Understanding Prediction Limits Through Unbiased Branches

  • Lucian Vintan
  • Arpad Gellert
  • Adrian Florea
  • Marius Oancea
  • Colin Egan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4186)


The majority of currently available branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches which are difficult-to-predict. In this paper, we quantify and evaluate the impact of unbiased branches and show that any gain in prediction accuracy is proportional to the frequency of unbiased branches. By using the SPECcpu2000 integer benchmarks we show that there are a significant proportion of unbiased branches which severely impact on prediction accuracy (averaging between 6% and 24% depending on the prediction context used).


Prediction Accuracy Distribution Index Polarisation Index High Prediction Accuracy Path Information 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lucian Vintan
    • 1
  • Arpad Gellert
    • 1
  • Adrian Florea
    • 1
  • Marius Oancea
    • 1
  • Colin Egan
    • 2
  1. 1.Computer Science Department“Lucian Blaga” University of SibiuSibiuRomania
  2. 2.School of Computer ScienceUniversity of HertfordshireHatfield, College LaneUK

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