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https://github.com/NVIDIA/nvbench.git
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445d881eda
Make it explicit in README that we build and locally install NVBench first, and then build Python package use the library as a dependency. The nvbench library is installed into Python layout alongside the native extension.
61 lines
1.5 KiB
Markdown
61 lines
1.5 KiB
Markdown
# CUDA Kernel Benchmarking Package
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This package provides Python API to CUDA Kernel Benchmarking Library `NVBench`.
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## Building
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### Build `NVBench` project
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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
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```
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conda create -n build_env --yes cmake ninja
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conda activate build_env
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```
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Now switch to python folder, configure and install NVBench library, and install the package in editable mode:
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```
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cd nvbench/python
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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
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cmake --build nvbench_build/ --config Release --target install
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```
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### Build Python extension
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Specify location local installation of `NVBench` library and perform editable `pip install`:
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```
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nvbench_DIR=$(pwd)/nvbench_install/lib/cmake CUDACXX=/usr/local/cuda/bin/nvcc pip install -e .
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```
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Note that `CUDACXX` must be set for NVBench cmake script to work, but Python extension itself only uses host compiler.
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### Verify that package works
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```
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python test/run_1.py
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```
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### Run examples
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```
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# Example benchmarking numba.cuda kernel
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python examples/throughput.py
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```
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```
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# Example benchmarking kernels authored using cuda.core
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python examples/axes.py
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```
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```
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# Example benchmarking algorithms from cuda.cccl.parallel
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python examples/cccl_parallel_segmented_reduce.py
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```
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```
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# Example benchmarking CuPy function
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python examples/cupy_extract.py
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```
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