Multi-core versus many-core computing for many-task Branch-and-Bound applied to big optimization problems

On the road to exascale, coprocessors are increasingly becoming key building blocks of High Performance Computing platforms. In addition to their energy efficiency, these many-core devices boost the performance of multi-core processors. In this paper, we revisit the design and implementation of Branch-and-Bound (B&B) algorithms for multi-core processors and Intel Xeon Phi coprocessors considering the offload mode as well as the native one. In addition, two major parallel models are considered: the master-worker and the work pool models. We address several parallel computing issues including processor-coprocessor data transfer optimization and vectorization. The proposed approaches have been experimented using the Flow-Shop scheduling problem (FSP) and two hardware configurations equivalent in terms of energy consumption: Intel Xeon E5-2670 processor and Intel Xeon Phi 5110P coprocessor. The reported results show that: (1) the proposed vectorization mechanism reduces the execution time by 55.4% (resp. 30.1%) in the many-core (resp. multi-core) approach ; (2) the offload mode allows a faster execution on MIC than the native mode for most FSP problem instances ; (3) the many-core approach (offload or native) is in average twice faster than the multi-core approach ; (4) the work pool parallel model is more suited for many/multi-core B&B applied to FSP than the master-worker model because of its irregular nature.