3872471c72
Implemented the clear-gap comparison, with the log-distance-equivalent
algebra and pessimistic SM-clock fallback.
What changed:
- Added TimingInterval and interval construction from summaries:
- robust interval: [min, q3], centered at median
- fallback interval: clipped [mean - stdev, mean + stdev] intersected with [min, max]
- Added CLEAR_GAP_RELATIVE_THRESHOLD = 0.005.
- FAST gap uses:
(ref.lower - cmp.upper) / cmp.upper >= delta
which is equivalent to log(ref.lower / cmp.upper) >= log(1 + delta).
- SLOW gap uses:
(cmp.lower - ref.upper) / ref.upper >= delta
- FAST/SLOW now requires SM clock summaries on both sides and the same clear-gap result after scaling intervals by sm_clock_rate_mean.
- If intervals are missing, overlap, fail the gap threshold, have missing/invalid clock summaries, or time/cycle comparison disagrees, status is UNDECIDED.
- Existing center/noise values are still computed and displayed, but no longer drive FAST/SLOW/SAME classification.
Updated tests to cover:
- center/noise-only comparisons becoming UNDECIDED
- clear FAST/SLOW with matching clock evidence
- missing clock fallback to UNDECIDED
- frequency-shift disagreement becoming UNDECIDED
- regression reporting with robust interval and clock evidence
CUDA Kernel Benchmarking Package
This package provides a Python API to the CUDA Kernel Benchmarking
Library NVBench.
Installation
Install from PyPI:
python -m pip install cuda-bench
Use an optional dependency if you want pip to install a compatible
cuda-bindings package as well:
python -m pip install "cuda-bench[cu12]" # Install cuda-bindings 12.x
python -m pip install "cuda-bench[cu13]" # Install cuda-bindings 13.x
The published Linux wheel is compatible with both CUDA 12.x and CUDA 13.x
Python environments. It contains two native extensions: one built with a CUDA
12.x Toolkit and installed under cuda.bench.cu12, and one built with a CUDA
13.x Toolkit and installed under cuda.bench.cu13. At runtime, cuda-bench
queries the installed cuda.bindings package to determine the CUDA major
version and loads the matching native extension.
The cu12 and cu13 extras do not select different cuda-bench wheels. They
only select the compatible cuda-bindings dependency family. If your
environment already provides an appropriate cuda-bindings 12.x or 13.x
package, installing plain cuda-bench is sufficient.
A local CUDA Toolkit is not required when installing a published wheel, but the
NVIDIA driver must support the CUDA runtime used by the installed
cuda.bindings package. Use the same CUDA major version for other CUDA Python
binary packages in the environment, for example cupy-cuda12x with
cuda-bench[cu12] or cupy-cuda13x with cuda-bench[cu13].
Building from source
Ensure recent version of CMake
Since nvbench requires CMake >=3.30.4, either install a recent CMake or
create a conda environment with CMake and Ninja:
conda create -n build_env --yes cmake ninja
conda activate build_env
Ensure CUDA compiler
Building cuda-bench from source requires a CUDA Toolkit with nvcc. Ensure
that the appropriate environment variables are set. For example, on Linux,
assuming the CUDA Toolkit is installed system-wide:
export CUDACXX=/usr/local/cuda/bin/nvcc
export CUDAARCHS=all-major
Unlike the published wheel, a local source build only builds the native extension for the CUDA Toolkit found by CMake. The CUDA major version selected in the install command below must match that Toolkit.
Build Python project
Now switch to the Python package directory and install cuda-bench from source:
cd nvbench/python
python -m pip install ".[cu12]" # If CUDACXX points to a CUDA 12.x toolkit
python -m pip install ".[cu13]" # If CUDACXX points to a CUDA 13.x toolkit
Editable installs (python -m pip install -e .) are currently not supported.
They do not install the versioned CUDA extension layout used by cuda-bench.
Re-run the non-editable install command after making source changes.
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