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Evaluating automatically parallelized versions of the support vector machine

  • Valeriu Codreanu
    ,
  • Bob Dröge
    ,
  • David Williams
    ,
  • Burhan Yasar
    ,
  • Po Yang
    ,
  • Baoquan Liu
Research Output: Contribution to journal Article Peer-review

Abstract

The support vector machine (SVM) is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the computational complexity of the kernelized version of the algorithm grows quadratically with the number of training examples. To tackle this high computational complexity, we have developed a directive-based approach that converts a gradient-ascent based training algorithm for the CPU to an efficient graphics processing unit (GPU) implementation. We compare our GPU-based SVM training algorithm to the standard LibSVM CPU implementation, a highly optimized GPU-LibSVM implementation, as well as to a directive-based OpenACC implementation. The results on different handwritten digit classification datasets demonstrate an important speed-up for the current approach when compared to the CPU and OpenACC versions. Furthermore, our solution is almost as fast and sometimes even faster than the highly optimized CUBLAS-based GPU-LibSVM implementation, without sacrificing the algorithm's accuracy.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 2274-2294

Journal (Volume, Issue Number)

Concurrency and Computation: Practice and Experience (Volume 28, Issue 7)

Publication milestones

  • Published - 09/10/2014

Publication status

Published - 09/10/2014

ISSN

1532-0626

External Publication IDs

  • handle.net: 10547/624418
  • Scopus: 84910617842

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