Abstract
In this paper, we present mechanisms that improve the accuracy and performance of history-based branch prediction. By studying the characteristics of the decision structures present in high-level languages, two mechanisms are proposed that reduce the number of wrong predictions made by a branch target buffer (BTB). Execution-driven modeling is used to evaluate the improvement in branch prediction accuracy, as well as the reduction in overall program execution.
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