Staff View: Parallel execution of distributed SVM using MPI (CoDLib)

Parallel execution of distributed SVM using MPI (CoDLib)

Support Vector Machine (SVM) is an efficient data mining approach for data classification. However, SVM algorithm requires very large memory requirement and computational time to deal with very large dataset. To reduce the computational time during the process of training the SVM, a combination of d...

Full description

Saved in:

Bibliographic Details
Main Authors:	Salleh N.S.M., Suliman A., Ahmad A.R.
Other Authors:	54946009300
Format:	Conference paper
Published:	2023
Subjects:	Distributed SVM LIBSVM Message Passing Interface (MPI) Support Vector Machine (SVM) Cluster computing Data mining Information technology Message passing Parallel architectures Computational time Data classification Data sets Distributed and parallel computing Memory requirements Message Passing Interface Multiple machine Parallel executions Support vector SVM algorithm Training time Support vector machines
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.uniten.dspace-30371
record_format	dspace
spelling	my.uniten.dspace-303712023-12-29T15:47:07Z Parallel execution of distributed SVM using MPI (CoDLib) Salleh N.S.M. Suliman A. Ahmad A.R. 54946009300 25825739000 35589598800 Distributed SVM LIBSVM Message Passing Interface (MPI) Support Vector Machine (SVM) Cluster computing Data mining Information technology Message passing Parallel architectures Computational time Data classification Data sets Distributed and parallel computing Distributed SVM LIBSVM Memory requirements Message Passing Interface Message Passing Interface (MPI) Multiple machine Parallel executions Support vector SVM algorithm Training time Support vector machines Support Vector Machine (SVM) is an efficient data mining approach for data classification. However, SVM algorithm requires very large memory requirement and computational time to deal with very large dataset. To reduce the computational time during the process of training the SVM, a combination of distributed and parallel computing method, CoDLib have been proposed. Instead of using a single machine for parallel computing, multiple machines in a cluster are used. Message Passing Interface (MPI) is used in the communication between machines in the cluster. The original dataset is split and distributed to the respective machines. Experiments results shows a great speed up on the training of the MNIST dataset where training time has been significantly reduced compared with standard LIBSVM without affecting the quality of the SVM. � 2011 IEEE. Final 2023-12-29T07:47:07Z 2023-12-29T07:47:07Z 2011 Conference paper 10.1109/ICIMU.2011.6122723 2-s2.0-84856509269 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856509269&doi=10.1109%2fICIMU.2011.6122723&partnerID=40&md5=470fbba73be6d7a87fca32af9639f744 https://irepository.uniten.edu.my/handle/123456789/30371 6122723 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/
topic	Distributed SVM LIBSVM Message Passing Interface (MPI) Support Vector Machine (SVM) Cluster computing Data mining Information technology Message passing Parallel architectures Computational time Data classification Data sets Distributed and parallel computing Distributed SVM LIBSVM Memory requirements Message Passing Interface Message Passing Interface (MPI) Multiple machine Parallel executions Support vector SVM algorithm Training time Support vector machines
spellingShingle	Distributed SVM LIBSVM Message Passing Interface (MPI) Support Vector Machine (SVM) Cluster computing Data mining Information technology Message passing Parallel architectures Computational time Data classification Data sets Distributed and parallel computing Distributed SVM LIBSVM Memory requirements Message Passing Interface Message Passing Interface (MPI) Multiple machine Parallel executions Support vector SVM algorithm Training time Support vector machines Salleh N.S.M. Suliman A. Ahmad A.R. Parallel execution of distributed SVM using MPI (CoDLib)
description	Support Vector Machine (SVM) is an efficient data mining approach for data classification. However, SVM algorithm requires very large memory requirement and computational time to deal with very large dataset. To reduce the computational time during the process of training the SVM, a combination of distributed and parallel computing method, CoDLib have been proposed. Instead of using a single machine for parallel computing, multiple machines in a cluster are used. Message Passing Interface (MPI) is used in the communication between machines in the cluster. The original dataset is split and distributed to the respective machines. Experiments results shows a great speed up on the training of the MNIST dataset where training time has been significantly reduced compared with standard LIBSVM without affecting the quality of the SVM. � 2011 IEEE.
author2	54946009300
author_facet	54946009300 Salleh N.S.M. Suliman A. Ahmad A.R.
format	Conference paper
author	Salleh N.S.M. Suliman A. Ahmad A.R.
author_sort	Salleh N.S.M.
title	Parallel execution of distributed SVM using MPI (CoDLib)
title_short	Parallel execution of distributed SVM using MPI (CoDLib)
title_full	Parallel execution of distributed SVM using MPI (CoDLib)
title_fullStr	Parallel execution of distributed SVM using MPI (CoDLib)
title_full_unstemmed	Parallel execution of distributed SVM using MPI (CoDLib)
title_sort	parallel execution of distributed svm using mpi (codlib)
publishDate	2023
_version_	1806424478337466368
score	13.222552

Parallel execution of distributed SVM using MPI (CoDLib)

Similar Items