Using Hybrid Branch Predictors to Improve Branch Prediction Accuracy

Pipeline stalls due to conditional branches represent one of the most significant impediments to realizing the performance potential of deeply pipelined, supers calar processors. Many branch predictors have been proposed to help aileviate this problem, including the Two-Level Adaptive Branch Predictor, and more recently, two-component hybrid branch predictors. In a less idealized environment, such as a time-shared system, code of interest involves context switches. Con-text switches, even at fairly large intervals, can seriously degrade the performance of many of the most accurate branch prediction schemes. In this paper, we introduce a new hybrid branch predictor and show that it is more accurate (for a given cost) than any previously published scheme, especially if the branch histories are periodically jlushed due to the presence of contezt switches