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1995
IEEE

A Comparative Analysis of Schemes for Correlated Branch Prediction

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A Comparative Analysis of Schemes for Correlated Branch Prediction
Modern high-performance architectures require extremely accurate branch prediction to overcome the performance limitations of conditional branches. We present a framework that categorizes branch prediction schemes by the way in which they partition dynamic branches and by the kind of predictor that they use. The framework allows us to compare and contrast branch prediction schemes, and to analyze why they work. We use the framework to show how a static correlated branch prediction scheme increases branch bias and thus improves overall branch prediction accuracy. We also use the framework to identify the fundamental differences between static and dynamic correlated branch prediction schemes. This study shows that there is room to improve the prediction accuracy of existing branch prediction schemes.
Cliff Young, Nicholas C. Gloy, Michael D. Smith
Added 26 Aug 2010
Updated 26 Aug 2010
Type Conference
Year 1995
Where ISCA
Authors Cliff Young, Nicholas C. Gloy, Michael D. Smith
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