Recent studies have shown significantly improved branch prediction through the use of branch classification. By separating static branches into groups, or classes, with similar dynamic behavior, predictors may be selected that are best suited for each class. Previous methods have classified branches according to taken rate (or bias). We propose a new metric for branch classification: branch transition rate, which is defined as the number of times a branch changes direction between taken and not taken during execution. We show that transition rate is a more appropriate indicator of branch behavior than taken rate for determining predictor performance. When both metrics are combined, an even clearer picture of dynamic branch behavior emerges, in which expected predictor performance for a branch is closely correlated with its combined taken and transition rate class. Using this classification, a small group of branches is identified for which two-level predictors are ineffective.


Computer Sciences

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