Using focused regression for accurate time-constrained scaling of scientific applications

Brad Barnes, Jeonifer Garren, David K. Lowenthal, Jaxk Reeves, Bronis R. De Supinski, Martin Schulz, Barry Rountree

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

5 Scopus citations

Abstract

Many large-scale clusters now have hundreds of thousands of processors, and processor counts will be over one million within a few years. Computational scientists must scale their applications to exploit these new clusters. Time-constrained scaling, which is often used, tries to hold total execution time constant while increasing the problem size along with the processor count. However, complex interactions between parameters, the processor count, and execution time complicate determining the input parameters that achieve this goal. In this paper we develop a novel gray-box, focused regression-based approach that assists the computational scientist with maintaining constant run time on increasing processor counts. Combining application-level information from a small set of training runs, our approach allows prediction of the input parameters that result in similar per-processor execution time at larger scales. Our experimental validation across seven applications showed that median prediction errors are less than 13%.

Original languageEnglish (US)
Title of host publicationProceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010
DOIs
StatePublished - 2010
Event24th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2010 - Atlanta, GA, United States
Duration: Apr 19 2010Apr 23 2010

Publication series

NameProceedings of the 2010 IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2010

Other

Other24th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2010
CountryUnited States
CityAtlanta, GA
Period4/19/104/23/10

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software
  • Theoretical Computer Science

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