https://github.com/llnl/apollo
Apollo: Online Machine Learning for Performance Portability
https://github.com/llnl/apollo
analytics-platform distributed-computing hpc in-situ machine-learning middleware monitoring parallel-programming performance programming-model runtime sampling tuning
Last synced: about 1 month ago
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Apollo: Online Machine Learning for Performance Portability
- Host: GitHub
- URL: https://github.com/llnl/apollo
- Owner: LLNL
- License: other
- Created: 2017-06-20T19:24:51.000Z (almost 8 years ago)
- Default Branch: develop
- Last Pushed: 2024-08-27T20:10:13.000Z (9 months ago)
- Last Synced: 2025-04-05T18:11:30.190Z (2 months ago)
- Topics: analytics-platform, distributed-computing, hpc, in-situ, machine-learning, middleware, monitoring, parallel-programming, performance, programming-model, runtime, sampling, tuning
- Language: C++
- Homepage:
- Size: 1.45 MB
- Stars: 22
- Watchers: 7
- Forks: 9
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Apollo
Apollo is a distributed tuning framework for parallel applications. You
instrument your code with the Apollo API, tell Apollo what the tuning
options are, and Apollo recommends tuning options so that your code
runs faster.
## Contributing
To contribute to Apollo please send a
[pull request](https://help.github.com/articles/using-pull-requests/) on the
`develop` branch of this repo. Apollo follows Gitflow for managing development.
## Authors
Apollo is currently developed by Giorgis Georgakoudis ([email protected]) and other
[contributors](https://github.com/LLNL/apollo/graphs/contributors).
Apollo was originally created by David Beckingsale, [email protected]
If you are referencing Apollo in a publication, please cite this repo and
the following papers:
* David Beckingsale and Olga Pearce and Ignacio Laguna and Todd Gamblin.
[**Apollo: Reusable Models for Fast, Dynamic Tuning of Input-Dependent Code**](https://www.osti.gov/biblio/1367962). In *IEEE International Parallel & Distributed Processing Symposium (IPDPS'17)*, Orlando, FL, May 29-June 2 2017. LLNL-CONF-723337.
* 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.
[**Artemis: Automatic Runtime Tuning of Parallel Execution Parameters Using Machine Learning**](https://www.osti.gov/servlets/purl/1778645).
In *In International Conference on High Performance Computing, pp. 453-472. Springer, Cham, 2021*. LLNL-CONF-809192.
## License
Apollo is distributed under the terms of both the MIT license. All new
contributions must be made under the MIT license.
See [LICENSE](https://github.com/LLNL/apollo/blob/master/LICENSE) and
[NOTICE](https://github.com/LLNL/apollo/blob/master/NOTICE) for details.
SPDX-License-Identifier: MIT
LLNL-CODE-733798