Enhancing branch prediction using software evolution

Software evolution has been extensively studied in the past decade for various properties and interesting patterns. In this work, we study the effect of evolution on branch prediction techniques. Typically for any program, at the hardware level, all dynamic branch prediction strategies learn the branch behaviors at run time and later re-use them to predict the direction of future branches. The duration of the learning curve depends heavily on the kind of technique used and also the complexity of the program at hand. We propose that saving the branch outcome profile from an older version and reusing it in a new version can significantly reduce this overhead and improve performance. In this paper, we discuss the effect of program evolution on the performance of branch prediction, study how the individual branches get affected during evolution, suggest a new method to reuse the branch behavior information from a previous version, and share our results on various software repositories. Preliminary results indicate our intuitions are well justified.