Correlation and aliasing in dynamic branch predictors

  • Authors:

  • Stuart Sechrest;Chih-Chieh Lee;Trevor Mudge

  • Affiliations:

  • EECS Department, University of Michigan, 1301 Beal Ave., Ann Arbor, Michigan;EECS Department, University of Michigan, 1301 Beal Ave., Ann Arbor, Michigan;EECS Department, University of Michigan, 1301 Beal Ave., Ann Arbor, Michigan

  • Venue:
  • ISCA '96 Proceedings of the 23rd annual international symposium on Computer architecture
  • Year:
  • 1996

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Abstract

Previous branch prediction studies have relied primarily upon the SPECint89 and SPECint92 benchmarks for evaluation. Most of these benchmarks exercise a very small amount of code. As a consequence, the resources required by these schemes for accurate predictions of larger programs have not been clear. Moreover, many of these studies have simulated a very limited number of configurations. Here we report on simulations of a variety of branch prediction schemes using a set of relatively large benchmark programs that we believe to be more representative of likely system workloads. We have examined the sensitivity of these prediction schemes to variation in workload, in resources, and in design and configuration. We show that for predictors with small available resources, aliasing between distinct branches can have the dominant influence on prediction accuracy. For global history based schemes, such as GAs and gshare, aliasing in the predictor table can eliminate any advantage gained through inter branch correlation. For self-history based prediction scheme, such as PAs, it is aliasing in the buffer recording branch history, rather than the predictor table, that poses problems. Past studies have sometimes confused these effects and allocated resources incorrectly.