Fast support vector machine training and classification on graphics processors

  • Authors:
  • Bryan Catanzaro;Narayanan Sundaram;Kurt Keutzer

  • Affiliations:
  • University of California, Berkeley, CA;University of California, Berkeley, CA;University of California, Berkeley, CA

  • Venue:
  • Proceedings of the 25th international conference on Machine learning
  • Year:
  • 2008

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Abstract

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).