Reducing Thread Divergence in GPU-based B&B Applied to the Flow-shop problem

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dc.contributor.authorChakroun, Imen
dc.contributor.authorBendjoudi, Ahcène
dc.contributor.authorMelab, Nouredine
dc.date.accessioned2013-06-12T10:14:47Z
dc.date.available2013-06-12T10:14:47Z
dc.date.issued2011-09-11
dc.description.abstractIn this paper,we propose a pioneering work on designing and programming B&B algorithms on GPU. To the best of our knowledge, no contribution has been proposed to raise such challenge. We focus on the parallel evaluation of the bounds for the Flow-shop scheduling problem. To deal with thread divergence caused by the bounding operation, we investigate two software based approaches called thread data reordering and branch refactoring. Experiments reported that parallel evaluation of bounds speeds up execution up to 54.5 times compared to a CPU version.fr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/195
dc.relation.ispartof10th International Conference on Parallel Processing and Applied Mathematics
dc.relation.ispartofseries10th International Conference on Parallel Processing and Applied Mathematics;
dc.relation.placeTorun, Polandfr_FR
dc.structureCalcul Pervasif et Mobilefr_FR
dc.subjectBranch and Bound, Data Parallelism, GPU Computing, Thread Divergence, Flow-shop Schedulingfr_FR
dc.titleReducing Thread Divergence in GPU-based B&B Applied to the Flow-shop problemfr_FR
dc.typeConference paper
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