ferrum-gpu

Pure-Rust GPU compute substrate with Python bindings. FFT kernels run on NVIDIA GPUs today via cuda-oxide (Rust source compiled to PTX, no CUDA C). Cross-vendor support via spirv-oxide → Vulkan is the v0.2 roadmap.

v0.1.0 is live on PyPI:

pip install ferrum-gpu
# or, faster:
uv pip install ferrum-gpu

import numpy as np
import ferrum_gpu as fgpu

dev = fgpu.cuda.Device(0)
arr = np.array([1+0j, 2+0j, 3+0j, 4+0j], dtype=np.complex64)
print(fgpu.fft.fft_1d_c2c_pow2(arr, log_n=2, device=dev))
# [10+0j, -2+2j, -2+0j, -2-2j]

29 GPU integration tests verify the 1D and 2D Stockham FFTs against numpy.fft.fft / numpy.fft.fft2 (1D: 16 cases, 2D: 13 cases) within 1e-3 to 1e-4 relative error.

The workspace ships:

• ferrum-gpu-core: Backend trait, KernelArtifact, errors. no_std + alloc.
• ferrum-gpu-cuda: impl Backend for Cuda over cudarc 0.19.
• ferrum-gpu: facade with Device<B> and Buffer<T, B>.
• ferrum-gpu-fft: 1D + 2D radix-2 power-of-2 C2C FFT host scaffolding + CPU Stockham reference.
• ferrum-gpu-py: Python bindings via PyO3 + maturin. ferrum_gpu.cuda.Device(0) persistent handle + ferrum_gpu.fft.fft_1d_c2c_pow2 + ferrum_gpu.fft.fft_2d_c2c_pow2.
• ferrum-gpu-bench: cuFFT comparison binary (1D, batched).
• examples/vector-add: end-to-end demo using hand-written PTX through the substrate.
• examples/vector-add-cuda-oxide: same kernel in Rust, compiled to PTX by cuda-oxide.
• examples/fft-1d-c2c: 1D Stockham FFT in Rust, GPU-vs-CPU on 8 cases (N from 4 to 4096, batched, forward + inverse).

Runtime requirements (wheel users)

• Linux x86_64 with glibc >= 2.34 (Ubuntu 22.04+, RHEL 9+, Fedora 36+, etc.)
• NVIDIA GPU of compute capability >= 8.0 (Ampere or newer); the wheel ships sm_80 PTX that the driver JITs forward to your architecture
• NVIDIA driver supporting CUDA 13.x (driver 580+)
• Python 3.10+

The wheel does not bundle libcuda; users need the NVIDIA driver installed.

Source build requirements (developers)

• Linux x86_64
• CUDA Toolkit 13.x
• NVIDIA driver compatible with the installed Toolkit
• Rust 1.99.1 (pinned via rust-toolchain.toml)
• cargo-oxide: cargo install --git https://github.com/NVIDIA/cuda-rust cargo-oxide
• For the Python bindings: Python 3.10+ with maturin + pytest + numpy

Quick start: vector-add via hand-written PTX

git clone https://github.com/alejandro-soto-franco/ferrum-gpu
cd ferrum-gpu
make example-vector-add

Expected:

vector_add: 1048576 elements verified

Quick start: vector-add via Rust source + cuda-oxide

cargo install --git https://github.com/NVIDIA/cuda-rust cargo-oxide
cargo oxide doctor       # one-time codegen-backend bootstrap
make example-vector-add-oxide

Expected:

vector_add (cuda-oxide): 1048576 elements verified

Quick start: 1D Stockham FFT

make example-fft

Runs 8 cases (N=4 through N=4096, batched, forward + inverse), each verified against a CPU Stockham reference within 1e-4 relative error.

Python development

uv is the Python package manager written in Rust by Astral; it is a drop-in faster replacement for pip + venv. The Makefile targets and the wheel install path work the same on pip for users who prefer it.

uv venv ~/.venvs/ferrum-gpu
source ~/.venvs/ferrum-gpu/bin/activate
uv pip install maturin pytest numpy
make develop                       # builds the cdylib + installs into the venv
python3 -c "
import numpy as np, ferrum_gpu as fgpu
arr = np.array([1+0j, 2+0j, 3+0j, 4+0j], dtype=np.complex64)
print(fgpu.fft.fft_1d_c2c_pow2(arr, log_n=2))
"

Pip equivalent:

python3 -m venv ~/.venvs/ferrum-gpu
source ~/.venvs/ferrum-gpu/bin/activate
pip install maturin pytest numpy
make develop

Run the pytest matrix:

make pytest

29 cases (16 1D + 13 2D), each compared against numpy.fft within 1e-3 to 1e-4 relative error.

Performance

make perf-gate runs the in-tree specialised kernels against cuFFT for batched 1D forward C2C FFTs at N in {256, 1024, 4096}, batch = 256, with the GPU graphics clock locked. Numbers below are medians over 100 trials with a 10-trial warmup, alternating ferrum and cuFFT launches per trial so DVFS affects both backends symmetrically.

These are pure-Rust kernels (Rust → PTX via cuda-oxide; no CUDA C) measured against NVIDIA's hand-tuned cuFFT. The specialised radix-4 (256, 1024) and radix-8 (4096) Stockham kernels are 2–5× faster than the generic radix-2 fallback; against cuFFT they range from within 1.3× at N=256 to 3.7× at N=4096.

Hardware: RTX 5060 Laptop (sm_120, Blackwell), graphics clock locked to 1500 MHz. Driver: 580.159.03. CUDA Toolkit: 13.1. Date: 2026-05-29.

ratio = ferrum_event_us / cufft_event_us; 1.0 is parity, lower is better. Event-time brackets each kernel launch with CUDA events (excludes the host launch path); cuFFT's plan-init is amortised across the loop and not counted. make perf-gate reports a 0.9× target (10 % faster than cuFFT); that gate is not yet met at any size and is tracked as an aspirational target — closing it needs a multi-FFT-per-block, profiler-guided kernel redesign. Per-launch wall-clock (which includes the launch path) is measured jointly in alternating mode and is ≈ 0.05 / 0.10 / 0.35 µs per FFT at the three sizes.

Testing

CPU-only tests: make test.

GPU tests + all examples + pytest (requires CUDA + NVIDIA GPU): make verify-all.

Publishing a new release

Maintainers only. The public wheel is built locally via maturin (manylinux_2_34 tag forced via --compatibility, libcuda excluded via --auditwheel skip) then attached to a GitHub Release. The release.yml workflow downloads the asset and uploads to PyPI via OIDC trusted publishing.

make wheel             # builds dist/ferrum_gpu-*-manylinux_2_34_x86_64.whl
gh release create vX.Y.Z dist/*.whl --title "vX.Y.Z" --notes "..."
gh workflow run release.yml --field release_tag=vX.Y.Z --field target_index=pypi

For a glibc 2.28 baseline (RHEL 8, Ubuntu 20.04), use make wheel-manylinux instead; it runs the build inside a manylinux_2_28 Docker image.

License

Apache-2.0.