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GPU-Accelerated Support Vector Machines for Traffic Classification

Volume 14, Number 5, May 2018, pp. 1088-1098
DOI: 10.23940/ijpe.18.05.p28.10881098

Guanglu Suna,b, Xuhang Lia, Xiangyu Houa, and Fei Langb,c

aSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
bResearch Center of Information Security & Intelligent Technology, Harbin University of Science and Technology, Harbin, 150080, China
cSchool of Foreign Languages, Harbin University of Science and Technology, Harbin, 150080, China

(Submitted on January 26, 2018; Revised on March 2, 2018; Accepted on April 26, 2018)


Machine learning model tackles traffic classification effectively. But, it consumes considerable computing resources and computing time, resulting in the difficulty to accommodate large-scale network. In the presented study, GPU-accelerated Support Vector Machines (SVM) is proposed for traffic classification. GPU is used to parallelly calculate the kernel matrix and process the grid traversal of iterative-tuning scheme, in order to accelerate the training and parameters optimization procedure of SVM. Parallel traffic classification is applied to accelerate the classification procedures through the single instruction multiple data paradigm, multithreading and the shared memory of the threads. The experimental results show that the presented method achieves the similar accuracy comparing to the existing CPU-based LibSVM. Furthermore, it ramps up the training speed to 1.53 times and the classification speed to 24 times, which is suitable for the real time classification of high speed backbone networks.


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