Performance Comparison of OpenMP, MPI, and MapReduce in Practical Problems

With problem size and complexity increasing, several parallel and distributed programming models and frameworks have been developed to efficiently handle such problems. This paper briefly reviews the parallel computing models and describes three widely recognized parallel programming frameworks: Ope...

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Bibliographic Details
Published in:Advances in Multimedia Vol. 2015; no. 2015; pp. 132 - 140
Main Authors: Kang, Sol Ji, Lee, Keon Myung, Lee, Sang Yeon
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Limiteds 01.01.2015
Hindawi Publishing Corporation
John Wiley & Sons, Inc
Wiley
Subjects:
Comparative analysis
Computer programming
ISSN:1687-5680, 1687-5699
Online Access:Get full text
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Summary:With problem size and complexity increasing, several parallel and distributed programming models and frameworks have been developed to efficiently handle such problems. This paper briefly reviews the parallel computing models and describes three widely recognized parallel programming frameworks: OpenMP, MPI, and MapReduce. OpenMP is the de facto standard for parallel programming on shared memory systems. MPI is the de facto industry standard for distributed memory systems. MapReduce framework has become the de facto standard for large scale data-intensive applications. Qualitative pros and cons of each framework are known, but quantitative performance indexes help get a good picture of which framework to use for the applications. As benchmark problems to compare those frameworks, two problems are chosen: all-pairs-shortest-path problem and data join problem. This paper presents the parallel programs for the problems implemented on the three frameworks, respectively. It shows the experiment results on a cluster of computers. It also discusses which is the right tool for the jobs by analyzing the characteristics and performance of the paradigms.
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ISSN:1687-5680
1687-5699
DOI:10.1155/2015/575687