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Benchmark function that sleeps for 1 seconda on the host using CPU-only timer, as well as CPU/GPU timer that does/doesn't use blocking kernel. All three methods must report consistent values close to 1 second.
CUDA Kernel Benchmarking Package
This package provides Python API to CUDA Kernel Benchmarking Library NVBench.
Building
Build NVBench project
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
Now switch to python folder, configure and install NVBench library, and install the package in editable mode:
cd nvbench/python
cmake -B nvbench_build --preset nvbench-ci -S $(pwd)/.. -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc -DNVBench_ENABLE_EXAMPLES=OFF -DCMAKE_INSTALL_PREFIX=$(pwd)/nvbench_install
cmake --build nvbench_build/ --config Release --target install
nvbench_DIR=$(pwd)/nvbench_install/lib/cmake CUDACXX=/usr/local/cuda/bin/nvcc pip install -e .
Verify that package works
export PYTHONPATH=$(pwd):${PYTHONPATH}
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