sfincs-jax 1.0.7

Neoclassical transport solver with CPU/GPU and differentiable JAX workflows

sfincs_jax

sfincs_jax is a standalone neoclassical transport code for radially local drift-kinetic calculations in stellarator and tokamak geometry. It combines high-fidelity kinetic models, CPU/GPU execution, modern matrix-free numerics, parallel workflows, and optional differentiable solve paths in one codebase.

On the current main branch, the full audited example suite runs cleanly on CPU and GPU. The default CLI path is tuned for robust production solves and practical throughput, while the Python API can opt into differentiable solve paths when gradients matter.

It is designed for:

• high-performance runs on CPU/GPU,
• research and production transport workflows,
• memory-efficient large solves,
• end-to-end differentiable workflows.

The figure above shows a representative transport benchmark. The release-facing validation and benchmark artifacts are documented in the docs and in the audit table below.

Installation

Install from PyPI:

pip install sfincs_jax

Install from source:

git clone https://github.com/uwplasma/sfincs_jax.git
cd sfincs_jax
pip install .

After installing from a source checkout, you can run the CLI immediately on the bundled tiny example. The suffix of --out selects the output format: .h5/.hdf5 for the Fortran-compatible HDF5 file, .nc/.netcdf for NetCDF4, and .npz for a fast NumPy archive.

cd sfincs_jax
sfincs_jax write-output \
  --input examples/getting_started/input.namelist \
  --out sfincsOutput.h5 \
  --geometry-only
sfincs_jax --plot sfincsOutput.h5

This is the fast installation smoke test. It writes sfincsOutput.h5 and then writes a multi-page PDF diagnostics panel next to it as sfincsOutput_summary.pdf. The same command works for NetCDF and NPZ:

sfincs_jax write-output --input examples/getting_started/input.namelist --out sfincsOutput.nc --geometry-only
sfincs_jax write-output --input examples/getting_started/input.namelist --out sfincsOutput.npz --geometry-only
sfincs_jax --plot sfincsOutput.nc

Physics in One Page

sfincs_jax solves the radially local, steady, linearized drift-kinetic equation for the non-adiabatic distribution-function perturbation f_s1 on a flux surface. In normalized form the solved kinetic balance is

(parallel streaming + mirror force + E x B drift + magnetic drift
 + energy/pitch-angle drifts - linearized collisions) f_s1 = thermodynamic drives.

The unknown distribution can be coupled to the flux-surface electrostatic potential variation Phi1(theta,zeta) through quasineutrality when requested. The output fluxes, flows, transport matrices, and diagnostics are moments of this solved f_s1. The full equations, normalizations, switches, and source-code mapping are documented in docs/system_equations.rst, docs/physics_models.rst, and docs/method.rst.

Quick Start (CLI)

You can run sfincs_jax from anywhere in your terminal. You do not need to be inside the repository folder.

Run an input file:

sfincs_jax /path/to/input.namelist

Write output explicitly:

sfincs_jax write-output --input /path/to/input.namelist --out /path/to/sfincsOutput.h5

Plot an existing output file:

sfincs_jax --plot /path/to/sfincsOutput.h5

By default this writes /path/to/sfincsOutput_summary.pdf, a multi-page panel with geometry, radial profiles, particle/heat/momentum fluxes, NTV, moments, and transport-matrix diagnostics when those datasets are present. Use sfincs_jax plot-output --input-h5 ... --out custom.pdf to choose a filename.

Override the equilibrium file at the CLI without changing input.namelist:

sfincs_jax write-output \
  --input /path/to/input.namelist \
  --out /path/to/sfincsOutput.h5 \
  --wout-path /path/to/wout.nc

The bare sfincs_jax /path/to/input.namelist form accepts the same --equilibrium-file and --wout-path overrides.

Quick Start (Python)

Read a namelist, run sfincs_jax, write an output file, and inspect results directly in memory:

from pathlib import Path

from sfincs_jax.io import write_sfincs_jax_output_h5

input_namelist = Path("input.namelist")
out_path, results = write_sfincs_jax_output_h5(
    input_namelist=input_namelist,
    output_path=Path("sfincsOutput.h5"),
    return_results=True,
)

print("Wrote:", out_path)
print("Available datasets:", len(results))
print("Example key:", "particleFlux_vm_psiHat" in results)

Set output_path=Path("sfincsOutput.nc") for NetCDF4 or output_path=Path("sfincsOutput.npz") for a fast NumPy archive. The calculation is identical; only the writer changes.

If you need to override the equilibrium file without editing the namelist, pass equilibrium_file=... or the VMEC-friendly alias wout_path=...:

write_sfincs_jax_output_h5(
    input_namelist=input_namelist,
    output_path=Path("sfincsOutput.h5"),
    wout_path=Path("/path/to/wout.nc"),
)

sfincs_jax write-output and the scan utilities use the explicit performance-oriented solve path by default. When calling write_sfincs_jax_output_h5(...) directly, pass differentiable=False for the same fast path or request the implicit/differentiable linear-solve path only when you need gradients:

write_sfincs_jax_output_h5(
    input_namelist=input_namelist,
    output_path=Path("sfincsOutput.h5"),
    differentiable=False,
)

write_sfincs_jax_output_h5(
    input_namelist=input_namelist,
    output_path=Path("sfincsOutput.h5"),
    differentiable=True,
)

Repository examples that map directly onto common first tasks:

• run the bundled tiny CLI example: sfincs_jax examples/getting_started/input.namelist
• write a tiny tokamak output: python examples/getting_started/write_sfincs_output_tokamak.py
• write a tiny VMEC output with wout_path: python examples/getting_started/write_sfincs_output_vmec.py
• plot an output file: python examples/getting_started/plot_sfincs_output.py
• write HDF5/NetCDF/NPZ and plot a PDF panel: python examples/getting_started/write_and_plot_multiple_formats.py
• run autodiff examples: python examples/autodiff/autodiff_gradient_nu_n_residual.py
• run the optional VMEC/Boozer differentiable geometry handoff: python examples/autodiff/vmec_jax_to_boozer_sfincs_pipeline.py --wout /path/to/wout.nc
• benchmark CPU/GPU parallel solves: python examples/performance/benchmark_sharded_solve_scaling.py --backend cpu --devices 1 2 --inner-warmup-solves 1 --sample-timeout-s 300 ...

Parallel CLI controls are now first-class:

# Multi-core CPU host sharding on one node
sfincs_jax --cores 8 --shard-axis auto /path/to/input.namelist

# Parallel transport-matrix RHS solves
sfincs_jax transport-matrix-v3 \
  --input /path/to/input.namelist \
  --transport-workers 4

# High-nu LHD/W7-X campaign pilot on a dual-GPU node
CUDA_VISIBLE_DEVICES=0,1 \
python examples/publication_figures/generate_sfincs_paper_figs.py \
  --case lhd \
  --collision-operators 0 \
  --nuprime-min 17.78279101649707 \
  --nuprime-max 17.78279101649707 \
  --n-points 1 \
  --transport-workers 2 \
  --transport-parallel-backend gpu \
  --transport-sparse-direct-max 30000 \
  --require-residuals \
  --max-transport-residual 1e-6 \
  --max-transport-relative-residual 1e-6 \
  --scan-only

# The current office dual-GPU LHD pilot for that point is residual-clean in
# ~262 s, compared with ~345 s on one GPU and ~569 s on the older implicit path.
# For the first W7-X FP high-nu point, use the bounded one-worker sparse-LU lane
# below: it closes all three RHS residual gates in ~9.7 min on one office GPU
# with sparse-helper factor reuse, compared with ~33.8 min before reuse.

# W7-X FP high-nu residual-clean pilot, intentionally one worker to limit sparse
# LU memory pressure:
CUDA_VISIBLE_DEVICES=0 \
SFINCS_JAX_TRANSPORT_SPARSE_FACTOR_DTYPE=float32 \
python examples/publication_figures/generate_sfincs_paper_figs.py \
  --case w7x \
  --collision-operators 0 \
  --nuprime-min 17.78332923601508 \
  --nuprime-max 17.78332923601508 \
  --n-points 1 \
  --transport-workers 1 \
  --transport-parallel-backend gpu \
  --transport-sparse-direct-max 40000 \
  --transport-maxiter 800 \
  --require-residuals \
  --max-transport-residual 1e-6 \
  --max-transport-relative-residual 1e-6 \
  --scan-only

# To compare candidate preconditioners before widening W7-X high-nu scans,
# isolate single-RHS behavior:
CUDA_VISIBLE_DEVICES=0 \
python examples/performance/benchmark_w7x_high_nu_preconditioners.py \
  --preconditioners auto,fp_tzfft,xmg \
  --which-rhs 2 \
  --sparse-direct-max 40000 \
  --sparse-factor-dtype float32 \
  --maxiter 800 \
  --timeout-s 900

The W7-X high-nu figure is generated by python examples/publication_figures/generate_w7x_high_nu_performance.py. The checked run preserves the previous residual-clean transport matrix exactly, reduces the full one-point wall time from about 2028 s to 582 s, and lowers measured peak RSS from about 19.9 GB to 15.3 GB.

# One-node multi-GPU sharded solve (experimental for very large single-RHS cases)
CUDA_VISIBLE_DEVICES=0,1 \
sfincs_jax write-output \
  --input /path/to/input.namelist \
  --shard-axis theta \
  --distributed-gmres auto

# Multi-host JAX distributed bootstrap
sfincs_jax write-output \
  --input /path/to/input.namelist \
  --distributed \
  --process-count 8 \
  --process-id ${RANK} \
  --coordinator-address node0 \
  --coordinator-port 1234

Use -v to have the executable print the active parallel runtime summary (cores, shard axis, transport workers, distributed Krylov mode, and multi-host bootstrap fields) before the solve starts.

Current recommendation:

• CPU host sharding is supported and deterministic, but the measured speedup is still case-dependent.
• The current sharded RHSMode=1 CPU path uses a wider Schwarz patch rule plus a bounded multilevel residual correction to avoid the worst 4/8-device fragmentation failures seen in earlier releases.
• Use one GPU per case or scan point for production throughput today.
• Multi-GPU single-case sharding is available for benchmarking and very large runs, but it remains experimental and is not yet the default recommendation.
• The sharded-solve benchmark helper supports both --backend cpu and --backend gpu; the GPU path uses CUDA_VISIBLE_DEVICES and disables JAX preallocation in the subprocess, with cuda_malloc_async enabled for the benchmark subprocess allocator, so one-node GPU scaling experiments are more reproducible.
• For practical multi-GPU usage today, the strongest measured path is transport-worker parallelism with one worker per GPU on RHSMode=2/3 runs. On the fresh office 2-GPU rerun of examples/performance/transport_parallel_2min.input.namelist, this path measured 351.1s -> 237.7s from 1 -> 2 GPU workers, i.e. 1.48x speedup on a 3-RHS case, essentially at the finite-task ideal of 1.5x.
• Multi-GPU single-case sharding remains experimental. Use it for research and benchmarking, not as the default production scaling path.

You can reproduce the recommended multi-GPU transport-worker benchmark with:

python examples/performance/benchmark_transport_parallel_scaling.py \
  --input examples/performance/transport_parallel_2min.input.namelist \
  --backend gpu \
  --workers 1 2

Compare two outputs:

sfincs_jax compare-h5 --a sfincsOutput_jax.h5 --b sfincsOutput_fortran.h5

Advanced CLI, plotting, and solver options are documented in docs/usage.rst, docs/outputs.rst, and docs/performance_techniques.rst.

Models, Numerics, and Validation

sfincs_jax solves the same class of neoclassical drift-kinetic problems as mature SFINCS workflows, but it is documented and maintained as its own code. In particular:

• the public executable favors bounded, performance-oriented solve strategies,
• the Python API can switch to differentiable solve paths when end-to-end sensitivities are needed,
• CPU runs lean on JIT-cached kernels and selected host sparse factorizations for hard linear branches,
• repeated RHSMode=1 output-writing runs reuse prebuilt grids, geometry, and operator state to cut setup cost on large HSX/geometry11 cases,
• GPU runs keep operator applications on device, then fall back to accelerator-safe or host rescue paths only when conditioning or memory demands it,
• and the documentation maps the governing equations directly onto the source tree.

The main documentation entry points are:

• physics and equations: docs/physics_models.rst, docs/system_equations.rst, docs/physics_reference.rst
• geometry and numerics: docs/geometry.rst, docs/method.rst, docs/numerics.rst
• inputs and outputs: docs/inputs.rst, docs/outputs.rst
• parallel and performance workflows: docs/parallelism.rst, docs/performance.rst
• examples, applications, and testing: docs/examples.rst, docs/applications.rst, docs/testing.rst
• external trust-building comparisons: docs/fortran_comparison.rst

Current Example-Suite Audit

Regenerate this block from the current main working tree with:

python scripts/run_scaled_example_suite.py \
  --examples-root examples/sfincs_examples \
  --resolution-reference-root /Users/rogeriojorge/local/tests/sfincs_original/fortran/version3/examples \
  --reference-results-root tests/scaled_example_suite_recheck_cpu_frozen_2026-04-23_postkeyfix \
  --out-root tests/scaled_example_suite_release_cpu_frozen_2026-04-25_v106 \
  --scale-factor 1.0 \
  --runtime-target-basis fortran \
  --fortran-min-runtime-s 0.0 \
  --runtime-adjustment-iters 0 \
  --runtime-baseline-report tests/scaled_example_suite_recheck_cpu_frozen_2026-04-23_postkeyfix/suite_report.json \
  --jax-profile-marks on
python scripts/generate_readme_fast_branch_audit.py \
  --out-root tests/scaled_example_suite_release_cpu_frozen_2026-04-25_v106 \
  --gpu-out-root tests/scaled_example_suite_release_gpu_2026-04-25_v106

The benchmark policy on main is:

• start from the original Fortran v3 example resolution,
• only downscale when a case is too expensive for a practical suite run,
• benchmark JAX CPU and GPU against a frozen CPU-generated Fortran reference root,
• and never intentionally push a reduced case below about 1s of Fortran wall time unless the original example is already that small.

That avoids the misleading sub-second Fortran rows that came from blind global downscaling, keeps the GPU lane tied to a deterministic reference, and makes the additional example part of the same artifact set as the standard suite.

Current main CPU audit comes from tests/scaled_example_suite_release_cpu_frozen_2026-04-25_v106. Matching frozen-reference GPU audit comes from tests/scaled_example_suite_release_gpu_2026-04-25_v106.

• Recorded cases: 39/39
• Practical status counts: parity_ok=39
• Strict status counts: parity_ok=39
• GPU practical status counts: parity_ok=39
• GPU strict status counts: parity_ok=39
• CPU output-key coverage: missing_total=0, extra_total=70, audited_cases=39, skipped_cases=0
• GPU output-key coverage: missing_total=0, extra_total=70, audited_cases=39, skipped_cases=0
• CPU runtime drift watchlist vs tests/scaled_example_suite_recheck_cpu_frozen_2026-04-23_postkeyfix/suite_report.json: none
• GPU runtime drift watchlist vs tests/scaled_example_suite_fast_gpu_full_v11_refresh/suite_report.json: none
• Resolution policy: reference_first_runtime_window, scale_factor=1.0, runtime_basis=fortran, fortran_min=0.0, fortran_max=None, adjust_iters=0
• Remaining cases: none
• Additional example: parity_ok on CPU and parity_ok on GPU

Current mismatches:

• CPU practical mismatches: none
• CPU strict mismatches: none
• GPU practical/strict mismatches: none

Full per-case runtime / memory table:

Case	Fortran CPU(s)	JAX CPU(s)	CPU x	JAX GPU(s)	GPU x	Fortran MB	JAX CPU MB	CPU MB x	JAX GPU MB	GPU MB x	CPU mismatch	GPU mismatch	CPU print	GPU print	CPU status	GPU status
HSX_FPCollisions_DKESTrajectories	29.664	3.060	0.10x	5.152	0.17x	103.0	496.9	4.82x	919.3	8.92x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
HSX_FPCollisions_fullTrajectories	88.504	3.054	0.03x	5.152	0.06x	100.8	506.8	5.03x	924.7	9.17x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
HSX_PASCollisions_DKESTrajectories	0.994	3.951	3.97x	6.508	6.55x	112.0	1146.3	10.23x	1183.5	10.56x	0/123 (strict 0/123)	0/123 (strict 0/123)	7/7	7/7	parity_ok	parity_ok
HSX_PASCollisions_fullTrajectories	2.510	3.724	1.48x	9.684	3.86x	179.2	1441.6	8.04x	2058.6	11.49x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
additional_examples	120.074	1.733	0.01x	2.682	0.02x	102.1	429.2	4.20x	884.3	8.66x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
filteredW7XNetCDF_2species_magneticDrifts_noEr	89.052	2.069	0.02x	3.035	0.03x	103.2	481.3	4.66x	898.4	8.70x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
filteredW7XNetCDF_2species_magneticDrifts_withEr	95.440	2.011	0.02x	3.188	0.03x	96.2	512.0	5.32x	904.6	9.40x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
filteredW7XNetCDF_2species_noEr	128.508	1.550	0.01x	2.785	0.02x	100.3	457.0	4.56x	892.7	8.90x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
geometryScheme4_1species_PAS_withEr_DKESTrajectories	1.365	2.209	1.62x	4.851	3.55x	127.3	1080.1	8.49x	1255.6	9.86x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
geometryScheme4_2species_PAS_noEr	0.953	2.622	2.75x	5.807	6.09x	162.7	1808.8	11.12x	1816.8	11.17x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
geometryScheme4_2species_noEr	139.240	1.733	0.01x	2.885	0.02x	92.2	468.0	5.07x	913.3	9.90x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
geometryScheme4_2species_noEr_withPhi1InDKE	293.275	1.936	0.01x	3.491	0.01x	100.6	480.9	4.78x	942.1	9.36x	0/265 (strict 0/265)	0/265 (strict 0/265)	9/9	9/9	parity_ok	parity_ok
geometryScheme4_2species_noEr_withQN	146.734	1.769	0.01x	3.136	0.02x	95.1	468.1	4.92x	930.2	9.79x	0/265 (strict 0/265)	0/265 (strict 0/265)	9/9	9/9	parity_ok	parity_ok
geometryScheme4_2species_withEr_fullTrajectories	58.053	1.710	0.03x	2.832	0.05x	113.4	475.5	4.19x	907.7	8.01x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
geometryScheme4_2species_withEr_fullTrajectories_withQN	211.358	1.889	0.01x	3.287	0.02x	98.8	486.4	4.92x	932.5	9.44x	0/251 (strict 0/251)	0/251 (strict 0/251)	9/9	9/9	parity_ok	parity_ok
geometryScheme5_3species_loRes	98.976	1.615	0.02x	3.942	0.04x	129.6	540.3	4.17x	911.1	7.03x	0/193 (strict 0/193)	0/193 (strict 0/193)	9/9	9/9	parity_ok	parity_ok
inductiveE_noEr	166.614	1.644	0.01x	3.037	0.02x	99.2	468.4	4.72x	912.5	9.20x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
monoenergetic_geometryScheme1	0.795	1.924	2.42x	12.959	16.30x	110.2	704.0	6.39x	995.1	9.03x	0/203 (strict 0/203)	0/203 (strict 0/203)	9/9	9/9	parity_ok	parity_ok
monoenergetic_geometryScheme11	0.861	2.853	3.31x	5.705	6.63x	118.7	1205.2	10.16x	1003.1	8.45x	0/210 (strict 0/210)	0/210 (strict 0/210)	9/9	9/9	parity_ok	parity_ok
monoenergetic_geometryScheme5_ASCII	1.052	1.678	1.59x	4.347	4.13x	142.1	497.0	3.50x	989.4	6.96x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
monoenergetic_geometryScheme5_netCDF	1.029	1.884	1.83x	4.446	4.32x	131.4	533.7	4.06x	988.5	7.52x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
quick_2species_FPCollisions_noEr	166.945	1.531	0.01x	2.836	0.02x	97.1	459.3	4.73x	913.2	9.40x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
sfincsPaperFigure3_geometryScheme11_FPCollisions_2Species_DKESTrajectories	76.666	1.653	0.02x	3.081	0.04x	106.7	479.6	4.49x	915.8	8.58x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
sfincsPaperFigure3_geometryScheme11_FPCollisions_2Species_fullTrajectories	93.439	1.767	0.02x	3.286	0.04x	94.0	491.9	5.24x	920.3	9.80x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
sfincsPaperFigure3_geometryScheme11_PASCollisions_2Species_DKESTrajectories	1.104	2.658	2.41x	6.606	5.98x	130.7	1458.0	11.16x	1587.7	12.15x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
sfincsPaperFigure3_geometryScheme11_PASCollisions_2Species_fullTrajectories	1.706	2.768	1.62x	7.363	4.32x	144.6	1482.2	10.25x	2097.7	14.51x	0/207 (strict 0/207)	0/207 (strict 0/207)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_FPCollisions_noEr	160.856	1.395	0.01x	2.530	0.02x	93.2	372.9	4.00x	860.4	9.23x	0/188 (strict 0/188)	0/188 (strict 0/188)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_FPCollisions_noEr_withPhi1InDKE	259.575	1.783	0.01x	3.442	0.01x	89.6	450.1	5.02x	932.9	10.41x	0/274 (strict 0/274)	0/274 (strict 0/274)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_FPCollisions_noEr_withQN	237.879	1.508	0.01x	2.886	0.01x	102.6	421.5	4.11x	918.4	8.95x	0/274 (strict 0/274)	0/274 (strict 0/274)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_FPCollisions_withEr_DKESTrajectories	155.955	1.510	0.01x	2.937	0.02x	103.1	431.4	4.18x	905.7	8.79x	0/214 (strict 0/214)	0/214 (strict 0/214)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_FPCollisions_withEr_fullTrajectories	154.953	1.623	0.01x	3.036	0.02x	101.1	437.6	4.33x	911.1	9.01x	0/214 (strict 0/214)	0/214 (strict 0/214)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_PASCollisions_noEr	0.309	2.098	6.79x	4.846	15.68x	114.2	579.0	5.07x	984.9	8.62x	0/212 (strict 0/212)	0/212 (strict 0/212)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_PASCollisions_noEr_Nx1	0.017	1.843	108.41x	3.484	204.96x	100.9	507.6	5.03x	929.4	9.21x	0/212 (strict 0/212)	0/212 (strict 0/212)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_PASCollisions_noEr_withQN	0.888	1.977	2.23x	3.388	3.82x	120.9	533.7	4.42x	987.8	8.17x	0/274 (strict 0/274)	0/274 (strict 0/274)	9/9	9/9	parity_ok	parity_ok
tokamak_1species_PASCollisions_withEr_fullTrajectories	0.017	2.770	162.92x	3.792	223.03x	102.0	609.1	5.97x	923.3	9.05x	0/212 (strict 0/212)	0/212 (strict 0/212)	9/9	9/9	parity_ok	parity_ok
tokamak_2species_PASCollisions_noEr	0.331	2.436	7.36x	4.698	14.19x	123.6	863.5	6.99x	1148.7	9.29x	0/212 (strict 0/212)	0/212 (strict 0/212)	9/9	9/9	parity_ok	parity_ok
tokamak_2species_PASCollisions_withEr_fullTrajectories	1.330	3.247	2.44x	7.315	5.50x	121.8	1607.5	13.19x	1246.0	10.23x	0/212 (strict 0/212)	0/212 (strict 0/212)	9/9	9/9	parity_ok	parity_ok
transportMatrix_geometryScheme11	0.025	1.582	63.28x	3.191	127.62x	102.6	438.8	4.28x	925.9	9.02x	0/194 (strict 0/194)	0/194 (strict 0/194)	9/9	9/9	parity_ok	parity_ok
transportMatrix_geometryScheme2	0.031	1.576	50.85x	3.188	102.83x	100.5	436.4	4.34x	924.7	9.20x	0/194 (strict 0/194)	0/194 (strict 0/194)	9/9	9/9	parity_ok	parity_ok

Largest CPU runtime improvements vs tests/scaled_example_suite_recheck_cpu_frozen_2026-04-23_postkeyfix/suite_report.json:

• monoenergetic_geometryScheme5_ASCII: 3.5s -> 1.7s (delta=1.8s)
• tokamak_2species_PASCollisions_noEr: 4.0s -> 2.4s (delta=1.6s)
• HSX_PASCollisions_fullTrajectories: 5.3s -> 3.7s (delta=1.6s)
• HSX_PASCollisions_DKESTrajectories: 5.5s -> 4.0s (delta=1.5s)
• sfincsPaperFigure3_geometryScheme11_PASCollisions_2Species_fullTrajectories: 3.8s -> 2.8s (delta=1.0s)

Largest CPU memory improvements vs tests/scaled_example_suite_recheck_cpu_frozen_2026-04-23_postkeyfix/suite_report.json:

• monoenergetic_geometryScheme5_ASCII: 3066.4 MB -> 497.0 MB (delta=2569.4 MB)
• tokamak_2species_PASCollisions_noEr: 2088.6 MB -> 863.5 MB (delta=1225.1 MB)
• HSX_PASCollisions_DKESTrajectories: 2053.6 MB -> 1146.3 MB (delta=907.3 MB)
• sfincsPaperFigure3_geometryScheme11_PASCollisions_2Species_fullTrajectories: 2298.6 MB -> 1482.2 MB (delta=816.4 MB)
• monoenergetic_geometryScheme5_netCDF: 1162.7 MB -> 533.7 MB (delta=629.0 MB)

Documentation

Build docs locally:

sphinx-build -b html -W docs docs/_build/html

Entry points:

• docs/index.rst
• docs/system_equations.rst
• docs/method.rst
• docs/normalizations.rst
• docs/performance.rst
• docs/parallelism.rst

Testing

pytest -q

License

See LICENSE.