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[![Run benchmark tests](https://github.com/IntelPython/optimizations_bench/actions/workflows/run_tests.yaml/badge.svg)](https://github.com/IntelPython/optimizations_bench/actions/workflows/run_tests.yaml) | ||
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# Optimization Benchmarks | ||
Collection of performance benchmarks used to present optimizations implemented for Intel(R) Distribution for Python* | ||
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## Environment Setup | ||
To install Python environments from Intel channel along with pip-installed packages | ||
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- `python3 create_python_envs.py` | ||
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- `conda env create -f environments/intel.yaml` | ||
- `conda activate intel_env` | ||
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## Run tests | ||
- `python3 run_tests.py` | ||
- `python numpy/umath/umath_mem_bench.py -v --size 10 --goal-time 0.01 --repeats 1` | ||
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## Run benchmarks | ||
### umath | ||
- To run python benchmarks: `python numpy/umath/umath_mem_bench.py` | ||
- To compile and run native benchmarks (requires icc): `make -C numpy/umath` | ||
- To compile and run native benchmarks (requires `icx`): `make -C numpy/umath` | ||
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### Random number generation | ||
- To run python benchmarks: `python numpy/random/rng.py` | ||
- To compile and run native benchmarks (requires icc): `make -C numpy/random` | ||
- To compile and run native benchmarks (requires `icx`): `make -C numpy/random` | ||
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## See also | ||
"[Accelerating Scientific Python with Intel Optimizations](http://conference.scipy.org/proceedings/scipy2017/pdfs/oleksandr_pavlyk.pdf)" by Oleksandr Pavlyk, Denis Nagorny, Andres Guzman-Ballen, Anton Malakhov, Hai Liu, Ehsan Totoni, Todd A. Anderson, Sergey Maidanov. Proceedings of the 16th Python in Science Conference (SciPy 2017), July 10 - July 16, Austin, Texas |
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