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Extending Hedgehog’s Dataflow Graphs to Multi-node GPU Architectures

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Asynchronous Many-Task Systems and Applications (WAMTA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13861))

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Abstract

Asynchronous task-based systems offer the possibility of making it easier to take advantage of scalable heterogeneous architectures. This paper extends the National Institute of Standards and Technology’s Hedgehog dataflow graph models, which target a single high-end compute node, to run on a cluster by borrowing aspects of Uintah’s cluster-scale task graphs and applying them to a sample implementation of matrix multiplication. These results are compared to implementations using the leading libraries, SLATE and DPLASMA, for illustrative purposes only. The motivation behind this work is to demonstrate that using general purpose high-level abstractions, such as Hedgehog’s dataflow graphs, does not negatively impact performance.

Supported by organization NIST.

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Author information

Authors and Affiliations

  1. University of Utah, Salt Lake City, UT, 84112, USA

    Nitish Shingde & Martin Berzins

  2. National Institute of Standards and Technology, Gaithersburg, MD, USA

    Timothy Blattner, Walid Keyrouz & Alexandre Bardakoff

Authors
  1. Nitish Shingde
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  2. Martin Berzins
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  3. Timothy Blattner
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  4. Walid Keyrouz
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  5. Alexandre Bardakoff
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Corresponding author

Correspondence to Nitish Shingde .

Editor information

Editors and Affiliations

  1. Louisiana State University, CCT, Baton Rouge, LA, USA

    Patrick Diehl

  2. University of Innsbruck, Innsbruck, Austria

    Peter Thoman

  3. Louisiana State University, CCT, Baton Rouge, LA, USA

    Hartmut Kaiser

  4. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Laxmikant Kale

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Shingde, N., Berzins, M., Blattner, T., Keyrouz, W., Bardakoff, A. (2023). Extending Hedgehog’s Dataflow Graphs to Multi-node GPU Architectures. In: Diehl, P., Thoman, P., Kaiser, H., Kale, L. (eds) Asynchronous Many-Task Systems and Applications. WAMTA 2023. Lecture Notes in Computer Science, vol 13861. Springer, Cham. https://doi.org/10.1007/978-3-031-32316-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-32316-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-32315-7

  • Online ISBN: 978-3-031-32316-4

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