Exascale Applications: Skin in the Game

Michael Sprague, Francis Alexander, Ann Almgren, John Bell, Amitava Bhattacharjee, Jacqueline Chen, Phil Colella, David Daniel, Jack DeSlippe, Lori Diachin, Erik Draeger, Anshu Dubey, Thom Dunning, Thomas Evans, Ian Foster, Marianne Francois, Tim Germann, Mark Gordon, Salman Habib, Mahantesh HalappanavarSteven Hamilton, William Hart, Zhenyu Huang, Aimee Hungerford, Daniel Kasen, Paul Kent, Tzanio Kolev, Douglas Kothe, Andreas Kronfeld, Ye Luo, Paul Mackenzie, David McCallen, Sue Mniszewski, Chris Oehmen, Chris Oehmen, Amedeo Perazzo, Danny Perez, David Richards, William Rider, Rob Rieben, Kenneth Roche, Andrew Siegel, Carl Steefel, Rick Stevens, Madhava Syamlal, Mark Taylor, John Turner, Jean-Luc Vay, Artur Voter, Theresa Windus, Katherine Yelick

Research output: Contribution to journalArticlepeer-review

65 Scopus Citations

Abstract

As noted in Wikipedia, skin in the game refers to having 'incurred risk by being involved in achieving a goal', where 'skin is a synecdoche for the person involved, and game is the metaphor for actions on the field of play under discussion'. For exascale applications under development in the US Department of Energy Exascale Computing Project, nothing could be more apt, with the skin being exascale applications and the game being delivering comprehensive science-based computational applications that effectively exploit exascale high-performance computing technologies to provide breakthrough modelling and simulation and data science solutions. These solutions will yield high-confidence insights and answers to the most critical problems and challenges for the USA in scientific discovery, national security, energy assurance, economic competitiveness and advanced healthcare. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.

Original languageAmerican English
Article number20190056
Number of pages31
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Volume378
Issue number2166
DOIs
StatePublished - 6 Mar 2020

Bibliographical note

Publisher Copyright:
© 2020 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

NREL Publication Number

  • NREL/JA-5000-75732

Keywords

  • Computational science applications
  • Exascale
  • High-performance computing
  • Machine learning
  • Modelling
  • Numerical algorithms
  • Simulation

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