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Parallel execution of SVM training using graphics processing units (SVMTrGPUs)

Parallel computing is a simultaneous use of multiple compute resources, for example, processors to solve complex computational problems. It has been used in high-end computing areas such as pattern recognition, medical diagnosis, national defense, and web search engine. This paper focuses on the imp...

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Main Authors: Salleh, N.S.M., Baharim, M.F.
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Published: 2018
Online Access:http://dspace.uniten.edu.my/jspui/handle/123456789/7252
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spelling my.uniten.dspace-72522018-01-11T09:14:50Z Parallel execution of SVM training using graphics processing units (SVMTrGPUs) Salleh, N.S.M. Baharim, M.F. Parallel computing is a simultaneous use of multiple compute resources, for example, processors to solve complex computational problems. It has been used in high-end computing areas such as pattern recognition, medical diagnosis, national defense, and web search engine. This paper focuses on the implementation of pattern classification technique, Support Vector Machine (SVM) using vector processor approach. We have carried out a performance analysis to benchmark the sequential SVM program against the Graphics Processing Units (GPUs) optimization. The result shows that the parallelization of SVM training duration achieves a better performance than the sequential code speedups by 6.40. © 2015 IEEE. 2018-01-11T09:14:50Z 2018-01-11T09:14:50Z 2016 http://dspace.uniten.edu.my/jspui/handle/123456789/7252
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Parallel computing is a simultaneous use of multiple compute resources, for example, processors to solve complex computational problems. It has been used in high-end computing areas such as pattern recognition, medical diagnosis, national defense, and web search engine. This paper focuses on the implementation of pattern classification technique, Support Vector Machine (SVM) using vector processor approach. We have carried out a performance analysis to benchmark the sequential SVM program against the Graphics Processing Units (GPUs) optimization. The result shows that the parallelization of SVM training duration achieves a better performance than the sequential code speedups by 6.40. © 2015 IEEE.
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author Salleh, N.S.M.
Baharim, M.F.
spellingShingle Salleh, N.S.M.
Baharim, M.F.
Parallel execution of SVM training using graphics processing units (SVMTrGPUs)
author_facet Salleh, N.S.M.
Baharim, M.F.
author_sort Salleh, N.S.M.
title Parallel execution of SVM training using graphics processing units (SVMTrGPUs)
title_short Parallel execution of SVM training using graphics processing units (SVMTrGPUs)
title_full Parallel execution of SVM training using graphics processing units (SVMTrGPUs)
title_fullStr Parallel execution of SVM training using graphics processing units (SVMTrGPUs)
title_full_unstemmed Parallel execution of SVM training using graphics processing units (SVMTrGPUs)
title_sort parallel execution of svm training using graphics processing units (svmtrgpus)
publishDate 2018
url http://dspace.uniten.edu.my/jspui/handle/123456789/7252
_version_ 1644494147356196864
score 13.160551

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