Finding the Best Compiler Optimization Option Set Rapidly via Machine Learning

For a compiler to find a set of options that result in an optimal program execution is a NP-hard problem, especially when there are a lot of options to choose. For a large program, finding the optimal set of compiler options can take an enormous amount of time. Thus, methods have been proposed to shorten the searching time by reducing the complexity of the problem. This thesis proposes a different approach to solve the problem via machine learning techniques. The goal of this approach is to give an acceptable answer in a short time. Using our approach we achieve a 5% improvement over the -O3 of GCC on a storage system and 7% on the MediaBench II. Moreover, the time our approach costs to get acceptable answer is only 1/100 of the time that CE Algorithm takes on a storage system.