Support Vector Machines on GPU with Sparse Matrix Format
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Ninth International Conference on Machine Learning and Applications (ICMLA), 2010
@article{10.1109/ICMLA.2010.53,
author={Tsung-Kai Lin and Shao-Yi Chien},
title={Support Vector Machines on GPU with Sparse Matrix Format},
journal={Machine Learning and Applications, Fourth International Conference on},
volume={0},
isbn={978-0-7695-4300-0},
year={2010},
pages={313-318},
doi={http://doi.ieeecomputersociety.org/10.1109/ICMLA.2010.53},
publisher={IEEE Computer Society},
address={Los Alamitos, CA, USA}
}
Emerging general-purpose Graphics Processing Unit (GPU) provides a multi-core platform for wide applications, including machine learning algorithms. In this paper, we proposed several techniques to accelerate Support Vector Machines (SVM) on GPUs. Sparse matrix format is introduced into parallel SVM to achieve better performance. Experimental results show that the speedup of 55x-133.8x over LIBSVM can be achieved in training process on NVIDIA GeForce GTX470.
April 8, 2011 by hgpu
