mirror of
https://github.com/NVIDIA/nvbench.git
synced 2026-06-29 18:57:44 +00:00
65abfbcfb2
- Add DecisionReason(code, message) and internal
TimingDecision(status, reason).
- SummaryComparison now carries reason
- ComparisonStats now aggregates undecided reasons.
- Final summary prints a reason breakdown only when
undecided reasons exist, e.g.:
- Undecided (comparison requires more evidence): 3
- Reasons:
- noise_too_high: 2 (relative dispersion is too
high to declare same)
- weak_interval_overlap: 1 (timing intervals do not
overlap strongly enough to declare same)
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