[2508.17493] Easy Acceleration with Distributed Arrays

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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2508.17493 (cs)

[Submitted on 24 Aug 2025]

Title:Easy Acceleration with Distributed Arrays

Authors:Jeremy Kepner, Chansup Byun, LaToya Anderson, William Arcand, David Bestor, William Bergeron, Alex Bonn, Daniel Burrill, Vijay Gadepally, Ryan Haney, Michael Houle, Matthew Hubbell, Hayden Jananthan, Michael Jones, Piotr Luszczek, Lauren Milechin, Guillermo Morales, Julie Mullen, Andrew Prout, Albert Reuther, Antonio Rosa, Charles Yee, Peter Michaleas<br>View a PDF of the paper titled Easy Acceleration with Distributed Arrays, by Jeremy Kepner and 22 other authors

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Abstract:High level programming languages and GPU accelerators are powerful enablers for a wide range of applications. Achieving scalable vertical (within a compute node), horizontal (across compute nodes), and temporal (over different generations of hardware) performance while retaining productivity requires effective abstractions. Distributed arrays are one such abstraction that enables high level programming to achieve highly scalable performance. Distributed arrays achieve this performance by deriving parallelism from data locality, which naturally leads to high memory bandwidth efficiency. This paper explores distributed array performance using the STREAM memory bandwidth benchmark on a variety of hardware. Scalable performance is demonstrated within and across CPU cores, CPU nodes, and GPU nodes. Horizontal scaling across multiple nodes was linear. The hardware used spans decades and allows a direct comparison of hardware improvements for memory bandwidth over this time range; showing a 10x increase in CPU core bandwidth over 20 years, 100x increase in CPU node bandwidth over 20 years, and 5x increase in GPU node bandwidth over 5 years. Running on hundreds of MIT SuperCloud nodes simultaneously achieved a sustained bandwidth $>$1 PB/s.

Comments:<br>8 pages, 4 figures, 2 tables, 2 algorithm listings, 2 code listings, to appear in IEEE HPEC 2025

Subjects:

Distributed, Parallel, and Cluster Computing (cs.DC); Computational Engineering, Finance, and Science (cs.CE); Mathematical Software (cs.MS); Performance (cs.PF)

Cite as:<br>arXiv:2508.17493 [cs.DC]

(or<br>arXiv:2508.17493v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2508.17493

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arXiv-issued DOI via DataCite

Related DOI:

https://doi.org/10.1109/HPEC67600.2025.11196478

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DOI(s) linking to related resources

Submission history<br>From: Jeremy Kepner [view email]<br>[v1]<br>Sun, 24 Aug 2025 19:05:52 UTC (359 KB)

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