Energy-Efficient Branch Prediction With Compiler-Guided History Stack

Branch prediction is critical in exploring instruction level parallelism for modern processors. Previous aggressive branch predictors generally require significant amount of hardware storage and complexity to pursue high prediction accuracy. This paper proposes the Compiler-guided History Stack (CHS), an energy-efficient compiler-microarchitecture cooperative technique for branch prediction. The key idea is to track very-longdistance branch correlation using a low-cost compiler-guided history stack. It relies on the compiler to identify branch correlation based on two program substructures: loop and procedure, and feed the information to the predictor by inserting guiding instructions. At runtime, the processor dynamically saves and restores the global history using a low-cost history stack structure according to the compiler-guided information. The modification on the global history enables the predictor to track very-longdistance branch correlation and thus improves the prediction accuracy. We show that CHS can be combined with most of existing branch predictors and it is especially effective with small and simple predictors. Our evaluations show that the CHS technique can reduce the average branch mispredictions by 28.7% over gshare predictor, resulting in average performance improvement of 10.4%. Furthermore, it can also improve those aggressive perceptron, OGEHL and TAGE predictors.