Bunt, R. A., Wright, S. A. orcid.org/0000-0001-7133-8533, Jarvis, S. A. et al. (2 more authors) (2017) Predictive Evaluation of Partitioning Algorithms through Runtime Modelling. In: Proceedings - 23rd IEEE International Conference on High Performance Computing, HiPC 2016. 23rd IEEE International Conference on High Performance Computing, HiPC 2016, 19-22 Dec 2016 IEEE International Conference on High Performance Computing . IEEE , IND , pp. 351-361.
Abstract
Performance modelling unstructured mesh codesis a challenging process, due to the difficulty of capturing theirmemory access patterns, and their communication patterns atvarying scale. In this paper we first develop extensions to anexisting runtime performance model, aimed at overcoming theformer, which we validate on up to 1,024 cores of a Haswell-based cluster, using both a geometric partitioning algorithmand ParMETIS to partition the input deck, with a maximumabsolute runtime error of 12.63% and 11.55% respectively. Toovercome the latter, we develop an application representative ofthe mesh partitioning process internal to an unstructured meshcode. This application is able to generate partitioning data thatis usable with the performance model to produce predictedapplication runtimes within 7.31% of those produced usingempirically collected data. We then demonstrate the use of theperformance model by undertaking a predictive comparisonamong several partitioning algorithms on up to 30,000 cores. Additionally, we correctly predict the ineffectiveness of thegeometric partitioning algorithm at 512 and 1024 cores.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Copyright 2017 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. 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. |
Keywords: | fluid dynamics,high performance computing,modelling,performance analysis,scientific computing |
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: | 06 Sep 2018 09:20 |
Last Modified: | 17 Dec 2024 00:33 |
Published Version: | https://doi.org/10.1109/HiPC.2016.048 |
Status: | Published |
Publisher: | IEEE |
Series Name: | IEEE International Conference on High Performance Computing |
Identification Number: | 10.1109/HiPC.2016.048 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:135340 |