The MHETA execution model for heterogeneous clusters

Mario Nakazawa, David K. Lowenthal, Wendou Zhou

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

2 Scopus citations

Abstract

The availability of inexpensive “off the shelf” machines increases the likelihood that parallel programs run on heterogeneous clusters of machines. These programs are increasingly likely to be out of core, meaning that portions of their datasets must be stored on disk during program execution. This results in significant, per-iteration, I/O cost. This paper describes an execution model, called MHETA, which is the key component to finding an effective data distribution on heterogeneous clusters. MHETA takes into account computation, communication, and I/O costs of iterative scientific applications. MHETA uses automatically extracted information from a single iteration to predict the execution time of the remaining iterations. Results show that MHETA predicts with on average 98% accuracy the execution time of several scientific benchmarks (with and without prefetching) and one full-scale scientific program that utilize pipelined and other communication. MHETA is thus an effective tool when searching for the most effective distribution on a heterogeneous cluster.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM/IEEE SC 2005 Conference, SC 2005
PublisherAssociation for Computing Machinery
ISBN (Electronic)1595930612
DOIs
StatePublished - 2005
Event2005 ACM/IEEE Conference on Supercomputing, SC 2005 - Seattle, United States
Duration: Nov 12 2005Nov 18 2005

Publication series

NameProceedings of the International Conference on Supercomputing
Volume2005-November

Conference

Conference2005 ACM/IEEE Conference on Supercomputing, SC 2005
Country/TerritoryUnited States
CitySeattle
Period11/12/0511/18/05

Keywords

  • Data distribution
  • I/O
  • Modeling parallel execution

ASJC Scopus subject areas

  • Computer Science(all)

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