Selective value prediction

  • Authors:

  • Brad Calder;Glenn Reinman;Dean M. Tullsen

  • Affiliations:

  • Department of Computer Science and Engineering, University of California, San Diego;Department of Computer Science and Engineering, University of California, San Diego;Department of Computer Science and Engineering, University of California, San Diego

  • Venue:
  • ISCA '99 Proceedings of the 26th annual international symposium on Computer architecture
  • Year:
  • 1999

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Abstract

Value Prediction is a relatively new technique to increase instruction-level parallelism by breaking true data dependence chains. A value prediction architecture produces values, which may be later consumed by instructions that execute speculatively using the predicted value.This paper examines selective techniques for using value prediction in the presence of predictor capacity constraints and reasonable misprediction penalties. We examine prediction and confidence mechanisms in light of these constraints, and we minimize capacity conflicts through instruction filtering. The latter technique filters which instructions put values into the value prediction table. We examine filtering techniques based on instruction type, as well as giving priority to instructions belonging to the longest data dependence path in the processor's active instruction window. We apply filtering both to the producers of predicted values and the consumers. In addition, we examine the benefit of using different confidence levels for instructions using predicted values on the longest dependence path.