A regression-based approach to scalability prediction

Bradley J. Barnes, Jaxk Reeves, Barry Rountree, Bronis De Supinski, David K. Lowenthal, Martind Schulz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

111 Scopus citations

Abstract

Many applied scientific domains are increasingly relying on largescale parallel computation. Consequently, many large clusters now have thousands of processors. However, the ideal number of processors to use for these scientific applications varies with both the input variables and the machine under consideration, and predicting this processor count is rarely straightforward. Accurate prediction mechanisms would provide many benefits, including improving cluster efficiency and identifying system configuration or hardware issues that impede performance. We explore novel regression-based approaches to predict parallel program scalability. We use several program executions on a small subset of the processors to predict execution time on larger numbers of processors. We compare three different regression-based techniques: one based on execution time only; another that uses per-processor information only; and a third one based on the global critical path. These techniques provide accurate scaling predictions, with median prediction errors between 6.2% and 17.3% for seven applications.

Original languageEnglish (US)
Title of host publicationICS'08 - Proceedings of the 2008 ACM International Conference on Supercomputing
Pages368-377
Number of pages10
DOIs
StatePublished - Dec 15 2008
Externally publishedYes
Event22nd ACM International Conference on Supercomputing, ICS'08 - Island of Kos, Greece
Duration: Jun 7 2008Jun 12 2008

Publication series

NameProceedings of the International Conference on Supercomputing

Other

Other22nd ACM International Conference on Supercomputing, ICS'08
CountryGreece
CityIsland of Kos
Period6/7/086/12/08

Keywords

  • MPI
  • Modeling
  • Prediction
  • Regression
  • Scalability

ASJC Scopus subject areas

  • Computer Science(all)

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