Unbiased Branches: An Open Problem

Abstract

The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches on prediction accuracy. In this paper we evaluate the impact of unbiased branches on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Our simulation results, with the SPEC2000 integer benchmark suite, are interesting even though they show that unbiased branches still restrict the ceiling of branch prediction and therefore accurately predicting unbiased branches remains an open problem.