The predictability of data values

  • Authors:

  • Yiannakis Sazeides;James E. Smith

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Wisconsin-Madison, 1415 Engr. Dr. Madison, WI;Department of Electrical and Computer Engineering, University of Wisconsin-Madison, 1415 Engr. Dr. Madison, WI

  • Venue:
  • MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
  • Year:
  • 1997

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Abstract

The predictability of data values is studied at a fundamental level. Two basic predictor models are defined: Computational predictors perform an operation on previous values to yield predicted next values. Examples we study are stride value prediction (which adds a delta to a previous value) and last value prediction (which performs the trivial identity operation on the previous value); Context Based} predictors match recent value history (context) with previous value history and predict values based entirely on previously observed patterns. To understand the potential of value prediction we perform simulations with unbounded prediction tables that are immediately updated using correct data values. Simulations of integer SPEC95 benchmarks show that data values can be highly predictable. Best performance is obtained with context based predictors; overall prediction accuracies are between 56% and 91%. The context based predictor typically has an accuracy about 20% better than the computational predictors (last value and stride). Comparison of context based prediction and stride prediction shows that the higher accuracy of context based prediction is due to relatively few static instructions giving large improvements; this suggests the usefulness of hybrid predictors. Among different instruction types, predictability varies significantly. In general, load and shift instructions are more difficult to predict correctly, whereas add instructions are more predictable.