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4a22923721
Move NVBench JSON result parsing into cuda.bench.results with explicit BenchmarkResult, BenchmarkResultDevice, BenchmarkResultSummary, SubBenchmarkResult, and SubBenchmarkState types. Remove the result reader from the top-level cuda.bench namespace and require construction through BenchmarkResult.from_json() or BenchmarkResult.empty(). Preserve bulk sample/frequency parsing and estimator helpers while making summaries rich objects that retain tag/name/hint/hide/description metadata. Add nvbench-json-summary to render NVBench JSON output as an NVBench-style markdown summary table, including axis formatting, device sections, hidden summary filtering, and summary hint formatting. Update packaging, type stubs, and tests for the new namespace, renamed classes, Python 3.10-compatible annotations, and summary-table generation.
CUDA Kernel Benchmarking Package
This package provides a Python API to the CUDA Kernel Benchmarking
Library NVBench.
Installation
Install from PyPi
pip install cuda-bench[cu13] # For CUDA 13.x
pip install cuda-bench[cu12] # For CUDA 12.x
Building from source
Ensure recent version of CMake
Since nvbench requires a rather new version of CMake (>=3.30.4), either build CMake from sources, or create a conda environment with a recent version of CMake, using
conda create -n build_env --yes cmake ninja
conda activate build_env
Ensure CUDA compiler
Since building NVBench library requires CUDA compiler, ensure that appropriate environment variables
are set. For example, assuming CUDA toolkit is installed system-wide, and assuming Ampere GPU architecture:
export CUDACXX=/usr/local/cuda/bin/nvcc
export CUDAARCHS=86
Build Python project
Now switch to python folder, configure and install NVBench library, and install the package in editable mode:
cd nvbench/python
pip install -e .
Verify that package works
python test/run_1.py
Run examples
# Example benchmarking numba.cuda kernel
python examples/throughput.py
# Example benchmarking kernels authored using cuda.core
python examples/axes.py
# Example benchmarking algorithms from cuda.cccl.parallel
python examples/cccl_parallel_segmented_reduce.py
# Example benchmarking CuPy function
python examples/cupy_extract.py