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Performance Improvement of Data Mining in Weka through GPU Acceleration

Tiago Augusto Engel, Andrea Schwertner Charao, Manuele Kirsch-Pinheiro, Luiz-Angelo Steffenel
Universidade Federal de Santa Maria, Santa Maria, Brazil
Procedia Computer Science, Volume 32, Pages 93-100, 2014

@article{Engel201493,

   title={Performance Improvement of Data Mining in Weka through GPU Acceleration},

   journal={Procedia Computer Science},

   volume={32},

   number={0},

   pages={93 – 100},

   year={2014},

   note={The 5th International Conference on Ambient Systems, Networks and Technologies (ANT-2014), the 4th International Conference on Sustainable Energy Information Technology (SEIT-2014)},

   issn={1877-0509},

   doi={http://dx.doi.org/10.1016/j.procs.2014.05.402},

   url={http://www.sciencedirect.com/science/article/pii/S1877050914006024},

   author={Tiago Augusto Engel and Andrea Schwertner Charao and Manuele Kirsch-Pinheiro and Luiz-Angelo Steffenel},

   keywords={GPU}

}

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Data mining tools may be computationally demanding, so there is an increasing interest on parallel computing strategies to improve their performance. The popularization of Graphics Processing Units (GPUs) increased the computing power of current desktop computers, but desktop-based data mining tools do not usually take full advantage of these architectures. This paper exploits an approach to improve the performance of Weka, a popular data mining tool, through parallelization on GPU-accelerated machines. From the profiling of Weka object-oriented code, we chose to parallelize a matrix multiplication method using state-of-the-art tools. The implementation was merged into Weka so that we could analyze the impact of parallel execution on its performance. The results show a significant speedup on the target parallel architectures, compared to the original, sequential Weka code.
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