Performance Enhancement of Tuning Time in GCC Compiler Optimization using Benchmark Applications

Compilers usually provide a larger number of optimization techniques. By applying all these techniques to a given application degrade the program performance, so selecting best set for a given application is not an easy task. Selecting best set of optimization techniques depends upon computer architecture and problem domain. Compilers provide different levels of optimization techniques. Each level is a pre selected group of optimization options and produces good efficiency for most programs. However, they exploit only a portion of the available optimization options. There is still a large potential that an even better efficiency can be gained for each specific source of code by exploiting the rest of the available optimization options. In this paper, it is proposed to study the various benchmark problems, identification of ideal objective functions for different tasks and the ordering of objective function for optimization. In this paper we proposed an automated framework to select the compiler options for a particular problem from a larger set of options. Many previous works consider only limited set of options. For this framework, we implemented compiler optimization selection algorithms such as advanced combined elimination algorithm and machine learning algorithm and evaluated its efficiencies to improve tuning time. We argue that machine learning algorithm works better when compared to advanced combined elimination by showing experimental results with the help of benchmark applications.