An Energy-Efficient Branch Prediction with Grouped Global History

Abstract

Branch prediction has been playing an increasingly important role in improving the performance and energy efficiency for modern microprocessors. The state-of-the-art branch predictors, such as the perceptron and TAGE predictors, leverage novel prediction algorithms to explore longer branch history for higher prediction accuracy. We observe that as the branch history is becoming longer, the efficiency of global history is degraded by the interference of different branch instructions. In order to mitigate the excessive influence of the branch history interference, we propose the Grouped Global History (GGH) based branch predictor, a lightweight yet efficient branch predictor. Unlike existing branch predictors that make use of a unified global history for prediction, GGH divides the global history into a set of subgroups such that the interference resulted by frequently executed branch instructions could be restricted. With subgroups of global history, GGH also enables us to track even longer effective branch correlation without introducing hardware storage overhead. Our experimental results based on SPEC CINT 2006 workloads demonstrate that our approach can significantly reduce the branch mispredictions per kilo instructions (MPKI) by 4.76 over the baseline perceptron predictor, with a simple control logic extension.