Performance issues in correlated branch prediction schemes

Accurate static branch prediction is the key to many techniques for exposing, enhancing, and exploiting Instruction Level Parallelism (ILP). The initial work on static correlated branch prediction (SCBP) demonstrated improvements in branch prediction accuracy, but did not address overall performance. In particular, SCBP expands the size of executable programs, which negatively affects the performance of the instruction memory hierarchy. Using the profile information available under SCBP, we can minimize these negative performance effects through the application of code layout and branch alignment techniques. We evaluate the performance effect of SCBP and these profile-driven optimizations on instruction cache misses, branch mispredictions, and branch misfetches for a number of recent processor implementations. We find that SCBP improves performance over (traditional) per-branch static profile prediction. We also find that SCBP improves the performance benefits gained from branch alignment. As expected, SCBP gives larger benefits on machine organizations with high mispredict/misfetch penalties and low cache miss penalties. Finally, we find that the application of profile-driven code layout and branch alignment techniques (without SCBP) can improve the performance of the dynamic correlated branch prediction techniques.Engineering and Applied Science