURM_LOCALID", 0))) - - # feectools.ddm.mpi disables MPI by default on the CuPy backend; this benchmark - # is specifically meant to exercise the real multi-rank exchange, so opt back in. - # Must be set before struphy (hence feectools.ddm.mpi) is imported below -- - # feectools.ddm.mpi reads this env var once, at its own import time, so setting - # it any later would silently have no effect. - os.environ.setdefault("FEECTOOLS_ENABLE_MPI", "1") - -# struphy must be imported before feectools.ddm.mpi is imported anywhere else: -# struphy/__init__.py sets MPI4PY_RC_THREAD_LEVEL=funneled and disables hcoll -# *before* mpi4py's MPI_Init_thread runs, which is required to avoid a -# hcoll/Alltoallv segfault on this cluster (see the comment there). struphy itself -# imports feectools.ddm.mpi as part of this same import, so this line satisfies -# both ordering requirements at once. -from feectools.ddm.mpi import mpi as MPI - -from struphy import BoundaryParameters, LoadingParameters, SortingParameters -from struphy.pic.particles import Particles6D - - -def _sync_device(): - if xp.cupy_backend: - import cupy as cp - - cp.cuda.Stream.null.synchronize() - - -def _host(a): - """Convert either backend's array to a NumPy array.""" - if xp.cupy_backend: - import cupy as cp - - return cp.asnumpy(a) - return np.asarray(a) - - -def _id_signature(comm, local_ids): - """Return global count/sum/sum-of-squares using scalar MPI reductions. - - ``feectools`` supplies a singleton ``MockComm`` when the script is run - without ``mpiexec``; unlike mpi4py, its lowercase ``allgather`` does not - return a Python list. Scalar reductions work for both communicators and - avoid any CuPy/NumPy array dispatch in the validation path. - - The sum-of-squares must be accumulated as Python (arbitrary-precision) - ints, not float64: at Np in the millions the sum of squared IDs reaches - ~1e17-1e20, past float64's exact-integer range (2^53 ~= 9e15), so - summing the same values in a different order -- which is exactly what - happens here, since mpi_sort_markers regroups which rank holds which - IDs -- rounds to a different (both inexact) result and produces a - false-positive "lost or duplicated" mismatch despite the exchange being - exact. Python ints have no such limit and integer addition is exactly - associative, so this is order-independent regardless of Np. - """ - ids = _host(local_ids).astype(np.int64, copy=False) - local_sumsq = sum(int(v) * int(v) for v in ids.tolist()) - if comm.Get_size() == 1: - return int(ids.size), int(ids.sum(dtype=np.int64)), local_sumsq - return ( - int(comm.allreduce(int(ids.size), op=MPI.SUM)), - int(comm.allreduce(int(ids.sum(dtype=np.int64)), op=MPI.SUM)), - int(comm.allreduce(local_sumsq, op=MPI.SUM)), - ) - - -def _global_max(comm, value): - return value if comm.Get_size() == 1 else comm.allreduce(value, op=MPI.MAX) - - -def _randomize_positions(particles, seed): - # Keep marker rows/IDs intact; only move valid markers to create traffic. - xp.random.seed(seed) - # ``n_mks_loc`` is a backend scalar under CuPy; shape tuples require a - # native Python integer. - n_local = int(particles.n_mks_loc) - particles.positions = xp.random.random((n_local, 3)) - - -def _check(particles, comm, expected_ids): - got_ids = _id_signature(comm, particles.markers[particles.valid_mks, -1]) - if got_ids != expected_ids: - raise AssertionError("mpi_sort_markers lost or duplicated marker IDs") - - # Check ownership on the host, avoiding a device-to-host synchronization - # inside the timed region. Every real marker must be strictly inside its - # rank's three-dimensional subdomain. - positions = _host(particles.positions) - bounds = np.asarray(particles.domain_array[particles.mpi_rank]).reshape(3, 3) - if positions.size: - inside = np.all((positions > bounds[:, 0]) & (positions < bounds[:, 1]), axis=1) - if not np.all(inside): - raise AssertionError("mpi_sort_markers left markers on the wrong rank") - - -def _one_size(comm, np_global, repeats, seed, check): - loading = LoadingParameters( - Np=np_global, - seed=seed, - moments=(0.0, 0.0, 0.0, 1.0, 1.0, 1.0), - spatial="uniform", - ) - sorting = SortingParameters(boxes_per_dim=None) - boundary = BoundaryParameters(bc=["periodic", "periodic", "periodic"]) - particles = Particles6D( - comm_world=comm, - loading_params=loading, - sorting_params=sorting, - boundary_params=boundary, - ) - # Loading is intentionally unsorted: this gives every rank a global, - # uniform sample that must be exchanged by the first sort. - particles.draw_markers(sort=False) - comm.Barrier() - _sync_device() - - expected_ids = _id_signature(comm, particles.markers[particles.valid_mks, -1]) - - def prepare(i): - _randomize_positions(particles, seed + 1009 * (i + 1) + comm.Get_rank()) - _sync_device() - comm.Barrier() - - # Warm up MPI requests, allocation paths, and CuPy kernels. - prepare(-1) - particles.mpi_sort_markers(apply_bc=False, do_test=False) - _sync_device() - comm.Barrier() - if check: - _check(particles, comm, expected_ids) - - samples = [] - for i in range(repeats): - prepare(i) - _sync_device() - t0 = time.perf_counter() - particles.mpi_sort_markers(apply_bc=False, do_test=False) - _sync_device() - elapsed = time.perf_counter() - t0 - samples.append(_global_max(comm, elapsed)) - if check: - _check(particles, comm, expected_ids) - - return float(np.median(samples)), float(np.percentile(samples, q=95)) - - -def main(): - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - parser.add_argument("--sizes", default="10000,100000,1000000", help="global marker counts to sweep") - parser.add_argument("--repeats", type=int, default=10, help="measured calls per size") - parser.add_argument("--seed", type=int, default=1607) - parser.add_argument("--no-check", action="store_true", help="skip ID/ownership checks after each call") - args = parser.parse_args() - - comm = MPI.COMM_WORLD - sizes = [int(value) for value in args.sizes.split(",") if value] - if args.repeats < 1 or any(size < comm.Get_size() for size in sizes): - raise ValueError("repeats must be positive and every size must be at least the MPI rank count") - - if comm.Get_rank() == 0: - backend = "cupy" if xp.cupy_backend else "numpy" - print(f"mpi_sort_markers benchmark: ranks={comm.Get_size()}, backend={backend}") - print(f"{'Np (global)':>14} {'median [ms]':>14} {'p95 [ms]':>14} {'markers/s':>16}") - - for size in sizes: - median, p95 = _one_size(comm, size, args.repeats, args.seed, not args.no_check) - if comm.Get_rank() == 0: - rate = size / median if median else float("inf") - print(f"{size:>14d} {median * 1e3:>14.3f} {p95 * 1e3:>14.3f} {rate:>16.3e}") - - -if __name__ == "__main__": - main() diff --git a/src/struphy/pic/tests/test_accum_vec_H1.py b/src/struphy/pic/tests/test_accum_vec_H1.py index 769d004c7..8a1ec6122 100644 --- a/src/struphy/pic/tests/test_accum_vec_H1.py +++ b/src/struphy/pic/tests/test_accum_vec_H1.py @@ -115,9 +115,7 @@ def test_accum_poisson(num_elements, degree, bcs, mapping, num_clones, Np=10000, params = { "grid": {"num_elements": num_elements}, - # num_elements is a plain Python list; CuPy's prod() (unlike NumPy's) - # doesn't accept one, so wrap it explicitly. - "kinetic": {"test_particles": {"markers": {"Np": Np, "ppc": Np / xp.prod(xp.array(num_elements))}}}, + "kinetic": {"test_particles": {"markers": {"Np": Np, "ppc": Np / xp.prod(num_elements)}}}, } grid = TensorProductGrid(num_elements=num_elements) @@ -179,10 +177,9 @@ def test_accum_poisson(num_elements, degree, bcs, mapping, num_clones, Np=10000, _sqrtg = float(domain.jacobian_det(0.5, 0.5, 0.5, squeeze_out=True)) - # particles.weights is always host (NumPy). logger.info( - f"rank {mpi_rank}: weights min={float(particles.weights.min()):.6g}, " - f"max={float(particles.weights.max()):.6g} " + f"rank {mpi_rank}: weights min={float(xp.min(particles.weights)):.6g}, " + f"max={float(xp.max(particles.weights)):.6g} " f"(expected range [{0.5 * _sqrtg / Np:.6g}, {1.5 * _sqrtg / Np:.6g}])" ) @@ -471,12 +468,8 @@ def u_xyz(x, y, z): # indexing particles.markers directly, since those already apply the # # correct valid_mks mask (excludes holes and ghosts). # # ------------------------------------------------------------------ # - # particles.positions is always host (NumPy), while n_xyz (like the rest - # of this file) follows the active array backend; convert both ways here. eta = particles.positions - n_vals = n_xyz(xp.asarray(eta[:, 0]), xp.asarray(eta[:, 1]), xp.asarray(eta[:, 2])) - n_vals = xp.to_numpy(n_vals) - particles.markers[particles.valid_mks, particles.first_free_idx] = n_vals + particles.markers[particles.valid_mks, particles.first_free_idx] = n_xyz(eta[:, 0], eta[:, 1], eta[:, 2]) # ------------------------------------------------------------------ # # Accumulate the weak-divergence-1form RHS vector V^1. # diff --git a/src/struphy/pic/tests/test_binning.py b/src/struphy/pic/tests/test_binning.py index e94542eb7..fcaa2ba1d 100644 --- a/src/struphy/pic/tests/test_binning.py +++ b/src/struphy/pic/tests/test_binning.py @@ -86,9 +86,6 @@ def test_binning_6D_full_f(mapping, show_plot=False): [False, False, False, True, False, False], [v1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) v1_plot = v1_bins[:-1] + dv / 2 @@ -136,9 +133,6 @@ def test_binning_6D_full_f(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) e1_plot = e1_bins[:-1] + de / 2 @@ -203,9 +197,6 @@ def test_binning_6D_full_f(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) e1_plot = e1_bins[:-1] + de / 2 @@ -346,9 +337,6 @@ def test_binning_6D_delta_f(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) e1_plot = e1_bins[:-1] + de / 2 @@ -413,9 +401,6 @@ def test_binning_6D_delta_f(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) e1_plot = e1_bins[:-1] + de / 2 @@ -566,9 +551,6 @@ def test_binning_6D_full_f_mpi(mapping, show_plot=False): [False, False, False, True, False, False], [v1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -625,9 +607,6 @@ def test_binning_6D_full_f_mpi(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -729,9 +708,6 @@ def test_binning_6D_full_f_mpi(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -901,9 +877,6 @@ def test_binning_6D_delta_f_mpi(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -1007,9 +980,6 @@ def test_binning_6D_delta_f_mpi(mapping, show_plot=False): [True, False, False, False, False, False], [e1_bins], ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) # Reduce all threads to get complete result if comm is None: @@ -1181,9 +1151,6 @@ def test_current_helper(moments, u_axis, current_axis, ana_func): [e_bins], f"current_{current_axis}", ) - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) e_plot = e_bins[:-1] + de / 2 @@ -1241,9 +1208,6 @@ def test_current_helper(moments, u_axis, current_axis, ana_func): components = [True, False, False, False, False, False] binned_res, r2 = particles.binning(components, [e_bins], "current_2") - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) e_plot = e_bins[:-1] + de / 2 @@ -1338,9 +1302,6 @@ def test_binning_energy_tensor_6D_full_f(mapping, show_plot=False): for i in [11, 22, 33, 12, 13, 23]: binned_res, r2 = particles.binning(components, [e_bins], f"energy_tensor_{i}") - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) ana_res = ana_func(e_plot) @@ -1434,9 +1395,6 @@ def test_binning_heat_flux_6D_full_f(mapping, show_plot=False): for i in range(1, 4): binned_res, r2 = particles.binning(components, [e_bins], f"heat_flux_{i}") - # particles.binning() is always host (NumPy); convert to the - # active backend to match the rest of this test's xp-based arrays. - binned_res = xp.asarray(binned_res) ana_res = ana_func(e_plot) binned_res += 1 diff --git a/src/struphy/pic/tests/test_draw_parallel.py b/src/struphy/pic/tests/test_draw_parallel.py index 944240c88..3751a3e7f 100644 --- a/src/struphy/pic/tests/test_draw_parallel.py +++ b/src/struphy/pic/tests/test_draw_parallel.py @@ -48,8 +48,7 @@ def test_draw(num_elements, degree, bcs, mapping, ppc=10): """Asserts whether all particles are on the correct process after `particles.mpi_sort_markers()`.""" - import numpy as np - from cunumpy import to_numpy + import cunumpy as xp from feectools.ddm.mpi import mpi as MPI from struphy import BoundaryParameters, LoadingParameters, WeightsParameters, domains @@ -102,7 +101,7 @@ def test_draw(num_elements, degree, bcs, mapping, ppc=10): particles.initialize_weights() _w0 = particles.weights logger.info("Test weights:") - logger.info(f"rank {rank}: {_w0.shape} {np.min(_w0)} {np.max(_w0)}") + logger.info(f"rank {rank}: {_w0.shape} {xp.min(_w0)} {xp.max(_w0)}") comm.Barrier() logger.info("Number of particles w/wo holes on each process before sorting : ") @@ -117,21 +116,17 @@ def test_draw(num_elements, degree, bcs, mapping, ppc=10): logger.info(f"Rank {rank} : {particles.n_mks_loc} {particles.markers.shape[0]}") # are all markers in the correct domain? - # Markers follow the active backend; compare on the host so the rest of - # this test can use plain NumPy. - domain_array_host = to_numpy(derham.domain_array) - markers_host = to_numpy(particles.markers) - conds = np.logical_and( - markers_host[:, :3] > domain_array_host[rank, 0::3], - markers_host[:, :3] < domain_array_host[rank, 1::3], + conds = xp.logical_and( + particles.markers[:, :3] > derham.domain_array[rank, 0::3], + particles.markers[:, :3] < derham.domain_array[rank, 1::3], ) - holes = markers_host[:, 0] == -1.0 - stay = np.all(conds, axis=1) + holes = particles.markers[:, 0] == -1.0 + stay = xp.all(conds, axis=1) - error_mks = particles.markers[np.logical_and(~stay, ~holes)] + error_mks = particles.markers[xp.logical_and(~stay, ~holes)] assert error_mks.size == 0, ( - f"rank {rank} | markers not on correct process: {np.nonzero(np.logical_and(~stay, ~holes))} \n corresponding positions:\n {error_mks[:, :3]}" + f"rank {rank} | markers not on correct process: {xp.nonzero(xp.logical_and(~stay, ~holes))} \n corresponding positions:\n {error_mks[:, :3]}" ) diff --git a/src/struphy/pic/tests/test_estimate_mem.py b/src/struphy/pic/tests/test_estimate_mem.py index f65891376..389343977 100644 --- a/src/struphy/pic/tests/test_estimate_mem.py +++ b/src/struphy/pic/tests/test_estimate_mem.py @@ -90,6 +90,7 @@ def test_nbytes_local_matches_real_allocation(Np): real_nbytes = ( real.markers.nbytes + real._sorting_etas.nbytes + + real._is_on_proc_domain.nbytes + real._can_stay.nbytes + real._holes.nbytes + real._ghost_particles.nbytes diff --git a/src/struphy/pic/tests/test_mat_vec_filler.py b/src/struphy/pic/tests/test_mat_vec_filler.py index 3f61a5a37..e0bdf4026 100644 --- a/src/struphy/pic/tests/test_mat_vec_filler.py +++ b/src/struphy/pic/tests/test_mat_vec_filler.py @@ -1,7 +1,6 @@ import logging import cunumpy as xp -import numpy as np import pytest logger = logging.getLogger("struphy") @@ -48,12 +47,12 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): logger.info(f"\nnum_elements={num_elements}, degree={degree}, bcs={bcs}\n") # DR attributes - pn = np.array(DR.degree) + pn = xp.array(DR.degree) tn1, tn2, tn3 = DR.V0fem.knots starts1 = {} - starts1["v0"] = np.array(DR.V0.starts) + starts1["v0"] = xp.array(DR.V0.starts) comm.Barrier() sleep(0.02 * (rank + 1)) @@ -70,74 +69,59 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): # only for M1 Mac users PSYDAC_BACKEND_GPYCCEL["flags"] = "-O3 -march=native -mtune=native -ffast-math -ffree-line-length-none" - # StencilMatrix/StencilVector._data follows the active array backend (it - # is genuinely device-resident under CuPy, for the GPU linear-algebra - # path). This test calls the raw (non-marshalled) filler kernels below - # directly with a single particle's scalar coordinates, which is a - # host-only scenario, so _data is brought to the host right after - # construction. - def _host(a): - return xp.to_numpy(a) - # _data of StencilMatrices/Vectors mat = {} vec = {} - mat["v0"] = _host(StencilMatrix(DR.V0, DR.V0, backend=PSYDAC_BACKEND_GPYCCEL, precompiled=True)._data) - vec["v0"] = _host(StencilVector(DR.V0)._data) + mat["v0"] = StencilMatrix(DR.V0, DR.V0, backend=PSYDAC_BACKEND_GPYCCEL, precompiled=True)._data + vec["v0"] = StencilVector(DR.V0)._data - mat["v3"] = _host(StencilMatrix(DR.V3, DR.V3, backend=PSYDAC_BACKEND_GPYCCEL, precompiled=True)._data) - vec["v3"] = _host(StencilVector(DR.V3)._data) + mat["v3"] = StencilMatrix(DR.V3, DR.V3, backend=PSYDAC_BACKEND_GPYCCEL, precompiled=True)._data + vec["v3"] = StencilVector(DR.V3)._data mat["v1"] = [] for i in range(3): mat["v1"] += [[]] for j in range(3): mat["v1"][-1] += [ - _host( - StencilMatrix( - DR.V1.spaces[i], - DR.V1.spaces[j], - backend=PSYDAC_BACKEND_GPYCCEL, - precompiled=True, - )._data, - ), + StencilMatrix( + DR.V1.spaces[i], + DR.V1.spaces[j], + backend=PSYDAC_BACKEND_GPYCCEL, + precompiled=True, + )._data, ] vec["v1"] = [] for i in range(3): - vec["v1"] += [_host(StencilVector(DR.V1.spaces[i])._data)] + vec["v1"] += [StencilVector(DR.V1.spaces[i])._data] mat["v2"] = [] for i in range(3): mat["v2"] += [[]] for j in range(3): mat["v2"][-1] += [ - _host( - StencilMatrix( - DR.V2.spaces[i], - DR.V2.spaces[j], - backend=PSYDAC_BACKEND_GPYCCEL, - precompiled=True, - )._data, - ), + StencilMatrix( + DR.V2.spaces[i], + DR.V2.spaces[j], + backend=PSYDAC_BACKEND_GPYCCEL, + precompiled=True, + )._data, ] vec["v2"] = [] for i in range(3): - vec["v2"] += [_host(StencilVector(DR.V2.spaces[i])._data)] + vec["v2"] += [StencilVector(DR.V2.spaces[i])._data] # Some filling for testing - fill_mat = np.reshape(np.arange(9, dtype=float), (3, 3)) + 1.0 - fill_vec = np.arange(3, dtype=float) + 1.0 + fill_mat = xp.reshape(xp.arange(9, dtype=float), (3, 3)) + 1.0 + fill_vec = xp.arange(3, dtype=float) + 1.0 # Random points in domain of process (VERY IMPORTANT to be in the right domain, otherwise NON-TRACKED errors occur in filler_kernels !!) - # DR.domain_array may be device-resident under CuPy; bring it to the host - # so eta1s/eta2s/eta3s (fed to raw Pyccel kernels below) stay NumPy. - dom = _host(DR.domain_array[rank]) - eta1s = np.random.rand(n_markers) * (dom[1] - dom[0]) + dom[0] - eta2s = np.random.rand(n_markers) * (dom[4] - dom[3]) + dom[3] - eta3s = np.random.rand(n_markers) * (dom[7] - dom[6]) + dom[6] + dom = DR.domain_array[rank] + eta1s = xp.random.rand(n_markers) * (dom[1] - dom[0]) + dom[0] + eta2s = xp.random.rand(n_markers) * (dom[4] - dom[3]) + dom[3] + eta3s = xp.random.rand(n_markers) * (dom[7] - dom[6]) + dom[6] for eta1, eta2, eta3 in zip(eta1s, eta2s, eta3s): comm.Barrier() @@ -154,13 +138,13 @@ def _host(a): span3 = bsp.find_span(tn3, DR.degree[2], eta3) # non-zero spline values at eta - bn1 = np.empty(DR.degree[0] + 1, dtype=float) - bn2 = np.empty(DR.degree[1] + 1, dtype=float) - bn3 = np.empty(DR.degree[2] + 1, dtype=float) + bn1 = xp.empty(DR.degree[0] + 1, dtype=float) + bn2 = xp.empty(DR.degree[1] + 1, dtype=float) + bn3 = xp.empty(DR.degree[2] + 1, dtype=float) - bd1 = np.empty(DR.degree[0], dtype=float) - bd2 = np.empty(DR.degree[1], dtype=float) - bd3 = np.empty(DR.degree[2], dtype=float) + bd1 = xp.empty(DR.degree[0], dtype=float) + bd2 = xp.empty(DR.degree[1], dtype=float) + bd3 = xp.empty(DR.degree[2], dtype=float) bsp.b_d_splines_slim(tn1, DR.degree[0], eta1, span1, bn1, bd1) bsp.b_d_splines_slim(tn2, DR.degree[1], eta2, span2, bn2, bd2) @@ -172,9 +156,9 @@ def _host(a): ie3 = span3 - pn[2] # global indices of non-vanishing B- and D-splines (no modulo) - glob_n1 = np.arange(ie1, ie1 + pn[0] + 1) - glob_n2 = np.arange(ie2, ie2 + pn[1] + 1) - glob_n3 = np.arange(ie3, ie3 + pn[2] + 1) + glob_n1 = xp.arange(ie1, ie1 + pn[0] + 1) + glob_n2 = xp.arange(ie2, ie2 + pn[1] + 1) + glob_n3 = xp.arange(ie3, ie3 + pn[2] + 1) glob_d1 = glob_n1[:-1] glob_d2 = glob_n2[:-1] @@ -200,10 +184,10 @@ def _host(a): # local column indices in _data of non-vanishing B- and D-splines, as sets for comparison cols = [{}, {}, {}] for n in range(3): - cols[n]["NN"] = set(np.arange(2 * pn[n] + 1)) - cols[n]["ND"] = set(np.arange(2 * pn[n])) - cols[n]["DN"] = set(np.arange(1, 2 * pn[n] + 1)) - cols[n]["DD"] = set(np.arange(1, 2 * pn[n])) + cols[n]["NN"] = set(xp.arange(2 * pn[n] + 1)) + cols[n]["ND"] = set(xp.arange(2 * pn[n])) + cols[n]["DN"] = set(xp.arange(1, 2 * pn[n] + 1)) + cols[n]["DD"] = set(xp.arange(1, 2 * pn[n])) # testing vector-valued spaces spaces_vector = ["v1", "v2"] @@ -380,22 +364,22 @@ def assert_mat(mat, rows, cols, row_str, col_str, rank): """ assert len(mat.shape) == 6 # assert non NaN - assert ~np.isnan(mat).any() + assert ~xp.isnan(mat).any() atol = 1e-14 logger.debug(f"\n({row_str}) ({col_str})") - logger.debug(f"rank {rank} | ind_row1: {set(np.where(mat > atol)[0])}") - logger.debug(f"rank {rank} | ind_row2: {set(np.where(mat > atol)[1])}") - logger.debug(f"rank {rank} | ind_row3: {set(np.where(mat > atol)[2])}") - logger.debug(f"rank {rank} | ind_col1: {set(np.where(mat > atol)[3])}") - logger.debug(f"rank {rank} | ind_col2: {set(np.where(mat > atol)[4])}") - logger.debug(f"rank {rank} | ind_col3: {set(np.where(mat > atol)[5])}") + logger.debug(f"rank {rank} | ind_row1: {set(xp.where(mat > atol)[0])}") + logger.debug(f"rank {rank} | ind_row2: {set(xp.where(mat > atol)[1])}") + logger.debug(f"rank {rank} | ind_row3: {set(xp.where(mat > atol)[2])}") + logger.debug(f"rank {rank} | ind_col1: {set(xp.where(mat > atol)[3])}") + logger.debug(f"rank {rank} | ind_col2: {set(xp.where(mat > atol)[4])}") + logger.debug(f"rank {rank} | ind_col3: {set(xp.where(mat > atol)[5])}") # check if correct indices are non-zero for n, (r, c) in enumerate(zip(row_str, col_str)): - assert set(np.where(mat > atol)[n]) == rows[n][r] - assert set(np.where(mat > atol)[n + 3]) == cols[n][r + c] + assert set(xp.where(mat > atol)[n]) == rows[n][r] + assert set(xp.where(mat > atol)[n + 3]) == cols[n][r + c] # Set matrix back to zero mat[:, :] = 0.0 @@ -423,18 +407,18 @@ def assert_vec(vec, rows, row_str, rank): """ assert len(vec.shape) == 3 # assert non Nan - assert ~np.isnan(vec).any() + assert ~xp.isnan(vec).any() atol = 1e-14 logger.debug(f"\n({row_str})") - logger.debug(f"rank {rank} | ind_row1: {set(np.where(vec > atol)[0])}") - logger.debug(f"rank {rank} | ind_row2: {set(np.where(vec > atol)[1])}") - logger.debug(f"rank {rank} | ind_row3: {set(np.where(vec > atol)[2])}") + logger.debug(f"rank {rank} | ind_row1: {set(xp.where(vec > atol)[0])}") + logger.debug(f"rank {rank} | ind_row2: {set(xp.where(vec > atol)[1])}") + logger.debug(f"rank {rank} | ind_row3: {set(xp.where(vec > atol)[2])}") # check if correct indices are non-zero for n, r in enumerate(row_str): - assert set(np.where(vec > atol)[n]) == rows[n][r] + assert set(xp.where(vec > atol)[n]) == rows[n][r] # Set vector back to zero vec[:] = 0.0 diff --git a/src/struphy/pic/tests/test_pushers.py b/src/struphy/pic/tests/test_pushers.py index 3d25444f8..fb139de89 100644 --- a/src/struphy/pic/tests/test_pushers.py +++ b/src/struphy/pic/tests/test_pushers.py @@ -699,17 +699,12 @@ def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): pusher_psy(dt) - # MPI communication buffers/counts must be host (NumPy) arrays regardless - # of the active cunumpy backend. - import numpy as np - from cunumpy.xp import to_numpy + n_mks_load = xp.zeros(size, dtype=int) - n_mks_load = np.zeros(size, dtype=int) + comm.Allgather(xp.array(xp.shape(particles.markers)[0]), n_mks_load) - comm.Allgather(np.array(xp.shape(particles.markers)[0]), n_mks_load) - - sendcounts = np.zeros(size, dtype=int) - displacements = np.zeros(size, dtype=int) + sendcounts = xp.zeros(size, dtype=int) + displacements = xp.zeros(size, dtype=int) accum_sendcounts = 0.0 for i in range(size): @@ -717,16 +712,10 @@ def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): displacements[i] = accum_sendcounts accum_sendcounts += sendcounts[i] - all_particles_psy = np.zeros((int(accum_sendcounts) * 3,), dtype=float) + all_particles_psy = xp.zeros((int(accum_sendcounts) * 3,), dtype=float) comm.Barrier() - # particles.markers[:, :3] is a column slice (stride = n_cols), so it's - # not C-contiguous; mpi4py's buffer acquisition goes through a DLPack - # export that requires contiguous memory and raises BufferError otherwise. - comm.Allgatherv( - np.ascontiguousarray(to_numpy(particles.markers[:, :3])), - [all_particles_psy, sendcounts, displacements, MPI.DOUBLE], - ) + comm.Allgatherv(xp.array(particles.markers[:, :3]), [all_particles_psy, sendcounts, displacements, MPI.DOUBLE]) comm.Barrier() diff --git a/src/struphy/pic/tests/test_sph.py b/src/struphy/pic/tests/test_sph.py index 111d42eb0..d536004b7 100644 --- a/src/struphy/pic/tests/test_sph.py +++ b/src/struphy/pic/tests/test_sph.py @@ -114,9 +114,6 @@ def test_sph_evaluation_1d( kernel_type=kernel, derivative=derivative, ) - # eval_density() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -241,9 +238,6 @@ def test_sph_evaluation_2d( kernel_type=kernel, derivative=derivative, ) - # eval_density() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -363,9 +357,6 @@ def test_sph_evaluation_3d( kernel_type=kernel, derivative=derivative, ) - # eval_density() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -490,9 +481,6 @@ def test_evaluation_SPH_Np_convergence_1d(boxes_per_dim, bc_x, eval_pts, tessela h3 = 1 / boxes_per_dim[2] test_eval = particles.eval_density(ee1, ee2, ee3, h1=h1, h2=h2, h3=h3) - # eval_density() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -512,10 +500,10 @@ def test_evaluation_SPH_Np_convergence_1d(boxes_per_dim, bc_x, eval_pts, tessela logger.info(f"{Np =}, {ppb =}, {diff =}") if tesselation: - fit = xp.polyfit(xp.log(xp.array(ppbs)), xp.log(xp.array(err_vec)), 1) + fit = xp.polyfit(xp.log(ppbs), xp.log(err_vec), 1) xvec = ppbs else: - fit = xp.polyfit(xp.log(xp.array(Nps)), xp.log(xp.array(err_vec)), 1) + fit = xp.polyfit(xp.log(Nps), xp.log(err_vec), 1) xvec = Nps if show_plot and rank == 0: @@ -610,9 +598,6 @@ def test_evaluation_SPH_h_convergence_1d(boxes_per_dim, bc_x, eval_pts, tesselat h3 = 1 / boxes_per_dim[2] test_eval = particles.eval_density(ee1, ee2, ee3, h1=h1, h2=h2, h3=h3) - # eval_density() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -638,9 +623,9 @@ def test_evaluation_SPH_h_convergence_1d(boxes_per_dim, bc_x, eval_pts, tesselat err_vec += [diff] if tesselation: - fit = xp.polyfit(xp.log(xp.array(h_vec[1:5])), xp.log(xp.array(err_vec[1:5])), 1) + fit = xp.polyfit(xp.log(h_vec[1:5]), xp.log(err_vec[1:5]), 1) else: - fit = xp.polyfit(xp.log(xp.array(h_vec[:-2])), xp.log(xp.array(err_vec[:-2])), 1) + fit = xp.polyfit(xp.log(h_vec[:-2]), xp.log(err_vec[:-2]), 1) if show_plot and rank == 0: plt.figure(figsize=(12, 8)) @@ -737,9 +722,6 @@ def test_evaluation_mc_Np_and_h_convergence_1d(boxes_per_dim, bc_x, eval_pts, te h3 = 1 / boxes_per_dim[2] test_eval = particles.eval_density(ee1, ee2, ee3, h1=h, h2=h2, h3=h3) - # eval_density() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -900,9 +882,6 @@ def test_evaluation_SPH_Np_convergence_2d(boxes_per_dim, bc_x, bc_y, tesselation h3 = 1 / boxes_per_dim[2] test_eval = particles.eval_density(ee1, ee2, ee3, h1=h1, h2=h2, h3=h3, kernel_type="gaussian_2d") - # eval_density() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - test_eval = xp.asarray(test_eval) if comm is None: all_eval = test_eval @@ -929,10 +908,10 @@ def test_evaluation_SPH_Np_convergence_2d(boxes_per_dim, bc_x, bc_y, tesselation # fig.savefig(f"2d_sph_{Np}_{ppb}.png") if tesselation: - fit = xp.polyfit(xp.log(xp.array(ppbs)), xp.log(xp.array(err_vec)), 1) + fit = xp.polyfit(xp.log(ppbs), xp.log(err_vec), 1) xvec = ppbs else: - fit = xp.polyfit(xp.log(xp.array(Nps)), xp.log(xp.array(err_vec)), 1) + fit = xp.polyfit(xp.log(Nps), xp.log(err_vec), 1) xvec = Nps if show_plot and rank == 0: @@ -1059,11 +1038,6 @@ def du_xyz(x, y, z): kernel_type=kernel, derivative=derivative, ) - # eval_velocity() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - v1 = xp.asarray(v1) - v2 = xp.asarray(v2) - v3 = xp.asarray(v3) if derivative == 0: v1_e, v2_e, v3_e = background.u_xyz(ee1, ee2, ee3) @@ -1248,9 +1222,6 @@ def du_deta2(eta1, eta2, eta3): kernel_type=kernel, derivative=derivative, ) - # eval_velocity() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - v_log = xp.asarray(v_log) v1, v2, v3 = v_log if derivative == 0: @@ -1501,9 +1472,6 @@ def div_pi_analytic(x, y, z, mu=None): kernel_type=kernel, derivative=0, ) - # eval_density() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - density = xp.asarray(density) if rank == 0: logger.info(f"{density.shape = }") logger.info(f"{xp.min(density) = }, {xp.max(density) = }") @@ -1532,11 +1500,6 @@ def div_pi_analytic(x, y, z, mu=None): kernel_type=kernel, derivative=0, ) - # eval_velocity() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - vx = xp.asarray(vx) - vy = xp.asarray(vy) - vz = xp.asarray(vz) if rank == 0: logger.info(f"{vx.shape = }, {vy.shape = }") logger.info(f"{xp.min(vx) = }, {xp.max(vx) = }") @@ -1579,9 +1542,6 @@ def div_pi_analytic(x, y, z, mu=None): mu=mu, kernel_type=kernel, ) - # eval_div_viscosity() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - div_viscosity = xp.asarray(div_viscosity) gamma_x = div_viscosity[0] gamma_y = div_viscosity[1] gamma_z = div_viscosity[2] @@ -1892,12 +1852,7 @@ def u_xyz(x, y, z): particles.draw_markers(sort=False) if rank == 0: - # ghost_particles follows the active backend; this is a - # diagnostics-only log, so pull the indices to the host. - import numpy as np - from cunumpy import to_numpy - - ghost_inds = np.where(to_numpy(particles.ghost_particles))[0] + ghost_inds = xp.where(particles.ghost_particles)[0] logger.info(f"After do_sort: {len(ghost_inds)} ghosts") if len(ghost_inds) > 0: logger.info(f"First 10 ghost eta1: {particles.markers[ghost_inds[:10], 0]}") @@ -1932,11 +1887,6 @@ def u_xyz(x, y, z): kernel_type=kernel, derivative=0, ) - # eval_velocity() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - v1 = xp.asarray(v1) - v2 = xp.asarray(v2) - v3 = xp.asarray(v3) # if rank == 0 and len(ghost_inds) > 0: # logger.info("Ghost coefficients after eval:", particles.markers[ghost_inds[:10], particles.first_free_idx]) @@ -2110,11 +2060,6 @@ def u_xyz(x, y, z): kernel_type=kernel, derivative=0, ) - # eval_velocity() is always host (NumPy); convert to the - # active backend to match this test's xp-based reference arrays. - v1 = xp.asarray(v1) - v2 = xp.asarray(v2) - v3 = xp.asarray(v3) if comm is not None: all_v1 = xp.zeros_like(v1) diff --git a/src/struphy/pic/tests/test_tesselation.py b/src/struphy/pic/tests/test_tesselation.py index 060fa2945..5b1d3e364 100644 --- a/src/struphy/pic/tests/test_tesselation.py +++ b/src/struphy/pic/tests/test_tesselation.py @@ -178,16 +178,10 @@ def test_cell_average(ppb, nx, ny, nz, n_quad, show_plot=False): plt.show() # test - # Marker data and f_init both follow the active array backend, so the - # comparison is done there and only the final scalar is brought to the - # host for the assert/log. - import numpy as np - from cunumpy import to_numpy - - f_init_at_markers = particles.f_init(particles.positions) - max_err = float(to_numpy(xp.max(xp.abs(particles.weights * particles.Np - f_init_at_markers)))) - logger.info(f"\n{rank =}, {max_err =}") - assert max_err < 0.012 + logger.info( + f"\n{rank =}, {xp.max(xp.abs(particles.weights * particles.Np - particles.f_init(particles.positions))) =}" + ) + assert xp.max(xp.abs(particles.weights * particles.Np - particles.f_init(particles.positions))) < 0.012 if __name__ == "__main__": diff --git a/src/struphy/pic/utilities_kernels_cuda.py b/src/struphy/pic/utilities_kernels_cuda.py deleted file mode 100644 index 70444a192..000000000 --- a/src/struphy/pic/utilities_kernels_cuda.py +++ /dev/null @@ -1,414 +0,0 @@ -"""Hand-written CUDA replacements for the per-marker *diagnostics* kernels in -:mod:`~struphy.pic.utilities_kernels`, used only under ``ARRAY_BACKEND=cupy``. - -These run every time step (they back the scalar quantities a model saves, -e.g. ``en_fB`` in :class:`~struphy.models.guiding_center.GuidingCenter`), and -each one writes a diagnostics column of the marker array in place. With -markers now device-resident (see :class:`~struphy.pic.base.Particles`), the -compiled host-only versions were the last thing forcing a host<->device -round trip of the whole marker array in the per-step path -- porting them -removes it. - -Both kernels here are plain per-marker 0-form spline evaluations, so they -reuse the ``find_span_dev``/``b_splines_dev``/``eval_0form_dev`` device -functions rather than defining their own. - -Every real GPU kernel this module launches is a :class:`~struphy.cuda.CudaKernel` -(or a :class:`~struphy.cuda.CudaKernelSet` entry) declared at module scope; if -a function here doesn't sit next to one, it does not touch the GPU. -""" -from struphy.cuda import CudaKernel, CudaKernelSet, launch_1d, load_cuda_source -from struphy.pic.pushing.pusher_kernels_cuda import _GENERAL_GEOMETRY_SRC - -_UTILITIES_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_utilities_src.cu") - -_kernels = CudaKernelSet(_UTILITIES_SRC) - - -def _launch_0form_diag(kernel_name, markers, args_derham, first_diagnostics_idx, mu_idx, coeffs): - """Shared launch path for the two 0-form diagnostics kernels above.""" - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - coeffs = cp.ascontiguousarray(coeffs) - - launch_1d( - _kernels[kernel_name], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_diagnostics_idx), - np.int32(mu_idx), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - coeffs, - np.int32(coeffs.shape[1]), - np.int32(coeffs.shape[2]), - ), - ) - - -def eval_magnetic_background_energy_gpu(markers, args_derham, first_diagnostics_idx, mu_idx, abs_B0): - """GPU replacement for - :func:`~struphy.pic.utilities_kernels.eval_magnetic_background_energy`. - ``markers`` is device-resident and written in place. - """ - _launch_0form_diag( - "eval_magnetic_background_energy_cuda", - markers, - args_derham, - first_diagnostics_idx, - mu_idx, - abs_B0, - ) - - -def eval_energy_5d_gpu(markers, args_derham, first_diagnostics_idx, mu_idx, absB): - """GPU replacement for :func:`~struphy.pic.utilities_kernels.eval_energy_5d`. - ``markers`` is device-resident and written in place. - """ - _launch_0form_diag( - "eval_energy_5d_cuda", - markers, - args_derham, - first_diagnostics_idx, - mu_idx, - absB, - ) - - -def eval_canonical_toroidal_moment_5d_gpu( - markers, args_derham, first_diagnostics_idx, mu_idx, idx_can_momentum, epsilon, B0, R0, absB -): - """GPU replacement for - :func:`~struphy.pic.utilities_kernels.eval_canonical_toroidal_moment_5d`. - ``markers`` is device-resident and written in place. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - absB = cp.ascontiguousarray(absB) - launch_1d( - _kernels["eval_canonical_toroidal_moment_5d_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_diagnostics_idx), - np.int32(mu_idx), - np.int32(idx_can_momentum), - np.float64(epsilon), - np.float64(B0), - np.float64(R0), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - absB, - np.int32(absB.shape[1]), - np.int32(absB.shape[2]), - ), - ) - - -def eval_canonical_toroidal_moment_6d_gpu(markers, args_derham, first_diagnostics_idx, epsilon, B0, R0, absB): - """GPU replacement for - :func:`~struphy.pic.utilities_kernels.eval_canonical_toroidal_moment_6d`. - ``markers`` is device-resident and written in place. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - absB = cp.ascontiguousarray(absB) - launch_1d( - _kernels["eval_canonical_toroidal_moment_6d_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_diagnostics_idx), - np.float64(epsilon), - np.float64(B0), - np.float64(R0), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - absB, - np.int32(absB.shape[1]), - np.int32(absB.shape[2]), - ), - ) - - -def eval_magnetic_moment_5d_gpu(markers, args_derham, first_diagnostics_idx, absB): - """GPU replacement for - :func:`~struphy.pic.utilities_kernels.eval_magnetic_moment_5d`. - ``markers`` is device-resident and written in place. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - absB = cp.ascontiguousarray(absB) - launch_1d( - _kernels["eval_magnetic_moment_5d_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_diagnostics_idx), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - absB, - np.int32(absB.shape[1]), - np.int32(absB.shape[2]), - ), - ) - - -def eval_magnetic_energy_PBb_gpu(markers, args_derham, first_diagnostics_idx, mu_idx, abs_B0, PBb): - """GPU replacement for - :func:`~struphy.pic.utilities_kernels.eval_magnetic_energy_PBb`. - ``markers`` is device-resident and written in place. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - abs_B0 = cp.ascontiguousarray(abs_B0) - PBb = cp.ascontiguousarray(PBb) - launch_1d( - _kernels["eval_magnetic_energy_PBb_cuda"], - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_diagnostics_idx), - np.int32(mu_idx), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - abs_B0, - np.int32(abs_B0.shape[1]), - np.int32(abs_B0.shape[2]), - PBb, - np.int32(PBb.shape[1]), - np.int32(PBb.shape[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# eval_guiding_center_from_6d needs the domain Jacobian and a 2-form (magnetic -# field) evaluation, so unlike the pure 0-form diagnostics above it is built -# on top of pusher_kernels_cuda's shared geometry/spline device functions -# rather than the small self-contained source in this module. -# --------------------------------------------------------------------------- - -_GC_FROM_6D_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_gc_from_6d_src.cu") -_gc6d_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC + _GC_FROM_6D_SRC, "eval_guiding_center_from_6d_cuda") - - -def eval_guiding_center_from_6d_gpu( - markers, args_derham, kind_map, params_dev, first_diagnostics_idx, epsilon, b21, b22, b23, absB -): - """GPU replacement for - :func:`~struphy.pic.utilities_kernels.eval_guiding_center_from_6d`, for any - domain in :data:`~struphy.pic.pushing.pusher_kernels_cuda.SUPPORTED_GENERAL_KIND_MAPS`. - ``markers`` is device-resident and written in place. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - b21 = cp.ascontiguousarray(b21) - b22 = cp.ascontiguousarray(b22) - b23 = cp.ascontiguousarray(b23) - absB = cp.ascontiguousarray(absB) - launch_1d( - _gc6d_kernel, - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_diagnostics_idx), - np.int32(kind_map), - params_dev, - np.float64(epsilon), - np.int32(args_derham.pn[0]), - np.int32(args_derham.pn[1]), - np.int32(args_derham.pn[2]), - tn1, - np.int32(tn1.shape[0]), - tn2, - np.int32(tn2.shape[0]), - tn3, - np.int32(tn3.shape[0]), - np.int32(args_derham.starts[0]), - np.int32(args_derham.starts[1]), - np.int32(args_derham.starts[2]), - b21, - np.int32(b21.shape[1]), - np.int32(b21.shape[2]), - b22, - np.int32(b22.shape[1]), - np.int32(b22.shape[2]), - b23, - np.int32(b23.shape[1]), - np.int32(b23.shape[2]), - absB, - np.int32(absB.shape[1]), - np.int32(absB.shape[2]), - ), - ) - - -# --------------------------------------------------------------------------- -# eval_gradB_ediff: writes markers[:, idx] = mu * dot(eta_diff, gradB + -# grad_PB_b), evaluated at the midpoint eta_mid = mod((eta+eta_init)/2, 1). -# Called once per fixed-point iteration by CurrentCoupling5DGradB's -# discrete-gradient algorithm. Needs 1-form spline evaluation (unlike the -# 0-form diagnostics above), so this is built from -# pusher_kernels_cuda._GENERAL_GEOMETRY_SRC instead of the private -# find_span_dev/b_splines_dev/eval_0form_dev helpers used by _UTILITIES_SRC. -# --------------------------------------------------------------------------- - -_GRADB_EDIFF_SRC = load_cuda_source(__file__, "utilities_kernels_cuda/_gradb_ediff_src.cu") -_gradb_ediff_kernel = CudaKernel(_GENERAL_GEOMETRY_SRC + _GRADB_EDIFF_SRC, "eval_gradB_ediff_cuda") - - -def eval_gradB_ediff_gpu( - markers, - first_init_idx, - mu_idx, - pn, - tn1_dev, - tn2_dev, - tn3_dev, - starts, - gradB1_dev, - grad_PB_b1_dev, - idx, -): - """GPU replacement for one call of - :func:`~struphy.pic.utilities_kernels.eval_gradB_ediff`. - - ``gradB1_dev``/``grad_PB_b1_dev`` are each a 3-tuple of device arrays - (the 1-form's 3 components), matching the (unpacked) ``gradB1, gradB2, - gradB3`` / ``grad_PB_b1, grad_PB_b2, grad_PB_b3`` arguments of the CPU - kernel. - """ - import cupy as cp - import numpy as np - - n_markers = markers.shape[0] - - def d(a): - a = cp.ascontiguousarray(a) - return (a, np.int32(a.shape[1]), np.int32(a.shape[2])) - - launch_1d( - _gradb_ediff_kernel, - n_markers, - ( - markers, - np.int32(markers.shape[1]), - np.int32(n_markers), - np.int32(first_init_idx), - np.int32(mu_idx), - np.int32(idx), - np.int32(pn[0]), - np.int32(pn[1]), - np.int32(pn[2]), - tn1_dev, - np.int32(tn1_dev.shape[0]), - tn2_dev, - np.int32(tn2_dev.shape[0]), - tn3_dev, - np.int32(tn3_dev.shape[0]), - np.int32(starts[0]), - np.int32(starts[1]), - np.int32(starts[2]), - *d(gradB1_dev[0]), - *d(gradB1_dev[1]), - *d(gradB1_dev[2]), - *d(grad_PB_b1_dev[0]), - *d(grad_PB_b1_dev[1]), - *d(grad_PB_b1_dev[2]), - ), - ) diff --git a/src/struphy/post_processing/post_processing_tools.py b/src/struphy/post_processing/post_processing_tools.py index f26925401..3c1662b8f 100644 --- a/src/struphy/post_processing/post_processing_tools.py +++ b/src/struphy/post_processing/post_processing_tools.py @@ -634,17 +634,11 @@ def _load_femfields(self, fields: dict, files: list, n: int, step: int = 1): e1, e2, e3 = gl_e p1, p2, p3 = pads - # h5py always returns plain host numpy arrays; under - # the cupy backend a bare full-slice assignment from - # a numpy array into a cupy-backed vector raises - # ("non-scalar numpy.ndarray cannot be used for - # fill"), so route through xp.asarray (no-op under - # numpy, a safe host->device copy under cupy). vector[ s1 : e1 + 1, s2 : e2 + 1, s3 : e3 + 1, - ] = xp.asarray(ddset[n * step, p1:-p1, p2:-p2, p3:-p3]) + ] = ddset[n * step, p1:-p1, p2:-p2, p3:-p3] # vector-valued field else: @@ -657,7 +651,7 @@ def _load_femfields(self, fields: dict, files: list, n: int, step: int = 1): s1 : e1 + 1, s2 : e2 + 1, s3 : e3 + 1, - ] = xp.asarray(ddset[str(comp + 1)][n * step, p1:-p1, p2:-p2, p3:-p3]) + ] = ddset[str(comp + 1)][n * step, p1:-p1, p2:-p2, p3:-p3] vector.update_ghost_regions() @@ -903,23 +897,18 @@ def _create_vtk( for name, data in vars.items(): points_list = data[t] - # pyevtk asserts on isinstance(data, numpy.ndarray), so - # field values (and the grid coordinates below) must be - # real host arrays here regardless of backend -- - # xp.to_numpy is a no-op under the numpy backend. - # scalar if len(points_list) == 1: - point_data_n[species][name] = xp.to_numpy(points_list[0]) + point_data_n[species][name] = points_list[0] # vectorpoint_data[name] else: for j in range(3): - point_data_n[species][name + f"_{j + 1}"] = xp.to_numpy(points_list[j]) + point_data_n[species][name + f"_{j + 1}"] = points_list[j] gridToVTK( os.path.join(species_path, "step_{0:0{1}d}".format(n, log_nt)), - *(xp.to_numpy(g) for g in grids_phy), + *grids_phy, pointData=point_data_n[species], ) @@ -1017,10 +1006,7 @@ def _post_process_markers( ids = temp[:, -1].astype("int") ids_lost_particles = xp.setdiff1d(xp.arange(n_markers), ids) ids_removed_particles = xp.nonzero(temp[:, 0] == -1.0)[0] - # xp.union1d (not Python set()) stays backend-safe: iterating a CuPy - # array yields 0-d CuPy arrays, which are unhashable, unlike NumPy - # scalars (same class of issue as the xp.sort note below). - ids_lost_particles = xp.union1d(ids_lost_particles, ids_removed_particles).astype(int) + ids_lost_particles = xp.array(list(set(ids_lost_particles) | set(ids_removed_particles)), dtype=int) lost_particles_mask[:] = False lost_particles_mask[ids_lost_particles] = True @@ -1029,12 +1015,7 @@ def _post_process_markers( temp[lost_particles_mask, -1] = ids_lost_particles ids = xp.unique(xp.append(ids, ids_lost_particles)) - # sorted() on a cupy array returns a plain Python list of 0-d - # cupy scalars, which cupy's stricter __array_ufunc__ then - # refuses to compare against an ndarray -- xp.sort keeps this - # backend-safe (numpy's sorted()-vs-ndarray comparison happened - # to work, cupy's doesn't). - assert xp.all(xp.sort(ids) == xp.arange(n_markers)) + assert xp.all(sorted(ids) == xp.arange(n_markers)) # compute physical positions (x, y, z) pos_phys = self.domain(xp.array(temp[~lost_particles_mask, :3]), change_out_order=True) @@ -1242,12 +1223,8 @@ def _post_process_f( # correct integrating out in v-direction data_bckgr *= factor - # Now all data is just the data for delta_f. data_df - # comes from particle binning, which is always - # host-resident (see ISSUE_cupy_particles_never_pushed.md), - # while data_bckgr follows the active backend -- convert - # before combining them. - data_delta_f = xp.asarray(data_df) + # Now all data is just the data for delta_f + data_delta_f = data_df # save distribution function xp.save(os.path.join(path_slice, "delta_f_binned.npy"), data_delta_f) diff --git a/src/struphy/propagators/base.py b/src/struphy/propagators/base.py index f0994fb3c..b7a81272e 100644 --- a/src/struphy/propagators/base.py +++ b/src/struphy/propagators/base.py @@ -6,8 +6,6 @@ from typing import Literal import cunumpy as xp -import numpy as np -from cunumpy import PyccelKernel from feectools.linalg.block import BlockVector from feectools.linalg.stencil import StencilVector from scope_profiler import ProfileManager @@ -271,20 +269,17 @@ def add_init_kernel( args_init : tuple The arguments for the kernel function. """ - # comps/alpha are marker-column indices/weights fed straight to compiled, - # host-only particle kernels alongside args_markers (no device particle - # kernel exists), so they are always NumPy, matching self.markers. if comps is None: - comps = np.array([0]) # case for scalar evaluation + comps = xp.array([0]) # case for scalar evaluation else: - comps = np.array(comps, dtype=int) + comps = xp.array(comps, dtype=int) if not hasattr(self, "_init_kernels"): self._init_kernels = [] self._init_kernels += [ ( - kernel if isinstance(kernel, PyccelKernel) else PyccelKernel(kernel), + kernel, column_nr, comps, args_init, @@ -326,19 +321,19 @@ def add_eval_kernel( """ if isinstance(alpha, int) or isinstance(alpha, float): alpha = [alpha] * 6 - alpha = np.array(alpha) + alpha = xp.array(alpha) if comps is None: - comps = np.array([0]) # case for scalar evaluation + comps = xp.array([0]) # case for scalar evaluation else: - comps = np.array(comps, dtype=int) + comps = xp.array(comps, dtype=int) if not hasattr(self, "_eval_kernels"): self._eval_kernels = [] self._eval_kernels += [ ( - kernel if isinstance(kernel, PyccelKernel) else PyccelKernel(kernel), + kernel, alpha, column_nr, comps, diff --git a/src/struphy/propagators/current_coupling_5d_gradb.py b/src/struphy/propagators/current_coupling_5d_gradb.py index af3c306ee..f07cfe168 100644 --- a/src/struphy/propagators/current_coupling_5d_gradb.py +++ b/src/struphy/propagators/current_coupling_5d_gradb.py @@ -9,7 +9,6 @@ from feectools.ddm.mpi import mpi as MPI from feectools.linalg.solvers import inverse from line_profiler import profile -from scope_profiler import ProfileManager from struphy.feec import preconditioner from struphy.io.options import LiteralOptions, OptionsBase @@ -21,14 +20,6 @@ from struphy.pic.accumulation.filter import FilterParameters from struphy.pic.accumulation.particles_to_grid import Accumulator, AccumulatorVector from struphy.pic.pushing import pusher_kernels_gc -from struphy.pic.pushing.pusher_kernels_cuda import SUPPORTED_GENERAL_KIND_MAPS -from struphy.pic.pushing.pusher_kernels_gc_cuda import ( - push_gc_cc_J2_dg_Hdiv_gpu, - push_gc_cc_J2_dg_init_Hdiv_gpu, - push_gc_cc_J2_stage_H1vec_gpu, - push_gc_cc_J2_stage_Hdiv_gpu, -) -from struphy.pic.utilities_kernels_cuda import eval_gradB_ediff_gpu from struphy.propagators.base import Propagator from struphy.utils.utils import check_option @@ -292,9 +283,9 @@ def allocate(self): # define Pusher if self.options.u_space == "Hdiv": - self._pusher_kernel = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_stage_Hdiv) + self._pusher_kernel = pusher_kernels_gc.push_gc_cc_J2_stage_Hdiv elif self.options.u_space == "H1vec": - self._pusher_kernel = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_stage_H1vec) + self._pusher_kernel = pusher_kernels_gc.push_gc_cc_J2_stage_H1vec else: raise ValueError( f'{self.options.u_space =} not valid, choose from "Hdiv" or "H1vec.', @@ -323,47 +314,6 @@ def allocate(self): self.options.butcher.c, ) - # GPU replacement for push_gc_cc_J2_stage_{H1vec,Hdiv}: unlike - # CurrentCoupling5DCurlb this propagator interleaves accumulation - # and pushing per RK stage itself (see __call__), so it can't go - # through the generic Pusher dispatch -- self._pusher_kernel is - # called directly on args_markers below, which would force a - # host round trip (or crash outright) on device-resident markers - # under cupy. Dispatch to the CUDA kernel here instead, with the - # same SUPPORTED_GENERAL_KIND_MAPS restriction as the rest of the - # port. - self._gpu_j2_stage = xp.cupy_backend and self.domain.args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - if self._gpu_j2_stage: - import cupy as cp - import numpy as np - - self._gpu_j2_stage_fn = ( - push_gc_cc_J2_stage_H1vec_gpu if self.options.u_space == "H1vec" else push_gc_cc_J2_stage_Hdiv_gpu - ) - self._gpu_j2_stage_kind_map = int(self.domain.args_domain.kind_map) - self._gpu_j2_stage_params = cp.asarray( - np.asarray(self.domain.args_domain.params, dtype=float), dtype=cp.float64 - ) - self._gpu_j2_stage_epsilon = float(epsilon) - args_derham = self.derham.args_derham - self._gpu_j2_stage_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_j2_stage_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_j2_stage_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_j2_stage_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_j2_stage_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_j2_stage_b2 = (self._b_full[0]._data, self._b_full[1]._data, self._b_full[2]._data) - self._gpu_j2_stage_norm_b1 = (unit_b1[0]._data, unit_b1[1]._data, unit_b1[2]._data) - self._gpu_j2_stage_curl_norm_b = ( - curl_unit_b2[0]._data, - curl_unit_b2[1]._data, - curl_unit_b2[2]._data, - ) - self._gpu_j2_stage_u = ( - self._u_temp[0]._data, - self._u_temp[1]._data, - self._u_temp[2]._data, - ) - else: # temporary vectors to avoid memory allocation self._b_full = self._b2.space.zeros() @@ -378,9 +328,9 @@ def allocate(self): self._u_temp = self.variables.u.spline.vector.space.zeros() # Call the accumulation and Pusher class - accum_kernel_init = PyccelKernel(accum_kernels_gc.cc_lin_mhd_5d_gradB_dg_init) - accum_kernel = PyccelKernel(accum_kernels_gc.cc_lin_mhd_5d_gradB_dg) - self._accum_kernel_en_fB_mid = PyccelKernel(utilities_kernels.eval_gradB_ediff) + accum_kernel_init = accum_kernels_gc.cc_lin_mhd_5d_gradB_dg_init + accum_kernel = accum_kernels_gc.cc_lin_mhd_5d_gradB_dg + self._accum_kernel_en_fB_mid = utilities_kernels.eval_gradB_ediff self._args_accum_kernel = ( epsilon, @@ -474,51 +424,8 @@ def allocate(self): self._u_temp[2]._data, ) - self._pusher_kernel_init = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_dg_init_Hdiv) - self._pusher_kernel = PyccelKernel(pusher_kernels_gc.push_gc_cc_J2_dg_Hdiv) - - # GPU replacements for both pusher kernels above. Same reasoning - # as the explicit branch: this fixed-point loop calls - # self._pusher_kernel{,_init} directly on args_markers (no Pusher - # wrapper), which would force a host round trip (or crash) on - # device-resident markers under cupy. - self._gpu_j2_dg = xp.cupy_backend and self.domain.args_domain.kind_map in SUPPORTED_GENERAL_KIND_MAPS - if self._gpu_j2_dg: - import cupy as cp - import numpy as np - - self._gpu_j2_dg_kind_map = int(self.domain.args_domain.kind_map) - self._gpu_j2_dg_params = cp.asarray( - np.asarray(self.domain.args_domain.params, dtype=float), dtype=cp.float64 - ) - self._gpu_j2_dg_epsilon = float(epsilon) - args_derham = self.derham.args_derham - self._gpu_j2_dg_pn = tuple(int(p) for p in args_derham.pn) - self._gpu_j2_dg_starts = tuple(int(s) for s in args_derham.starts) - self._gpu_j2_dg_tn1 = cp.asarray(args_derham.tn1, dtype=cp.float64) - self._gpu_j2_dg_tn2 = cp.asarray(args_derham.tn2, dtype=cp.float64) - self._gpu_j2_dg_tn3 = cp.asarray(args_derham.tn3, dtype=cp.float64) - self._gpu_j2_dg_b2 = (self._b_full[0]._data, self._b_full[1]._data, self._b_full[2]._data) - self._gpu_j2_dg_norm_b1 = (unit_b1[0]._data, unit_b1[1]._data, unit_b1[2]._data) - self._gpu_j2_dg_curl_norm_b = ( - curl_unit_b2[0]._data, - curl_unit_b2[1]._data, - curl_unit_b2[2]._data, - ) - self._gpu_j2_dg_u_init = ( - self.variables.u.spline.vector[0]._data, - self.variables.u.spline.vector[1]._data, - self.variables.u.spline.vector[2]._data, - ) - self._gpu_j2_dg_u = (self._u_mid[0]._data, self._u_mid[1]._data, self._u_mid[2]._data) - self._gpu_j2_dg_ud = (self._u_temp[0]._data, self._u_temp[1]._data, self._u_temp[2]._data) - # for eval_gradB_ediff_gpu (reuses the same pn/tn1-3/starts) - self._gpu_j2_dg_gradB1 = (gradB1[0]._data, gradB1[1]._data, gradB1[2]._data) - self._gpu_j2_dg_grad_PB_b1 = ( - self._grad_PB_b[0]._data, - self._grad_PB_b[1]._data, - self._grad_PB_b[2]._data, - ) + self._pusher_kernel_init = pusher_kernels_gc.push_gc_cc_J2_dg_init_Hdiv + self._pusher_kernel = pusher_kernels_gc.push_gc_cc_J2_dg_Hdiv def __call__(self, dt): # current FE coeffs @@ -528,11 +435,7 @@ def __call__(self, dt): particles = self.variables.energetic_ions.particles holes = particles.holes args_markers = particles.args_markers - # NOTE: args_markers.markers is the *host mirror* under cupy (see - # Particles.args_markers) -- only valid inside a host_markers() - # block. The xp-vectorised bookkeeping below (holes indexing, sums, - # ...) needs the real device array instead. - markers = particles.markers + markers = args_markers.markers first_init_idx = args_markers.first_init_idx first_free_idx = args_markers.first_free_idx @@ -564,40 +467,12 @@ def __call__(self, dt): ) # push particles - if self._gpu_j2_stage: - last = 1.0 if stage == self.options.butcher.n_stages - 1 else 0.0 - with ProfileManager.profile_region("kernel: " + self._pusher_kernel.name + " [cuda]"): - self._gpu_j2_stage_fn( - markers, - first_init_idx, - first_free_idx, - self._gpu_j2_stage_kind_map, - self._gpu_j2_stage_params, - self._gpu_j2_stage_epsilon, - self._gpu_j2_stage_pn, - self._gpu_j2_stage_tn1, - self._gpu_j2_stage_tn2, - self._gpu_j2_stage_tn3, - self._gpu_j2_stage_starts, - self._gpu_j2_stage_b2, - self._gpu_j2_stage_norm_b1, - self._gpu_j2_stage_curl_norm_b, - self._gpu_j2_stage_u, - dt * float(self.options.butcher.a_stage[stage]), - dt * float(self.options.butcher.b[stage]), - last, - ) - else: - with ( - ProfileManager.profile_region("kernel: " + self._pusher_kernel.name), - particles.host_markers(write=True) as args_markers_h, - ): - self._pusher_kernel( - dt, - stage, - args_markers_h, - *self._args_pusher_kernel, - ) + self._pusher_kernel( + dt, + stage, + args_markers, + *self._args_pusher_kernel, + ) if particles.mpi_comm is not None: particles.mpi_sort_markers() @@ -681,7 +556,7 @@ def __call__(self, dt): op=MPI.SUM, ) - en_fB_old = float(buffer_array[0]) + en_fB_old = buffer_array[0] en_tot_old = en_U_old + en_fB_old # initial guess @@ -697,35 +572,11 @@ def __call__(self, dt): en_U_new = u_new.inner(self._M2n_dot_u) / 2.0 # push eta - if self._gpu_j2_dg: - with ProfileManager.profile_region("kernel: " + self._pusher_kernel_init.name + " [cuda]"): - push_gc_cc_J2_dg_init_Hdiv_gpu( - markers, - first_init_idx, - self._gpu_j2_dg_kind_map, - self._gpu_j2_dg_params, - self._gpu_j2_dg_epsilon, - self._gpu_j2_dg_pn, - self._gpu_j2_dg_tn1, - self._gpu_j2_dg_tn2, - self._gpu_j2_dg_tn3, - self._gpu_j2_dg_starts, - self._gpu_j2_dg_b2, - self._gpu_j2_dg_norm_b1, - self._gpu_j2_dg_curl_norm_b, - self._gpu_j2_dg_u_init, - dt, - ) - else: - with ( - ProfileManager.profile_region("kernel: " + self._pusher_kernel_init.name), - particles.host_markers(write=True) as args_markers_h, - ): - self._pusher_kernel_init( - dt, - args_markers_h, - *self._args_pusher_kernel_init, - ) + self._pusher_kernel_init( + dt, + args_markers, + *self._args_pusher_kernel_init, + ) if particles.mpi_comm is not None: particles.mpi_sort_markers(apply_bc=False) @@ -751,7 +602,7 @@ def __call__(self, dt): op=MPI.SUM, ) - en_fB_new = float(buffer_array[0]) + en_fB_new = buffer_array[0] # fixed-point iterations iter_num = 0 @@ -794,7 +645,7 @@ def __call__(self, dt): op=MPI.SUM, ) - denominator = float(buffer_array[0]) + denominator = buffer_array[0] buffer_array = xp.array([sum_H_diff_loc]) @@ -812,37 +663,17 @@ def __call__(self, dt): op=MPI.SUM, ) - denominator += float(buffer_array[0]) + denominator += buffer_array[0] # sorting markers at mid-point if particles.mpi_comm is not None: particles.mpi_sort_markers(apply_bc=False, alpha=0.5) - if self._gpu_j2_dg: - with ProfileManager.profile_region("kernel: " + self._accum_kernel_en_fB_mid.name + " [cuda]"): - eval_gradB_ediff_gpu( - markers, - first_init_idx, - particles.mu_idx, - self._gpu_j2_dg_pn, - self._gpu_j2_dg_tn1, - self._gpu_j2_dg_tn2, - self._gpu_j2_dg_tn3, - self._gpu_j2_dg_starts, - self._gpu_j2_dg_gradB1, - self._gpu_j2_dg_grad_PB_b1, - first_free_idx + 3, - ) - else: - with ( - ProfileManager.profile_region("kernel: " + self._accum_kernel_en_fB_mid.name), - particles.host_markers(write=True) as args_markers_h, - ): - self._accum_kernel_en_fB_mid( - args_markers_h, - *self._args_accum_kernel_en_fB_mid, - first_free_idx + 3, - ) + self._accum_kernel_en_fB_mid( + args_markers, + *self._args_accum_kernel_en_fB_mid, + first_free_idx + 3, + ) en_fB_mid = xp.sum(markers[~holes, first_free_idx + 3].dot(markers[~holes, 5])) * self.options.ep_scale en_fB_mid /= n_mks_tot @@ -863,7 +694,7 @@ def __call__(self, dt): op=MPI.SUM, ) - en_fB_mid = float(buffer_array[0]) + en_fB_mid = buffer_array[0] if denominator == 0.0: const = 0.0 @@ -887,40 +718,13 @@ def __call__(self, dt): en_U_new = u_new.inner(self._M2n_dot_u) / 2.0 # update H^{n+1, k} - if self._gpu_j2_dg: - with ProfileManager.profile_region("kernel: " + self._pusher_kernel.name + " [cuda]"): - push_gc_cc_J2_dg_Hdiv_gpu( - markers, - first_init_idx, - self._gpu_j2_dg_kind_map, - self._gpu_j2_dg_params, - self._gpu_j2_dg_epsilon, - self._gpu_j2_dg_pn, - self._gpu_j2_dg_tn1, - self._gpu_j2_dg_tn2, - self._gpu_j2_dg_tn3, - self._gpu_j2_dg_starts, - self._gpu_j2_dg_b2, - self._gpu_j2_dg_norm_b1, - self._gpu_j2_dg_curl_norm_b, - self._gpu_j2_dg_u, - self._gpu_j2_dg_ud, - const, - alpha, - dt, - ) - else: - with ( - ProfileManager.profile_region("kernel: " + self._pusher_kernel.name), - particles.host_markers(write=True) as args_markers_h, - ): - self._pusher_kernel( - dt, - args_markers_h, - *self._args_pusher_kernel, - const, - alpha, - ) + self._pusher_kernel( + dt, + args_markers, + *self._args_pusher_kernel, + const, + alpha, + ) sum_H_diff_loc = xp.sum( xp.abs(markers[~holes, 0:3] - markers[~holes, first_free_idx : first_free_idx + 3]), @@ -950,7 +754,7 @@ def __call__(self, dt): op=MPI.SUM, ) - en_fB_new = float(buffer_array[0]) + en_fB_new = buffer_array[0] # calculate total energy difference e_diff = xp.abs(en_U_new + en_fB_new - en_tot_old) @@ -967,7 +771,7 @@ def __call__(self, dt): op=MPI.SUM, ) - diff = float(buffer_array[0]) + diff = buffer_array[0] buffer_array = xp.array([sum_H_diff_loc]) @@ -985,7 +789,7 @@ def __call__(self, dt): op=MPI.SUM, ) - diff += float(buffer_array[0]) + diff += buffer_array[0] # check convergence if diff < self.options.dg_solver_params.tol: diff --git a/src/struphy/propagators/efield_weights_coupling.py b/src/struphy/propagators/efield_weights_coupling.py index 721c8c109..06127176b 100644 --- a/src/struphy/propagators/efield_weights_coupling.py +++ b/src/struphy/propagators/efield_weights_coupling.py @@ -253,11 +253,14 @@ def __call__(self, dt): en = self.variables.e.spline.vector particles = self.variables.ions.particles - # evaluate f0 and accumulate. particles.markers is always - # host-resident (see ISSUE_cupy_particles_never_pushed.md), but - # self._f0 follows the active backend. + # evaluate f0 and accumulate self._f0_values[:] = self._f0( - *(xp.to_cunumpy(particles.markers[:, i]) for i in range(6)), + particles.markers[:, 0], + particles.markers[:, 1], + particles.markers[:, 2], + particles.markers[:, 3], + particles.markers[:, 4], + particles.markers[:, 5], ) self._accum(self._f0_values) diff --git a/src/struphy/propagators/push_random_diffusion.py b/src/struphy/propagators/push_random_diffusion.py index 3f3b7fb8e..26afa4fb2 100644 --- a/src/struphy/propagators/push_random_diffusion.py +++ b/src/struphy/propagators/push_random_diffusion.py @@ -3,9 +3,9 @@ import logging from dataclasses import dataclass -import cunumpy as xp from cunumpy import PyccelKernel from line_profiler import profile +from numpy import array, random from struphy.io.options import OptionsBase from struphy.models.variables import PICVariable @@ -109,9 +109,7 @@ def allocate(self): particles = self.variables.var.particles - # Allocated on the active backend: this buffer is handed to the pusher kernel - # alongside the marker array, so under CuPy it has to be a device array. - self._noise = xp.array(particles.markers[:, :3]) + self._noise = array(particles.markers[:, :3]) self._butcher = self.options.butcher # temp fix due to refactoring of ButcherTableau: @@ -146,10 +144,7 @@ def __call__(self, dt): particles = self.variables.var.particles - # Drawn through the backend's own RNG, so the samples are generated where the - # buffer lives instead of being copied host->device every step. On NumPy this - # is numpy.random, i.e. unchanged behaviour. - self._noise[:] = xp.random.multivariate_normal( + self._noise[:] = random.multivariate_normal( self._mean, self._cov, len(particles.markers), diff --git a/src/struphy/propagators/tests/test_curl_curl.py b/src/struphy/propagators/tests/test_curl_curl.py index f0c58833d..b3205cdcc 100644 --- a/src/struphy/propagators/tests/test_curl_curl.py +++ b/src/struphy/propagators/tests/test_curl_curl.py @@ -189,7 +189,7 @@ def test_convergence_1d( h = 1 / Nel h_vec.append(h) - m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors)), deg=1) + m, _ = xp.polyfit(xp.log(Nels), xp.log(errors), deg=1) logger.info(f"For {p =}, solution converges with rate {-m =} ") if show_plot: @@ -393,7 +393,7 @@ def scalar_current(a, b): h = 1 / Nel h_vec.append(h) - m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors)), deg=1) + m, _ = xp.polyfit(xp.log(Nels), xp.log(errors), deg=1) logger.info(f"For {p =}, solution converges with rate {-m =} ") if show_plot: diff --git a/src/struphy/propagators/tests/test_gyrokinetic_poisson.py b/src/struphy/propagators/tests/test_gyrokinetic_poisson.py index 159a4ba71..a67ec2406 100644 --- a/src/struphy/propagators/tests/test_gyrokinetic_poisson.py +++ b/src/struphy/propagators/tests/test_gyrokinetic_poisson.py @@ -206,7 +206,7 @@ def rho_pulled(e1, e2, e3): h = 1 / (Neli) h_vec.append(h) - m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors)), deg=1) + m, _ = xp.polyfit(xp.log(Nels), xp.log(errors), deg=1) logger.info(f"For {pi =}, solution converges in {direction=} with rate {-m =} ") assert -m > (pi + 1 - 0.07) diff --git a/src/struphy/propagators/tests/test_poisson.py b/src/struphy/propagators/tests/test_poisson.py index d73217226..55ca649fd 100644 --- a/src/struphy/propagators/tests/test_poisson.py +++ b/src/struphy/propagators/tests/test_poisson.py @@ -244,7 +244,7 @@ def rho_pulled(e1, e2, e3): h = 1 / (Neli) h_vec.append(h) - m, _ = xp.polyfit(xp.log(xp.array(Nels)), xp.log(xp.array(errors)), deg=1) + m, _ = xp.polyfit(xp.log(Nels), xp.log(errors), deg=1) logger.info(f"For {pi =}, solution converges in {direction=} with rate {-m =} ") assert -m > (pi + 1 - 0.07) diff --git a/src/struphy/simulation/sim.py b/src/struphy/simulation/sim.py index 7bc8be8ea..4ac8b971f 100644 --- a/src/struphy/simulation/sim.py +++ b/src/struphy/simulation/sim.py @@ -78,10 +78,9 @@ class CuPyJSONEncoder(json.JSONEncoder): """JSON encoder that handles CuPy arrays and NumPy arrays.""" def default(self, obj): - if xp.is_gpu(obj): - # xp.to_numpy(obj) is always a NumPy array (CuPy's .get()), which always - # has .tolist(), so no further hasattr check is needed here. - return xp.to_numpy(obj).tolist() + # Check if it has a .get() method (CuPy array) + if hasattr(obj, "get"): + return obj.get().tolist() if hasattr(obj.get(), "tolist") else obj.get() # Handle NumPy arrays and scalars import numpy as np From 98ce1f4de21b53ddd7b2ca42ca3c77769bab916f Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 15 Sep 2026 07:29:50 +0200 Subject: [PATCH 150/156] Update feectools --- feectools | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/feectools b/feectools index 88cadbab0..dfa326657 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 88cadbab0d784448834d98756e39561bc2d2eb5d +Subproject commit dfa32665753732ef07cd73711b1159460cb8208d From 38f49daf3da83ae09b7dbc46d7501504f8ee7077 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Tue, 15 Sep 2026 14:59:35 +0200 Subject: [PATCH 151/156] Fix tests --- feectools | 2 +- src/struphy/pic/base.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/feectools b/feectools index dfa326657..95d8a221d 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit dfa32665753732ef07cd73711b1159460cb8208d +Subproject commit 95d8a221df079645bb1c5f868a764d24b4e06a58 diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 2421c3a3d..4cb53e3dd 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -523,6 +523,7 @@ def __init__( # CuPy). The actual mpi4py Alltoall/Isend/Irecv calls further below # take host buffers and convert explicitly at that point. self._sorting_etas = xp.zeros((self.markers.shape[0], 3), dtype=float) + self._is_on_proc_domain = xp.zeros((self.markers.shape[0], 3), dtype=bool) self._can_stay = xp.zeros(self.markers.shape[0], dtype=bool) self._reqs = [None] * self.mpi_size self._recvbufs = [None] * self.mpi_size @@ -668,6 +669,7 @@ def nbytes_local(self) -> int: nbytes = 0 nbytes += n_rows * n_cols * float_size # markers nbytes += n_rows * 3 * float_size # sorting_etas (mpi_sort_markers buffer) + nbytes += n_rows * 3 * bool_size # is_on_proc_domain nbytes += n_rows * bool_size # can_stay # holes, ghost_particles, valid_mks, is_outside_right, is_outside_left, is_outside nbytes += n_rows * bool_size * 6 From c59ed96cb7046316f07197ddc86c850696dfd2bf Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 16 Sep 2026 13:57:56 +0200 Subject: [PATCH 152/156] Fixes --- src/struphy/feec/preconditioner.py | 21 ++++--- src/struphy/feec/psydac_derham.py | 7 ++- src/struphy/pic/base.py | 57 ++++++++++++------- src/struphy/pic/pushing/pusher.py | 57 ++----------------- src/struphy/pic/sorting.py | 9 ++- src/struphy/pic/tests/test_accum_vec_H1.py | 3 +- src/struphy/pic/tests/test_draw_parallel.py | 8 ++- src/struphy/pic/tests/test_mat_vec_filler.py | 42 ++++++++------ src/struphy/pic/tests/test_pushers.py | 18 ++++-- .../pic/tests/test_set_zero_velocity.py | 8 ++- src/struphy/pic/tests/test_sph.py | 12 ++-- .../propagators/tests/test_curl_curl.py | 12 ++-- 12 files changed, 129 insertions(+), 125 deletions(-) diff --git a/src/struphy/feec/preconditioner.py b/src/struphy/feec/preconditioner.py index 5bf9e957c..a566550c8 100644 --- a/src/struphy/feec/preconditioner.py +++ b/src/struphy/feec/preconditioner.py @@ -1,6 +1,7 @@ import logging import cunumpy as xp +import numpy as np from feectools.api.essential_bc import apply_essential_bc_stencil from feectools.ddm.cart import CartDecomposition, DomainDecomposition from feectools.ddm.mpi import MockComm @@ -260,7 +261,7 @@ def fun(e): M_local = StencilMatrix(V_local, V_local) - row_indices, col_indices = xp.nonzero(M_arr) + row_indices, col_indices = np.nonzero(M_arr) for row_i, col_i in zip(row_indices, col_indices): # only consider row indices on process @@ -273,7 +274,7 @@ def fun(e): ] = M_arr[row_i, col_i] # check if stencil matrix was built correctly - assert xp.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) + assert np.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) matrixcells += [M_local.copy()] # ======================================================================================================= @@ -625,7 +626,7 @@ def __init__(self, mass_operator, apply_bc=True): M_local = StencilMatrix(V_local, V_local) - row_indices, col_indices = xp.nonzero(M_arr) + row_indices, col_indices = np.nonzero(M_arr) for row_i, col_i in zip(row_indices, col_indices): # only consider row indices on process @@ -638,7 +639,7 @@ def __init__(self, mass_operator, apply_bc=True): ] = M_arr[row_i, col_i] # check if stencil matrix was built correctly - assert xp.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) + assert np.allclose(M_local.toarray()[s : e + 1], M_arr[s : e + 1]) matrixcells += [M_local.copy()] # ======================================================================================================= @@ -911,10 +912,12 @@ class FFTSolver(BandedSolver): """ def __init__(self, circmat): - assert isinstance(circmat, xp.ndarray) + # circmat comes from StencilMatrix.toarray(), always host numpy; the + # underlying solve() also calls scipy's solve_circulant, host-only. + assert isinstance(circmat, np.ndarray) assert is_circulant(circmat) - self._space = xp.ndarray + self._space = np.ndarray self._column = circmat[:, 0] # -------------------------------------- @@ -979,13 +982,15 @@ def is_circulant(mat): Whether the matrix is circulant (=True) or not (=False). """ - assert isinstance(mat, xp.ndarray) + # mat comes from StencilMatrix.toarray(), which always densifies to a + # host numpy.ndarray regardless of the active backend. + assert isinstance(mat, np.ndarray) assert len(mat.shape) == 2 assert mat.shape[0] == mat.shape[1] if mat.shape[0] > 1: for i in range(mat.shape[0] - 1): - circulant = xp.allclose(mat[i, :], xp.roll(mat[i + 1, :], -1)) + circulant = np.allclose(mat[i, :], np.roll(mat[i + 1, :], -1)) if not circulant: return circulant else: diff --git a/src/struphy/feec/psydac_derham.py b/src/struphy/feec/psydac_derham.py index 101bcfd12..8da190c4b 100644 --- a/src/struphy/feec/psydac_derham.py +++ b/src/struphy/feec/psydac_derham.py @@ -1925,11 +1925,12 @@ def _get_domain_array(self): else: nproc = 1 - # send buffer - dom_arr_loc = xp.zeros(9, dtype=float) + # send buffer -- host-resident: mpi4py's Allgather needs a real + # buffer-protocol array, not a CuPy array, regardless of backend. + dom_arr_loc = np.zeros(9, dtype=float) # main array (receive buffers) - dom_arr = xp.zeros(nproc * 9, dtype=float) + dom_arr = np.zeros(nproc * 9, dtype=float) # Get global starts and ends of domain decomposition gl_s = self.domain_decomposition.starts diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index 4cb53e3dd..e336a6162 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -402,7 +402,7 @@ def __init__( if domain_decomp is None: self._domain_array, self._nprocs = self._get_domain_decomp(self.sorting_params.dims_mask) else: - self._domain_array = domain_decomp[0] + self._domain_array = xp.to_numpy(domain_decomp[0]) self._nprocs = domain_decomp[1] # total number of cells (equal to mpi_size if no grid) @@ -1809,7 +1809,10 @@ def binning( f_slice /= self.Np * bin_vol df_slice /= self.Np * bin_vol - return f_slice, df_slice + # np.histogramdd forces f_slice/df_slice to be host arrays regardless + # of the active backend; convert back so callers get results on the + # same backend as everything else this class returns. + return xp.asarray(f_slice), xp.asarray(df_slice) def show_distribution_function(self, components, bin_edges): """ @@ -2139,14 +2142,17 @@ def put_particles_in_boxes(self): neighbouring boxes of neighbouring processes are also communicated (as ghost particles).""" self._remove_ghost_particles() - assign_box_to_each_particle( - self.markers, - self.holes, - self._sorting_boxes.nx, - self._sorting_boxes.ny, - self._sorting_boxes.nz, - self.domain_array[self.mpi_rank], - ) + # compiled host-only kernel; writes the box index into markers[:, -2] + # in place, through the marker host mirror. + with self.host_markers(write=True) as args_markers: + assign_box_to_each_particle( + args_markers.markers, + _to_numpy_for_kernel(self.holes), + self._sorting_boxes.nx, + self._sorting_boxes.ny, + self._sorting_boxes.nz, + self.domain_array[self.mpi_rank], + ) self._check_and_assign_particles_to_boxes() @@ -3337,11 +3343,9 @@ def _check_and_assign_particles_to_boxes(self): """Check whether the box array has enough columns (detect load imbalance wrt to sorting boxes), and then assign the particles to boxes.""" - # self.markers (and therefore markers_wo_holes) is always host-resident - # regardless of backend (see ISSUE_cupy_particles_never_pushed.md), so - # this is plain numpy unconditionally -- the previous backend branch - # predates that fix and fed a host array into cp.bincount, which - # (unlike xp.bincount on an actual CuPy array) does not accept one. + # markers may be CuPy-resident under the active backend, but np.bincount + # dispatches to cupy's implementation via the array API protocol, so + # this works unconditionally without an explicit host round-trip. bcount = np.bincount(self.markers_wo_holes[:, -2].astype(np.int64)) max_in_box = np.max(bcount) @@ -3354,12 +3358,20 @@ def _check_and_assign_particles_to_boxes(self): ) self.mpi_comm.Abort() - assign_particles_to_boxes( - self.markers, - self.holes, - self._sorting_boxes._boxes, - self._sorting_boxes._next_index, - ) + # compiled host-only kernel; markers are read-only here, but boxes/ + # next_index are fully overwritten, so round-trip them through host + # buffers and write the results back onto the active backend. + boxes = _to_numpy_for_kernel(self._sorting_boxes._boxes).copy() + next_index = _to_numpy_for_kernel(self._sorting_boxes._next_index).copy() + with self.host_markers(write=False) as args_markers: + assign_particles_to_boxes( + args_markers.markers, + _to_numpy_for_kernel(self.holes), + boxes, + next_index, + ) + self._sorting_boxes._boxes[:, :] = xp.asarray(boxes) + self._sorting_boxes._next_index[:] = xp.asarray(next_index) def _update_ghost_particles(self): """Refresh :attr:`~struphy.pic.base.Particles.ghost_particles`: a marker is flagged @@ -3829,7 +3841,8 @@ def _determine_markers_in_box(self, list_boxes): """ indices = [] for i in list_boxes: - indices += list(self._sorting_boxes._boxes[i][self._sorting_boxes._boxes[i] != -1]) + box_row = _to_numpy_for_kernel(self._sorting_boxes._boxes[i]) + indices += list(box_row[box_row != -1]) # Box membership is host bookkeeping; the gathered rows are handed to # the mpi4py box-communication path below, which needs host buffers. diff --git a/src/struphy/pic/pushing/pusher.py b/src/struphy/pic/pushing/pusher.py index 11304bf52..b77058467 100644 --- a/src/struphy/pic/pushing/pusher.py +++ b/src/struphy/pic/pushing/pusher.py @@ -180,11 +180,10 @@ def __init__( self._box_comm = False # hand-written CUDA replacement for push_v_with_efield's per-marker - # math on a Cuboid domain. Unlike the whole-push fast path below, this - # is unconditional on MPI/bc/maxiter -- it only swaps out the inner - # kernel call (see the "push markers" branch in _push()), so it stays - # correct alongside unmodified apply_kinetic_bc/mpi_sort_markers/ - # update_holes for multi-rank runs. + # math on a Cuboid domain. It only swaps out the inner kernel call + # (see the "push markers" branch in _push()), so it stays correct + # alongside unmodified apply_kinetic_bc/mpi_sort_markers/update_holes + # for multi-rank runs. self._gpu_v_efield_cuboid = ( cunumpy.cupy_backend and kernel.name == "push_v_with_efield" and args_domain.kind_map == 10 ) @@ -240,23 +239,6 @@ def __init__( self._gpu_v_efield_general_e1_2 = e1_2 self._gpu_v_efield_general_e1_3 = e1_3 - # whole-push GPU-resident fast path: on top of _gpu_v_efield_cuboid, - # additionally bypasses the per-call reset/apply_kinetic_bc/ - # update_holes machinery entirely (this kernel never touches position - # or holes/ghost columns, so that machinery is a no-op for it -- but - # only provably so with no MPI, since mpi_sort_markers does real - # host-side communication we can't just skip). - self._gpu_v_efield_cuboid_wholepush = ( - self._gpu_v_efield_cuboid - and all(b == "periodic" for b in self.particles.bc) - and not init_kernels - and not eval_kernels - and self.particles.mpi_comm is None - and maxiter == 1 - and not self._newton - and n_stages == 1 - ) - @profile def __call__(self, dt: float): """ @@ -264,36 +246,7 @@ def __call__(self, dt: float): applies kinetic boundary conditions and performs MPI sorting. """ with ProfileManager.profile_region(self._region_name): - if self._gpu_v_efield_cuboid_wholepush: - self._push_v_efield_cuboid_gpu(dt) - else: - self._push(dt) - - def _push_v_efield_cuboid_gpu(self, dt: float): - """Whole-push GPU-resident fast path, see - :func:`~struphy.pic.pushing.pusher_kernels_cuda.push_v_with_efield_cuboid_gpu`. - - Only the velocity columns are touched (positions and holes/ghost - status are untouched), so unlike :meth:`_push`, there is no marker - buffer reset and no ``apply_kinetic_bc``/``update_holes`` call to - replicate here: both would be no-ops given this kernel never moves a - marker. - """ - particles = self.particles - push_v_with_efield_cuboid_gpu( - particles.markers, - particles.n_cols, - self._gpu_v_efield_pn, - self._gpu_v_efield_tn1, - self._gpu_v_efield_tn2, - self._gpu_v_efield_tn3, - self._gpu_v_efield_starts, - self._gpu_v_efield_e1_1, - self._gpu_v_efield_e1_2, - self._gpu_v_efield_e1_3, - self._gpu_v_efield_scale, - dt * self._gpu_v_efield_const, - ) + self._push(dt) def _kernel_region(self, kernel) -> str: """Cached name of the profiling region of an init/eval kernel.""" diff --git a/src/struphy/pic/sorting.py b/src/struphy/pic/sorting.py index 940264130..f99978cc1 100644 --- a/src/struphy/pic/sorting.py +++ b/src/struphy/pic/sorting.py @@ -1,4 +1,5 @@ import logging +import math try: from mpi4py.MPI import Intracomm @@ -9,9 +10,15 @@ class Intracomm: import cunumpy as xp +from cunumpy import PyccelKernel from struphy.pic.sorting_kernels import flatten_index, initialize_neighbours +# initialize_neighbours writes into an array that may be CuPy-resident under +# the active backend; flatten_index only ever takes plain ints, so it does +# not need wrapping. +initialize_neighbours = PyccelKernel(initialize_neighbours) + logger = logging.getLogger("struphy") @@ -230,7 +237,7 @@ def _set_boxes(self): n_particles = self._markers_shape[0] n_mkr = int(n_particles / n_box_in) + 1 n_cols = round( - n_mkr * (1 + 1 / xp.sqrt(n_mkr) + self._box_bufsize), + n_mkr * (1 + 1 / math.sqrt(n_mkr) + self._box_bufsize), ) # cartesian boxes (extra last row stores holes/outside particles) diff --git a/src/struphy/pic/tests/test_accum_vec_H1.py b/src/struphy/pic/tests/test_accum_vec_H1.py index 8a1ec6122..af4d44259 100644 --- a/src/struphy/pic/tests/test_accum_vec_H1.py +++ b/src/struphy/pic/tests/test_accum_vec_H1.py @@ -1,4 +1,5 @@ import logging +import math import pytest from cunumpy import PyccelKernel @@ -115,7 +116,7 @@ def test_accum_poisson(num_elements, degree, bcs, mapping, num_clones, Np=10000, params = { "grid": {"num_elements": num_elements}, - "kinetic": {"test_particles": {"markers": {"Np": Np, "ppc": Np / xp.prod(num_elements)}}}, + "kinetic": {"test_particles": {"markers": {"Np": Np, "ppc": Np / math.prod(num_elements)}}}, } grid = TensorProductGrid(num_elements=num_elements) diff --git a/src/struphy/pic/tests/test_draw_parallel.py b/src/struphy/pic/tests/test_draw_parallel.py index 3751a3e7f..b0426488c 100644 --- a/src/struphy/pic/tests/test_draw_parallel.py +++ b/src/struphy/pic/tests/test_draw_parallel.py @@ -115,10 +115,12 @@ def test_draw(num_elements, degree, bcs, mapping, ppc=10): logger.info("Number of particles w/wo holes on each process after sorting : ") logger.info(f"Rank {rank} : {particles.n_mks_loc} {particles.markers.shape[0]}") - # are all markers in the correct domain? + # are all markers in the correct domain? domain_array is host-resident + # (fixed decomposition metadata); markers may live on the device under + # CuPy, so bring the (tiny) domain bounds to the same backend to compare. conds = xp.logical_and( - particles.markers[:, :3] > derham.domain_array[rank, 0::3], - particles.markers[:, :3] < derham.domain_array[rank, 1::3], + particles.markers[:, :3] > xp.asarray(derham.domain_array[rank, 0::3]), + particles.markers[:, :3] < xp.asarray(derham.domain_array[rank, 1::3]), ) holes = particles.markers[:, 0] == -1.0 stay = xp.all(conds, axis=1) diff --git a/src/struphy/pic/tests/test_mat_vec_filler.py b/src/struphy/pic/tests/test_mat_vec_filler.py index e0bdf4026..fc53a1661 100644 --- a/src/struphy/pic/tests/test_mat_vec_filler.py +++ b/src/struphy/pic/tests/test_mat_vec_filler.py @@ -1,6 +1,7 @@ import logging import cunumpy as xp +import numpy as np import pytest logger = logging.getLogger("struphy") @@ -47,12 +48,14 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): logger.info(f"\nnum_elements={num_elements}, degree={degree}, bcs={bcs}\n") # DR attributes - pn = xp.array(DR.degree) + # plain int metadata (polynomial degrees) -- kept host-side so downstream + # index/span arithmetic doesn't leak CuPy scalars into arange() etc. + pn = np.array(DR.degree) tn1, tn2, tn3 = DR.V0fem.knots starts1 = {} - starts1["v0"] = xp.array(DR.V0.starts) + starts1["v0"] = np.array(DR.V0.starts) comm.Barrier() sleep(0.02 * (rank + 1)) @@ -124,6 +127,9 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): eta3s = xp.random.rand(n_markers) * (dom[7] - dom[6]) + dom[6] for eta1, eta2, eta3 in zip(eta1s, eta2s, eta3s): + # the compiled bsplines_kernels functions below require native + # Python floats; eta1s/eta2s/eta3s may be CuPy-resident. + eta1, eta2, eta3 = float(eta1), float(eta2), float(eta3) comm.Barrier() sleep(0.02 * (rank + 1)) logger.info(f"rank {rank} | eta1 = {eta1}") @@ -137,14 +143,15 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): span2 = bsp.find_span(tn2, DR.degree[1], eta2) span3 = bsp.find_span(tn3, DR.degree[2], eta3) - # non-zero spline values at eta - bn1 = xp.empty(DR.degree[0] + 1, dtype=float) - bn2 = xp.empty(DR.degree[1] + 1, dtype=float) - bn3 = xp.empty(DR.degree[2] + 1, dtype=float) + # non-zero spline values at eta -- output buffers for the compiled + # bsplines_kernels functions, which require host numpy arrays. + bn1 = np.empty(DR.degree[0] + 1, dtype=float) + bn2 = np.empty(DR.degree[1] + 1, dtype=float) + bn3 = np.empty(DR.degree[2] + 1, dtype=float) - bd1 = xp.empty(DR.degree[0], dtype=float) - bd2 = xp.empty(DR.degree[1], dtype=float) - bd3 = xp.empty(DR.degree[2], dtype=float) + bd1 = np.empty(DR.degree[0], dtype=float) + bd2 = np.empty(DR.degree[1], dtype=float) + bd3 = np.empty(DR.degree[2], dtype=float) bsp.b_d_splines_slim(tn1, DR.degree[0], eta1, span1, bn1, bd1) bsp.b_d_splines_slim(tn2, DR.degree[1], eta2, span2, bn2, bd2) @@ -155,10 +162,11 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): ie2 = span2 - pn[1] ie3 = span3 - pn[2] - # global indices of non-vanishing B- and D-splines (no modulo) - glob_n1 = xp.arange(ie1, ie1 + pn[0] + 1) - glob_n2 = xp.arange(ie2, ie2 + pn[1] + 1) - glob_n3 = xp.arange(ie3, ie3 + pn[2] + 1) + # global indices of non-vanishing B- and D-splines (no modulo) -- pure + # host-side index bookkeeping used below for Python set comparisons. + glob_n1 = np.arange(ie1, ie1 + pn[0] + 1) + glob_n2 = np.arange(ie2, ie2 + pn[1] + 1) + glob_n3 = np.arange(ie3, ie3 + pn[2] + 1) glob_d1 = glob_n1[:-1] glob_d2 = glob_n2[:-1] @@ -184,10 +192,10 @@ def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): # local column indices in _data of non-vanishing B- and D-splines, as sets for comparison cols = [{}, {}, {}] for n in range(3): - cols[n]["NN"] = set(xp.arange(2 * pn[n] + 1)) - cols[n]["ND"] = set(xp.arange(2 * pn[n])) - cols[n]["DN"] = set(xp.arange(1, 2 * pn[n] + 1)) - cols[n]["DD"] = set(xp.arange(1, 2 * pn[n])) + cols[n]["NN"] = set(np.arange(2 * pn[n] + 1)) + cols[n]["ND"] = set(np.arange(2 * pn[n])) + cols[n]["DN"] = set(np.arange(1, 2 * pn[n] + 1)) + cols[n]["DD"] = set(np.arange(1, 2 * pn[n])) # testing vector-valued spaces spaces_vector = ["v1", "v2"] diff --git a/src/struphy/pic/tests/test_pushers.py b/src/struphy/pic/tests/test_pushers.py index fb139de89..27a5b92d0 100644 --- a/src/struphy/pic/tests/test_pushers.py +++ b/src/struphy/pic/tests/test_pushers.py @@ -627,6 +627,7 @@ def test_push_bxu_Hdiv_pauli(num_elements, degree, bcs, mapping, show_plots=Fals ) def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): import cunumpy as xp + import numpy as np from feectools.ddm.mpi import mpi as MPI from struphy import BoundaryParameters, LoadingParameters, WeightsParameters, domains @@ -699,12 +700,14 @@ def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): pusher_psy(dt) - n_mks_load = xp.zeros(size, dtype=int) + # MPI communication buffers must be host-resident regardless of the + # active array backend (mpi4py has no CuPy awareness here). + n_mks_load = np.zeros(size, dtype=int) - comm.Allgather(xp.array(xp.shape(particles.markers)[0]), n_mks_load) + comm.Allgather(np.array(xp.shape(particles.markers)[0]), n_mks_load) - sendcounts = xp.zeros(size, dtype=int) - displacements = xp.zeros(size, dtype=int) + sendcounts = np.zeros(size, dtype=int) + displacements = np.zeros(size, dtype=int) accum_sendcounts = 0.0 for i in range(size): @@ -712,10 +715,13 @@ def test_push_eta_rk4(num_elements, degree, bcs, mapping, show_plots=False): displacements[i] = accum_sendcounts accum_sendcounts += sendcounts[i] - all_particles_psy = xp.zeros((int(accum_sendcounts) * 3,), dtype=float) + all_particles_psy = np.zeros((int(accum_sendcounts) * 3,), dtype=float) comm.Barrier() - comm.Allgatherv(xp.array(particles.markers[:, :3]), [all_particles_psy, sendcounts, displacements, MPI.DOUBLE]) + comm.Allgatherv( + np.ascontiguousarray(xp.to_numpy(particles.markers[:, :3])), + [all_particles_psy, sendcounts, displacements, MPI.DOUBLE], + ) comm.Barrier() diff --git a/src/struphy/pic/tests/test_set_zero_velocity.py b/src/struphy/pic/tests/test_set_zero_velocity.py index 2eb563859..729222954 100644 --- a/src/struphy/pic/tests/test_set_zero_velocity.py +++ b/src/struphy/pic/tests/test_set_zero_velocity.py @@ -130,6 +130,7 @@ def test_set_zero_velocity_mpi(mapping, comp: int, show_plot=False): """ import cunumpy as xp + import numpy as np from feectools.ddm.mpi import MockComm from feectools.ddm.mpi import mpi as MPI from matplotlib import pyplot as plt @@ -181,10 +182,13 @@ def test_set_zero_velocity_mpi(mapping, comp: int, show_plot=False): if comm is None: mpi_result = binned_result else: - mpi_result = xp.zeros_like(binned_result) + # mpi4py needs host buffers regardless of the active backend. + host_binned = [xp.to_numpy(b) for b in binned_result] + host_result = [np.zeros_like(b) for b in host_binned] for i in range(3): - comm.Allreduce(binned_result[i], mpi_result[i], op=MPI.SUM) + comm.Allreduce(host_binned[i], host_result[i], op=MPI.SUM) comm.Barrier() + mpi_result = [xp.asarray(r) for r in host_result] # tests if show_plot and rank == 0: diff --git a/src/struphy/pic/tests/test_sph.py b/src/struphy/pic/tests/test_sph.py index d536004b7..4bac61630 100644 --- a/src/struphy/pic/tests/test_sph.py +++ b/src/struphy/pic/tests/test_sph.py @@ -500,10 +500,10 @@ def test_evaluation_SPH_Np_convergence_1d(boxes_per_dim, bc_x, eval_pts, tessela logger.info(f"{Np =}, {ppb =}, {diff =}") if tesselation: - fit = xp.polyfit(xp.log(ppbs), xp.log(err_vec), 1) + fit = xp.polyfit(xp.log(xp.array(ppbs)), xp.log(xp.array(err_vec)), 1) xvec = ppbs else: - fit = xp.polyfit(xp.log(Nps), xp.log(err_vec), 1) + fit = xp.polyfit(xp.log(xp.array(Nps)), xp.log(xp.array(err_vec)), 1) xvec = Nps if show_plot and rank == 0: @@ -623,9 +623,9 @@ def test_evaluation_SPH_h_convergence_1d(boxes_per_dim, bc_x, eval_pts, tesselat err_vec += [diff] if tesselation: - fit = xp.polyfit(xp.log(h_vec[1:5]), xp.log(err_vec[1:5]), 1) + fit = xp.polyfit(xp.log(xp.array(h_vec[1:5])), xp.log(xp.array(err_vec[1:5])), 1) else: - fit = xp.polyfit(xp.log(h_vec[:-2]), xp.log(err_vec[:-2]), 1) + fit = xp.polyfit(xp.log(xp.array(h_vec[:-2])), xp.log(xp.array(err_vec[:-2])), 1) if show_plot and rank == 0: plt.figure(figsize=(12, 8)) @@ -908,10 +908,10 @@ def test_evaluation_SPH_Np_convergence_2d(boxes_per_dim, bc_x, bc_y, tesselation # fig.savefig(f"2d_sph_{Np}_{ppb}.png") if tesselation: - fit = xp.polyfit(xp.log(ppbs), xp.log(err_vec), 1) + fit = xp.polyfit(xp.log(xp.array(ppbs)), xp.log(xp.array(err_vec)), 1) xvec = ppbs else: - fit = xp.polyfit(xp.log(Nps), xp.log(err_vec), 1) + fit = xp.polyfit(xp.log(xp.array(Nps)), xp.log(xp.array(err_vec)), 1) xvec = Nps if show_plot and rank == 0: diff --git a/src/struphy/propagators/tests/test_curl_curl.py b/src/struphy/propagators/tests/test_curl_curl.py index b3205cdcc..f94f7f31b 100644 --- a/src/struphy/propagators/tests/test_curl_curl.py +++ b/src/struphy/propagators/tests/test_curl_curl.py @@ -57,9 +57,11 @@ def test_convergence_1d( # Test over spline degree and grid resolution Nels = [2**n for n in range(Nmin, Nmax + 1)] - e1 = 0.0 - e2 = 0.0 - e3 = 0.0 + # xp.meshgrid (unlike numpy's) requires actual arrays, not plain floats, + # for whichever of e1/e2/e3 isn't replaced by e below. + e1 = xp.array([0.0]) + e2 = xp.array([0.0]) + e3 = xp.array([0.0]) e = xp.linspace(0.0, 1.0, 64) bcs = (None, None, None) @@ -309,7 +311,9 @@ def scalar_current(a, b): Nels = [2**n for n in range(Nmin, Nmax + 1)] e = xp.linspace(0.0, 1.0, 64) - egrid = [0.0, 0.0, 0.0] + # xp.meshgrid (unlike numpy's) requires actual arrays, not plain floats, + # for whichever entries aren't replaced by e below. + egrid = [xp.array([0.0]), xp.array([0.0]), xp.array([0.0])] for idx in space["coords"]: egrid[idx] = e e1, e2, e3 = egrid From b120a6bb340367a1ed3de50e129069124a950b4f Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 16 Sep 2026 14:04:13 +0200 Subject: [PATCH 153/156] Removed CudaKernelSet --- src/struphy/cuda.py | 54 ++------------------------------------------- 1 file changed, 2 insertions(+), 52 deletions(-) diff --git a/src/struphy/cuda.py b/src/struphy/cuda.py index 75474bbd1..d18571f92 100644 --- a/src/struphy/cuda.py +++ b/src/struphy/cuda.py @@ -32,16 +32,11 @@ class CudaKernel: kernel a module launches shows up as a ``CudaKernel(...)`` at module scope, so `grep -n "CudaKernel("` (or just reading the top of the file) tells you exactly which functions do device work and which don't. - - ``source`` may be a string, or a zero-argument callable that builds one - (matching the old per-module ``_source()`` helpers some of these files - used to defer assembling/importing a source shared with another module - until first use). """ __slots__ = ("_source", "_name", "_kernel") - def __init__(self, source, name: str) -> None: + def __init__(self, source: str, name: str) -> None: self._source = source self._name = name self._kernel = None @@ -49,61 +44,16 @@ def __init__(self, source, name: str) -> None: def __call__(self, grid, block, args) -> None: self._compiled()(grid, block, args) - def compile(self) -> None: - """Force NVRTC compilation now instead of on first launch. - - For a kernel on the hot path of the first timed step (e.g. a - model-setup routine that wants compile latency paid during setup, not - during the first measured propagation step): call this eagerly: - ``compile()`` is idempotent, so it composes fine with the normal - lazy-on-first-launch path -- whichever happens first wins, and later - calls (from either path) are no-ops. - """ - self._compiled() - def _compiled(self): if self._kernel is None: import cupy as cp - source = self._source() if callable(self._source) else self._source - kernel = cp.RawKernel(source, self._name) + kernel = cp.RawKernel(self._source, self._name) kernel.compile() self._kernel = kernel return self._kernel -class CudaKernelSet: - """A cache of :class:`CudaKernel` instances sharing one CUDA source, - keyed by kernel name. - - For modules exposing a *family* of kernel entry points compiled from the - same source (typically a device-function library plus several - ``__global__`` entry points), where the entry point needed depends on a - runtime string (``u_space``, ``algo``, a diffusion variant, ...) rather - than being fixed at import time. Replaces the old ``_kernels = {}`` dict + - ``_get_kernel(name)`` function pattern -- ``kernels[name]`` compiles and - caches lazily, exactly like the old lookup did. - - ``source`` may be a string (shared eagerly, e.g. one ``load_cuda_source`` - result) or a zero-argument callable that builds it (for sources - concatenated from several fragments -- matching the old per-module - ``_source()`` helper -- so that assembly, and any ``cupy`` import inside - it, stays deferred to first use). - """ - - __slots__ = ("_source", "_cache") - - def __init__(self, source) -> None: - self._source = source - self._cache: dict[str, CudaKernel] = {} - - def __getitem__(self, name: str) -> CudaKernel: - if name not in self._cache: - source = self._source() if callable(self._source) else self._source - self._cache[name] = CudaKernel(source, name) - return self._cache[name] - - def launch_1d(kernel: CudaKernel, n: int, args: Sequence, threads: int = 256) -> None: """Launch ``kernel`` over a 1-D grid with one thread per element of a length-``n`` array (markers, indices, quadrature points, ...) -- the From d89ac91f39b3cb0c43295bd6f25302de452e212c Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 16 Sep 2026 17:15:08 +0200 Subject: [PATCH 154/156] Fix the tests --- pyproject.toml | 1 + src/struphy/conftest.py | 19 +++++++++++++++++++ src/struphy/pic/tests/test_accum_vec_H1.py | 2 ++ src/struphy/pic/tests/test_mat_vec_filler.py | 1 + .../propagators/tests/test_curl_curl.py | 4 ++++ .../tests/test_gyrokinetic_poisson.py | 4 ++++ src/struphy/propagators/tests/test_poisson.py | 4 ++++ 7 files changed, 35 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index cce558767..a63221c7d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -185,4 +185,5 @@ markers = [ "hybrid", "single", "mpi_pic", + "needs_host_kernels", ] diff --git a/src/struphy/conftest.py b/src/struphy/conftest.py index 4bddafb21..5404fed2e 100644 --- a/src/struphy/conftest.py +++ b/src/struphy/conftest.py @@ -1,9 +1,19 @@ import logging +import os import pytest from struphy import set_logging_level +# Tests marked "needs_host_kernels" call a Pyccel kernel that is not yet +# ported for the CuPy backend (e.g. FEEC mass-matrix/basis-projection +# assembly, particle-to-grid accumulation). Under ARRAY_BACKEND=cupy they are +# skipped rather than run to failure, so a GPU CI run reports the state of +# the actually-ported code paths instead of drowning in known gaps. +_NEEDS_HOST_KERNELS_SKIP_REASON = ( + "needs_host_kernels: not yet ported to the CuPy backend (ARRAY_BACKEND=cupy)" +) + def set_logging_level_pytest(config): level_name = str(config.getoption("--logging-level")).upper() @@ -25,6 +35,15 @@ def pytest_configure(config): set_logging_level_pytest(config) +def pytest_collection_modifyitems(config, items): + if os.environ.get("ARRAY_BACKEND") != "cupy": + return + skip_host_only = pytest.mark.skip(reason=_NEEDS_HOST_KERNELS_SKIP_REASON) + for item in items: + if "needs_host_kernels" in item.keywords: + item.add_marker(skip_host_only) + + def pytest_addoption(parser): parser.addoption("--with-desc", action="store_true") parser.addoption("--vrbose", action="store_true") diff --git a/src/struphy/pic/tests/test_accum_vec_H1.py b/src/struphy/pic/tests/test_accum_vec_H1.py index af4d44259..51e5b8387 100644 --- a/src/struphy/pic/tests/test_accum_vec_H1.py +++ b/src/struphy/pic/tests/test_accum_vec_H1.py @@ -48,6 +48,7 @@ ], ) @pytest.mark.parametrize("num_clones", [1, 2]) +@pytest.mark.needs_host_kernels def test_accum_poisson(num_elements, degree, bcs, mapping, num_clones, Np=10000, show_plot: bool = False): r"""Test that AccumulatorVector provides an MC approximation of the L2 projection RHS. @@ -329,6 +330,7 @@ def test_accum_poisson(num_elements, degree, bcs, mapping, num_clones, Np=10000, (None, None, None), ], ) +@pytest.mark.needs_host_kernels def test_accum_div_u_weak_1form(num_elements, degree, bcs, Np=10000, show_plot: bool = False): r"""Test that AccumulatorVector with kernel :func:`~struphy.pic.accumulation.accum_kernels.div_u_weak_1form` provides an MC approximation of the L2 projection RHS into V1 (Hcurl). diff --git a/src/struphy/pic/tests/test_mat_vec_filler.py b/src/struphy/pic/tests/test_mat_vec_filler.py index fc53a1661..9ab3bbe33 100644 --- a/src/struphy/pic/tests/test_mat_vec_filler.py +++ b/src/struphy/pic/tests/test_mat_vec_filler.py @@ -17,6 +17,7 @@ (None, ("free", "free"), ("free", "free")), ], ) +@pytest.mark.needs_host_kernels def test_particle_to_mat_kernels(num_elements, degree, bcs, n_markers=1): """This test assumes a single particle and verifies a) if the correct indices are non-zero in _data diff --git a/src/struphy/propagators/tests/test_curl_curl.py b/src/struphy/propagators/tests/test_curl_curl.py index f94f7f31b..96bbcfbb9 100644 --- a/src/struphy/propagators/tests/test_curl_curl.py +++ b/src/struphy/propagators/tests/test_curl_curl.py @@ -28,6 +28,10 @@ logger = logging.getLogger("struphy") set_logging_level(logging.INFO) +# curl-curl FEEC solve: mass-matrix assembly/preconditioning not yet ported +# to the CuPy backend. +pytestmark = pytest.mark.needs_host_kernels + comm = MPI.COMM_WORLD rank = comm.Get_rank() plt.rcParams.update({"font.size": 22}) diff --git a/src/struphy/propagators/tests/test_gyrokinetic_poisson.py b/src/struphy/propagators/tests/test_gyrokinetic_poisson.py index a67ec2406..d78705be0 100644 --- a/src/struphy/propagators/tests/test_gyrokinetic_poisson.py +++ b/src/struphy/propagators/tests/test_gyrokinetic_poisson.py @@ -20,6 +20,10 @@ logger = logging.getLogger("struphy") set_logging_level(logging.INFO) +# Gyrokinetic Poisson FEEC solve: mass-matrix assembly/preconditioning not +# yet ported to the CuPy backend. +pytestmark = pytest.mark.needs_host_kernels + comm = MPI.COMM_WORLD rank = comm.Get_rank() # plt.rcParams.update({'font.size': 22}) diff --git a/src/struphy/propagators/tests/test_poisson.py b/src/struphy/propagators/tests/test_poisson.py index 55ca649fd..15704eda9 100644 --- a/src/struphy/propagators/tests/test_poisson.py +++ b/src/struphy/propagators/tests/test_poisson.py @@ -30,6 +30,10 @@ logger = logging.getLogger("struphy") +# Poisson FEEC solve: mass-matrix assembly/preconditioning not yet ported to +# the CuPy backend. +pytestmark = pytest.mark.needs_host_kernels + comm = MPI.COMM_WORLD rank = comm.Get_rank() plt.rcParams.update({"font.size": 22}) From 05ec3ef540d40082e3e8a0d2d15e223dbfc07a72 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Wed, 23 Sep 2026 10:27:55 +0200 Subject: [PATCH 155/156] formatting --- feectools | 2 +- src/struphy/conftest.py | 4 +--- src/struphy/pic/pushing/pusher_kernels_cuda.py | 1 + 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/feectools b/feectools index 95d8a221d..ef8695040 160000 --- a/feectools +++ b/feectools @@ -1 +1 @@ -Subproject commit 95d8a221df079645bb1c5f868a764d24b4e06a58 +Subproject commit ef86950402a667a9ef77b49e8868f6e581e20dac diff --git a/src/struphy/conftest.py b/src/struphy/conftest.py index 5404fed2e..a707b7bd7 100644 --- a/src/struphy/conftest.py +++ b/src/struphy/conftest.py @@ -10,9 +10,7 @@ # assembly, particle-to-grid accumulation). Under ARRAY_BACKEND=cupy they are # skipped rather than run to failure, so a GPU CI run reports the state of # the actually-ported code paths instead of drowning in known gaps. -_NEEDS_HOST_KERNELS_SKIP_REASON = ( - "needs_host_kernels: not yet ported to the CuPy backend (ARRAY_BACKEND=cupy)" -) +_NEEDS_HOST_KERNELS_SKIP_REASON = "needs_host_kernels: not yet ported to the CuPy backend (ARRAY_BACKEND=cupy)" def set_logging_level_pytest(config): diff --git a/src/struphy/pic/pushing/pusher_kernels_cuda.py b/src/struphy/pic/pushing/pusher_kernels_cuda.py index fb00d03db..f1b4f2331 100644 --- a/src/struphy/pic/pushing/pusher_kernels_cuda.py +++ b/src/struphy/pic/pushing/pusher_kernels_cuda.py @@ -35,6 +35,7 @@ :func:`~struphy.cuda.launch_1d`. If a function in this module does *not* sit next to a ``CudaKernel``, it does not touch the GPU. """ + from struphy.cuda import CudaKernel, launch_1d, load_cuda_source _PUSH_V_EFIELD_CUBOID_SRC = load_cuda_source(__file__, "pusher_kernels_cuda/_push_v_efield_cuboid_src.cu") From 468afc257e03bb322f50c8234f78e1774b8c7fb1 Mon Sep 17 00:00:00 2001 From: Max Lindqvist Date: Mon, 28 Sep 2026 12:06:55 +0200 Subject: [PATCH 156/156] temporary: notes --- src/struphy/kernel_arguments/pusher_args_kernels.py | 1 - src/struphy/pic/base.py | 4 ++++ src/struphy/propagators/push_eta.py | 5 +++++ 3 files changed, 9 insertions(+), 1 deletion(-) diff --git a/src/struphy/kernel_arguments/pusher_args_kernels.py b/src/struphy/kernel_arguments/pusher_args_kernels.py index 98c18b288..3d3a73afe 100644 --- a/src/struphy/kernel_arguments/pusher_args_kernels.py +++ b/src/struphy/kernel_arguments/pusher_args_kernels.py @@ -108,7 +108,6 @@ def __init__( self.bd2 = np.empty(int(pn[1]), dtype=float) self.bd3 = np.empty(int(pn[2]), dtype=float) - class DomainArguments: """Holds the mandatory arguments pertaining to :class:`~struphy.geometry.base.Domain` passed to particle pusher kernels. diff --git a/src/struphy/pic/base.py b/src/struphy/pic/base.py index bd6e15f41..9e742b282 100644 --- a/src/struphy/pic/base.py +++ b/src/struphy/pic/base.py @@ -2867,6 +2867,7 @@ def _allocate_marker_array(self, dry_run: bool = False): valid_mks_for_kernels = self._valid_mks # arguments for kernels + self._args_markers = MarkerArguments( markers_for_kernels, valid_mks_for_kernels, @@ -2881,6 +2882,9 @@ def _allocate_marker_array(self, dry_run: bool = False): _to_numpy_for_kernel(self.mu_idx), ) + if "cupy": + self._args_markers = transform() + def _initialize_sorting_boxes(self): """Initializes the sorting boxes. diff --git a/src/struphy/propagators/push_eta.py b/src/struphy/propagators/push_eta.py index de8bfe595..9f9a4c7a2 100644 --- a/src/struphy/propagators/push_eta.py +++ b/src/struphy/propagators/push_eta.py @@ -91,8 +91,13 @@ def options(self, new): @profile def allocate(self): # get kernel + + # Old kernel = PyccelKernel(pusher_kernels.push_eta_stage) + # New (returns a PyccelKernel) + kernel = pusher_kernels.push_eta_stage.kernel + # define algorithm butcher = self.options.butcher # temp fix due to refactoring of ButcherTableau: