An Enhanced Two-Level Adaptive Multiple Branch Prediction for Superscalar Processors

This paper proposes an enhanced method of multiple branch prediction using a per-primary branch history table. This scheme improves the previous ones based on a single global branch history register, by reducing interferences among histories of diierent branches caused by sharing a single register. This scheme also allows the prediction of a branch not to aaect the prediction of other branches that are predicted in the same cycle, thus allowing independent and parallel prediction of multiple branches. Our experimental results indicate that these features help to achieve higher prediction accuracy than that of the previous global history scheme (which is already high) with the less hardware cost (i.e., 96.1% vs. 95.1% for integer code and 95.7% vs. 94.9% for oating-point code including nasa7, for a given hardware budget of 128K bits). Moreover, the increased prediction accuracy causes better fetch bandwidth of a superscalar machine (i.e., 7.1 vs. 6.9 instructions per clock cycle for integer code and 11.0 vs. 10.9 instructions per cycle for oating-point code).