TITLE

Comparison of conjugate gradient method and jacobi method algorithm on MapReduce framework

TYPE

Journal Article

Abstract

As the volume of data continues to grow across many areas of sc ience, parallel computing is a solution to the scaling problem many applications fac e. The goal of a parallel program is to enable the execution of larger proble ms and to reduce the execution time compared to sequential programs. Among parallel computing frameworks, MapReduce is a framework that enables parallel pr ocessing of data on collections of commodity computing nodes without the need to handle the complexities of implementing a parallel program. This paper pre sents implementations of the parallel Jacobi and Conjugate Gradient m ethods using MapReduce. A performance analysis shows that MapReduce can spe ed up the Jacobi method over sequential processing for dense matrices wi th dimension ≥ 14,000.

Citation

Kacamarga M. F, Pardamean B and Baurley J. (2014). Comparison of conjugate gradient method and jacobi method algorithm on MapReduce framework. Applied Mathematical Sciences, 8 (17), 837-849.

Keywords

Parallel Computing, MapReduce, Jacobi Method, Conjugate Gr adient, Iterative Method

Published On

Applied Mathematical Sciences

Author

  • Bens Pardamean
  • James Baurley

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