Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Open Computer Science

Editor-in-Chief: van den Broek, Egon

Covered by:
Web of Science - Emerging Sources Citation Index

CiteScore 2018: 0.63
Source Normalized Impact per Paper (SNIP) 2018: 0.604

ICV 2017: 98.90

Open Access
See all formats and pricing
More options …

GPUSVM: a comprehensive CUDA based support vector machine package

Qi Li / Raied Salman / Erik Test / Robert Strack / Vojislav Kecman
Published Online: 2011-12-27 | DOI: https://doi.org/10.2478/s13537-011-0028-7


GPUSVM (Graphic Processing Unit Support Vector Machine) is a Computing Unified Device Architecture (CUDA) based Support Vector Machine (SVM) package. It is designed to offer an end-user a fully functional and user friendly SVM tool which utilizes the power of GPUs. The core package includes an efficient cross validation tool, a fast training tool and a predicting tool. In this article, we first introduce the background theory of how we build our parallel SVM solver using CUDA programming model. Then we compare our GPUSVM package with the popular state of the art Libsvm package on several well known datasets. The preliminary results have shown one to two orders of magnitude speed improvement in both training and predicting phases compared to Libsvm using our Tesla server.

Keywords: parallel support vector machine; CUDA; GPU; cross validation

  • [1] Abe S., Support vector machines for pattern classification advances in pattern recognition, Springer-Verlag, London, 2005 Google Scholar

  • [2] Cao L.J., Keerthi S.S., Ong C.J., Zhang J.Q., Periyathamby U., Fu X.J., Lee H.P., Parallel sequential minimal optimization for the training of support vector machines, IEEE T. Neural. Network., 17(4), 1039–1049, 2006 http://dx.doi.org/10.1109/TNN.2006.875989CrossrefGoogle Scholar

  • [3] Catanzaro B., Sundaram N., Keutzer K., Fast support vector machine training and classification on graphics processors, Proceedings of the 25th International Conference on Machine Learning, 104–111, 2008 Google Scholar

  • [4] Collobert R., Bengio S., Bengio Y., A parallel mixture of SVMs for very large scale problems, Neural Comput., 14(5), 1105–1114, 2002 http://dx.doi.org/10.1162/089976602753633402CrossrefGoogle Scholar

  • [5] Cortes C., Vapnik V., Support-vector networks, Mach. Learn., 20(3), 273–297, 1995 Google Scholar

  • [6] Dong J.X., Krzyzak A., Suen C.Y., A fast parallel optimization for training support vector machine, In: Machine Learning and Data Mining in Pattern Recognition, Perner, P. and Rosenfeld, A. (Eds.), Lect. Notes Comput. Sc., 2734, 96–105, 2003 Google Scholar

  • [7] Fan R.E., Chen P.H., Lin C.J., Working set selection using second order information for training support vector machines, J. Mach. Learn. Res., 6, 1889–1918, 2005 Google Scholar

  • [8] Frank A., Asuncion A., UCI machine learning repository, Irvine, CA: University of California, School of Information and Computer Science, 2010 Google Scholar

  • [9] Graf H.P., Cosatto E., Bottou L., Durdanovic I., Vapnik V., Parallel support vector machines: the cascade SVM, In: Adv. Neur. In., Weiss, T., Schölkopf, B. and Platt, J. (Eds.), MIT Press, Cambridge, MA, USA, 17, 521–528, 2005 Google Scholar

  • [10] Herrero-Lopez S., Williams J.R., Sanchez A., Parallel multiclass classification using SVMs on GPUs, Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, 2–11, 2010 Google Scholar

  • [11] Hillis W.D., Steele Jr. G.L., Data parallel algorithms, Commun. ACM, 29, 1170–1183, 1986 http://dx.doi.org/10.1145/7902.7903CrossrefGoogle Scholar

  • [12] Huang G.B., Mao K.Z., Siew C.K., Huang D.S., Fast modular network implementation for support vector machines, IEEE T. Neural. Network., 16(6), 1651–1663, 2005 http://dx.doi.org/10.1109/TNN.2005.857952CrossrefGoogle Scholar

  • [13] Huang T.M., Kecman V., Kopriva I., Iterative single data algorithm for kernel machines from huge data sets: theory and performance, Stud. Comp. Intell., 17, 61–95, 2006 http://dx.doi.org/10.1007/3-540-31689-2_3CrossrefGoogle Scholar

  • [14] Hull J., A database for handwritten text recognition research, IEEE T. Pattern Anal., 16(5), 550–554, 1994 http://dx.doi.org/10.1109/34.291440CrossrefGoogle Scholar

  • [15] Joachims T., Making large-scale support vector machine learning practical, Schölkopf, B. Burges, C.J.C. and Smola, A.J. (Eds.), 169–184, MIT Press, Cambridge, MA, USA, 1999 Google Scholar

  • [16] Keerthi S.S., Shevade S.K., Bhattacharyya C., Murthy K.R.K., Improvements to Platt’s SMO algorithm for SVM classifier design, Neural Comput., 13(3), 637–649, 2001 http://dx.doi.org/10.1162/089976601300014493CrossrefGoogle Scholar

  • [17] Lecun Y., Bottou L., Bengio Y., Haffner P., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86(11), 2278–2324, 1998 http://dx.doi.org/10.1109/5.726791CrossrefGoogle Scholar

  • [18] Li Q., Kecman V., Salman R., A chunking method for Euclidean distance matrix calculation on large dataset using multi-GPU, In: 2010 9th International Conference on Machine Learning and Applications (ICMLA), Draghici, S., Khoshgoftaar, T.M., Palade, V., Pedrycz, W., Wani, M.A. and Zhu, X. (Eds.), Washington D.C., US, 208–213, 2010 Google Scholar

  • [19] Li Q., Salman R., Kecman V., An intelligent system for accelerating parallel SVM classification problems on large datasets using GPU, In: 10th International Conference on Intelligent Systems Design and Applications (ISDA), Hassanien, A.E., Abraham, A., Marcelloni, F., Hagras, H., Antonelli, M. and Hong, T.-P. (Eds.), Cairo, Egypt, 1131–1135, 2010 Google Scholar

  • [20] Osuna E., Freund R., Girosi F., An improved training algorithm for support vector machines, In: Neural Networks for Signal Processing, Principe, J., Gile, L., Nelson, M. and Wilson, E. (Eds.), 7th Proceedings of the 1997 IEEE Workshop, 276–285, 1997 Google Scholar

  • [21] Platt J.C., Fast training of support vector machines using sequential minimal optimization, In: Advances in kernel methods — support vector learning, Schölkopf, B. and Burges, C.J.C and Smola, A.J. (Eds.), 185–208, MIT Press, Cambridge, MA, USA, 1999 Google Scholar

  • [22] Vapnik V.N., The nature of statistical learning theory, Springer Verlag, New York, 2000 Google Scholar

  • [23] Zanghirati G., Zanni L., A parallel solver for large quadratic programs in training support vector machines, Parallel Comput., 29(4), 535–551, 2003 http://dx.doi.org/10.1016/S0167-8191(03)00021-8CrossrefGoogle Scholar

About the article

Published Online: 2011-12-27

Published in Print: 2011-12-01

Citation Information: Open Computer Science, Volume 1, Issue 4, Pages 387–405, ISSN (Online) 2299-1093, DOI: https://doi.org/10.2478/s13537-011-0028-7.

Export Citation

© 2011 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Shirin Tavara
ACM Computing Surveys, 2019, Volume 51, Number 6, Page 1
Jan Vanek, Josef Michalek, and Josef Psutka
IEEE Transactions on Parallel and Distributed Systems, 2017, Volume 28, Number 12, Page 3330
Lin Wang, Xuehui Zhu, Bo Yang, Jifeng Guo, Shuangrong Liu, Meihui Li, Jian Zhu, and Ajith Abraham
Engineering Applications of Artificial Intelligence, 2018, Volume 68, Page 53
Pablo Quesada-Barriuso, Francisco Arguello, Dora B. Heras, and Jon Atli Benediktsson
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, Volume 8, Number 6, Page 2962
Mingfeng Jiang, Yaming Wang, Ling Xia, Feng Liu, Shanshan Jiang, and Wenqing Huang
Computers & Mathematics with Applications, 2013, Volume 66, Number 10, Page 1981
Robert Strack, Vojislav Kecman, Beata Strack, and Qi Li
Neurocomputing, 2013, Volume 101, Page 59

Comments (0)

Please log in or register to comment.
Log in