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High-Performance Parallel Support Vector Machine Training

In: Parallel Scientific Computing and Optimization

Author

Listed:
  • Kristian Woodsend

    (University of Edinburgh)

  • Jacek Gondzio

    (University of Edinburgh)

Abstract

Support vector machines are a powerful machine learning technology, but the training process involves a dense quadratic optimization problem and is computationally expensive. We show how the problem can be reformulated to become suitable for high-performance parallel computing. In our algorithm, data is pre-processed in parallel to generate an approximate low-rank Cholesky decomposition. Our optimization solver then exploits the problem’s structure to perform many linear algebra operations in parallel, with relatively low data transfer between processors, resulting in excellent parallel efficiency for very-large-scale problems.

Suggested Citation

  • Kristian Woodsend & Jacek Gondzio, 2009. "High-Performance Parallel Support Vector Machine Training," Springer Optimization and Its Applications, in: Parallel Scientific Computing and Optimization, pages 83-92, Springer.
    • Handle: RePEc:spr:spochp:978-0-387-09707-7_7
    DOI: 10.1007/978-0-387-09707-7_7
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