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Reseach Article

Evaluation of Tuning and Normalized Tuning Time using an Effective and Automated Framework pertained to Benchmark Applications

by J.andrews, T.sasikala
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 9
Year of Publication: 2013
Authors: J.andrews, T.sasikala
10.5120/11874-7679

J.andrews, T.sasikala . Evaluation of Tuning and Normalized Tuning Time using an Effective and Automated Framework pertained to Benchmark Applications. International Journal of Computer Applications. 69, 9 ( May 2013), 48-53. DOI=10.5120/11874-7679

@article{ 10.5120/11874-7679,
author = { J.andrews, T.sasikala },
title = { Evaluation of Tuning and Normalized Tuning Time using an Effective and Automated Framework pertained to Benchmark Applications },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 9 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 48-53 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number9/11874-7679/ },
doi = { 10.5120/11874-7679 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:29:50.582800+05:30
%A J.andrews
%A T.sasikala
%T Evaluation of Tuning and Normalized Tuning Time using an Effective and Automated Framework pertained to Benchmark Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 9
%P 48-53
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Tuning compiler optimization for a given application of particular computer architecture is not an easy task, because modern computer architecture reaches higher levels of compiler optimization. These modern compilers usually provide a larger number of optimization techniques. By applying all these techniques to a given application degrade the program performance as well as more time consuming. The performance of the program measured by time and space depends on the machine architecture, problem domain and the settings of the compiler. The brute-force method of trying all possible combinations would be infeasible, as it's complexity O(2n ) even for "n" on-off optimizations. Even though many existing techniques are available to search the space of compiler options to find optimal settings, most of those approaches can be expensive and time consuming. In this paper, machine learning algorithm has been modified and used to reduce the complexity of selecting suitable compiler options for programs running on a specific hardware platform. This machine learning algorithm is compared with advanced combined elimination strategy to determine tuning time and normalized tuning time. The experiment is conducted on core i7 processor. These algorithms are tested with different mibench benchmark applications. It has been observed that performance achieved by a machine learning algorithm is better than advanced combined elimination strategy algorithm.

References
  1. GCC online documentation http://gcc. gnu. org/onlinedocs/
  2. Grigori Fursin,Oliver Temam et al 2011. Milepost GCC:machine learning enabled self tuning compiler,,International journal on parallel programming,Springer,vol. 39,pp. 296-327.
  3. shih-Hao Hung, Chia-Heng Tu, Huang-Sen Lin,and Chi-Meng Chen2009, An Automatic Compiler Optimizations Selection Framework for Embedded Applications. In proceddings . of international . conference on Embedded Software and Systems,pp. 381-387.
  4. Z. Pan and R. Eigenmann2006. Fast and effective orchestration of compiler optimizations for automatic performance tuning, In proceddings . of the international . symposium on code generation and optimization, pp. 319-332.
  5. Agakov. F. Bonilla. E Cavzos and Williams 2006. Using machine learning to focus iterative optimization, In proceddings of the Intenational Symposium on code generation and optimization,pp. 295-305.
  6. Chow. K and Wu. Y(2001),'Feed back directed selection and characterization of compiler optimization.
  7. Mathew R. Guthaus,Jeffry S. Ringberg et al. 2001. Mibench:A free commercially representative embedded benchmark suite,Workload characterization,2001. WWC-4. IEEE International. workshop pp. 3-14.
  8. William Von Hagen,2006. The definitive guide to GCC',Apress publications ,second edition,ISBN:1590595858, pp. 101-117.
  9. Brian J. Gough,2005. An introduction to GCC for gcc and g++ ',Revised edition,ISBN:0954161793,pp 45-53.
  10. www. networktheroy. co. uk
  11. www. sourceforge. net
  12. LLVM:the low level virtual machine compiler infrastructure. http://llvm. org
  13. Open64:an open source optimizing compiler. www. open64. net
  14. ROSE:www. rosecompiler. org.
  15. Intel VTune Performance Analyzer http://www. intel. com/cd/software/products/asmona/eng/vtune/239144. html.
Index Terms

Computer Science
Information Sciences

Keywords

Machine learning Program features Compiler optimization Mibench