Symbolic Partitioning and Scheduling of Parameterized Task Graphs

The DAG-based task graph model has been found effective in scheduling for performance prediction and optimization of parallel applications. However the scheduling complexity and solution normally depend on the problem size. In this paper, we propose a symbolic scheduling scheme for a parameterized task graph which models coarse-grain DAG parallelism independent of the problem size. The algorithm first derives symbolic clusters to group of tasks in order to minimize communication while preserving parallelism and then it evenly assigns task clusters to processors. The runtime system executes clusters on each processor in a multithreaded fashion. This paper also presents preliminary experimental results to demonstrate the effectiveness of our techniques.