Evaluation of Performance on Open MP Parallel Platform based on Problem Size

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Author(s)

Yajnaseni Dash 1,* Sanjay Kumar 1 V.K. Patle 1

1. School of Studies in Computer Science & IT, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India, 492010

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2016.06.05

Received: 9 Mar. 2016 / Revised: 1 Apr. 2016 / Accepted: 2 May 2016 / Published: 8 Jun. 2016

Index Terms

Open MP, parallel algorithm, matrix multiplication, performance analysis, speed up, efficiency

Abstract

This paper evaluates the performance of matrix multiplication algorithm on dual core 2.0 GHz processor with two threads. A novel methodology was designed to implement this algorithm on Open MP platform by selecting time of execution, speed up and efficiency as performance parameters. Based on the experimental analysis, it was found that a good performance can be achieved by executing the problem in parallel rather than sequential after a certain problem size.

Cite This Paper

Yajnaseni Dash, Sanjay Kumar, V.K. Patle, "Evaluation of Performance on Open MP Parallel Platform based on Problem Size", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.6, pp.35-40, 2016. DOI:10.5815/ijmecs.2016.06.05

Reference

[1]Y. Dash, S. Kumar, V.K Patle. , “A Survey on Serial and Parallel Optimization Techniques Applicable for Matrix Multiplication Algorithm,” American Journal of Computer Science and Engineering Survey (AJCSES), vol 3, issue 1, Feb 2015.
[2]K. Hwang, N. Jotwani, “Advanced Computer Architecture”, Tata McGraw Hill education Private Limited, Second Edition, 2001.
[3]J. Ali, R.Z. Khan, “Performance Analysis of Matrix Multiplication Algorithms Using MPI,” International Journal of Computer Science and Information Technologies (IJCSIT), vol. 3 (1), pp. 3103 -3106, 2012.
[4]R. Patel, S. Kumar, “Effect of problem size on parallelism”, Proc. of 2nd International conference on Biomedical Engineering & Assistive Technologies at NIT Jalandhar, pp. 418-420, 2012.
[5]S.K. Sharma, K. Gupta, “Performance Analysis of Parallel Algorithms on Multi-core System using OpenMP Programming Approaches”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), vol.2, No.5, 2012.
[6]S. Kathavate, N.K. Srinath, “Efficiency of Parallel Algorithms on Multi Core Systems Using OpenMP,” International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue 10, pp. 8237-8241, October 2014..
[7]P. Kulkarni, S. Pathare, “Performance analysis of parallel algorithm over sequential using OpenMP,” IOSR Journal of Computer Engineering (IOSR-JCE), Volume 16, Issue 2, pp. 58-62.
[8]P. Graham, “OpenMP: A Parallel Programming Model for Shared Memory Architectures,” Edinburgh Parallel Computing Centre, The University of Edinburgh, Version 1.1, March 1999.
[9]M. J. Quinn, “ Parallel Programming in C with MPI and OpenMP,” McGraw Hill, 1st edition, 2004
[10]Uusheikh, “Begin Parallel Programming with OpenMP” CPOL Malaysia 5 Jun 2007.
[11]OpenMP: https://computing.llnl.gov/tutorials/openMP/.
[12]K. Asanovic, R. Bodik, B. Catanzaro et al., “The landscape of parallel computing research: A view from Berkeley,” Technical Report UCB/EECS-2006-183, EECS Department, University of California, Berkeley, December 2006.
[13]K. Thouti, S.R. Sathe, “Comparison of OpenMP and OpenCL Parallel processing Technologies”, UACSA, vol. 3, issue 4, pp. 56-61, 2012.
[14]R. Choy, A. Edelman, “Parallel MATLAB: Doing it Right,” Computer Science AI Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
[15]L.Chai, “Understanding the Impact of Multi-Core Architecture in Cluster Computing: A Case Study with Intel Dual-Core System,” Seventh IEEE International Symposium on Cluster Computing and the Grid, pp 471-478, 14-17 May 2007.
[16]D. Geer, “Chip makers turn to multicore processors,” Computer, IEEE Explore, Vol.38, May 2005, pp 11-13.