Multi-objective circuit partitioning for cutsize and path-based delay minimization
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Sourcebook of parallel computing
A parallel rendezvous algorithm for interpolation between multiple grids
Journal of Parallel and Distributed Computing
Multi.Objective Hypergraph Partitioning Algorithms for Cut and Maximum Subdomain Degree Minimization
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Traffic-based Load Balance for Scalable Network Emulation
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Multi-Constraint Mesh Partitioning for Contact/Impact Computations
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
International Journal of Parallel Programming
Efficient Partitioning Strategies for Distributed Web Crawling
Information Networking. Towards Ubiquitous Networking and Services
Multicriteria global minimum cuts
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
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Recently, a number of graph partitioning applications have emerged with additional requirements that the traditional graph partitioning model alone cannot effectively handle. One such class of problems is those in which multiple objectives, each of which can be modeled as a sum of weights of the edges of a graph, must be simultaneously optimized. This class of problems can be solved utilizing a multi-objective graph partitioning algorithm. We present a new formulation of the multi-objective graph partitioning problem and describe an algorithm that computes partitionings with respect to this formulation. We explain how this algorithm provides the user with a fine-tuned control of the tradeoffs among the objectives, results in predictable partitionings, and is able to handle both similar and dissimilar objectives. We show that this algorithm is better able to find a good tradeoff among the objectives than partitioning with respect to a single objective only. Finally, we show that by modifying the input preference vector, the multi-objective graph partitioning algorithm is able to gracefully tradeoff decreases in one objective for increases in the others.