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https://github.com/kostrykin/blas-benchmark
A benchmark comparison of different BLAS backends for NumPy.
https://github.com/kostrykin/blas-benchmark
benchmark blas cvxpy mkl numpy openblas python
Last synced: 15 days ago
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A benchmark comparison of different BLAS backends for NumPy.
- Host: GitHub
- URL: https://github.com/kostrykin/blas-benchmark
- Owner: kostrykin
- License: mit
- Created: 2024-02-23T15:42:25.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2024-02-26T19:53:36.000Z (11 months ago)
- Last Synced: 2024-12-26T13:19:30.690Z (21 days ago)
- Topics: benchmark, blas, cvxpy, mkl, numpy, openblas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 133 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# blas-benchmark
Tasks are defined in `tasks/*.py`. The Conda environments, which specify the different BLAS configurations, are defined in `results/*/environment.yml`. Python versions and numbers of threads are defined in `profiles.yml`.
To determine the runtime of a task, each task is repeated for at least 10 seconds, and the average is determined. The repetition and averaging procedure is repeated 3 times, and the best result is used.
## Main results:
The configurations
mkl2020.0_debug
andmkl2020.1_fakeintel
perform overall best:
AMD Ryzen Threadripper 3970X
AMD EPYC 7763
2 threads
16 threads
2 threads
openblas
1.003829
1.019895
1.016262
mkl2024.0
1.128423
1.213864
1.065984
mkl2020.0_debug
1.156737
1.261223
1.162273
mkl2020.1_fakeintel
1.144065
1.281782
1.164156
The *score* of a configuration is the *geometric mean* of the best possible speed-up in comparison to the other configurations. See `reports/*.ipynb` for details.
## Benchmark CLI:
Run the benchmark on your CPU:
```
python -m benchmark.cli --profiles py38_2threads py38_16threads --run
```Or only update the reports:
```
python -m benchmark.cli
```## Acknowledgements:
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