Reproduce

From the eagle repository root, in an environment with eagle, hawk and CuPy, an NVIDIA driver for the GPU cards (no CUDA toolkit or nvcc: hawk compiles kernels with NVRTC) and (optionally) Nsight Systems:

python benchmarks/perf_card/perf_card.py       # RK4 oscillators, GPU
python benchmarks/perf_card/cpu_card.py        # RK4 oscillators, CPU
python benchmarks/rk78_card/rk78_card.py       # RK7(8) Kepler orbits, GPU
python benchmarks/rk78_card/cpu_rk78_card.py   # RK7(8) Kepler orbits, CPU

Each run builds the hawk kernel(s), verifies the arms against each other, times them, and writes its own card_<device>.json together with the card_<device>.md table the results pages show. The JSON carries the md5 of the script that produced it; a test re-renders the table from the JSON so the two cannot drift. --quick runs a reduced matrix as a smoke test.

To reproduce the cards on another machine (Kaggle, a rented GPU or your own box) with the same pinned software, see benchmarks/reproduce/. The P2000 card ran locally, the T4 on Kaggle, and the other cards on cloud VMs through benchmarks/reproduce/run_cards.sh. On Kaggle, choose the GPU T4 x2 accelerator, turn Internet on and run the notebook:

Adding a device. Drop the new card_<slug>.json/.md (GPU) or cpu_card_<slug>.json/.md (CPU) next to the existing ones under benchmarks/perf_card/ or benchmarks/rk78_card/ – benchmarks/reproduce/run_cards.sh --publish writes them there directly. Rebuilding the docs (make html or make strict) then regenerates the overview and results pages’ tabs and tables on its own; re-running a card for a device already listed replaces that device’s rows the same way. No page needs editing.