Fast Support Vector Machine Training and Classification on Graphics Processors
published: Aug. 5, 2008, recorded: July 2008, views: 9886
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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).
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !