Message strip-mining heuristics for high speed networks

  • Authors:

  • Costin Iancu;Parry Husbands;Wei Chen

  • Affiliations:

  • Computational Research Division, Lawrence Berkeley National Laboratory;Computational Research Division, Lawrence Berkeley National Laboratory;Computer Science Division, University of California at Berkeley

  • Venue:
  • VECPAR'04 Proceedings of the 6th international conference on High Performance Computing for Computational Science
  • Year:
  • 2004

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Abstract

In this work we investigate how the compiler technique of message strip-mining performs in practice on contemporary high performance networks. Message strip-mining attempts to reduce the overall cost of communication in parallel programs by breaking up large message transfers into smaller ones that can be overlapped with computation. In practice, however, network resource constraints may negate the expected performance gains. By deriving a performance model and synthetic benchmarks we determine how network and application characteristics in.uence the applicability of this optimization. We use these .ndings to determine heuristics to follow when performing this optimization on parallel programs. We propose strip-mining with variable block size as an alternative strategy that performs almost as well as a highly tuned .xed block strategy and has the advantage of being performance portable across systems and application input sets. We evaluate both techniques using synthetic benchmarks and an application from the NAS Parallel Benchmark suite.