document: A GPU-tailored approach for training kernelized SVMs We present a method for efficiently training binary and multiclass kernelized SVMs on a Graphics Processing Unit ( GPU ) . Our methods apply to a broad range of kernels , including the popular Gaus - sian kernel , on datasets as large as the amount of available memory on the graphics card . Our approach is distinguished from earlier work in that it cleanly and efficiently handles sparse datasets through the use of a novel clustering technique . Our optimization algorithm is also specifically designed to take advantage of the graphics hardware . This leads to different algorithmic choices then those preferred in serial implementations . Our easy-to-use library is orders of magnitude faster then existing CPU libraries , and several times faster than prior GPU approaches .
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