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Visualizing, Measuring, and Tuning Adaptive MPI Parameters

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Programming and Performance Visualization Tools (ESPT 2017, ESPT 2018, VPA 2017, VPA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11027))

Abstract

Adaptive MPI (AMPI) is an advanced MPI runtime environment that offers several features over traditional MPI runtimes, which can lead to a better utilization of the underlying hardware platform and therefore higher performance. These features are overdecomposition through virtualization, and load balancing via rank migration. Choosing which of these features to use, and finding the optimal parameters for them is a challenging task however, since different applications and systems may require different options. Furthermore, there is a lack of information about the impact of each option. In this paper, we present a new visualization of AMPI in its companion Projections tool, which depicts the operation of an MPI application and details the impact of the different AMPI features on its resource usage. We show how these visualizations can help to improve the efficiency and execution time of an MPI application. Applying optimizations indicated by the performance analysis to two MPI-based applications results in performance improvements of up 18% from overdecomposition and load balancing.

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Notes

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  2. 2.

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  3. 3.

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Acknowledgments

This paper is based in part upon work supported by the Department of Energy, National Nuclear Security Administration, under Award Number DE-NA0002374.

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Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, Urbana, USA

    Matthias Diener, Sam White & Laxmikant V. Kale

Authors
  1. Matthias Diener
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  2. Sam White
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  3. Laxmikant V. Kale
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Corresponding author

Correspondence to Matthias Diener .

Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Livermore, CA, USA

    Abhinav Bhatele

  2. Lawrence Livermore National Laboratory, Livermore, CA, USA

    David Boehme

  3. The University of Arizona, Tucson, AZ, USA

    Joshua A. Levine

  4. University of Oregon, Eugene, OR, USA

    Allen D. Malony

  5. Technical University of Munich, Munich, Germany

    Martin Schulz

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Diener, M., White, S., Kale, L.V. (2019). Visualizing, Measuring, and Tuning Adaptive MPI Parameters. In: Bhatele, A., Boehme, D., Levine, J., Malony, A., Schulz, M. (eds) Programming and Performance Visualization Tools. ESPT ESPT VPA VPA 2017 2018 2017 2018. Lecture Notes in Computer Science(), vol 11027. Springer, Cham. https://doi.org/10.1007/978-3-030-17872-7_13

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  • DOI: https://doi.org/10.1007/978-3-030-17872-7_13

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  • Online ISBN: 978-3-030-17872-7

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