摘要：The literature presents several auto-tunning systems for compiler optimizations, which employ a variety of techniques; however, most systems do not explore the premise that a large amount of program runtime is spent by hot functions which are the portions at which compiler optimizations will provide the greatest benefit. In this paper, we propose Pinhão , an auto-tunning system for compiler optimizations that uses hot functions to guide the process of exploring which compiler optimizations should be enabled during target code generation. Pinhão employs a hybrid technique - a machine learning technique, as well as an iterative compilation technique - to find an effective compiler optimization sequence that fits the characteristics of the unseen program. We implemented Pinhão as a LLVM tool, and the experimental results indicate that Pinhão finds effective sequences evaluating a few points in the search space. Furthermore, Pinh~ao outperforms the well-engineered compiler optimization levels, as well as other techniques.
