research-article

Public Access

Accelerating Scientific Computing in the Post-Moore’s Era

Authors:

Kathleen E. Hamilton,

Catherine D. Schuman,

Steven R. Young,

Ryan S. Bennink,

Neena Imam, and

Travis S. HumbleAuthors Info & Claims

ACM Transactions on Parallel Computing (TOPC), Volume 7, Issue 1

Article No.: 6, Pages 1 - 31

https://doi.org/10.1145/3380940

Published: 29 March 2020 Publication History

All formats PDF

Abstract

Novel uses of graphical processing units for accelerated computation revolutionized the field of high-performance scientific computing by providing specialized workflows tailored to algorithmic requirements. As the era of Moore’s law draws to a close, many new non–von Neumann processors are emerging as potential computational accelerators, including those based on the principles of neuromorphic computing, tensor algebra, and quantum information. While development of these new processors is continuing to mature, the potential impact on accelerated computing is anticipated to be profound. We discuss how different processing models can advance computing in key scientific paradigms: machine learning and constraint satisfaction. Significantly, each of these new processor types utilizes a fundamentally different model of computation, and this raises questions about how to best use such processors in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This also hints at the infrastructure needed to integrate next-generation processing units into future high-performance computing systems.

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Index Terms

Accelerating Scientific Computing in the Post-Moore’s Era
1. Hardware
  1. Emerging technologies
    1. Biology-related information processing
      1. Neural systems
    2. Quantum technologies
      1. Quantum computation
2. Theory of computation
  1. Models of computation

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cover image ACM Transactions on Parallel Computing

ACM Transactions on Parallel Computing Volume 7, Issue 1

Special Issue on Innovations in Systems for Irregular Applications, Part 1 and Regular Paper

March 2020

182 pages

ISSN:2329-4949

EISSN:2329-4957

DOI:10.1145/3387354

Editor:
David A. Bader
New Jersey Institute of Technology, USA

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Copyright © 2020 ACM.

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Publication History

Published: 29 March 2020

Accepted: 01 January 2020

Revised: 01 September 2019

Received: 01 November 2018

Published in TOPC Volume 7, Issue 1

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Department of Energy, Office of Science, Early Career Research Program
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https://doi.org/10.1080/10643389.2024.2365907
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