179004faee
Add versioned TOML configuration support for nvbench-compare threshold
settings. The new --config option reads grouped settings for clear-gap,
same-result, bulk coverage, and rare-support filtering thresholds. The parser
validates the schema strictly so unknown tables, unknown keys, invalid types,
unsupported versions, and out-of-range values fail early.
Add --dump-config to print the effective configuration without requiring input
JSON files. This makes the currently selected preset and resolved threshold
values discoverable and gives users a starting point for custom configuration.
Preset resolution is:
- default is used when neither TOML nor CLI selects a preset
- [preset] name = "..." in TOML selects the base preset
- --preset ... overrides the TOML preset selection
- explicit threshold values in TOML override whichever base preset was selected
For example:
- nvbench-compare --dump-config
Prints the built-in default settings as grouped TOML.
- nvbench-compare --preset permissive --dump-config
Prints the permissive preset values as TOML.
- nvbench-compare --config compare.toml ref.json cmp.json
Compares using the preset named in compare.toml, plus any explicit TOML
threshold overrides.
- nvbench-compare --config compare.toml --preset strict ref.json cmp.json
Uses the strict preset as the base, while preserving explicit threshold
overrides from compare.toml.
Keep TOML parsing lazy: Python 3.11+ uses tomllib, while Python 3.10 only
requires tomli when --config is used. Add focused tests for grouped config
dumping, strict validation, preset/override precedence, and CLI dump behavior.
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