Compiler techniques for data partitioning of sequentially iterated parallel loops

Abstract

This paper uses bottom-up, static program partitioning to minimize the execution time of parallel programs by reducing interprocessor communication. Program partitioning is applied to a parallel programming construct known as a sequentially iterated parallel loop. This paper develops and evaluates compiler techniques to automatically generate data partitions for sequentially iterated parallel loops that minimize interprocessor communication. These techniques could be included as a communication-reducing back end for existing parallelizing compilers. A multiprocessor model is defined to analyze program execution factors that lead to interprocessor communication. A model for the sequentially iterated parallel loop is defined to quantify communication patterns within a program. A vector notation is chosen to quantify communication across a global data set. Communication parameters used in generating data partitions are found and used within equations which are shown to generate rectangular and hexagonal partition shapes that minimize interprocessor communication. Experiments measuring the impact of communication on program performance and the improvements gained by using the automatically generated data partitions were conducted on a sixteen-processor Encore Multimax. The new method outperformed established data partitioning methods.