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Dynamic Load Balancing for Large-Scale Multiphysics Simulations

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High-Performance Scientific Computing (JHPCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10164))

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Abstract

In parallel computing load balancing is an essential component of any efficient and scalable simulation code. Static data decomposition methods have proven to work well for symmetric workloads. But, in today’s multiphysics simulations, with asymmetric workloads, this imbalance prevents good scalability on future generation of parallel architectures. We present our work on developing a general dynamic load balancing framework for multiphysics simulations on hierarchical Cartesian meshes. Using a weighted dual graph based workload estimation and constrained multilevel graph partitioning, the required runtime for industrial applications could be reduced by 40\(\%\) of the runtime, running on the K computer.

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Acknowledgments

This work was supported through the computing resources provided on the K computer by RIKEN Advanced Institute for Computational Science.

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Authors and Affiliations

  1. RIKEN Advanced Institute for Computational Science, Kobe, Japan

    Niclas Jansson, Rahul Bale, Keiji Onishi & Makoto Tsubokura

  2. Department of Computational Science, Graduate School of System Informatics, Kobe University, Kobe, Japan

    Makoto Tsubokura

Authors
  1. Niclas Jansson
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  2. Rahul Bale
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  3. Keiji Onishi
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  4. Makoto Tsubokura
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Corresponding authors

Correspondence to Niclas Jansson or Rahul Bale .

Editor information

Editors and Affiliations

  1. Forschungszentrum Jülich, Jülich, Germany

    Edoardo Di Napoli

  2. Forschungszentrum Jülich, Jülich, Germany

    Marc-André Hermanns

  3. RWTH Aachen University, Aachen, Germany

    Hristo Iliev

  4. RWTH Aachen University, Aachen, Germany

    Andreas Lintermann

  5. Forschungszentrum Jülich, Jülich, Germany

    Alexander Peyser

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Jansson, N., Bale, R., Onishi, K., Tsubokura, M. (2017). Dynamic Load Balancing for Large-Scale Multiphysics Simulations. In: Di Napoli, E., Hermanns, MA., Iliev, H., Lintermann, A., Peyser, A. (eds) High-Performance Scientific Computing. JHPCS 2016. Lecture Notes in Computer Science(), vol 10164. Springer, Cham. https://doi.org/10.1007/978-3-319-53862-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-53862-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53861-7

  • Online ISBN: 978-3-319-53862-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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