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

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Abstract

Data mining tools may be computationally demanding, which leads to an increasing interest on parallel computing strategies in order to improve their performance. While multi-core processors and Graphics Processing Units (GPUs) accelerators increased the computing power of current desktop computers, we observe that desktop-based data mining tools do not take full advantage of these architectures yet. This paper investigates 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 execution 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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Notes

  1. See KDNuggets polls at http://www.kdnuggets.com/polls/2013/analytics-big-data-mining-data-science-software.html.

  2. http://romeo.univ-reims.fr.

  3. http://romeo.univ-reims.fr.

  4. http://www.top500.org.

  5. http://www.green500.org.

  6. http://cosy.univ-reims.fr/PER-MARE.

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Acknowledgments

The authors would like to thank the LSC Laboratory and the ROMEO Computing Center for the access to their resources. This project is partially financed by the STIC-AmSud PER-MARE project\(^{6}\) (project number 13STIC07), an international collaboration program supported by CAPES/MAEE/ANII agencies.

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Correspondence to Tiago Augusto Engel.

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Engel, T.A., Charão, A.S., Kirsch-Pinheiro, M. et al. Performance improvement of data mining in Weka through multi-core and GPU acceleration: opportunities and pitfalls. J Ambient Intell Human Comput 6, 377–390 (2015). https://doi.org/10.1007/s12652-015-0292-9

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