International Conference Papers

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    Parallel BSO Algorithm for Association Rules Mining Using Master/Worker Paradigm
    (2015-09-06) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, Djamel; Habbas, Zineb
    The extraction of association rules from large transactional databases is considered in the paper using cluster architecture parallel computing. Motivated by both the successful sequential BSO-ARM algorithm, and the strong matching between this algorithm and the structure of the cluster architectures, we present in this paper a new parallel ARM algorithm that we call MW-BSO-ARM for Master/Workers version of BSO-ARM. The goal is to deal with large databases by minimizing the communication and synchronization costs, which represent the main challenges that faces any cluster architecture. The experimental results are very promising and show clear improvement that reaches 300% for large instances. For examples, in big transactional database such as WebDocs, the proposed approach generates 107 satisfied rules in only 22 minutes, while a previous GPU-based approach cannot generate more than 103 satisfied rules into 10 hours. The results also reveal that MWBSO-ARM outperforms the PGARM cluster-based approach in terms of computation time.
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    Parallel B&B Algorithm on Hybrid Multicore/GPU Architecture
    (IEEE, 2013-11-15) Bendjoudi, Ahcène; Chekini, Mehdi; Gharbi, Makhlouf; Mehdi, Malika; Benatchba, Karima; Sitayeb-Benbouzid, Fatima; Melab, Nouredine
    B&B algorithms are well known techniques for exact solving of combinatorial optimization problems (COP). They perform an implicit enumeration of the search space instead of exhaustive one. Based on a pruning technique, they reduce considerably the computation time required to explore the whole search space. Nevertheless, these algorithms remain inefficient when dealing with large combinatorial optimization instances. They are time-intensive and they require a huge computing power to be solved optimally. Nowadays, multi-core-based processors and GPU accelerators are often coupled together to achieve impressive performances. However, classical B&B algorithms must be rethought to deal with their two divergent architectures. In this paper, we propose a new B&B approach exploiting both the multi-core aspect of actual processors and GPU accelerators. The proposed approaches have been executed to solve FSP instances that are well-known combinatorial optimization benchmarks. Real experiments have been carried out on an Intel Xeon 64-bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our hybrid B&B approach speeds up the execution time up to x123 over the sequential mono-core B&B algorithm.