Two branch predictor schemes for reduction of misprediction rate in conditions of frequent context switches

Abstract

Branch misprediction is one of the important causes of performance degradation in superpipelined and superscalar processors. Most of the existing branch predictors, based on the exploiting of branch history, suffer from prediction accuracy decrease caused by frequent context switches. The goal of this research is to reduce misprediction rate (MPR) when the context switches are frequent, and not to increase the MPR when the context switches are relatively rare. We propose two independent, but closely related modifications of global adaptive prediction mechanisms: first, to flush only the branch history register (BHR) at context switch, instead of reinitialization of the whole predictor, and second, to use two separated BHRs, one for user and one for kernel branches, instead of one global history register. We have evaluated the ideas by measurements on real traces from IBS (Instruction Benchmark Set), and have shown that both modifications reduce MPR at negligible hardware cost.