[gpu-1] End-to-end GPU test of VlasovAmpereOneSpecies on 1 and 2 ranks

[max-models]

Solves the following issue(s):

Part of #689, tracked in #650. This PR covers the items of #689 that are left for VlasovAmpereOneSpecies: running on 1 and 2 ranks, no host/device transfers in the time loop, and the diagnostics it needs. It does not close #689, because these runs use the CPU emulation; the first run on a real GPU is #687.

Stack: this is gpu-1 of a new stack. Merge it after #722 (cuda-namespaces), #716, #717 and #719. It merges those branches in, so until they are merged, this diff also shows their commits. gpu-2 (the benchmark script) is based on this branch.

Core changes:

• New test models/tests/test_gpu_VlasovAmpereOneSpecies.py. A small Landau-damping run on NumPy and on CuPy: 8 cells, 320 Sobol markers, 3 steps, full-f weights, and an e1_v1 binning plot so that the diagnostics run on the device too. It compares the energies, the e_field and phi coefficients, and the markers by ID (tolerances explained in compare_runs), and checks the transfer budget of the time loop.
• Shared helpers in models/tests/gpu_e2e.py. The transfer budget, the instrumentation, gathering markers across ranks and the device context, moved out of End-to-end GPU test for LinearVlasovAmpereOneSpecies #719's test. test_gpu_LinearVlasovAmpereOneSpecies.py now uses them.
• The GPU tests also run without a GPU. On cunumpy's fake CuPy (CUNUMPY_FAKE_CUPY=1), every CUDA launch is emulated on the CPU and CudaKernel.compile is skipped. Simulation.compile_cuda_kernels still checks that every kernel of the time loop has a CUDA version. Without a GPU and without the fake CuPy, test_fake_cupy_matches_numpy runs the comparison in a serial child process.
• Transfer budget of model.integrate. Only scalar downloads are allowed: the residual norm of each Krylov iteration, which feectools copies for its convergence test (feectools#98 keeps everything else of the solve on the device). No arrays, no host-to-device copies, no host kernels.
• Particles. Checking whether MPI is CUDA-aware now happens when the marker array is allocated (in the setup), not on the first marker exchange. Before, that check was the only array transfer inside the time loop on 2 ranks.

Results (fake CuPy, every CUDA launch emulated on the CPU; macOS; not run on a GPU):

test	1 rank	2 ranks
test_gpu_VlasovAmpereOneSpecies.py (NumPy reference + CuPy vs NumPy + transfer budget)	passed	passed
test_gpu_LinearVlasovAmpereOneSpecies.py::test_cupy_matches_numpy_without_transfers	passed	not run

Transfers in the time loop of the CuPy run:

• integrate: one 8-byte residual norm per pcg iteration.
• Diagnostics: one 24-byte copy of the three scalars per update.
• Output: one copy per saved dataset.

Diagnostics: no new CUDA kernels are needed for this model. The scalars use xp reductions and feectools' device inner products, and binning uses xp.histogramdd.

Found along the way (not changed here):

• The initial Poisson solve with the default stab_eps=1e-14 puts (Monte-Carlo net charge) / 1e-14 into the constant mode of phi. That makes phi about 1e14, so E = -grad(phi) loses about 5% to cancellation on either backend. The verification tests use this default. The new test uses stab_eps=1e-6, as End-to-end GPU test for LinearVlasovAmpereOneSpecies #719 does.
• With control_variate=True, kinetic_energy at t=0 is computed with the full-f weights and afterwards with the delta-f weights, so total_energy jumps after the first step. With full-f weights the total is conserved from step to step to 1e-9, but the t=0 value still differs by about 2e-4 (not investigated).

Documentation changes:

CUDA_STRATEGY.md: the end-to-end entry now covers both models, the CPU emulation and the transfer budget.

🤖 Generated with Claude Code