Benchmarks

This page shows a checked-in benchmark snapshot for the numpy and jax backends. The artifacts are generated manually so routine docs builds stay fast and deterministic. The figure includes forward runtime, inverse runtime, and round-trip reconstruction error against the original real-valued input signal.

The raw numbers used for the plot are available in benchmark_results.json.

Refreshing The Benchmark Snapshot

Regenerate the plot and JSON artifact manually from the repository root:

uv run python docs/examples/generate_benchmark_plot.py --backends numpy jax

If jax is not installed in the active environment, the script will warn and only emit the available backends.

Notes

• The default benchmark range covers N = 2048 through 33554432 and uses 7 timed runs per point.
• Refreshing the full default snapshot is now substantially more expensive than before.
• Each measurement uses one warmup call before timed runs.
• JAX timings are synchronized before the timer stops, so they include the actual device work.
• The timing panels show mean runtimes in milliseconds, with a shaded band for one standard deviation.
• The error panel shows the maximum absolute difference after from_wdm_to_time(from_time_to_wdm(x)).