International Journal Papers

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    GPU-based Bees Swarm Optimization for Association Rules Mining
    (Springer, 2014) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, Zineb
    Association Rules Mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large scale data sets. According to the state of the art, the bio-inspired methods proved their efficiency by generating acceptable solutions in a reasonable time when dealing with small and medium size instances. Unfortunately, to cope with large instances such as the webdocs benchmark, these methods require more and more powerful processors and are time expensive. Nowadays, computing power is no longer a real issue. It can be provided by the power of emerging technologies such as GPUs that are massively multi-threaded processors. In this paper, we investigate the use of GPUs to speed up the computation. We propose two GPU-based bees swarm algorithms for association rules mining (SE-GPU and ME-GPU). SE-GPU aims at evaluating one rule at a time where each thread is associated with one transaction, whereas ME-GPU evaluates multiple rules in parallel on GPU where each thread is associated with several transactions. To validate our approaches, the two algorithms have been executed to solve well-known large ARM instances. 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 approaches improve the execution time up to x100 over the sequential mono-core BSO-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1 to 8 cores). The results show that they are faster than the multi-core versions what ever the number of used cores.
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    GPU-accelerated Bounding for Branch-and-Bound applied to a Permutation Problem using Data Access Optimization
    (John Wiley & Sons, 2013-11) Melab, Nouredine; Chakroun, Imen; Bendjoudi, Ahcène
    Branch-and-Bound (B\&B) algorithms are attractive methods for solving to optimality combinatorial optimization problems using an implicit enumeration of a dynamically built tree-based search space. Nevertheless, they are time-consuming when dealing with large problem instances. Therefore, pruning tree nodes (subproblems) is traditionally used as a powerful mechanism to reduce the size of the explored search space. Pruning requires to perform the bounding operation which consists of applying a lower bound function to the subproblems generated during the exploration process. Preliminary experiments performed on the Flow-Shop scheduling problem (FSP) have shown that the bounding operation consumes over $98\%$ of the execution time of the B\&B algorithm. In this paper, we investigate the use of GPU computing as a major complementary way to speed up the search. We revisit the design and implementation of the parallel bounding model on GPU accelerators. The proposed approach enables data access optimization. Extensive experiments have been carried out on well-known FSP benchmarks using an Nvidia Tesla C2050 GPU card. Compared to a CPU-based single core execution using an Intel Core i7-970 processor without GPU, speedups higher than $100$ times faster are achieved for large problem instances. At an equivalent peak performance, GPU-accelerated B\&B is twice faster than its multi-core counterpart.