A framework for characterizing overlap of communication and computation in parallel applications

Aniruddha G. Shet, P. Sadayappan, David E. Bernholdt, Jarek Nieplocha, Vinod Tipparaju

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Effective overlap of computation and communication is a well understood technique for latency hiding and can yield significant performance gains for applications on high-end computers. In this paper, we propose an instrumentation framework for message-passing systems to characterize the degree of overlap of communication with computation in the execution of parallel applications. The inability to obtain precise time-stamps for pertinent communication events is a significant problem, and is addressed by generation of minimum and maximum bounds on achieved overlap. The overlap measures can aid application developers and system designers in investigating scalability issues. The approach has been used to instrument two MPI implementations as well as the ARMCI system. The implementation resides entirely within the communication library and thus integrates well with existing approaches that operate outside the library. The utility of the framework is demonstrated by analyzing communication-computation overlap for micro-benchmarks and the NAS benchmarks, and the insights obtained are used to modify the NAS SP benchmark, resulting in improved overlap.

Original languageEnglish
Pages (from-to)75-90
Number of pages16
JournalCluster Computing
Volume11
Issue number1
DOIs
StatePublished - Mar 2008

Funding

This research was supported by the FASTOS program of the Mathematical, Information, and Computational Sciences Division, Office of Advanced Scientific Computing Research, U.S. Department of Energy. Part of this work was performed at Pacific Northwest National Laboratory and Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725.

Keywords

  • Communication-computation overlap
  • Latency hiding
  • Parallel applications
  • Performance instrumentation and monitoring

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