Artificial intelligence
Scalable load balancing strategies for parallel A* algorithms
Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
Scalable, high-performance parallel branch-and-bound algorithms for solving large combinatorial optimization problems
Random Seeking: A General, Efficient, and Informed Randomized Scheme for Dynamic Load Balancing
IPPS '96 Proceedings of the 10th International Parallel Processing Symposium
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In this paper, we present an adaptive version of our previously proposed quality equalizing (QE) load balancing strategy that attempts to maximize the performance of parallel branch-and-bound (B&B) by adapting to application and target computing system characteristics. Adaptive QE (AQE) incorporates the following salient adaptive features: (1) Anticipatory quantitative and qualitative load balancing mechanisms. (2) Regulation of load information exchange overhead. (3) Deterministic load balancing in extended neighborhoods instead of just immediate neighborhoods as in non-adaptive QE. (4) Randomized global load balancing to fetch work from outside the extended neighborhood. AQE yields speedup improvements of up to 80%, and 15% on the average, compared to that provided by QE for several real-world mixed-integer programming (MIP) problems, and near-ideal speedups for two of the largest problems in the MIPLIB benchmark suite on an IBM SP2 system.