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.
