Improving the Accuracy of Indirect Branch Predication via Branch Classification

Providing accurate branch prediction is critical to effectively exploit superscalar execution. While most modern processors employ speculative execution to overcome the branch hazard problem, some number of the instructions will have to be discarded when a branch misprediction occurs. Even though existing branch prediction schemes can accurately predict the direction of conditional branches, they still have difficulty predicting the correct targets of indirect branches. This type of branch occurs more frequently in languages used in Object-Oriented Programming (OOP), as well as in Dynamically-Linked Libraries (DLLs), two programming environment rapidly increasing in popularity. In this paper, we investigate the performance of several predictors used to predict the targets of indirect branches. We present indirect branch classification as a mechanism to characterize the behavior of indirect branches. We then propose hybrid predictors utilizing static and profile-guided branch c..