Semidefinite programming for graph partitioning with preferences in data distribution

Abstract

Graph partitioning with preferences is one of the data distribution models for parallel computer, where partitioning and mapping are generated together. It improves the overall throughput of message traffic by having communication restricted to processors which are near each other, whenever possible. This model is obtained by associating to each vertex a value which reflects its net preference for being in one partition or another of the recursive bisection process. We have formulated a semidefinite programming relaxation for graph partitioning with preferences and implemented efficient subspace algorithm for this model. We numerically compared our new algorithm with a standard semidefinite programming algorithm and show that our subspace algorithm performs better. © Springer-Verlag Berlin Heidelberg 2003.