mirror of
https://github.com/NVIDIA/nvbench.git
synced 2026-06-30 19:27:34 +00:00
db4db61596
Store JSON-bin sample time and frequency metadata in GpuTimingData instead of reading the binary files during summary extraction. Add Float32BinarySource and lazy cached accessors for samples and frequencies. Use np.fromfile by default, but allow tests and alternate callers to inject a float32 reader returning any buffer-compatible object convertable to "<f4" data type. Treat optional bulk-data failures as unavailable evidence instead of aborting comparison: unreadable files, invalid buffers, count mismatches, and mismatched sample/frequency metadata now emit RuntimeWarning and return None. Update nvbench_compare tests to verify lazy loading, cache reuse, injected reader behavior, warning-based degradation, and count mismatch handling.
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