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Accelerating scientific computing in the post-Moore’s era

Kathleen E. Hamilton, Catherine D. Schuman, Steven R. Young, Ryan S. Bennink, Neena Imam, and Travis S. Humble

March, 2020

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

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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.

Citation Information


author      K. E. Hamilton and C. D. Schuman and S. R. Young and R. S. Bennink 
            and N. Imam and and T. S. Humble
title       Accelerating Scientific Computing in the Post-Moore’s Era
year        2020
month       March
publisher   Association for Computing Machinery
volume      7
number      1
issn        2329-4949
doi         10.1145/3380940
journal     ACM Transactions on Parallel Computing
pages       1-31


    author = "K. E. Hamilton and C. D. Schuman and S. R. Young and R. S. Bennink and N. Imam and and T. S. Humble",
    title = "Accelerating Scientific Computing in the Post-Moore’s Era",
    year = "2020",
    month = "March",
    publisher = "Association for Computing Machinery",
    volume = "7",
    number = "1",
    issn = "2329-4949",
    where = "",
    doi = "10.1145/3380940",
    journal = "ACM Transactions on Parallel Computing",
    pages = "1-31"