Performance prediction of large parallel applications using parallel simulations

  • Authors:

  • Rajive Bagrodia;Ewa Deeljman;Steven Docy;Thomas Phan

  • Affiliations:

  • Computer Science Department, University of California, Los Angeles, CA;Computer Science Department, University of California, Los Angeles, CA;Computer Science Department, University of California, Los Angeles, CA;Computer Science Department, University of California, Los Angeles, CA

  • Venue:
  • Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
  • Year:
  • 1999

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Abstract

Accurate simulation of large parallel applications can be facilitated with the use of direct execution and parallel discrete event simulation. This paper describes the use of COMPASS, a direct execution-driven, parallel simulator for performance prediction of programs that include both communication and I/O intensive applications. The simulator has been used to predict the performance of such applications on both distributed memory machines like the IBM SP and shared-memory machines like the SGI Origin 2000. The paper illustrates the usefulness of COMPASS as a versatile performance prediction tool. We use both real-world applications and synthetic benchmarks to study application scalability, sensitivity to communication latency, and the interplay between factors like communication pattern and parallel file system caching on application performance. We also show that the simulator is accurate in its predictions and that it is also efficient in its ability to use parallel simulation to reduce its own execution time which, in some cases, has yielded a nearlinear speedup.