Improving the accuracy of static branch prediction using branch correlation

Abstract

Recent work in history-based branch prediction uses novel hardware structures to capture branch correlation and increase branch prediction accuracy. We present a profile-based code transformation that exploits branch correlation to improve the accuracy of static branch prediction schemes. Our general method encodes branch history information in the program counter through the duplication and placement of program basic blocks. For correlation histories of eight branches, our experimental results achieve up to a 14.7% improvement in prediction accuracy over conventional profile-based prediction without any increase in the dynamic instruction count of our benchmark applications. In the majority of these applications, code duplication increases code size by less than 30%. For the few applications with code segments that exhibit exponential branching paths and no branch correlation, simple compile-time heuristics can eliminate these branches as code-transformation candidates.