Bird, R. F., Wright, S. A. orcid.org/0000-0001-7133-8533, Beckingsale, D. A. et al. (1 more author) (2013) Performance modelling of magnetohydrodynamics codes. In: Computer Performance Engineering - 9th European Workshop, EPEW 2012, Revised Selected Papers. 9th European Performance Engineering Workshop, EPEW 2012, 30 Jul 2012 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . , DEU , pp. 197-209.
Abstract
Performance modelling is an important tool utilised by the High Performance Computing industry to accurately predict the run-time of science applications on a variety of different architectures. Performance models aid in procurement decisions and help to highlight areas for possible code optimisations. This paper presents a performance model for a magnetohydrodynamics physics application, Lare. We demonstrate that this model is capable of accurately predicting the run-time of Lare across multiple platforms with an accuracy of 90% (for both strong and weak scaled problems). We then utilise this model to evaluate the performance of future optimisations. The model is generated using SST/macro, the machine level component of the Structural Simulation Toolkit (SST) from Sandia National Laboratories, and is validated on both a commodity cluster located at the University of Warwick and a large scale capability resource located at Lawrence Livermore National Laboratory.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | 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: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 04 Oct 2018 09:20 |
Last Modified: | 17 Dec 2024 00:33 |
Published Version: | https://doi.org/10.1007/978-3-642-36781-6_14 |
Status: | Published |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Identification Number: | 10.1007/978-3-642-36781-6_14 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136614 |
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