Vectorization of local search for solving flow-shop scheduling problem on Xeon Phi™ MIC co-processors

Vaillant Gautier 1, 2 Mohand Mezmaz 1 Daniel Tuyttens 1 Nouredine Melab 2
1 Institut de Mathématiques [Mons]
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : This paper aims to propose a vectorizable cost function for the permutation flow-shop problem (PFSP) with the makespan criterion. This vectorization has been tested on a Xeon Phi core, using a local search. Indeed, Xeon Phi co-processors require vectorization in order to get the best performance from the device. Taillard's benchmark instances are used for the validation of the algorithm. The obtained results show that vectorization is more efficient as the number of jobs and the number of machines increase. Speedups up to 4.5× are recorded.
Keywords : Local search MIC architecture Intel Xeon Phi Permutation problems Flow-Shop Problem SIMD Vectorization
Type de document :
Communication dans un congrès
2016 International Conference on High Performance Computing & Simulation (HPCS), Jul 2016, Innsbruck, Austria. IEEE, 2016, 〈10.1109/HPCSim.2016.7568407〉
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https://hal.inria.fr/hal-01419077
Contributeur : Nouredine Melab <>
Soumis le : dimanche 18 décembre 2016 - 15:30:41
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13

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Vaillant Gautier, Mohand Mezmaz, Daniel Tuyttens, Nouredine Melab. Vectorization of local search for solving flow-shop scheduling problem on Xeon Phi™ MIC co-processors. 2016 International Conference on High Performance Computing & Simulation (HPCS), Jul 2016, Innsbruck, Austria. IEEE, 2016, 〈10.1109/HPCSim.2016.7568407〉. 〈hal-01419077〉

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