In this paper we develop simple index-based graph-partitioning techniques. We show that our methods are very fast and provide good-quality mappings. We also show that they are easily parallelizable. These properties make them useful for the parallelization of a number of irregular and adaptive applications.
Partitioning graphs into equally large groups of nodes while minimizing the numberofedges between di erent groups is an extremely important problem in parallel computing. For instance, e ciently parallelizing several scienti c and engineering applications requires the partitioning of data or tasks among processors such that the computational load on each node is roughly the same, while communication is minimized. Obtaining exact solutions is computationally intractable, since graph-partitioning is an NP-complete. For a large class of irregular and adaptive data parallel applications (such as adaptive meshes), the computational structure changes from one phase to another in an incremental fashion. In incremental graph-partitioning problems the partitioning of the graph needs to be updated as the graph changes over time a small number of nodes or edges may be added or deleted at any g i v en instant. In this paper we use a linear programming-based method to solve the incremental graph partitioning problem. All the steps used by our method are inherently parallel and hence our approach can be easily parallelized. By using an initial solution for the graph partitions derived from recursive spectral bisectionbased methods, our methods can achieve repartitioning at considerably lower cost than can be obtained by applying recursive spectral bisection. Further, the quality of the partitioning achieved is comparable to that achieved by applying recursive spectral bisection to the incremental graphs from scratch.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.