An optimal repartitioning decision policy

Authors:
David M. Nicol;Paul F. Reynolds, Jr.
Affiliations:
ICASE, Mail Stop 132C, NASA Langley Research Center, Hampton, VA and Department of Computer Science, Thornton Hall, University of Virginia, Charlottesville, Virginia;Department of Computer Science, Thornton Hall, University of Virginia, Charlottesville, Virginia
Venue:
WSC '85 Proceedings of the 17th conference on Winter simulation
Year:
1985

Citing 1
Cited 2

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ACM Transactions on Modeling and Computer Simulation (TOMACS)

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Abstract

The automated partitioning of simulations for parallel execution is a timely research problem. A simulation's run-time performance depends heavily on the nature of the inputs the simulation responds to. Consequently, a simulation's run-time behavior varies as a function of time. Since a simulation's run-time behavior is generally too complex to analytically predict, partitioning algorithms must be statistically based: they base their partitioning decisions on the simulation's observed behavior. Simulations which are partitioned statistically are vulnerable to radical changes in the run-time dynamics of the simulation. In this paper we discuss a dynamic repartitioning decision policy which detects change in a simulation's run-time behavior and reacts to this change. This decision policy optimally balances the costs and potential benefits of repartitioning a running simulation.