{"id":17125448,"url":"https://github.com/daleroberts/pypar","last_synced_at":"2026-03-04T03:02:05.044Z","repository":{"id":8559395,"uuid":"10184854","full_name":"daleroberts/pypar","owner":"daleroberts","description":"Efficient and scalable parallelism using the message passing interface (MPI) to handle big data and highly computational problems.","archived":false,"fork":false,"pushed_at":"2016-11-11T00:51:58.000Z","size":619,"stargazers_count":69,"open_issues_count":5,"forks_count":16,"subscribers_count":14,"default_branch":"master","last_synced_at":"2025-04-13T06:41:57.012Z","etag":null,"topics":["big-data","map-reduce","mpi","python"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/daleroberts.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":null,"support":null}},"created_at":"2013-05-21T00:41:23.000Z","updated_at":"2024-01-13T23:51:30.000Z","dependencies_parsed_at":"2022-08-25T21:10:39.367Z","dependency_job_id":null,"html_url":"https://github.com/daleroberts/pypar","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/daleroberts/pypar","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daleroberts%2Fpypar","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daleroberts%2Fpypar/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daleroberts%2Fpypar/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daleroberts%2Fpypar/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/daleroberts","download_url":"https://codeload.github.com/daleroberts/pypar/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/daleroberts%2Fpypar/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30070479,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-04T01:03:42.280Z","status":"online","status_checked_at":"2026-03-04T02:00:07.464Z","response_time":59,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["big-data","map-reduce","mpi","python"],"created_at":"2024-10-14T18:44:55.332Z","updated_at":"2026-03-04T03:02:05.030Z","avatar_url":"https://github.com/daleroberts.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"[![Logo](https://raw.githubusercontent.com/daleroberts/pypar/master/doc/logo.png)](https://raw.githubusercontent.com/daleroberts/pypar/master/doc/logo.png)\n\n**PyPar** is a python library that provides efficient and scalable parallelism using the message passing interface (MPI) to handle **big data** and **highly computational problems**.\n\n[![Build Status](https://travis-ci.org/daleroberts/pypar.svg?branch=master)](https://travis-ci.org/daleroberts/pypar)\n\n**PyPar** is used by a number of large projects, such as:\n\n - [ANUGA shallow water equation solver](https://github.com/GeoscienceAustralia/anuga_core)\n - [TCRM A statistical-parametric model for assessing wind hazard from tropical cyclones](https://github.com/GeoscienceAustralia/tcrm)\n - [Wind multipliers: for produce wind terrain, shielding and topographic multipliers](https://github.com/GeoscienceAustralia/Wind_multipliers)\n\n## Example\n\nA simple 'pass the parcel' example.\n\n```python\nimport pypar as pp\n\nncpus = pp.size()\nrank = pp.rank()\nnode = pp.get_processor_name()\n\nprint 'I am rank %d of %d on node %s' % (rank, ncpus, node)\n\nif rank == 0:\n  msg = 'P0'\n  pp.send(msg, destination=1)\n  msg = pp.receive(source=rank-1)\n  print 'Processor 0 received message \"%s\" from rank %d' % (msg, rank-1)\nelse:\n  source = rank-1\n  destination = (rank+1) % ncpus\n  msg = pp.receive(source)\n  msg = msg + 'P' + str(rank)\n  pp.send(msg, destination)\n\npp.finalize()\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaleroberts%2Fpypar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdaleroberts%2Fpypar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdaleroberts%2Fpypar/lists"}