Advancing value prediction




Subramanian, Anjana

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Read after write dependencies form a key bottleneck in single thread performance. Value prediction [9][10][18] is a speculative technique that overcomes these dependencies by predicting results of instruction execution, thereby preventing dependent instructions from stalling. Usually, the penalties for value mispredictions are extremely high. As a result, value predictors have evolved to prioritize accuracy over coverage. To improve upon the state-of-the-art, our goals are: (i) to develop more powerful prediction mechanisms that have a better accuracy-coverage tradeoff (ii) to maximize performance gains obtained from correct predictions. We present two independent pieces of work that address each of these. To achieve the first goal, we design a Heterogeneous Context-based Value Predictor (HCVP) that combines the use of branch history with value history to represent program context information. We demonstrate that this combination provides better predictability than using either of them individually and that it allows for the use of relatively short value history lengths that provide more coverage than very long ones. HCVP does not maintain speculative value histories as it more tolerant to the update problem that occurs when back to back instances of the same instruction are predicted. Our predictor performs better than the state-of-the-art value predictors (E VTAGE and DFCM++) to achieve a 29% speedup over a baseline with no value prediction. When combined with the E Stride predictor, it achieves a speedup of 46%, which is 9% higher than that achieved by E VTAGE E Stride (EVES), the winner of the First Championship Value Prediction. To achieve the second goal, we exploit the fact that some instructions are more performance critical than others. We categorize instructions by various parameters to find one or more classes of instructions that provide high performance benefits for correct predictions. We find that loads, address producing instructions, and high fanout instructions are extremely beneficial for value prediction.


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