Improving the Accuracy of History Based Branch Prediction

نویسندگان

  • David R. Kaeli
  • Philip G. Emma
چکیده

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 buer (BTB). Execution-driven modeling is used to evaluate the improvement in branch prediction accuracy, as well as the reduction in overall program execution. stack, case block table.

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عنوان ژورنال:
  • IEEE Trans. Computers

دوره 46  شماره 

صفحات  -

تاریخ انتشار 1997