Improving superscalar instruction dispatch and issue by exploiting dynamic code sequences

  • Authors:
  • Sriram Vajapeyam;Tulika Mitra

  • Affiliations:
  • Supercomputer Education and Research Centre and Dept. of Computer Science & Automation, Indian Institnte of Science, Bangalore, India 560012;Dept. of Computer Science & Automation, Indian Institute of science, Bangalore, India 560012

  • Venue:
  • Proceedings of the 24th annual international symposium on Computer architecture
  • Year:
  • 1997

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Abstract

Superscalar processors currently have the potential to fetch multiple basic blocks per cycle by employing one of several recently proposed instruction fetch mechanisms. However, this increased fetch bandwidth cannot be exploited unless pipeline stages further downstream correspondingly improve. In particular, register renaming a large number of instructions per cycle is difficult. A large instruction window, needed to receive multiple basic blocks per cycle, will slow down dependence resolution and instruction issue. This paper addresses these and related issues by proposing (i) partitioning of the instruction window into multiple blocks, each holding a dynamic code sequence; (ii) logical partitioning of the register file into a global file and several local files, the latter holding registers local to a dynamic code sequence; (iii) the dynamic recording and reuse of register renaming information for registers local to a dynamic code sequence. Performance studies show these mechanisms improve performance over traditional superscalar processors by factors ranging from 1.5 to a little over 3 for the SPEC Integer programs. Next, it is observed that several of the loops in the benchmarks display vector-like behavior during execution, even if the static loop bodies are likely complex for compile-time vectorization. A dynamic loop vectorization mechanism that builds on top of the above mechanisms is briefly outlined. The mechanism vectorizes up to 60% of the dynamic instructions for some programs, albeit the average number of iterations per loop is quite small.