Clustering and intra-processor scheduling for explicitly-parallel programs on distributed-memory systems

When mapping a parallel program onto a parallel architecture, the number of available processors is usually less than the number of tasks in the program. This gives rise to clustering and intra-processor scheduling problems. We address these two problems for distributed-memory systems where programs are explicitly-parallel in nature. We show that existing models of program representation are insufficient to capture temporal behavior of such programs. We use a new temporal communication graph model that allows identification of overlap of communication with computation and inter-task parallelism. Clustering and intra-processor scheduling heuristics, attempting to minimize program completion time, are proposed using this model. Simulation results on random task graphs show 10-25% improvement in completion time over existing heuristics