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Journal Articles Journal of Supercomputing Year : 2015

GPU-based bees swarm optimization for association rules mining


Association rules mining (ARM) is a well-known combinatorial optimization problem aiming at extracting relevant rules from given large-scale datasets. 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 graphics processing units (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 (single evaluation in GPU, SE-GPU and multiple evaluation in GPU, 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 100× over the sequential mono-core bees swarm optimization-ARM algorithm. Moreover, the proposed approaches have been compared with CPU multi-core ones (1–8 cores). The results show that they are faster than the multi-core versions whatever is the number of used cores.
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Dates and versions

hal-01783628 , version 1 (02-05-2018)



Youcef Djenouri, Ahcène Bendjoudi, Malika Mehdi, Nadia Nouali-Taboudjemat, Zineb Habbas. GPU-based bees swarm optimization for association rules mining. Journal of Supercomputing, 2015, 71 (4), pp.1318-1344. ⟨10.1007/s11227-014-1366-8⟩. ⟨hal-01783628⟩
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