A periodic scheduling heuristic for mapping iterative task graphs onto distributed memory multiprocessors

Abstract

This thesis investigates the problem of statically assigning the tasks of applications represented by repetitive task graphs (such as sonar or radar signal processing) to the processors of a distributed memory multiprocessor system with the objective of maximizing graph instance throughput. The repetitive nature of these task graphs allows for pipelining and the overlapping of successive graph instances, suggesting a departure from classical directed acyclic graph scheduling techniques. To investigate such a claim, a version of the Mapping Heuristic (MH) [ELR 90] is extended for use with iterative applications. Then a new heuristic, Periodic Scheduling (PS), is developed to capitalize on the repetitive nature of these task graphs by overlapping successive graph instances. The PS heuristic assigns tasks to processors in such a way so as to minimize the maximal utilization of the processors and the communications links between them. This maximal utilization figure dictates the interval between successive instances of the task graph. We conduct experiments in which the graph instance throughput of PS is compared to that of MH across a broad range of processor topologies, utilizing several communications/computation ratios. It is shown that, compared to MH, the PS heuristic improves the throughput perfonnance between two and 50 percent Particularly noteworthy improvement is noted on systems with high average inter-node communications costs.