Data parallel programming in an adaptive environment

Guy Edjlali, Gagan Agrawal, Alan Sussman, Joel Saltz

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations

Abstract

For better utilization of computing resources, it is important to consider parallel programming environments in which the number of available processors varies at runtime. In this paper, we discuss runtime support for data parallel programming in such an adaptive environment. Executing data parallel programs in an adaptive environment requires redistributing data when the number of processors changes, and also requires determining new loop bounds and communication patterns for the new set of processors. We have developed a runtime library to provide this support. We also present performance results for a multiblock Navier-Stokes solver run on a network of workstations using PVM for message passing. Our experiments show that if the number of processors is not varied frequently, the cost of data redistribution is not significant compared to the time required for the actual computations.

Original languageEnglish (US)
Pages (from-to)827-832
Number of pages6
JournalIEEE Symposium on Parallel and Distributed Processing - Proceedings
StatePublished - 1995
EventProceedings of the IEEE 9th International Parallel Processing Symposium - Santa Barbara, CA, USA
Duration: Apr 25 1995Apr 28 1995

ASJC Scopus subject areas

  • Engineering(all)

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