GPU stack 2/8: host/device conversions in post-processing; math.sqrt in Species

[max-models]

Part 2 of 8 of the split of #772 (cuda-aware-mpi-fixes) into stacked PRs. Requires struphy-hub/feectools#104 to be merged into feectools devel-tiny first (PR 1 bumps the submodule).

• post_processing/output.py: on the CuPy backend the fields are evaluated on the device and written from the host. The evaluation grids, point data and coordinates are converted where they cross, and HDF5 data is moved to the device before it goes into the spline coefficients.
• Species: the plasma frequency of the unit conversion is a host scalar (math.sqrt instead of xp.sqrt).

Tests

Local, macOS without a GPU, in a fresh environment with feectools 1d8d47c and the pinned cunumpy, after merging devel (with #774 and #775). The PR passes test_cuda_parity, test_cuda_emulation, test_kernel_backends, test_sorting, test_accum_matrix_cupy, test_preconditioner_cupy, test_poisson and test_output on 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 all pic tests (1477 passed), the examples test for the two profiling cases, the sorting, Poisson and fused-gradient tests under mpirun -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.

Stack (merge in order)

0. Device ghost-exchange plans, fused block exchanges, single-reduction PCG (CUDA-aware MPI) feectools#104 (feectools, into devel-tiny)

#	PR	base
1	#782 update feectools (device ghost exchange, single-reduction PCG); pcg_sr solver option	devel
2	#783 host/device conversions in post-processing; math.sqrt in Species (this PR)	#782
3	#784 CUDA mass-matrix assembly kernel kernel_3d_mat	#783
4	#785 apply separable mass matrices as Kronecker products of 1D banded matrices	#784
5	#786 Poisson/diffusion solve with fewer passes over the vectors	#785
6	#787 marker rows prepared inside the push kernels	#786
7	#788 chunked marker loading, row-based weight update, leaner MPI sorting; static field in PushVxB	#787
8	#789 profiling cases Poisson cube and Vlasov slab (CPU node vs GPU, GPU scaling)	#788

🤖 Generated with Claude Code