The YAGS branch prediction scheme

The importance of an accurate branch prediction mechanism has been well documented. Since the introduction of gshare and the observation that aliasing in the PHT is a major factor in reducing prediction accuracy, several schemes have been proposed to reduce aliasing in the PHT. All these schemes are aimed at maximizing the prediction accuracy with the fewest resources. In this paper we introduce Yet Another Global Scheme (YAGS)-a new scheme to reduce the aliasing in the PHT-that combines the strong points of several previous schemes. YAGS introduces tags into the PHT that allows it to be reduced without sacrificing key branch outcome information. The size reduction more than offsets the cost of the tags. Our experimental results show that YAGS gives better prediction accuracy for the SPEC95 benchmark suite than several leading prediction schemes, for the same cost. It also performs better than the other schemes in the presence of a context switch. Finally, YAGS displays good results for the go benchmark, which is of special interest since it has a large number of static branches and reflects situations where aliasing in the PHT can be a problem.