A decomposition advisory system for heterogeneous data-parallel processing

Networked computing has become a popular method for using parallelism to solve a variety of computationally intense problems. However, high communication costs and processor heterogeneity may limit performance unless the problem space is carefully partitioned. We propose a decomposition advisory system that is designed to help choose the best data partitioning strategy. The goal of this research is to determine the partitioning scheme(s) expected to yield the best performance for a particular data-parallel problem with known regular communication patterns on a collection of heterogeneous processors. Given information about the problem space and the network, the system provides a ranking of standard partitioning methods