Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls

Abstract : Data mining tools may be computationally demanding, which leads to an increasing interest on par- allel computing strategies in order to improve their per- formance. While multi-core processors and Graphics Processing Units (GPUs) accelerators increased the com- puting power of current desktop computers, we observe that desktop-based data mining tools do not take full advantage of these architectures yet. This paper investi- gates strategies to improve the performance of Weka, a popular data mining tool, through multi-core and GPU acceleration. Using performance profiling of Weka, we identify operations that could improve the data mining performance when parallelized. We selected two of these operations, and analyze the impact of their parallel exe- cution on Weka’s performance. These experiments demonstrate that while significant speedups can be achieved, all operations are not prone to be parallelized, which reinforces the need for a careful and well-studied selection of the candidates.
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Journal of ambient intelligence and humanized computing, 2015, 6 (4), pp.407-423. 〈10.1007/s12652-015-0292-9 〉
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https://hal-paris1.archives-ouvertes.fr/hal-01196967
Contributeur : Manuele Kirsch Pinheiro <>
Soumis le : jeudi 10 septembre 2015 - 17:08:33
Dernière modification le : mercredi 14 février 2018 - 16:54:01

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Engel Tiago Augusto, Andrea Schwertner Charão, Manuele Kirsch Pinheiro, Luiz Angelo Steffenel. Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls. Journal of ambient intelligence and humanized computing, 2015, 6 (4), pp.407-423. 〈10.1007/s12652-015-0292-9 〉. 〈hal-01196967〉

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