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First Generation

1. Improving Branch Predictors by Correlating on Data Values 2. A Language for Describing Predictors and its Application to Automatic Synthesis. Input. PC. First Generation. Feedback. PC. history. Prediction. PC: CondJmpEq Ra , Rb , Target. Input. PC GBH (global branch history).

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First Generation

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  1. 1. Improving Branch Predictors by Correlating on Data Values2. A Language for Describing Predictors and its Application to Automatic Synthesis

  2. Input PC First Generation Feedback PC history Prediction PC: CondJmpEq Ra, Rb, Target

  3. Input PC GBH (global branch history) PC GBH Prediction Improvement 1 Feedback history PC: CondJmpEq Ra, Rb, Target

  4. Input PC GBH Feedback PC GBH Improvement 2 history Prediction PC history Feedback PC: CondJmpEq Ra, Rb, Target

  5. Input Feedback PC GBH PC Prediction history GBH Improvement 3 PC: CondJmpEq Ra, Rb, Target Reduce table interference through more intelligent table indexing scheme.

  6. Input to the Predictor • PC, GBH • What else? • PC: CondJmpEq Ra, Rb, Target • Ra, Rb? • What is the main obstacle to this strategy? • Solution: predict the values of Ra and Rb. • Still impractical? • Solution: predict the difference between Ra and Rb. • Still impractical? • Solution: store only a few bits of the difference values.

  7. Branch Difference Predictor

  8. PC Rare Event Predictor REP PC value pattern counter Prediction GBH value pattern VHT PC: CondJmpEq Ra, Rb, Target

  9. Value Pattern Value Pattern = [Branch count, Branch difference]

  10. Experimental Results • REP • 6K bytes • VHT • BCT, 64 bytes • BDC, 100 bytes

  11. Discussion • BDP can be used with other predictors. • Why? • Significant improvement? • BDP removes 13% to 9% of the misprediction over gShare. • and 12% to 8% of the misprediction over Bi-Mode. • 1% of overall improvement? • Can BDP be applied to any architecture? • Set instructions in the MIPS-based SimpleScalar • BEQ Ra, Target in the Alpha family.

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