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
Load balancing loosely synchronous problems with a neural network
C3P Proceedings of the third conference on Hypercube concurrent computers and applications: Architecture, software, computer systems, and general issues - Volume 1
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
Performance of dynamic load balancing algorithms for unstructured mesh calculations
Concurrency: Practice and Experience
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Physical optimization algorithms for mapping data to distributed-memory multiprocessors
Genetic algorithms for graph partitioning and incremental graph partitioning
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
IEEE Transactions on Parallel and Distributed Systems
Hypergraph-Partitioning-Based Decomposition for Parallel Sparse-Matrix Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems
A Dynamic Diffusion Optimization Method for Irregular Finite Element Graph Partitioning
The Journal of Supercomputing
An improved network clustering method for I/O-efficient query processing
Proceedings of the 8th ACM international symposium on Advances in geographic information systems
An Efficient Partitioning Algorithm for Distributed Virtual Environment Systems
IEEE Transactions on Parallel and Distributed Systems
A hypergraph-partitioning approach for coarse-grain decomposition
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Performance Surface Prediction for WAN-Based Clusters
The Journal of Supercomputing
Load balancing for unstructured mesh applications
Progress in computer research
A Web-Based Parallel PDE Solver Generation System for Distributed Memory Computing Environments
COMPSAC '00 24th International Computer Software and Applications Conference
Synthesizing Distributed Controllers for the Safe Operation of ConnectedSpaces
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Proceedings of the 22nd annual international conference on Supercomputing
Heat diffusion based dynamic load balancing for distributed virtual environments
Proceedings of the 17th ACM Symposium on Virtual Reality Software and Technology
Dynamic load balancing in distributed virtual environments using heat diffusion
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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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 graphs), 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. Further, the quality of the partitioning achieved is comparable to that achieved by applying recursive spectral bisection to the incremental graphs from scratch.