Difficult-path branch prediction using subordinate microthreads

  • Authors:

  • Robert S. Chappell;Francis Tseng;Adi Yoaz;Yale N. Patt

  • Affiliations:

  • The University of Michigan, Ann Arbor, Michigan;The University of Texas at Austin, Austin, Texas;Intel Corporation, Austin, TX;The University of Texas at Austin, Austin, Texas

  • Venue:
  • ISCA '02 Proceedings of the 29th annual international symposium on Computer architecture
  • Year:
  • 2002

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Abstract

Branch misprediction penalties continue to increase as microprocessor cores become wider and deeper. Thus, improving branch prediction accuracy remains an important challenge. Simultaneous Subordinate Microthreading (SSMT) provides a means to improve branch prediction accuracy. SSMT machines run multiple, concurrent microthreads in support of the primary thread. We propose to dynamically construct microthreads that can speculatively and accurately pre-compute branch outcomes along frequently mispredicted paths. The mechanism is intended to be implemented entirely in hardware. We present the details for doing so. We show how to select the right paths, how to generate accurate predictions, and how to get this information in a timely way. We achieve an average gain of 8.4% (42% maximum) over a very aggressive baseline machine on the SPECint95 and SPECint2000 benchmark suites.