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Value Prediction. Kyaw Kyaw, Min Pan. Final Project. What is Value Prediction?. Predict the value of instructions before they are executed Branch Prediction – eliminates the control dependences  Value Prediction – eliminates the data dependences. Why Value Prediction?.

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Value prediction

Value Prediction

Kyaw Kyaw, Min Pan

Final Project


What is value prediction
What is Value Prediction?

  • Predict the value of instructions before they are executed

  • Branch Prediction – eliminates the control dependences

  • Value Prediction – eliminates the data dependences


Why value prediction
Why Value Prediction?

  • Results of many instructions can be accurately predicted before they are issued or executed.

  • Dependent instructions are no longer bound by the serialization constraints imposed by data dependences.

  • More parallelism can be explored




Causes of value locality
Causes of Value Locality

  • Data redundancy

  • Error checking

  • Program constants

  • Computed branches

    ……

  • Virtual function calls

  • Glue code

  • Addressability

  • Memory alias resolution


Value prediction units
Value Prediction Units

Three factors determine the efficacy

  • Accuracy

    ability to avoid mispredictions

  • Coverage

    ability to predict as many instruction outcomes as possible

  • Scope

    The set of instructions that the predictor targets


Relationships between factors
Relationships between factors

  • Accuracy ↔ Coverage

    trade-off

  • Scope

    • Low implementation cost

    • Achieve better accuracy and coverage

    • Mispredictions for useless predictions are eliminated


Value prediction units1
Value Prediction Units

  • Three types

    • History-Based Predictors

    • Computational Predictors

    • Hybrid Predictors




Value prediction techniques
Value Prediction Techniques

  • Last Value Predictor

  • Register Value Predictor

  • Stride 2-delta Predictor

  • Last Four value Predictor

  • Finite Context Method Predictor

  • Confidence Estimation


Sample research works
Sample Research Works

  • “Value Locality and Load Value Prediction” M. H. Lipasti, C. B. Wilkerson, J. P. Shen ASPLOS-VII, October 1996

  • “Selective Value Prediction” B. Calder, G. Reinman, D. M. Tullsen Proceedings of 26th International Symposium on Computer Architecture, May 1999


Value locality prediction
Value Locality & Prediction

  • Likelihood of a previously-seen value recurring repeatedly within a storage location

  • Exists primarily due to an effective compile-time optimization


Load value prediction
Load Value Prediction

  • Based on Branch Prediction idea, tries to predict all 32-bit or 64-bit value

  • Load Value Prediction Table ~ branch target buffer

  • Load Classification Table ~ branch history table

  • Constant Verification Unit – to avoid accessing memory and force LVPT entries coherent with main memory



Results
Results

  • PowerPC 620 = avg. 3% (max 21%) performance gain

  • Alpha 21164 = avg. 6% (max 17%) performance gain


Selective value prediction
Selective Value Prediction

  • Not only on Load instructions but on all important instructions

  • To speculate on operations with large gains and small losses even when confidence is low, and on operations with small gains and large losses when confidence is high

  • To intelligently choose –

    • When to use value prediction

    • Which instructions to use value prediction


Confidence prediction
Confidence Prediction

  • Predicted value is used if the confidence associated with that value is above given threshold.

  • Confidence Saturating Counter – (low, med, high)

  • Confidence History Counter – similar to local branch history


Minimizing capacity misses
Minimizing Capacity Misses

  • To prevent unnecessary replacement in the value prediction table

  • Replacement Counter – increment on correct prediction, decrement otherwise

  • Also decrement when another instruction attempts to use that entry

  • Warn-up Counter – increment only every time an instruction hits in the value table, set to 0 on a replacement

  • Only after the warn-up counter has reached a certain threshold, predictions made, confidence counter updated and VPT allowed to be modified


Filtering producers of predicted values
Filtering Producers of Predicted Values

  • To reduce pressure on the prediction table, predict fewer instructions

  • Only allow entries to be allocated to instructions that define registers which are actually used by another instruction in the current instruction window

  • Limit the instructions that are on the critical path to be inserted into VPT


Filtering producers of predicted values1
Filtering Producers of Predicted Values

  • To reduce pressure on the prediction table, predict fewer instructions

  • Only allow entries to be allocated to instructions that define registers which are actually used by another instruction in the current instruction window

  • Limit the instructions that are on the critical path to be inserted into VPT


Finding the important consumers
Finding the Important Consumers

  • Filtering which instructions use a predicted value

  • Use confidence bits (low, med, high)

  • Path Heuristic – use predicted values for instructions that have low confidence but are on the longest path


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