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Reseach Article

Evaluate the Prediction Accuracy and Confidence Intervals of Intel Nehalem base on Regression Model

by Mahmoud Askari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 3
Year of Publication: 2014
Authors: Mahmoud Askari
10.5120/15987-4927

Mahmoud Askari . Evaluate the Prediction Accuracy and Confidence Intervals of Intel Nehalem base on Regression Model. International Journal of Computer Applications. 92, 3 ( April 2014), 6-9. DOI=10.5120/15987-4927

@article{ 10.5120/15987-4927,
author = { Mahmoud Askari },
title = { Evaluate the Prediction Accuracy and Confidence Intervals of Intel Nehalem base on Regression Model },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 3 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number3/15987-4927/ },
doi = { 10.5120/15987-4927 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:13:18.517344+05:30
%A Mahmoud Askari
%T Evaluate the Prediction Accuracy and Confidence Intervals of Intel Nehalem base on Regression Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 3
%P 6-9
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, has been investigated the predicted accuracy and confidence intervals of performance on multi–core processor i5–460M in various modes of processor included: single, parallel and hyper–threading on SPEC CPU2000 with fixed point operations. The experiments have been performed by Intel–vtune 2013 and have been modeled base on two methods of regression analysis that are Multi–linear and Robust regression along with the accuracy of their predictions. Result of this paper is applicable for producers and users of operating systems and applications due to more accurate models have a lower risk in prediction and thus they can contribute to the better scheduling of applications.

References
  1. Thomadakis, E. M. 2011 The architecture of the Nehalem processor and Nehalem-EP SMP platforms. Texas A&M University.
  2. Hennessy, J. L. and Patterson, D. A. 2011 Computer Architecture: A Quantitive Approach. Morgan & Kaufmann Publishers.
  3. Y. S. Kim, "Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size", Expert System with Application: An International Journal, 2008, No 2. pp. 1227-1234.
  4. Lee, B. C. and Brooks, D. M. 2006. Accurate and efficient regression modeling for microarchitectural performance and power prediction. In Proceeding of the 12th international conference on Architectural support for programming languages and operating systems. pp. 185-194.
  5. Ould, E. , Woodlee, J. , Yount, C. et al. 2008. On the Comparison of Regression Algorithms for Computer Architecture Performance Analysis of Software Applications. In International Symposium on Performance Analysis of Systems and Software. pp. 179-190.
  6. Mousa, H. , Doshi, K. , Sherwood, R. et al. 2010. VrtProf: Vertical Profiling for System Virtualization. In Hawaii International Conference on System Science. pp. 1-10.
  7. Rai, J. K. , Negi, A. , Wankar, R. et al. 2010. Characterizing L2 cache behavior of programs on multi-core processors: Regression models and their transferability. In International Journal of Computer Information Systems and Industrial Management Applications. pp. 212-221.
  8. Joseph, P. J. , Vaswani, K. , Thanzhuthaveetil, M. J. et al. 2006. Construction and Use of Linear Regression Models for Processor Performance Analysis. In Twelfth International Symposium on High-Performance Computer Architecture. pp. 99-108.
  9. Xu, Z. , Sohani, S. , Min, R. et al. "An Analysis of Cache Performance of Multimedia Applications", IEEE Transactions on Computers, 2004, pp. 20-38.
  10. Choi, Y. 2001. Design and Experience: Using the Intel Itanium 2 Processor Performance Monitoring Unit to Implement Feedback Optimizations. In Proceedings of the 34th Annual International Symposium on Microarchitecture. pp. 182-191.
  11. Reinders, J. 2005 Vtune Performance Analyzer Essentials. Intel.
  12. Gfroerer, D. , Tricket, N. , Nakagawa, T. et al. 2003 Understanding IBM eServer pSeries Performance and Sizing. IBM.
  13. Intel Corporation: Intel 64 and IA-32 Architectures Optimization Manual. [Online]: www. intel. com/content/www/us/en/architecture-and-technology/64-ia-32-architectures-optimization-manual. html.
  14. Solka, M. 2011 Exploratory Data Analysis with MATLAB. Chapman & Hall.
  15. Askari, M. , Ivanov, N. N. , "The Dependence of Physical Memory Footprint of Processor on the Applications", Asian Journal of Computer Science and Technology, No 2. Vol. 2. pp. 4-10.
Index Terms

Computer Science
Information Sciences

Keywords

Nehalem Performance SPEC CPU2000 Regression Prediction accuracy Confidence interval