Abstract

In this paper, we describe a general interprocedural framework for partitioning a program dependence graph into parallel tasks for execution on a multiprocessor system. Partitioning techniques are necessary to execute a parallel program at the appropriate granularity for a given target multiprocessor. The problem is to determine the best trade-off between parallelism and overhead. It is desirable for the partitioning to be performed automatically, so that the programmer can write a parallel program without being burdened by details of the overhead target multiprocessor, and so that the same parallel program can be made to execute efficiently on different multiprocessors. For each procedure, the partitioning algorithm attempts to minimize the estimated parallel execution time. The estimated parallel execution time reflects a trade-off between parallelism and overhead and is minimized at an optimal intermediate granularity of parallelism. Execution-profiling information is used to obtain accurate execution-time estimates. The partitioning framework has been completely implemented in the PTRAN system at the IBM Thomas J. Watson Research Center. Partitioned parallel programs generated by this prototype system have been executed on the IBM 3090™ and RP3 multiprocessor systems.