Files

muh-bot 2a7ca101d7 feat(cccl): integrate missing CCCL directories — python/, ci/, .agent/, docs/, test/

Sparse-checkout from NVIDIA/cccl main branch to complete cccl_upstream:

Added:
- python/cuda_cccl/ (226 files) — Python bindings for device-level algorithms
  Critical for muh toolchain: cuda.compute.reduce_into, scan, radix_sort, etc.
  Includes 204 .py files with full test coverage for all 27 algorithms
- ci/ (163 files) — Build/test infrastructure
  build_cub.sh, test_cub.sh, build_and_test_targets.sh, matrix.yaml
  Directly maps to our [INFRA-CI] and [INFRA-BUILD] items
- .agent/skills/ (7 files) — NVIDIA's own agent skills for CCCL
  cccl-style/SKILL.md, cccl-test/SKILL.md, sass-diff/SKILL.md
- docs/ (491 files) — Official CCCL documentation
  CI references, CMake guides, Python compute docs, libcudacxx PTX docs
- test/ (12 files) — Top-level integration tests (cuda_smoke, stdpar)
- Root configs: .clang-format, .clang-tidy, CONTRIBUTING.md, pyproject.toml
- CLAUDE.md symlink → AGENTS.md (NVIDIA's standard)

cccl_upstream now mirrors full NVIDIA/cccl structure:
  Before: 42M (cub + thrust + libcudacxx + cudax + c + examples + benchmarks)
  After:  53M (+python +ci +docs +.agent +test +configs)

This completes the CCCL base needed for:
- [muh-bench] items: ci/util/build_and_test_targets.sh for targeted builds
- [CCCL-verify] items: python/cuda_cccl/tests/ as reference implementations
- [CCCL-test] items: ci/test_cub.sh, ci/test_thrust.sh
- Agent workflow: .agent/skills/ for consistent style and test patterns

2026-08-07 02:34:33 +00:00

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cuda.compute Benchmarks

Compare Python cuda.compute performance against C++ CUB implementations.

Setup

This project uses pixi to manage environments and dependencies.

Two environments are available:

wheel - Uses the released cuda-cccl package
source - Builds cuda-cccl from the local repository

Build C++ Benchmarks

Build CUB benchmarks using the CI script (one-time, ~13 minutes):

cd /path/to/cccl
./ci/build_cub.sh -arch 89  # Use your GPU arch (89=RTX 4090, 80=A100, 90=H100)

Binaries are built to: build/cub/bin/

Run Benchmarks

Using pixi tasks

# Run Python benchmarks (released cuda-cccl)
pixi run -e wheel bench

# Run Python benchmarks (local source build)
pixi run -e source bench

# Run Python benchmarks with reduced parameter set
pixi run -e wheel bench-quick

# Run just one benchmark
pixi run -e wheel bench -b transform/fill

# Run C++ benchmarks
pixi run -e wheel bench-cpp

# Run both Python and C++ benchmarks
pixi run -e wheel bench-all

Using run_benchmarks.py directly

# Run both C++ and Python (default)
pixi run -e wheel python run_benchmarks.py -b transform/fill -d 0

# Run only C++
pixi run -e wheel python run_benchmarks.py -b transform/fill --cpp

# Run only Python
pixi run -e wheel python run_benchmarks.py -b transform/fill --py

# Show help
pixi run -e wheel python run_benchmarks.py --help

To run the benchmarks using the "quick" configuration:

pixi run -e wheel python run_benchmarks.py --quick

Compare Results

pixi run -e wheel python analysis/python_vs_cpp_summary.py -b transform/fill

Web Report

A simple page used to visualize a set of results.

Requires results/ to be populated with benchmark results.

First generate a manifest:

pixi run -e wheel python analysis/generate_web_report_manifest.py \
  --results-dir results \
  --output results/manifest.json

Build the web report single file app:

cd analysis/web-report
npm install
npm run build

This will output a single file app to analysis/web-report/dist/ copy it to the results/ directory and:

cd results/
python3 -m http.server

Now its possible to share the results directory as a zip/tar file.

Manual Usage

List benchmark configurations

# Python
pixi run -e wheel python transform/fill.py --list

# C++
/path/to/cccl/build/cub/bin/cub.bench.transform.fill.base --list

Run with custom options

# Python - specific type and size
pixi run -e wheel python transform/fill.py --axis "T=I32" --axis "Elements[pow2]=20" --devices 0

# C++ - save JSON
/path/to/cccl/build/cub/bin/cub.bench.transform.fill.base \
  --json results/transform/fill_cpp.json \
  --devices 0

Compare manually

pixi run -e wheel python analysis/python_vs_cpp_summary.py \
  results/transform/fill_py.json \
  results/transform/fill_cpp.json \
  --device 0

AI commands

These are using the .opencode folder but can be moved to other Agents.

/migration-status

Generates a report of the migration status for each benchmark in CUB.

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