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Dynamic Load Balancing for Large-Scale Multiphysics Simulations
RIKEN Advanced Institute for Computational Science.ORCID iD: 0000-0002-5020-1631
RIKEN Advanced Institute for Computational Science.
RIKEN Advanced Institute for Computational Science.
Department of Computational Science, Graduate School of System Informatics, Kobe University and RIKEN Advanced Institute for Computational Science.
2017 (English)In: High-Performance Scientific Computing: Jülich Aachen Research Alliance (JARA) High-Performance Computing Symposium / [ed] Edoardo Di Napoli, Marc-André Hermanns, Hristo Iliev, Andreas Lintermann, Alexander Peyser, 2017, 13-23 p.Conference paper, (Refereed)
Abstract [en]

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%" role="presentation" style="box-sizing: border-box; display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">%% of the runtime, running on the K computer.

Place, publisher, year, edition, pages
2017. 13-23 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10164
Keyword [en]
HPC, Load balancing, Multiphysics, BCM
National Category
Computational Mathematics Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-202723DOI: 10.1007/978-3-319-53862-4_2ISBN: 978-3-319-53861-7 (print)ISBN: 978-3-319-53862-4 (electronic)OAI: oai:DiVA.org:kth-202723DiVA: diva2:1078342
Conference
Jülich Aachen Research Alliance (JARA) High-Performance Computing Symposium
Note

QC 20170314

Available from: 2017-03-03 Created: 2017-03-03 Last updated: 2017-03-14Bibliographically approved

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