Tight analyses of two local load balancing algorithms
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Computational Optimization and Applications
Scalable Global and Local Hashing Strategies for Duplicate Pruning in Parallel A* Graph Search
IEEE Transactions on Parallel and Distributed Systems
Theoretical Computer Science - Phase transitions in combinatorial problems
Control strategies for parallel mixed integer branch and bound
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Control Schemes in a Generalized Utility for Parallel Branch-and-Bound Algorithms
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
Hybrid evolutionary algorithms on minimum vertex cover for random graphs
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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The authors present a new load balancing strategy and its application to distributed branch & bound algorithms and demonstrate its efficiency by solving some NP-complete problems on a network of up to 256 transputers. The parallelization of their branch & bound algorithm is fully distributed. Every processor performs the same algorithm but each on a different part of the solution tree. In this case it is necessary to distribute subproblems among the processors to achieve a well balanced workload. Their load balancing method overcomes the problem of search overhead and idle times by an appropriate load model and avoids trashing effects by a feedback control method. Using this strategy they were able to achieve a speedup of up to 237.32 on a 256 processor network for very short parallel computation times, compared to an efficient sequential algorithm.