Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing

@article{Ward2021ColmenaSM,
  title={Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing},
  author={Logan T. Ward and Ganesh Sivaraman and J. Gregory Pauloski and Yadu N. Babuji and Ryan Chard and Naveen K. Dandu and Paul C. Redfern and Rajeev S. Assary and Kyle Chard and Larry A. Curtiss and Rajeev Thakur and Ian T. Foster},
  journal={2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC)},
  year={2021},
  pages={9-20}
}
Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform. Methods that use machine learning (ML) to create proxy models of simulations show particular promise for guiding ensembles but are challenging to deploy because of the need to coordinate dynamic mixes of simulation and learning tasks. We present Colmena, an open-source Python framework that allows users to steer campaigns by providing… 

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