Clustered indexing for branch predictors

As a result of resource limitations, state in branch predictors is frequently shared between uncorrelated branches. This interference can significantly limit prediction accuracy. In current predictor designs, the branches sharing prediction information are determined by their branch addresses and thus branch groups are arbitrarily chosen during compilation. This feasibility study explores a more analytic and systematic approach to classify branches into clusters with similar behavioral characteristics. We present several ways to incorporate this cluster information as an additional information source in branch predictors.