Confidence Counter Modelling for Value Predictor

Value prediction suffers from high penalties for mispredictions, so confidence mechanisms using saturation counters are often introduced to increase the output threshold of the value predictor. Statistics from saturation counters allow the fine-grained analysis of value prediction performance. However, for architects, traditional simulator is time-consuming and non-scalable, and for software developers, value predictors under the microarchitecture are usually invisible, which makes it difficult to optimize software. In this paper, we model saturation counters commonly used in value predictors with the Markov method, which enables offline estimation of misprediction rates. Such offline analysis can better provide information for performance estimation and compiler optimization. The final experimental results show that the difference between the misprediction rate obtained by the model and the simulator is very small, with the arithmetic average of 0.07% and 0.19% for the two commonly used counters, respectively.