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8c85393ee2
Add a bulk-data SAME path to nvbench_compare for cases where summary intervals do not provide a clear FAST/SLOW decision. The new path compares sample times and SM-clock-adjusted cycles with symmetric nearest-neighbor coverage over unique values and sample counts. The comparison now requires both sample-weight coverage and unique-support coverage to pass before declaring SAME. If bulk data is available but coverage does not pass, the result remains UNDECIDED instead of falling back to the summary-only SAME rule. Also improve undecided diagnostics by aggregating reason codes while preserving the most severe representative detail, including observed coverage values and thresholds for bulk support mismatches. Add tests for: - bulk data confirming SAME despite changed mode weights; - bulk time mismatch overriding summary-only SAME; - cycle coverage vetoing time-only agreement; - sample-weight and unique-support coverage diagnostics; - aggregation of undecided reason details.
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