Semidefinite programming for graph partitioning with preferences in data distribution

Authors:
Suely Oliveira;David Stewart;Takako Soma
Affiliations:
The Department of Computer Science, The University of Iowa, Iowa City, IA;The Department of Mathematics, The University of Iowa, Iowa City, IA;The Department of Computer Science, The University of Iowa, Iowa City, IA
Venue:
VECPAR'02 Proceedings of the 5th international conference on High performance computing for computational science
Year:
2002

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Abstract

Graph partitioning with preferences is one of the data distribution models for parallel computer, where partitioning and mapping are generatedto gether. It improves the overall throughput of message traffic by having communication restrictedto 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.