An Analytical Evaluation of Matricizing Least-Square-Errors Curve Fitting to Support High Performance Computation on Large Datasets

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2015 by IJCTT Journal
Volume-30 Number-2
Year of Publication : 2015
Authors : Poorna Banerjee Dasgupta
  10.14445/22312803/IJCTT-V30P120

MLA

Poorna Banerjee Dasgupta "An Analytical Evaluation of Matricizing Least-Square-Errors Curve Fitting to Support High Performance Computation on Large Datasets". International Journal of Computer Trends and Technology (IJCTT) V30(2):113-115, December 2015. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
The procedure of Least Square-Errors curve fitting is extensively used in many computer applications for fitting a polynomial curve of a given degree to approximate a set of data. Although various methodologies exist to carry out curve fitting on data, most of them have shortcomings with respect to efficiency especially where huge datasets are involved. This paper proposes and analyzes a matricized approach to the Least Square-Errors curve fitting with the primary objective of parallelizing the whole algorithm so that high performance efficiency can be achieved when algorithmic execution takes place on colossal datasets.

References
[1] S. S Sastry. "Introductory Methods of Numerical analysis". PHI Publications, 2007.
[2] Giorgia Zucchelli , Marieke van Gere. "Speed up numerical analysis with MATLAB". 2011 Technology Trend Seminar, MathWorks Inc.
[3] Shane Ryoo, Christopher I. Rodrigues, Sara S. Baghsorkhi, Sam S. Stone. "Optimization Principles and Application Performance Evaluation of a Multithreaded GPU Using CUDA". Proceedings of 13th ACM SIGPLAN Symposium on Principles and Practices of Parallel Programming, Pages 73-82, 2008.
[4] Zhe Fan, Feng Qiu, Arie Kaufman, Suzanne Yoakum- Stover. "GPU Cluster for High Performance Computing". ACM / IEEE Supercomputing Conference 2004, November 06-12, Pittsburgh, PA.
[5] Poorna Banerjee, Amit Dave. “GPGPU Based Parallelized Client-Server Framework For Providing High Performance Computation Support”. International Journal of Computer Science & Telecommunications, Vol-4, Issue- 1, 2013.
[6] David B. Kirk, Wen-mei W. Hwu. "Programming Massively Parallel Processors - A Hands-on Approach". Morgan Kaufman Publishers, 2010.
[7] (2002) Least Squares - Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Least_squares.

Keywords
Data Approximation, Least Square- Errors, Parallel Computing, High Performance Computing.