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General Information
    • ISSN: 2382-6185
    • Frequency: Quarterly (2015-2016); semiyearly (Since 2017)
    • DOI: 10.18178/IJKE
    • Editor-in-Chief: Prof. Chen-Huei Chou
    • Executive Editor: Ms. Nina Lee
    • Indexed by: Google Scholar, DOAJ, Engineering & Technology Digital Library, Crossref, ProQuest
    • E-mail: ijke@ejournal.net
Editor-in-chief
Prof. Chen-Huei Chou
College of Charleston, SC, USA
It is my honor to be the editor-in-chief of IJKE. I will do my best to help develop this journal better.
IJKE 2016 Vol.2(4): 182-186 ISSN: 2382-6185
doi: 10.18178/ijke.2016.2.4.076

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

Yi-Yan Nan, Quan-Zhe Li, Jin-Chun Piao, and Shin-Dug Kim
Abstract—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.

Index Terms—Support vector machine algorithm, parallel computing, GPU and OpenCL based SVM, image classification, matrix multiplication.

The authors are with the Yonsei University, Republic of Korea (e-mail: nanyiyan@yonsei.ac.kr, liquanzhe@yonsei.ac.kr, kumcun@yonsei.ac.kr, sdkim@yonsei.ac.kr).

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Cite: Yi-Yan Nan, Quan-Zhe Li, Jin-Chun Piao, and Shin-Dug Kim, "GPU-Accelerated SVM Training Algorithm Based on PC and Mobile Device," International Journal of Knowledge Engineering vol. 2, no. 4, pp. 182-186, 2016.

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E-mail: ijke@ejournal.net