Multi-core versus Many-core Computing for Many-task Branch-and-Bound applied to Big Optimization Problems

Nouredine Melab 1 Jan Gmys 2, 3 Mohand Mezmaz 3 Daniel Tuyttens 3
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : 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 42.1% (resp. 21.2%) in the many-core (resp. multi-core) approach ; (2) the native mode allows a faster execution on MIC than the offload mode for all 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.
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Future Generation Computer Systems, A Paraître, pp.20
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https://hal.inria.fr/hal-01419079
Contributeur : Nouredine Melab <>
Soumis le : dimanche 18 décembre 2016 - 15:55:55
Dernière modification le : jeudi 11 janvier 2018 - 06:27:32

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Nouredine Melab, Jan Gmys, Mohand Mezmaz, Daniel Tuyttens. Multi-core versus Many-core Computing for Many-task Branch-and-Bound applied to Big Optimization Problems. Future Generation Computer Systems, A Paraître, pp.20. 〈hal-01419079〉

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