Using graph-based program characterization for predictive modeling

Abstract

Using machine learning has proven effective at choosing the right set of optimizations for a particular program. For machine learning techniques to be most effective, compiler writers have to develop expressive means of characterizing the program being optimized. The current state-of-the-art techniques for characterizing programs include using a fixed-length feature vector of either source code features extracted during compile time or performance counters collected when running the program. For the problem of identifying optimizations to apply, models constructed using performance counter characterizations of a program have been shown to outperform models constructed using source code features. However, collecting performance counters requires running the program multiple times, and this "dynamic" method of characterizing programs can be specific to inputs of the program. It would be preferable to have a method of characterizing programs that is as expressive as performance counter features, but that is "static" like source code features and therefore does not require running the program.