Understanding prediction limits through unbiased branches

Authors:
Lucian Vintan;Arpad Gellert;Adrian Florea;Marius Oancea;Colin Egan
Affiliations:
Computer Science Department, “Lucian Blaga” University of Sibiu, Sibiu, Romania;Computer Science Department, “Lucian Blaga” University of Sibiu, Sibiu, Romania;Computer Science Department, “Lucian Blaga” University of Sibiu, Sibiu, Romania;Computer Science Department, “Lucian Blaga” University of Sibiu, Sibiu, Romania;School of Computer Science, University of Hertfordshire, Hatfield, College Lane, UK
Venue:
ACSAC'06 Proceedings of the 11th Asia-Pacific conference on Advances in Computer Systems Architecture
Year:
2006

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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).