Autocorrelation analysis: a new and improved method for measuring branch predictability

Authors:
Jian Chen;Lizy Kurian John
Affiliations:
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA;Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA
Venue:
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
Year:
2011

Citing 2
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Abstract

Branch taken rate and transition rate have been proposed as metrics to characterize the branch predictability. However, these two metrics may misclassify branches with regular history patterns as hard-to-predict branches, causing an inaccurate and ambiguous view of branch predictability. This study uses autocorrelation to analyze the branch history patterns and presents a new metric Degree of Pattern Irregularity (DPI) for branch classification. The proposed metric is evaluated with different branch predictors, and the results show that DPI significantly improves the quality and the accuracy of branch classification over traditional taken rate and transition rate.