A GPU-Based Model For Accelerating Support Vector Machines

Amin Nezarat, Farshad Khunjush


This paper presents a GPU-assisted version of the LIB-SVM library for Support Vector Machines. SVMs are particularly popular for classification procedures among thx research community, but for large training data the processing time becomes unrealistic. The modification that is proposed is porting the computation ox the kernel matrix elements to the GPU, to significantly decrease the processing time for SVM training without altering the classification results compared to the original LIBSVM. The experimental evaluation of the proposed approach highlights how the GPU-accelerated persion of LIBSVM enables the move efficient handling of large problems, such as large-scale concept detection in video.

Full Text:



E. Tsamoura, V. Mezaris, and I. Kompatsiaris, “Gradual transition detection using color coherence and other criteria in a video shot meta-segmentation framework,” in Proc. ICIP-MIR, San Diego, CA, SA, Oct. 2008.

V. Mezaris, A. Dimou, and I. Kompatsiaris, “On the use of feature tracks for dynamic concept detection in video,” in Proc. ICIP, Hong Kong, China, Sept. 2010.

K.E.A. van de Sande, T. Gevers, and C.G.M. Snoek, “Evaluation of color descriptors for object and scene recognition,” in Proc. CVPR, Anchorage, Alaska, USA, June 2008.

C. Cortes and V. Vapnik, “Support-vector neworks,” Machine Learning, vol. 20, pp. 273–297, 1995.

C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” 2001, Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm.

E.Y. Chang, K. Zhu, H. Wang, H. Bai, J. Li, Z. Qiu, and H. Cui, “PSVM: Parallelizing Support Vector Machines on Distributed Computers,” in Proc. NIPS, Vancouver, B.C., Canada, Dec. 2007.

NVIDIA, CUDA Programming Guide 2.0, 2008.

T. Joachims, “SVMlight, http://svmlight.joachims.org,” 2002.

S. Rueping, “mySVM-Manual, http://www-ai.cs.unidortmund.de/SOFTWARE/MYSVM/,” 2000.

T. Kudo, “Tinysvm : Support vector machines,” http://www.chasen.org/ taku/software/TinySVM/.

B. Catanzaro, N. Sundaram, and K. Keutzer, “Fast support vector machine training and classification on graphics processors,” in Proc. ICML, Helsinki, Finland, 2008.

J. Platt, “Sequential minimal optimization: A fast algorithm for training support vector machines,” Technical Report MSR-TR-98-14, Microsoft Research, 1998.

A. Carpenter, “cuSVM: a CUDA implementation of support vector classification and regression,” 2009, Software available at http://patternsonascreen.net/cuSVM.html.

K.E.A. van de Sande, T. Gevers, and C.G.M. Snoek, “Empowering visual categorization with the gpu,” IEEE Trans. on Multimedia, vol. 13, no. 1, pp. 60–70, 2011.

NVIDIA, CUBLAS Library 2.0, 2008.

D. G. Lowe, “Distinctive image features from scale invariant keypoints,” Int. J. of Computer Vision, vol. 60, pp. 91–110, 2004.

DOI: http://dx.doi.org/10.7613/hccaj.v1i1.7


  • There are currently no refbacks.

The Official Journal of Nanostructured Coatings Institute of Payame Noor University and HPCLab Company.