Albucore Benchmarks

Albucore Benchmarks

Before designing a performance comparison, read ../performance-optimization/SKILL.md and ../../../docs/performance-optimization.md completely. Extend the benchmark along the dimension that controls the candidate, such as label density for bincount, table and channel layout for LUTs, or output size and dtype for random generation.

Use exactly one CPU thread per process for every candidate, following the thread controls in the canonical performance guide. Benchmark additional thread counts only when the user explicitly requests thread scaling.

Layout

• benchmarks/ - Python timing scripts. Run from repo root: uv run python benchmarks/<script>.py.
• benchmarks/timing.py - Shared median_ms helper for scripts executed as python benchmarks/foo.py.
• ./benchmark.sh - Dataset-driven runner; expects an external benchmark package that is not always present in-tree. Prefer synthetic scripts for CI-style checks.
• benchmarks/benchmark_router_synthetic.py - Times public routers on synthetic uint8 and float32 arrays: HWC, plus NHWC for mean, std, and mean_std only.
• benchmarks/compare_router_json.py - Builds a Markdown table from two JSON outputs.
• benchmarks/benchmark_resize3d_tensor.py - Times direct Tensor, zero-copy Tensor→NumPy→Tensor, and public resize3d routes for contiguous and channel-last-strided CPU CDHW Tensors.
• benchmarks/benchmark_warp_affine3d.py - Times full single-volume NumPy DHWC affine paths, including the NumPy→Torch bridge and public router.
• benchmarks/benchmark_warp_affine3d_tensor.py - Times native Torch affine-grid, manual-grid and coverage-fill probes, and public single-volume CDHW routing.

Canonical Shape Grid

Benchmark shape sweeps use channel-last Albucore conventions.

HWC images:

• 128x160 with 1, 3, 9 channels - small / warm-cache, non-square.
• 240x320 with 1, 3, 9 channels - mid-size crop, non-square.
• 480x640 with 1, 3, 9 channels - typical augmentation training crop, non-square.
• 768x1024 with 1, 3, 9 channels - high-res / full-image pass, non-square.

Use non-square H/W pairs so height-width swaps fail visibly. Avoid square-only benchmark grids.

DHWC volumes:

• 16x128x160x1, 16x128x160x3 - thin slab, non-square in-plane.
• 32x128x160x1, 32x128x160x3 - common nnU-Net patch depth.
• 64x128x160x3 - deeper slab.
• 96x128x160x1 - deep single-channel slab.
• 48x240x320x3 - large in-plane, multi-channel.

For resize3d, also include C=5, unit input/output spatial axes, and an explicit D*C value on both sides of the OpenCV encoded-channel boundary. Time its public NumPy route end-to-end, including channel packing, Torch conversions, and output repair. For Tensor input, sweep contiguous and channel-last-strided CDHW, direct interpolation, the zero-copy bridge, and the public router. Use uv run python benchmarks/benchmark_resize3d.py --quick and uv run python benchmarks/benchmark_resize3d_tensor.py --quick while iterating; record any resulting routing decision in docs/numkong-performance.md or a focused report under benchmarks/results/.

For warp_affine3d, benchmark only one volume per call: NumPy DHWC or CPU Tensor CDHW. The full matrix uses uint8/float32, C=1/3/5/9, canonical output sizes including a unit output axis, nearest/trilinear interpolation, one 3×4 forward matrix per scenario, and zero/nonzero fill. NumPy timings use contiguous inputs; Tensor timings add contiguous and channel-last-strided inputs. Test the equivalent homogeneous 4×4 representation as a contract, not a timing route. Run uv run python benchmarks/benchmark_warp_affine3d.py --quick --threads 1 and uv run python benchmarks/benchmark_warp_affine3d_tensor.py --quick --threads 1. A manual grid, coverage sampler, tiled route, or native extension remains a diagnostic candidate until it has exact correctness parity and a sustained full-path win. Channel choices: 1 for grayscale, 3 for RGB / 3-channel, and 9 for hyperspectral paths that exceed MAX_OPENCV_WORKING_CHANNELS=4.

Compare the current tree with a previous release

uv run python benchmarks/benchmark_router_synthetic.py \
  --output-json benchmarks/results/router-current.json

uv run --no-project --with albucore==<previous-version> --with opencv-python-headless \
  --with numkong --with stringzilla --with numpy \
  python benchmarks/benchmark_router_synthetic.py \
  --output-json benchmarks/results/router-previous.json

uv run python benchmarks/compare_router_json.py \
  benchmarks/results/router-current.json \
  benchmarks/results/router-previous.json \
  benchmarks/results/REPORT_router_current_vs_previous.md

Replace <previous-version> with the release that answers the current question. Use --quick for smaller shape/channel grids while iterating, and do not accumulate version-specific baselines in the repository.

Docs

• Current NumKong route decisions: docs/numkong-performance.md
• Generated benchmark evidence: benchmarks/results/
• General performance policy: docs/performance-optimization.md