Abstract
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).
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© 2006 Springer-Verlag Berlin Heidelberg
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Vintan, L., Gellert, A., Florea, A., Oancea, M., Egan, C. (2006). Understanding Prediction Limits Through Unbiased Branches. In: Jesshope, C., Egan, C. (eds) Advances in Computer Systems Architecture. ACSAC 2006. Lecture Notes in Computer Science, vol 4186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11859802_47
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DOI: https://doi.org/10.1007/11859802_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40056-1
Online ISBN: 978-3-540-40058-5
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