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https://github.com/NVIDIA/nvbench.git
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4cf75dcaf5
Extend nvbench_compare with multiple table display modes and richer interval
formatting for timing comparisons.
Highlights:
- add `--display` with `intervals`, `legacy`, and `explain` modes
- keep `legacy` output using scalar Diff/%Diff
- make `intervals` the default, showing compact center-plus-delta timing
intervals
- add `explain` mode with explicit `[L | C | H]` interval rendering and
self-describing headers
- compute and store diff and relative-diff intervals in SummaryComparison
- add formatting helpers for absolute and relative interval displays
- make default preset slightly more permissive by lowering
`bulk_same_sample_coverage` to 0.97
Add focused tests covering:
- diff/%diff interval computation
- compact and explicit interval formatting
- default, legacy, and explain table layouts
- CLI propagation of `--display` and preset selection
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