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System and Application Performance Modeling and Simulation in the AI Era

Published: 15 June 2020 Publication History

Abstract

The increasing complexity and heterogeneity of systems at large scale, combined with challenging characteristics of applications driven by data dominated by adaptivity and irregularity, pose a need for fundamental rethinking and retooling of modeling and simulation (ModSim) for systems and applications. ModSim as a science and practice will be discussed from the perspectives of methods and tools and its myriad of uses, such as system-application co-design, performance prediction, or system and application optimization. To achieve this demanding goal, the presentation initially will offer an analysis and critique of the traditional methodologies and their state of the art. Then, attention will focus on new ideas related to machine learning-both as an increasingly important application workload and a method for ModSim. The context of mapping these applications to leading-edge systems will include analysis and the need for "dynamic performance modeling" as an actionable way to optimize effectively for performance during execution. Throughout, particular emphasis will be on methods and practices that are practical, accurate, and can be applied to extreme-scale computing (as broadly defined).

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  1. System and Application Performance Modeling and Simulation in the AI Era

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    cover image ACM Conferences
    SIGSIM-PADS '20: Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
    June 2020
    204 pages
    ISBN:9781450375924
    DOI:10.1145/3384441
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 15 June 2020

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    Author Tags

    1. artificial intelligence
    2. modeling
    3. modsim
    4. simulation
    5. systems

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    • Keynote

    Funding Sources

    • U.S. Department of Defense

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    SIGSIM-PADS '20
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    Overall Acceptance Rate 398 of 779 submissions, 51%

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