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SIGMETRICS
2011
ACM

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

8 years 2 months ago
Autocorrelation analysis: a new and improved method for measuring branch predictability
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 hardto-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. Categories and Subject Descriptors: C.4 [Performance of Systems][Measurement techniques, Modeling techniques] General Terms: Measurement, Performance
Jian Chen, Lizy Kurian John
Added 17 Sep 2011
Updated 17 Sep 2011
Type Journal
Year 2011
Where SIGMETRICS
Authors Jian Chen, Lizy Kurian John
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