Statement-Level Communication-Free Partitioning Techniques for Parallelizing Compilers

Abstract

This paper addresses the problem of communication-free partition of iteration spaces and data spaces along hyperplanes. To finding more possible communication-free hyperplane partitions, we treat statements within a loop body as separate schedulable units. Instead of using the information about data dependence distance or direction vectors, our technique explicitly formulates array references as transformations from statement-iteration spaces to data spaces. Based on these transformations, the necessary and sufficient conditions for communication-free partition along hyperplanes to be feasible have been proposed. This approach can be applied to all programs with an imperfectly nested loop or sequences of imperfectly nested loops, whose array references are affine functions of outer loop indices or loop invariant variables. The proposed approach is more practical than existing methods in finding the data and computation distribution patterns that can cause the processor to execute fully-parallel on multicomputers without any interprocessor communication.