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Performance Comparison of Parallel Execution Using GPU and CPU in SVM Training Session

Application programming interfaces (API); Array processing; Artificial intelligence; Benchmarking; Computer graphics; Image coding; Learning systems; Multiprocessing systems; Parallel processing systems; Program processors; Vectors; CUDA; Graphics processing units; Machine learning approaches; OpenM...

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Main Authors: Salleh N.S.M., Baharim M.F.
Other Authors: 54946009300
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-227222023-05-29T14:11:50Z Performance Comparison of Parallel Execution Using GPU and CPU in SVM Training Session Salleh N.S.M. Baharim M.F. 54946009300 57190275900 Application programming interfaces (API); Array processing; Artificial intelligence; Benchmarking; Computer graphics; Image coding; Learning systems; Multiprocessing systems; Parallel processing systems; Program processors; Vectors; CUDA; Graphics processing units; Machine learning approaches; OpenMP; Performance analysis; Performance comparison; Symmetric multi-processors; UCW dataset; Support vector machines Support Vector Machine (SVM) is a machine learning approach, which is used in a growing number of applications. SVM is a useful technique for data classification. This machine learning approach has been optimized using two (2) parallel computing approaches. This includes symmetric multiprocessor (SMP) approach and vector processor approach. The outcome performance of the implementation of symmetric multiprocessor approach and vector processor approach on SVM training session is the focus of this paper. We have carried out a performance analysis to benchmark between Central Processing Unit (CPU) and Graphics Processing Units (GPUs) optimization. The result shows the GPU optimization of SVM training duration achieves better performance than the CPU optimized program by 3.11 of speedup. � 2015 IEEE. Final 2023-05-29T06:11:50Z 2023-05-29T06:11:50Z 2016 Conference Paper 10.1109/ACSAT.2015.31 2-s2.0-84979074755 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979074755&doi=10.1109%2fACSAT.2015.31&partnerID=40&md5=ea598a0d5a870388369d83196bc16d3c https://irepository.uniten.edu.my/handle/123456789/22722 7478746 214 217 Institute of Electrical and Electronics Engineers Inc. Scopus
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 Application programming interfaces (API); Array processing; Artificial intelligence; Benchmarking; Computer graphics; Image coding; Learning systems; Multiprocessing systems; Parallel processing systems; Program processors; Vectors; CUDA; Graphics processing units; Machine learning approaches; OpenMP; Performance analysis; Performance comparison; Symmetric multi-processors; UCW dataset; Support vector machines
author2 54946009300
author_facet 54946009300
Salleh N.S.M.
Baharim M.F.
format Conference Paper
author Salleh N.S.M.
Baharim M.F.
spellingShingle Salleh N.S.M.
Baharim M.F.
Performance Comparison of Parallel Execution Using GPU and CPU in SVM Training Session
author_sort Salleh N.S.M.
title Performance Comparison of Parallel Execution Using GPU and CPU in SVM Training Session
title_short Performance Comparison of Parallel Execution Using GPU and CPU in SVM Training Session
title_full Performance Comparison of Parallel Execution Using GPU and CPU in SVM Training Session
title_fullStr Performance Comparison of Parallel Execution Using GPU and CPU in SVM Training Session
title_full_unstemmed Performance Comparison of Parallel Execution Using GPU and CPU in SVM Training Session
title_sort performance comparison of parallel execution using gpu and cpu in svm training session
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806428073883598848
score 13.222552

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