Keal, Thomas, Elena, Alin-Marin, Stoneham, Karen et al. (11 more authors) (2022) Materials and Molecular Modelling at the Exascale. Computing in Science & Engineering. pp. 36-45. ISSN 1558-366X
Abstract
Progression of computational resources towards exascale computing makes possible simulations of unprecedented accuracy and complexity in the fields of materials and molecular modelling (MMM), allowing high fidelity in silico experiments on complex materials of real technological interest. However, this presents demanding challenges for the software used, especially the exploitation of the huge degree of parallelism available on exascale hardware, and the associated problems of developing effective workflows and data management on such platforms. As part of the UKs ExCALIBUR exascale computing initiative, the UK-led MMM Design and Development Working Group has worked with the broad MMM community to identify a set of high priority application case studies which will drive future exascale software developments. We present an overview of these case studies, categorized by the methodological challenges which will be required to realize them on exascale platforms, and discuss the exascale requirements, software challenges and impact of each application area.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Physics (York) |
Funding Information: | Funder Grant number EPSRC EP/W026775/1 |
Depositing User: | Pure (York) |
Date Deposited: | 15 Feb 2022 17:40 |
Last Modified: | 08 Feb 2025 00:45 |
Published Version: | https://doi.org/10.1109/MCSE.2022.3141328 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/MCSE.2022.3141328 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183655 |
Download
Filename: Materials_and_Molecular_Modelling_at_the_Exascale.pdf
Description: Materials_and_Molecular_Modelling_at_the_Exascale