Automatic coarse grain task parallel processing on SMP using openMP

Abstract

This paper proposes a simple and efficient implementation method for a hierarchical coarse grain task parallel processing scheme on a SMP machine. OSCAR multigrain parallelizing compiler automatically generates parallelized code including OpenMP directives and its performance is evaluated on a commercial SMP machine. The coarse grain task parallel processing is important to improve the effective performance of wide range of multiprocessor systems from a single chip multiprocessor to a high performance computer beyond the limit of the loop parallelism. The proposed scheme decomposes a Fortran program into coarse grain tasks, analyzes parallelism among tasks by “Earliest Executable Condition Analysis” considering control and data dependencies, statically schedules the coarse grain tasks to threads or generates dynamic task scheduling codes to assign the tasks to threads and generates OpenMP Fortran source code for a SMP machine. The thread parallel code using OpenMP generated by OSCAR compiler forks threads only once at the beginning of the program and joins only once at the end even though the program is processed in parallel based on hierarchical coarse grain task parallel processing concept. The performance of the scheme is evaluated on 8-processor SMP machine, IBM RS6000 SP 604e High Node, using a newly developed OpenMP backend of OSCAR multigrain compiler. The evaluation shows that OSCAR compiler with IBM XL Fortran compiler version 5.1 gives us 1.5 to 3 times larger speedup than the native XL Fortran compiler for SPEC 95fp SWIM, TOMCATV, HYDRO2D, MGRID and Perfect Benchmarks ARC2D.