Performance

All numbers are medians over repeated runs of benchmarks/benchmark.py, measured on an NVIDIA GeForce RTX 5090 with CUDA 12.8, extracting a sphere SDF at level 0. Timings include the full call from Python: case classification, extraction, and vertex welding.

Extraction time (median, ms)

Grid	Algorithm	128³	512³
uniform	marching_cubes	0.55	4.8
uniform	get_intersection	0.30	5.3
uniform	dual_contouring	0.82	7.0
sparse	marching_cubes	0.44	1.4
sparse	get_intersection	0.17	0.53
sparse	dual_contouring	0.78	2.3

Sparse grids only touch cells near the surface, so their cost scales with the surface area rather than the volume. At 512³ that is 300k cells instead of 134M.

Reproducing

pixi run --environment cu128 bench

# or directly, with options:
pixi run --environment cu128 python benchmarks/benchmark.py \
    --res 32 64 128 256 512 --json results.json

The harness reports the median, 10th and 90th percentiles, and the first call separately.

Notes

• The first call after installation is slow (a few seconds): the driver compiles the shipped PTX for your GPU and caches the result. See Installation. The benchmark warms up before timing, so the table excludes this.

• The kernels compute cell corners on the fly instead of storing them, so memory use grows with the extracted surface, not the grid volume. A dense 1024³ grid fits on a consumer GPU.

• Numbers depend on the GPU, driver, and field. Expect different results on other machines.