Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases

Djenouri, Youcef; Djenouri, Djamel; Belhadi, Asma; Fournier-Viger, Philippe; Chun-Wei Lin, Jerry; Bendjoudi, Ahcene

Abstract

© 2018 This paper investigates the use of GPU (Graphics Processing Unit) in improving the bees swarm optimization metaheuristic performance for solving the association rule mining problem. Although this metaheuristic proved its effectiveness, it requires huge computational resource when considering big databases for mining. To overcome this limitation, we develop in this paper a GPU-based Bees Swarm Optimization Miner (GBSO-Miner) where the GPU is used as a co-processor to compute the CPU-time intensive steps of the algorithm. Unlike state-of-the-art GPU-based ARM methods, all BSO steps including the determination of search area, the local search, the evaluation, and the dancing are performed on GPU. A mapping method between the data input of each task and the GPU blocks/threads is developed. To demonstrate the effectiveness of the GBSO-Miner framework, intensive experiments have been carried out. The results show that GBSO-Miner outperforms the baseline methods of the literature (GPApriroi, MEGPU, and Dmine) using big textual and graph databases. The results reveal that GBSO-Miner is up to 800 times faster than an optimized CPU-Implementation.

Citation

Djenouri, Y., Djenouri, D., Belhadi, A., Fournier-Viger, P., Chun-Wei Lin, J., & Bendjoudi, A. (2019). Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases. Information Sciences, 496, 326-342. https://doi.org/10.1016/j.ins.2018.06.060

• Journal Article Type: Article
• Acceptance Date: Jun 29, 2018
• Online Publication Date: Jul 3, 2018
• Publication Date: Sep 1, 2019
• Deposit Date: Jan 23, 2020
• Journal: Information Sciences
• Print ISSN: 0020-0255
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 496
• Pages: 326-342
• DOI: https://doi.org/10.1016/j.ins.2018.06.060
• Keywords: Control and Systems Engineering; Theoretical Computer Science; Software; Information Systems and Management; Artificial Intelligence; Computer Science Applications
• Public URL: https://uwe-repository.worktribe.com/output/5208778
• Additional Information: This article is maintained by: Elsevier; Article Title: Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases; Journal Title: Information Sciences; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.ins.2018.06.060; Content Type: article; Copyright: Published by Elsevier Inc.