Min-Cut Methods for Mapping Dataflow Graphs

High performance applications and the underlying hardware platforms are becoming increasingly dynamic; runtime changes in the behavior of both are likely to result in inappropriate mappings of tasks to parallel machines during application execution. This fact is prompting new research on mapping and scheduling the dataflow graphs that represent parallel applications. In contrast to recent research which focuses on critical paths in dataflow graphs, this paper presents new mapping methods that compute near-min-cut partitions of the dataflow graph. Our methods deliver mappings that are an order of magnitude more efficient than those of DSC, a state-of-the-art critical-path algorithm, for sample high performance applications.