{"id":19706211,"url":"https://github.com/llnl/apollo","last_synced_at":"2025-04-29T16:33:07.767Z","repository":{"id":44702883,"uuid":"94927354","full_name":"LLNL/apollo","owner":"LLNL","description":"Apollo: Online Machine Learning for Performance Portability","archived":false,"fork":false,"pushed_at":"2024-08-27T20:10:13.000Z","size":1518,"stargazers_count":22,"open_issues_count":3,"forks_count":9,"subscribers_count":7,"default_branch":"develop","last_synced_at":"2025-04-05T18:11:30.190Z","etag":null,"topics":["analytics-platform","distributed-computing","hpc","in-situ","machine-learning","middleware","monitoring","parallel-programming","performance","programming-model","runtime","sampling","tuning"],"latest_commit_sha":null,"homepage":"","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LLNL.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-06-20T19:24:51.000Z","updated_at":"2024-08-27T20:10:17.000Z","dependencies_parsed_at":"2024-05-11T14:15:31.879Z","dependency_job_id":null,"html_url":"https://github.com/LLNL/apollo","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fapollo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fapollo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fapollo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fapollo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LLNL","download_url":"https://codeload.github.com/LLNL/apollo/tar.gz/refs/heads/develop","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251540472,"owners_count":21605910,"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":["analytics-platform","distributed-computing","hpc","in-situ","machine-learning","middleware","monitoring","parallel-programming","performance","programming-model","runtime","sampling","tuning"],"created_at":"2024-11-11T21:34:39.477Z","updated_at":"2025-04-29T16:33:02.757Z","avatar_url":"https://github.com/LLNL.png","language":"C++","readme":"# Apollo\n\nApollo is a distributed tuning framework for parallel applications.  You\ninstrument your code with the Apollo API, tell Apollo what the tuning\noptions are, and Apollo recommends tuning options so that your code\nruns faster.\n\n## Contributing\n\nTo contribute to Apollo please send a\n[pull request](https://help.github.com/articles/using-pull-requests/) on the\n`develop` branch of this repo. Apollo follows Gitflow for managing development.\n\n## Authors\n\nApollo is currently developed by Giorgis Georgakoudis (georgakoudis1@llnl.gov) and other\n[contributors](https://github.com/LLNL/apollo/graphs/contributors).\n\nApollo was originally created by David Beckingsale, david@llnl.gov\n\nIf you are referencing Apollo in a publication, please cite this repo and\nthe following papers:\n\n* David Beckingsale and Olga Pearce and Ignacio Laguna and Todd Gamblin.\n  [**Apollo: Reusable Models for Fast, Dynamic Tuning of Input-Dependent Code**](https://www.osti.gov/biblio/1367962). In *IEEE International Parallel \u0026 Distributed Processing Symposium (IPDPS'17)*, Orlando, FL, May 29-June 2 2017. LLNL-CONF-723337.\n* Chad Wood and Giorgis Georgakoudis and David Beckingsale and David Poliakoff and Alfredo Giménez and Kevin A. Huck and Allen D. Mallony and Todd Gamblin.\n[**Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning**](https://www.osti.gov/servlets/purl/1778645).\nIn *In International Conference on High Performance Computing, pp. 453-472. Springer, Cham, 2021*. LLNL-CONF-809192.\n\n## License\n\nApollo is distributed under the terms of both the MIT license.  All new\ncontributions must be made under the MIT license.\n\nSee [LICENSE](https://github.com/LLNL/apollo/blob/master/LICENSE) and\n[NOTICE](https://github.com/LLNL/apollo/blob/master/NOTICE) for details.\n\nSPDX-License-Identifier: MIT\n\nLLNL-CODE-733798\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fllnl%2Fapollo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fllnl%2Fapollo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fllnl%2Fapollo/lists"}