Mitigating the compiler optimization phase-ordering problem using machine learning

Abstract

Today's compilers have a plethora of optimizations to choose from, and the correct choice of optimizations can have a significant impact on the performance of the code being optimized. Furthermore, choosing the correct order in which to apply those optimizations has been a long standing problem in compilation research. Each of these optimizations interacts with the code and in turn with all other optimizations in complicated ways. Traditional compilers typically apply the same set of optimization in a fixed order to all functions in a program, without regard the code being optimized.