{"id":20433773,"url":"https://github.com/intelpython/blackscholes_bench","last_synced_at":"2025-04-12T21:07:01.070Z","repository":{"id":66109066,"uuid":"82985631","full_name":"IntelPython/BlackScholes_bench","owner":"IntelPython","description":"Benchmark computing Black Scholes formula using different technologies","archived":false,"fork":false,"pushed_at":"2024-08-14T19:21:17.000Z","size":111,"stargazers_count":14,"open_issues_count":1,"forks_count":7,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-04-12T21:06:55.393Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","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":".github/CODEOWNERS","security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-02-24T00:42:27.000Z","updated_at":"2024-10-14T13:15:49.000Z","dependencies_parsed_at":"2024-04-03T17:56:02.046Z","dependency_job_id":"3da1524a-2870-40e3-962a-f425cb9007c7","html_url":"https://github.com/IntelPython/BlackScholes_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%2FBlackScholes_bench","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IntelPython%2FBlackScholes_bench/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IntelPython%2FBlackScholes_bench/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/IntelPython%2FBlackScholes_bench/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/IntelPython","download_url":"https://codeload.github.com/IntelPython/BlackScholes_bench/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248631678,"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":[],"created_at":"2024-11-15T08:21:07.859Z","updated_at":"2025-04-12T21:07:01.030Z","avatar_url":"https://github.com/IntelPython.png","language":"Python","readme":"[![Build status](https://ci.appveyor.com/api/projects/status/eqwaj633uivd3nnv/branch/master?svg=true)](https://ci.appveyor.com/project/anton-malakhov/blackscholes-bench/branch/master)\n[![Build Status](https://travis-ci.org/IntelPython/BlackScholes_bench.svg?branch=master)](https://travis-ci.org/IntelPython/BlackScholes_bench)\n\n# BlackScholes benchmark\nBenchmark computing Black Scholes formula using different technologies.\n\n## Prerequisites\n- `icc`, if compiling native benchmarks. Intel Distribution for Python*\n  2019 Gold benchmarks used icc 17.0.1.\n- `mkl`, if compiling native benchmarks with MKL.\n\n## Setup\n\n### Linux \u0026 Mac\n- Run `. activate-conda.sh` to install miniconda on Linux and Mac\n- Run `make` to build and run native benchmarks\n  - Run `make mkl` to build and run MKL version\n  - Run `make nomkl` to build and run non-MKL version\n  - Run `make black_scholes_mkl` to only build MKL version\n  - Run `make black_scholes` to only build non-MKL version\n\n### Windows\n- Download \u0026 install Miniconda3 and MSYS2\n- Run bash from MSYS2 and activate miniconda environment\n- Run `./install-conda-envs.sh` to install Python environments\n\n\n## Usage\n\n### Native benchmarks\n- Non-MKL version: Run the compiled binary `./black_scholes`.\n- MKL version: Run the compiled binary `./black_scholes_mkl`.\n\n### Python benchmarks\n```\nusage: {bs_erf_*.py|run.sh} [-h]\n                       [--steps STEPS] [--step STEP] [--chunk CHUNK]\n                       [--size SIZE] [--repeat REPEAT] [--dask DASK]\n                       [--text TEXT]\n\n\noptional arguments:\n  -h, --help       show this help message and exit\n  --steps STEPS    Number of steps\n  --step STEP      Factor for each step\n  --chunk CHUNK    Chunk size for Dask\n  --size SIZE      Initial data size\n  --repeat REPEAT  Iterations inside measured region\n  --dask DASK      Dask scheduler: sq, mt, mp\n  --text TEXT      Print with each result\n```\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%2Fblackscholes_bench","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fintelpython%2Fblackscholes_bench","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fintelpython%2Fblackscholes_bench/lists"}