Efficient Support for Irregular Applications on Distributed-Memory Machines

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.


Published in:
Fifth ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 68-79
Year:
1995
Publisher:
ACM
Laboratories:




 Record created 2013-12-23, last modified 2018-03-17

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