Inadequacy of Genetic Algorithm as Scheduling Optimization to Enhance the Performance of Source Code for Real Time System
M R Dhande, R A Tiwari, R R Tuteja, R V Wasu and V K Patil. Article: Inadequacy of Genetic Algorithm as Scheduling Optimization to Enhance the Performance of Source Code for Real Time System. IJCA Proceedings on International Conference on Benchmarks in Engineering Science and Technology 2012 ICBEST(1):10-11, October 2012. Full text available. BibTeX
@article{key:article,
author = {M R Dhande and R A Tiwari and R R Tuteja and R V Wasu and V K Patil},
title = {Article: Inadequacy of Genetic Algorithm as Scheduling Optimization to Enhance the Performance of Source Code for Real Time System},
journal = {IJCA Proceedings on International Conference on Benchmarks in Engineering Science and Technology 2012},
year = {2012},
volume = {ICBEST},
number = {1},
pages = {10-11},
month = {October},
note = {Full text available}
}
Abstract
Without any optimization option, the compiler's goal is to reduce the cost of compilation and to make debugging produce the expected results. Statements are independent: if you stop the program with a breakpoint between statements, you can then assign a new value to any variable or change the program counter to any other statement in the function and get exactly the results you would expect from the source code. Turning on optimization flags makes the compiler attempt to improve the performance and/or code size at the expense of compilation time and possibly the ability to debug the program. The compiler performs optimization based on the knowledge it has of the program. To optimizing performance of program for real time system doesn't always mean what we might think. It's not just a matter of outright speed; sometimes it's about tuning the code and data so that it fits into a small memory footprint. It is hard-pressed to find a programmer that does not want to make programs that run faster, regardless of the platform. Real time system programmers are not exception for that some take an almost over-enthusiastic approach to the job of optimizing their code for performance.
References
- GNU Compiler Collection http://gcc. gnu. org/onlinedocs/gcc/Gcov. html
- Keith D. Cooper, Philip J. Schielke, and Devika Subramanian , "Optimizing for Reduced Code Space using Genetic Algorithms", Rice University Houston,Texas, USA.
- Goldberg D. E. , Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing, Boston, MA, USA, 1989.