Parallel one-versus-rest SVM training on the GPU

Sander Dieleman, Aaron van den Oord, Benjamin Schrauwen
Electronics and Information Systems (ELIS), Ghent University, Ghent, Belgium
Ghent University, 2012


   title={Parallel one-versus-rest SVM training on the GPU},

   author={Dieleman, S. and van den Oord, A. and Schrauwen, B.},



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Linear SVMs are a popular choice of binary classifier. It is often necessary to train many different classifiers on a multiclass dataset in a one-versus-rest fashion, and this for several values of the regularization constant. We propose to harness GPU parallelism by training as many classifiers as possible at the same time. We optimize the primal L2-loss SVM objective using the conjugate gradient method, with an adapted backtracking line search strategy. We compared our approach to liblinear and achieved speedups of up to 17 times on our available hardware.
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