Efficient support for irregular applications on distributed-memory machines

  • Authors:

  • Shubhendu S. Mukherjee;Shamik D. Sharma;Mark D. Hill;James R. Larus;Anne Rogers;Joel Saltz

  • Affiliations:

  • Computer Sciences Department, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, WI;Department of Computer Science, University of Maryland, 4166 A.V. Williams Building, College Park, MD;Computer Sciences Department, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, WI;Computer Sciences Department, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, WI;Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ;Department of Computer Science, University of Maryland, 4166 A.V. Williams Building, College Park, MD

  • Venue:
  • PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
  • Year:
  • 1995

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Abstract

Irregular computation problems underlie many important scientific applications. Although these problems are computationally expensive, and so would seem appropriate for parallel machines, their irregular and unpredictable run-time behavior makes this type of parallel program difficult to write and adversely affects run-time performance.This paper explores three issues—partitioning, mutual exclusion, and data transfer—crucial to the efficient execution of irregular problems on distributed-memory machines. Unlike previous work, we studied the same programs running in three alternative systems on the same hardware base (a Thinking Machines CM-5): the CHAOS irregular application library, Transparent Shared Memory (TSM), and eXtensible Shared Memory (XSM). CHAOS and XSM performed equivalently for all three applications. Both systems were somewhat (13%) to significantly faster (991%) than TSM.