{"id":20433764,"url":"https://github.com/intelpython/optimizations_bench","last_synced_at":"2025-04-12T21:06:36.149Z","repository":{"id":20768530,"uuid":"88307691","full_name":"IntelPython/optimizations_bench","owner":"IntelPython","description":"Collection of performance benchmarks used to present optimizations implemented for Intel(R) Distribution for Python*","archived":false,"fork":false,"pushed_at":"2024-08-16T18:34:22.000Z","size":78,"stargazers_count":3,"open_issues_count":0,"forks_count":8,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-12T21:06:30.129Z","etag":null,"topics":["benchmark","mkl-umath","numpy","python"],"latest_commit_sha":null,"homepage":null,"language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/IntelPython.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-04-14T22:26:09.000Z","updated_at":"2024-08-16T18:34:24.000Z","dependencies_parsed_at":"2024-08-16T19:51:33.371Z","dependency_job_id":null,"html_url":"https://github.com/IntelPython/optimizations_bench","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IntelPython%2Foptimizations_bench","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IntelPython%2Foptimizations_bench/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IntelPython%2Foptimizations_bench/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IntelPython%2Foptimizations_bench/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/IntelPython","download_url":"https://codeload.github.com/IntelPython/optimizations_bench/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248631685,"owners_count":21136562,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["benchmark","mkl-umath","numpy","python"],"created_at":"2024-11-15T08:20:58.932Z","updated_at":"2025-04-12T21:06:36.117Z","avatar_url":"https://github.com/IntelPython.png","language":"C++","readme":"[![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)\n\n# Optimization Benchmarks\nCollection of performance benchmarks used to present optimizations implemented for Intel(R) Distribution for Python*\n\n## Environment Setup\nTo install Python environments from Intel channel along with pip-installed packages\n\n- `conda env create -f environments/intel.yaml`\n- `conda activate intel_env`\n\n## Run tests\n- `python numpy/umath/umath_mem_bench.py -v --size 10 --goal-time 0.01 --repeats 1`\n\n## Run benchmarks\n### umath\n- To run python benchmarks: `python numpy/umath/umath_mem_bench.py`\n- To compile and run native benchmarks (requires `icx`): `make -C numpy/umath`\n\n### Random number generation\n- To run python benchmarks: `python numpy/random/rng.py`\n- To compile and run native benchmarks (requires `icx`): `make -C numpy/random`\n\n## See also\n\"[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\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fintelpython%2Foptimizations_bench","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fintelpython%2Foptimizations_bench","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fintelpython%2Foptimizations_bench/lists"}