Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Solving problems on concurrent processors. Vol. 1: General techniques and regular problems
Heuristic approaches to task allocation for parallel computing
Heuristic approaches to task allocation for parallel computing
Solving problems on concurrent processors: vol. 2
Solving problems on concurrent processors: vol. 2
Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Nonlinear adaptive finite element systems on distributed memory computers
EDMCC2 Proceedings of the 2nd European conference on Distributed memory computing
Performance of dynamic load balancing algorithms for unstructured mesh calculations
Concurrency: Practice and Experience
Genetic algorithms for graph partitioning and incremental graph partitioning
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
ICS '95 Proceedings of the 9th international conference on Supercomputing
A unified algorithm for load-balancing adaptive scientific simulations
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Wavefront Diffusion and LMSR: Algorithms for Dynamic Repartitioning of Adaptive Meshes
IEEE Transactions on Parallel and Distributed Systems
Sourcebook of parallel computing
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Partitioning graphs into equally large groups of nodes while minimizing the number of edges between different groups is an extremely important problem in parallel computing. For instance, efficiently parallelizing several scientific 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 given 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 bisection-based methods, our methods can achieve repartitioning at considerably lower cost than can be obtained by applying recursive spectral bisection from scratch. Further, the quality of the partitioning achieved is comparable to that achieved by applying recursive spectral bisection to the incremental graphs from scratch.