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GPU-Accelerated SVM Training Algorithm Based on PC and Mobile Device

Yi-Yan Nan, Quan-Zhe Li, Jin-Chun Piao, Shin-Dug Kim
Yonsei University, Republic of Korea
International Journal of Knowledge Engineering, Vol. 2, No. 4, 2016

@article{nan2016gpu,

   title={GPU-Accelerated SVM Training Algorithm Based on PC and Mobile Device},

   author={Nan, Yi-Yan and Li, Quan-Zhe and Piao, Jin-Chun and Kim, Shin-Dug},

   year={2016}

}

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This work is to design an accelerated SVM (Support Vector Machine) which is suitable for Android operating system. SVM is widely used in the health-related applications. The SVM provides a potential classification technology based on the pattern recognition method and statistical learning theory. This paper proposes a parallel SVM algorithm based on GPU accelerator. GPU can provide better performance on matrix multiplication through parallelization which is the main drawback of conventional SVM execution. The cross validation function in the personal computer is designed and improved, and SVM training function in the mobile devices in addition. Through the above approach, the influence of matrix calculation on the whole system can be reduced to a certain extent. In the experiment of image classification, compared to the serial SVM, the proposed approach can achieve 3.3x speed up in the PC, and 1.5x speed up in the mobile devices. But the accuracy rate is not greatly improved both. Since the experiment mainly focuses on improving the execution time, no optimization is considered on the prediction process.
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