Fast Support Vector Machine Training and Classification on Graphics Processors

author: Bryan Catanzaro, Department of Electrical Engineering and Computer Sciences, UC Berkeley
published: Aug. 5, 2008,   recorded: July 2008,   views: 1648
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Description

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, using Platt's 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).

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Download slides icon Download slides: icml08_catanzaro_fsvm_01.pptx (2.1┬áMB)


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