NDLI: Comparison of Conjugate Gradient Method and Jacobi Method Algorithm on MapReduce Framework

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Comparison of Conjugate Gradient Method and Jacobi Method Algorithm on MapReduce Framework

Content Provider	Semantic Scholar
Author	Kacamarga, Muhamad Fitra Pardamean, Bens
Copyright Year	2014
Abstract	As the volume of data continues to grow across many areas of science, parallel computing is a solution to the scaling problem many applications face. The goal of a parallel program is to enable the execution of larger problems and to reduce the execution time compared to sequential programs. Among parallel computing frameworks, MapReduce is a framework that enables parallel processing of data on collections of commodity computing nodes without the need to handle the complexities of implementing a parallel program. This paper presents implementations of the parallel Jacobi and Conjugate Gradient methods using MapReduce. A performance analysis shows that MapReduce can speed up the Jacobi method over sequential processing for dense matrices with dimension ≥ 14,000.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.m-hikari.com/ams/ams-2014/ams-17-20-2014/pardameanAMS17-20-2014.pdf
Language	English
Access Restriction	Open
Subject Keyword	Algorithm Amazon Elastic Compute Cloud (EC2) Amazona Apache Hadoop Apache Hama CPU (central processing unit of computer system) Central processing unit Collections (publication) Commodity computing Computation (action) Conjugate gradient method Fault tolerance Hamilton Anxiety Rating Scale Questionnaire Immunostimulating conjugate (antigen) Iteration Iterative method Jacobi method Large Linear system MapReduce Parallel computing Run time (program lifecycle phase) Sparse matrix System of linear equations Twister
Content Type	Text
Resource Type	Article

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