Abstract
Graph scheduling has been shown effective for solving irregular problems represented as directed acyclic graphs(DAGs) on distributed memory systems. Many scientific applications can also be modeled as iterative task graphs(ITGs). In this paper, we model the SOR computation for solving sparse matrix systems in terms of ITGs and address the optimization issues for scheduling ITGs when communication overhead is not zero. We present an approach that incorporates techniques of software pipelining and graph scheduling. We demonstrate the effectiveness of our approach in mapping SOR computation and compare it with the multi-coloring method.
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