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<p>We present a low-complexity heuristic, named the dominant sequence clusteringalgorithm (DSC), for scheduling parallel tasks on an unbounded number of completelyconnected processors. The performance of DSC is on average, comparable to, or evenbetter than, other higher-complexity algorithms. We assume no task duplication andnonzero communication overhead between processors. Finding the optimum solution forarbitrary directed acyclic task graphs (DAG's) is NP-complete. DSC finds optimalschedules for special classes of DAG's, such as fork, join, coarse-grain trees, and somefine-grain trees. It guarantees a performance within a factor of 2 of the optimum forgeneral coarse-grain DAG's. We compare DSC with three higher-complexity generalscheduling algorithms: the ETF by J.J. Hwang, Y.C. Chow, F.D. Anger, and C.Y. Lee(1989); V. Sarkar's (1989) clustering algorithm; and the MD by M.Y. Wu and D. Gajski(1990). We also give a sample of important practical applications where DSC has beenfound useful.</p>
Index Termsscheduling; directed graphs; computational complexity; parallel algorithms; trees(mathematics); parallel programming; DSC; parallel task scheduling; low-complexityheuristic; dominant sequence clustering algorithm; completely connected processor;unbounded number; performance; nonzero communication overhead; arbitrary directedacyclic task graphs; DAGs; NP-complete; optimal schedules; special classes; fork; join;coarse-grain trees; fine-grain trees; general scheduling algorithms; ETF; MD

T. Yang and A. Gerasoulis, "DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors," in IEEE Transactions on Parallel & Distributed Systems, vol. 5, no. , pp. 951-967, 1994.
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