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338936b6fe
Implements `cuda.bench.results.BenchmarkResult` class to represent data from JSON output of benchmark execution.
The contains implements two class methods `BenchmarkResult.from_json(filename : str | os.PathLike, *, metadata : Any = None)` which expects well-formed JSON filename and `BenchmarkResult.empty(*, metadata : Any = None)` intended to represent failed result with reasons that can be recorded in metadata at user's discretion.
The `BenchmarkResult` implements mapping interface, supporting `.keys()`, `.values()`, `.items()` methods, `__len__`, `__contains__`, `__getitem__` and `__iter__` special methods.
Values in `BenchmarkResult` has type `cuda.bench.results.SubBenchmarkResult` which implements a list-like interface, i.e. implements `__len__`, `__getitem__`, and `__iter__` special methods. Values in this list-like structure correspond to measurements of individual states of a particular benchmark (the key in `BenchmarkResult`).
Elements of `SubBenchmarkResult` structure have type `SubBenchmarkState` that supports mapping protocol with axis_values as a key and represent data corresponding to measurements for a particular state (combination of settings for each axis).
The state provides `.samples` and `.frequencies` attributes storing raw execution duration values and estimates for average GPU frequencies.
Example usage:
```
import array, numpy as np, cuda.bench.results
r = cuda.bench.results.BenchmarkResult("perf_data/axes_run1.json")
r["copy_sweep_grid_shape"].centers_with_frequencies(
lambda t, f: np.median(np.asarray(t)*np.asarray(f)))
```
```
In [1]: import array, numpy as np, cuda.bench.results
In [2]: r = cuda.bench.results.BenchmarkResult("temp_data/axes_run1.json")
In [3]: list(r)
Out[3]:
['simple',
'single_float64_axis',
'copy_sweep_grid_shape',
'copy_type_sweep',
'copy_type_conversion_sweep',
'copy_type_and_block_size_sweep']
In [4]: r["simple"].centers(lambda t: np.percentile(t, [25,75]))
Out[4]: {'Device=0': array([0.00100966, 0.00101299])}
In [5]: r.centers(lambda t: np.percentile(t, [25,75]))["simple"]
Out[5]: {'Device=0': array([0.00100966, 0.00101299])}
In [6]: len(r)
Out[6]: 6
In [7]: "fake" in r
Out[7]: False
```
Each `SubBenchmarkState` implements
`.summaries` attribute - rich object that retains tag/name/hint/hide/description metadata.
* Add nvbench-json-summary to render NVBench JSON output as an NVBench-style
markdown summary table, including axis formatting, device sections, hidden
summary filtering, and summary hint formatting.
Update packaging, type stubs, and tests for the new namespace, renamed
classes, Python 3.10-compatible annotations, and summary-table generation.
* Split tests in test_benchmark_result into smaller tests
* Fix break due to file name change
* Add python/examples/benchmark_result_autotune.py
This example demonstrates using cuda.bench and cuda.bench.results
to implement simple auto-tuning, demonstrated on selecting of
tile shape hyperparameter for naive stencil kernel implemented
in numba-cuda.
* Resolve ruff PLE0604
* Fix for format_axis_value in json format script to handle None value
Add tests to cover such input.
* Address code rabbit review feedback
* Fix license header, add validation
* Addressed both issues raised in review
Malformed values are now represented in result as None.
Skipped benchmarks are no longer dropped, i.e., they are present
in BenchmarkResult data, but they are not reflected in summary
table in line with what NVBench-instrumented benchmarks do.
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