The Significance of Affectors and Affectees Correlations for Branch Prediction

This work investigates the potential of direction-correlations to improve branch prediction. There are two types of direction-correlation: affectors and affectees. This work considers for the first time their implications at a basic level. These correlations are determined based on dataflow graph information and are used to select the subset of global branch history bits used for prediction. If this subset is small then affectors and affectees can be useful to cut down learning time, and reduce aliasing in prediction tables. This paper extends previous work explaining why and how correlation-based predictors work by analyzing the properties of direction-correlations. It also shows that branch history selected using oracle knowledge of direction-correlations improves the accuracy of the limit and realistic conditional branch predictors, that won at the recent branch prediction contest, by up to 30% and 17% respectively. The findings in this paper call for the investigation of predictors that can learn efficiently correlations from long branch history that may be non-consecutive with holes between them.