Correlation and Aliasing in Dynamic Branch Predictors

Previous branch prediction studies have relied primarily upon the SPECint89 and SPECint92 benchmarks for evaluation. Most of these benchmarks exercise a very small amount of code. As a consequence, the resources required by these schemes for accurate predictions of larger programs has not been clear. Moreover, many of these studies have simulated a very limited number of configurations. Here we report on simulations of a variety of branch prediction schemes using a set of relatively large benchmark programs that we believe to be more representative of likely system workloads. We have examined the sensitivity of these prediction schemes to variation in workload, in resources, and in design and configuration. We show that for predictors with small available resources, aliasing between distinct branches can have the dominant influence on prediction accuracy. As a result, the simple scheme of selecting a predictor using the branch address can be more effective than more elaborate correlating branch predictors. With larger resources, the designer may have more latitudes, but we show that past studies have sometimes overlooked the importance of correct allocation of these resources.