Fast support vector machine training and classification on graphics processors
Electrical Engineering and Computer Sciences, University of California at Berkeley
In ICML ’08: Proceedings of the 25th international conference on Machine learning (2008), pp. 104-111
Recent developments in programmable, highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algorithms. We describe a solver for Support Vector Machine training running on a GPU, using the Sequential Minimal Optimization algorithm and an adaptive first and second order working set selection heuristic, which achieves speedups of 9-35x over LIBSVM running on a traditional processor. We also present a GPU-based system for SVM classification which achieves speedups of 81-138x over LIBSVM (5-24x over our own CPU based SVM classifier).
December 7, 2010 by hgpu
