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CUDT: A CUDA Based Decision Tree Algorithm

Win-Tsung Lo, Yue-Shan Chang, Ruey-Kai Sheu, Chun-Chieh Chiu, Shyan-Ming Yuan
Department of Computer Science, Tung Hai University, Taichung 40704, Taiwan
The Scientific World Journal, Volume 2014, Article ID 745640, 12 pages, 2014

@article{lo2014cudt,

   title={CUDT: A CUDA Based Decision Tree Algorithm},

   author={Lo, Win-Tsung and Chang, Yue-Shan and Sheu, Ruey-Kai and Chiu, Chun-Chieh and Yuan, Shyan-Ming},

   journal={The Scientific World Journal},

   volume={2014},

   year={2014},

   publisher={Hindawi Publishing Corporation}

}

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Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5~55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set.
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