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 language | English |
|---|---|
| Article number | 20190056 |
| Journal | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |
| Volume | 378 |
| Issue number | 2166 |
| DOIs | |
| State | Published - Mar 6 2020 |
Funding
Data accessibility. This article has no additional data. Competing interests. We declare we have no competing interests. Funding. This research was supported by the Exascale Computing Project (grant no. 17-SC-20-SC), a collaborative effort of two US DOE organizations (Office of Science and the National Nuclear Security Administration) responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware technology R&D, and integration of these technologies onto DOE HPC systems, in support of the nation’s exascale computing imperative. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. Acknowledgements. The authors would like to extend a special thanks to the many computer and computational science researchers (hundreds of them) who have committed their time, talents, experience and passion to the ECP efforts summarized in this paper. This group represents the best and brightest leaders and doers the HPC and computational science community has to offer. Without their engagement and commitment, the ECP would not succeed in achieving its aggressive goals and realizing its overall vision. Disclaimer. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy. gov/downloads/doe-public-access-plan). This work was also authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. This research was supported by the Exascale Computing Project (grant no. 17-SC-20-SC), a collaborative effort of two US DOE organizations (Office of Science and the National Nuclear Security Administration) responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware technology R&D, and integration of these technologies onto DOE HPC systems, in support of the nation's exascale computing imperative. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the US Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a US Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. The authors would like to extend a special thanks to the many computer and computational science researchers (hundreds of them) who have committed their time, talents, experience and passion to the ECP efforts summarized in this paper. This group represents the best and brightest leaders and doers the HPC and computational science community has to offer. Without their engagement and commitment, the ECP would not succeed in achieving its aggressive goals and realizing its overall vision.
Keywords
- Computational science applications
- Exascale
- High-performance computing
- Machine learning
- Modelling
- Numerical algorithms
- Simulation