Parallel execution of distributed SVM using MPI (CoDLib)

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Salleh N.S.M.
Suliman A.
Ahmad A.R.
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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.
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