emu EMEWS

Extreme-scale Model Exploration with Swift

What is EMEWS?

As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation have become possible. This tutorial presents the Extreme-scale Model Exploration with Swift (EMEWS) framework for combining existing capabilities for model exploration approaches (e.g., model calibration, metaheuristics, data assimilation) and simulations (or any “black box” application code) with the Swift/T parallel scripting language to run scientific workflows on a variety of computing resources, from desktop to academic clusters to Top 500 level supercomputers. This tutorial presents a number of use-cases, starting with a simple agent-based model parameter sweep, and ending with a complex adaptive parameter space exploration workflow coordinating ensembles of distributed (MPI) simulations. The use-cases are available for interested parties to download and run on their own.

Authors
  • Jonathan Ozik
    Argonne National Laboratory

  • Nicholson Collier
    Argonne National Laboratory

  • Justin Wozniak
    Argonne National Laboratory

  • Carmine Spagnuolo
    Univeristà degli Studi di Salerno



  • For questions about EMEWS or to access archived questions, please subscribe to the EMEWS mailing list.


  • To cite EMEWS, please use:
    Ozik, J., N. Collier, J. M. Wozniak and C. Spagnuolo. 2016. "From Desktop to Large-Scale Model Exploration with Swift/T." In Proceedings of the 2016 Winter Simulation Conference.
  • ANL
  • unisa uchicago


Research reported in this website was supported by the National Institute of General Medical Sciences (R01GM115839), National Institute on Drug Abuse (R01DA039934), and National Institute on Aging (R01AG047869) of the National Institutes of Health. This material is based upon work supported by the National Science Foundation under Grant DEB1516428. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation.
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Copyright © 2016 Argonne National Laboratory. All rights reserved. Website developed by Carmine Spagnuolo