Support Vector Machines on GPU with Sparse Matrix Format

Authors:
Tsung-Kai Lin;Shao-Yi Chien
Affiliations:
-;-
Venue:
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Year:
2010

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Cited 1

SVM with CUDA accelerated kernels for big sparse problems

ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I

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Abstract

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.