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Now:
- establish a candidate clear timing gap from summary timing intervals, as before
- if bulk sample times and frequencies are available on both sides,
compute cycles = time * frequency
- derive bulk cycle intervals from min/q1/median/q3
- confirm the gap direction from those bulk cycle intervals
- only fall back to summary sm_clock_rate_mean confirmation when bulk cycle data
is unavailable
I also split the reason codes so the evidence source is visible:
- clear_gap_confirmed_by_bulk_cycles
- bulk_cycle_gap_not_confirmed
- clear_gap_confirmed_by_summary_cycles
- summary_cycle_gap_not_confirmed
Updated tests in python/test/test_nvbench_compare.py cover both the bulk-confirmed
and bulk-rejected paths, along with the renamed summary reason codes.
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