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Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM

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High Performance Computing for Computational Science -- VECPAR 2014 (VECPAR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8969))

Abstract

Load balancing scientific codes on massively parallel architectures is becoming an increasingly challenging task. In this paper, we focus on the Community Earth System Model, a widely used climate modeling code. It comprises six components each of which exhibits different scalability patterns. Previously, an analytical performance model has been used to find optimal load-balancing parameter configurations for each component. Nevertheless, for the Community Ice Code component, the analytical performance model is too restrictive to capture its scalability patterns. We therefore developed machine-learning-based load-balancing algorithm. It involves fitting a surrogate model to a small number of load-balancing configurations and their corresponding runtimes. This model is then used to find high-quality parameter configurations. Compared with the current practice of expert-knowledge-based enumeration over feasible configurations, the machine-learning-based load-balancing algorithm requires six times fewer evaluations to find the optimal configuration.

The submitted manuscript has been created by the UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne) under Contracts No. DE-AC02-06CH11357 and DE-FG02-05ER25694 with the U.S. Department of Energy. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The NCAR is sponsored by the National Science Foundation.

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Acknowledgments

This work was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under Contract DE-AC02-06CH11357. An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357.

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Authors and Affiliations

  1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, USA

    Prasanna Balaprakash, Sheri A. Mickelson, Sven Leyffer & Robert Jacob

  2. Leadership Computing Facility, Argonne National Laboratory, Argonne, IL, USA

    Prasanna Balaprakash & Yuri Alexeev

  3. UCAR, Seattle, WA, USA

    Anthony Craig

Authors
  1. Prasanna Balaprakash
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  2. Yuri Alexeev
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  3. Sheri A. Mickelson
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  4. Sven Leyffer
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  5. Robert Jacob
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  6. Anthony Craig
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Corresponding author

Correspondence to Prasanna Balaprakash .

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Editors and Affiliations

  1. IRIT, ENSEEIHT, Toulouse Cedex, France

    Michel Daydé

  2. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Osni Marques

  3. Information Technology Center, The University of Tokyo, Tokyo, Japan

    Kengo Nakajima

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Balaprakash, P., Alexeev, Y., Mickelson, S.A., Leyffer, S., Jacob, R., Craig, A. (2015). Machine-Learning-Based Load Balancing for Community Ice Code Component in CESM. In: Daydé, M., Marques, O., Nakajima, K. (eds) High Performance Computing for Computational Science -- VECPAR 2014. VECPAR 2014. Lecture Notes in Computer Science(), vol 8969. Springer, Cham. https://doi.org/10.1007/978-3-319-17353-5_7

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  • DOI: https://doi.org/10.1007/978-3-319-17353-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17352-8

  • Online ISBN: 978-3-319-17353-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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