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Predictive Evaluation of Partitioning Algorithms through Runtime Modelling

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Title of host publicationProceedings - 23rd IEEE International Conference on High Performance Computing, HiPC 2016
DateAccepted/In press - 6 Sep 2016
DatePublished (current) - 1 Feb 2017
Pages351-361
Number of pages11
PublisherInstitute of Electrical and Electronics Engineers Inc.
Original languageEnglish
ISBN (Electronic)9781509054114

Publication series

NameIEEE International Conference on High Performance Computing
PublisherIEEE
ISSN (Print)1094-7256
ISSN (Electronic)2640-0316

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.

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    Research areas

  • fluid dynamics, high performance computing, modelling, performance analysis, scientific computing

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