Traditional branch predictors exploit correlations between pattern history and branch outcome to predict branches, but there is a stronger and more natural correlation between path history and branch outcome. I exploit this correlation with piecewise linear branch prediction, an idealized branch predictor that develops a set of linear functions, one for each program path to the branch to be predicted, that separate predicted taken from predicted not taken branches. Taken together, all of these

more »

... inear functions form a piecewise linear decision surface. Disregarding implementation concerns modulo a 64.25 kilobit hardware budget, I present this idealized branch predictor for the first Championship Branch Predictor competition. I describe the idea of the algorithm and as well as tricks used to squeeze it into 64.25 kilobits while maintaining good accuracy.