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GPU stack 6/8: marker rows prepared inside the push kernels - #787
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…the push kernels - Particles allocates the marker buffer column-major on the CuPy backend: kernels and the Python column operations access a few columns of many markers, so with a row-major buffer every access touched whole rows and the threads of a warp read addresses a row apart. The CUDA kernels address the buffer through its strides (Array2D<double> in MarkerArgs); NumPy keeps C order for the pyccel kernels. - push_eta_stage and push_vxb_analytic prepare each marker row themselves at stage 0 (MarkerArgs.prepare_rows), so Pusher._push skips the separate prepare_push pass when no init or evaluation kernel runs before them. prepare_push is launched on a 2D grid. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
…repare_rows in the CUDA emulation Kernels without a CUDA version run on host copies of the device arrays, which keep the F order (cunumpy.to_numpy uses get(order='A')), and the pyccel kernels reject it: the sorting-box kernels failed on the fake CuPy (test_sorting_boxes_on_cupy_fake_cupy), and the same holds on a GPU for every marker kernel without a CUDA version. The buffer is therefore C-ordered unless the variable is set. The CUDA emulation of the tests reads the struct fields from the pyccel argument objects, which do not have the CUDA-only prepare_rows flag (every emulated marker kernel failed). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This was referenced Oct 16, 2026
max-models
added this pull request to stack #790
October 16, 2026 08:51
…k-5-diffusion-solve) # Conflicts: # src/struphy/kernel_arguments/pusher_args_cuda.py # src/struphy/pic/base.py # src/struphy/pic/pushing/prepare_push_cuda.cu # src/struphy/pic/pushing/pusher.py
…yout (feectools update)
max-models
changed the title
…yout (fused gradient)
…yout (feectools update, CachedLaunch)
…k-5-diffusion-solve)
…yout (pcg_sr in the shear Alfven solves)
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Part 6 of 8 of the split of #772 (
cuda-aware-mpi-fixes) into stacked PRs. Requires struphy-hub/feectools#104 to be merged into feectoolsdevel-tinyfirst (PR 1 bumps the submodule).push_eta_stageandpush_vxb_analyticprepare each marker themselves at stage 0 (MarkerArgs.prepare_rows): they copy the phase-space coordinates to the rows fromfirst_pusher_idxand zero the rows after them, asprepare_pushdoes.Pusher._pushthen skips the separateprepare_pushlaunch when no init or evaluation kernel runs before them (those may read the prepared rows). The kernels cover the same markers, one thread per used marker (n_markers_used).prepare_rowsis a CUDA-only field ofMarkerArgs. The CUDA emulation of the tests reads it with default 0.Updated after #774 and #775 were merged into devel: on the original branch this PR also stored the marker buffer column-major on the GPU (
order="F"). devel now stores it as(n_cols, n_markers)on both backends, so that part is gone. The row preparation is ported tomarkers(j, ip)indexing.Tests
Local, macOS without a GPU, in a fresh environment with feectools
1d8d47cand the pinned cunumpy, after merging devel (with #774 and #775). The PR passestest_cuda_parity,test_cuda_emulation,test_kernel_backends,test_sorting,test_accum_matrix_cupy,test_preconditioner_cupy,test_poissonandtest_outputon its own. The top of the stack also passes the mass-matrix, preconditioner, Derham-GPU, fused-gradient, Kronecker-block, Poisson, shear Alfvén, post-processing, polar fake-CuPy and allpictests (1477 passed), the examples test for the two profiling cases, the sorting, Poisson and fused-gradient tests undermpirun -n 2, and the device MPI sort on the fake CuPy on 2 and 4 ranks. The Kronecker products match the stencil mass matrices to 5e-16 for M0, M1, M2, M3, Mv, M1n and M2n on a Cuboid, periodic and Dirichlet, on 1 and 2 ranks.Not tested on a GPU here. The CUDA kernels and the device paths need a run on the cluster.
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