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Construction of Locality-Aware Algorithms to Optimize Performance of Stencil Codes on Heterogeneous Hardware

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Supercomputing (RuSCDays 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14389))

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Abstract

Recently, an increase in code performance has been obtained mainly through parallelism. For codes that implement stencil schemes, parallel processing requires data-intensive exchange. When parallel threads need to communicate, memory bandwidth becomes the bottleneck in performance. To overcome this bottleneck, processors have advanced caches. However, when developing codes for the purposes of scientific modeling, it is still the task of a programmer to make sure that every tool available is used to its highest limit and the best performance is obtained. To simplify this task, we use locally recursive non-locally asynchronous (LRnLA) algorithms. In this work, we develop an algorithm that efficiently localizes data in caches for advanced many-core CPUs with heterogeneous cores. We demonstrate the method by optimizing the performance of a fluid dynamics code on a computer with a many-core CPU with different types of cores.

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Authors and Affiliations

  1. Keldysh Institute of Applied Mathematics RAS, Moscow, Russia

    Vadim Levchenko & Anastasia Perepelkina

Authors
  1. Vadim Levchenko
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  2. Anastasia Perepelkina
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Corresponding author

Correspondence to Anastasia Perepelkina .

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Editors and Affiliations

  1. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Vladimir Voevodin

  2. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Sergey Sobolev

  3. Russian Academy of Sciences (RAS), Keldysh Institute of Applied Mathematics, Moscow, Russia

    Mikhail Yakobovskiy

  4. Russian Federal Nuclear Center, Sarov, Russia

    Rashit Shagaliev

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Levchenko, V., Perepelkina, A. (2023). Construction of Locality-Aware Algorithms to Optimize Performance of Stencil Codes on Heterogeneous Hardware. In: Voevodin, V., Sobolev, S., Yakobovskiy, M., Shagaliev, R. (eds) Supercomputing. RuSCDays 2023. Lecture Notes in Computer Science, vol 14389. Springer, Cham. https://doi.org/10.1007/978-3-031-49435-2_11

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  • DOI: https://doi.org/10.1007/978-3-031-49435-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49434-5

  • Online ISBN: 978-3-031-49435-2

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