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bd2b536ab4
Change example to illustrate timing CPU work. First example does only CPU work (sleeps), use CPU-only timer. Second examples does both CPU and GPU work (sleeps in either case). Use cold-run timer with/without sync tag to measure both CPU and GPU times.
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