Using hybrid branch predictors to improve branch prediction accuracy in the presence of context switches

  • Authors:

  • Marius Evers;Po-Yung Chang;Yale N. Patt

  • Affiliations:

  • Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, Michigan;Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, Michigan;Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, Michigan

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

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Abstract

Pipeline stalls due to conditional branches represent one of the most significant impediments to realizing the performance potential of deeply pipelined, superscalar processors. Many branch predictors have been proposed to help alleviate this problem, including the Two-Level Adaptive Branch Predictor, and more recently, two-component hybrid branch predictors.In a less idealized environment, such as a time-shared system, code of interest involves context switches. Context switches, even at fairly large intervals, can seriously degrade the performance of many of the most accurate branch prediction schemes. In this paper, we introduce a new hybrid branch predictor and show that it is more accurate (for a given cost) than any previously published scheme, especially if the branch histories are periodically flushed due to the presence of context switches.