mirror of
https://github.com/NVIDIA/nvbench.git
synced 2026-07-01 11:47:33 +00:00
4744d26d26
* Define nan and inf in generated --bulk-debug-python scripts so pprint output for non-finite timing values remains valid Python code. Add a regression test that executes the generated script and verifies nan/inf values round-trip. * Sharpen bulk-cycle confirmation gating. Only suppress summary-clock fallback when both reference and compare inputs provide paired, non-empty bulk sample/frequency payloads. Missing or empty bulk files are treated as unavailable evidence and still allow sm_clock_rate/mean fallback, while malformed non-empty payloads continue to produce AMBG. Add regression coverage for missing bulk files falling back to summary-cycle confirmation. These changes resolve automated review feedback
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