Data reordering for minimizing threads divergence in GPU-based evaluating association rules

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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorBendjoudi, Ahcène
dc.contributor.authorMehdi, Malika
dc.contributor.authorHabbas, Zineb
dc.contributor.authorNouali-Taboudjemat, Nadia
dc.date.accessioned2015-03-26T10:16:28Z
dc.date.available2015-03-26T10:16:28Z
dc.date.issued2015-03-26
dc.description.abstractThis last decade, the success of Graphics Processor Units (GPUs) has led researchers to launch a lot of works on solving large complex problems by using these cheap and powerful architecture. Association Rules Mining (ARM) is one of these hard problems requiring a lot of computational resources. Due to the exponential increase of data bases size, existing algorithms for ARM problem become more and more inefficient. Thus, research has been focusing on parallelizing these algorithms. Recently, GPUs are starting to be used to this task. However, their major drawback is the threads divergence problem. To deal with this issue, we propose in this paper an intelligent strategy called Transactions- based Reordering "TR" allowing an efficient evaluation of association rules on GPU by minimizing threads divergence. This strategy is based on data base re-organization. To validate our proposition, theoretical and experimental studies have been carried out using well-known synthetic data sets. The results are very promising in terms of minimizing the number of threads divergence.fr_FR
dc.identifier.isrnCERIST-DTISI/RR--15-00000009--dzfr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/722
dc.publisherCERIST
dc.relation.ispartofRapports de recherche internes
dc.relation.placeAlger
dc.structureCalcul Parallèle et Applicationsfr_FR
dc.subjectAssociation Rules Miningfr_FR
dc.subjectGPU Computingfr_FR
dc.subjectThreads Divergencefr_FR
dc.titleData reordering for minimizing threads divergence in GPU-based evaluating association rulesfr_FR
dc.typeTechnical Report
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