Maximizing parallelism for nested loops with non-uniform dependences

Abstract

Partitioning of loops is a very important optimization issue and requires the efficient and exact data dependence analysis. Although several methods exist in order to parallelize loops with non-uniform dependences, most of them perform poorly due to irregular and complex dependence constraints. This paper proposes Improved Region Partitioning Method for minimizing the size of the sequential region and maximizing parallelism. Our approach is based on the Convex Hull theory that has adequate information to handle non-uniform dependences. By parallelizing anti dependence region using variable renaming, we will divide the iteration space into two parallel regions and one or less sequential region. Comparison with other schemes shows more parallelism than the existing techniques. © Springer-Verlag Berlin Heidelberg 2004.