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CCCL (CUDA C++ Core Libraries) provides: - CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk) - Thrust: high-level parallel algorithms (transform_reduce, sort, scan) - libcudacxx: CUDA C++ standard library (atomics, barriers, memory) - cudax: experimental features (memory resources, allocators) - Tuning policies: per-SM hardware-specific algorithm parameters Competition optimization vectors mapped to CCCL: - Output TPS (83% weight): warp_reduce, block_reduce, device_topk - Input TPS (14% weight): device_scan, block_load, prefetch - Cache TPS (3% weight): prefix caching strategy patterns - Memory (0.9 util): pooled/cached/buddy allocators Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only) License: Apache-2.0
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 releasedcuda-ccclpackagesource- Buildscuda-ccclfrom 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.