The role of adaptivity in two-level adaptive branch prediction

Seeking high branch-prediction accuracy, architects are making use of the extended history of individual branches. One approach is to divide the branch prediction task into two levels: the first records the results of previous branches; the second makes predictions based upon previous instances in which particular patterns arose. PAg predictors use simple state machines in the second level to provide adaptive predictions. We show that this adaptive level benefits from a high level of hysteresis. We further show that, if the predictions for this second level are fixed rather than adaptive, i.e. a PSg organization, the performance can be superior to that of PAg predictors for short branch histories and close to PAg performance for longer predictors. The patterns of errors among these schemes provide insight into the workings of a wide variety of two-level schemes