Automatic data partitioning for irregular and adaptive applications

An important component in compiling for distributed memory machines is data partitioning. While a number of automatic analysis techniques have been proposed for this phase, none of them is applicable for irregular problems. In this paper, we present compile-time analysis for determining data partitioning for such applications. We have developed a set of cost functions for determining communication and redistribution costs in irregular codes. We first determine the appropriate distributions for a single data parallel statement, and then use the cost functions with a greedy algorithm for computing distributions for the full program. Initial performance results on a 16-processor IBM SP-2 are also presented