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High performance evaluation of evolutionary-mined association rules on GPUs

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Association rule mining is a well-known data mining task, but it requires much computational time and memory when mining large scale data sets of high dimensionality. This is mainly due to the evaluation process, where the antecedent and consequent in each rule mined are evaluated for each record. This paper presents a novel methodology for evaluating association rules on graphics processing units (GPUs). The evaluation model may be applied to any association rule mining algorithm. The use of GPUs and the compute unified device architecture (CUDA) programming model enables the rules mined to be evaluated in a massively parallel way, thus reducing the computational time required. This proposal takes advantage of concurrent kernels execution and asynchronous data transfers, which improves the efficiency of the model. In an experimental study, we evaluate interpreter performance and compare the execution time of the proposed model with regard to single-threaded, multi-threaded, and graphics processing unit implementation. The results obtained show an interpreter performance above 67 billion giga operations per second, and speed-up by a factor of up to 454 over the single-threaded CPU model, when using two NVIDIA 480 GTX GPUs. The evaluation model demonstrates its efficiency and scalability according to the problem complexity, number of instances, rules, and GPU devices.

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This work was supported by the Regional Government of Andalusia and the Ministry of Science and Technology, projects P08-TIC-3720 and TIN-2011-22408, and FEDER funds. This research was also supported by the Spanish Ministry of Education under FPU grants AP2010-0042 and AP2010-0041.

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Correspondence to Sebastián Ventura.

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Cano, A., Luna, J.M. & Ventura, S. High performance evaluation of evolutionary-mined association rules on GPUs. J Supercomput 66, 1438–1461 (2013).

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  • Performance evaluation
  • Association rules
  • Parallel computing
  • GPU