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Hybrid Indirect Branch Predictors

  • Karel Driesen
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 596)

Abstract

As discussed in the previous section, predictors with short path lengths adapt more quickly when the program goes through a phase change because it doesn’t take much time for a short history to fill up. Longer path length predictors are capable of detecting longer-term correlations but take longer to adapt and suffer more from table size limitations because a larger pattern set is mapped to the same number of targets. In this section we combine basic predictors into a hybrid predictor in order to obtain the advantages of both.

Keywords

Path Length Short Path Length Table Entry Table Size Component Predictor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Karel Driesen
    • 1
  1. 1.McGill UniversityCanada

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