Reducing thread divergence in GPU‐based bees swarm optimization applied to association rule mining

• Published in: Concurrency and computation Vol. 29; no. 9; pp. np - n/a
• Main Authors: Djenouri, Youcef, Bendjoudi, Ahcene, Habbas, Zineb, Mehdi, Malika, Djenouri, Djamel
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 10.05.2017
• Subjects: association rules mining; Bees; bees swarm optimization; Central processing units; Computing time; Concurrency; CPUs; Data mining; Divergence; GPU computing; Graphics processing units; Heuristic methods; Microprocessors; Optimization; Search process; Swarm intelligence; thread divergence
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.3836

Summary The association rules mining (ARM) problem is one of the most important problems in the area of data mining. It aims at finding all relevant association rules from transactional databases. It is CPU time intensive and requires a huge computing power when dealing with large transactional databases. To deal with this issue, Graphics Processing Units (GPUs) are a powerful tool to speed up the search process. However, their performance is closely subject to thread/branch divergence resulting from the single instruction multiple data parallel model of GPUs. In this paper, we propose three approaches based on database reorganization, aiming to reduce thread divergence in GPU‐based bees swarm optimization metaheuristic for ARM, respectively, named block‐based reordering, transactions‐based reordering, and transactions‐based reordering with median value. Theoretical and experimental studies have been carried out using well‐known large ARM instances. The experiments have been performed on an Intel Xeon 64 bit quad‐core processor E5520 coupled to Nvidia Tesla C2075 448 cores. The results show that the proposed approaches minimize considerably the number of thread divergence and improve the overall execution time. Indeed, the number of thread divergence occurrences has been reduced by up to eight times making the execution much faster. Copyright © 2016 John Wiley & Sons, Ltd.