Adaptive Quality Equalizing: High-performance load balancing for parallel branch-and-bound across applications and computing systems
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.
parallel branch-and-bound mixed-integer programming adaptive load balancing best-first search dynamic load balancing granularity