Efficient Load Value Prediction Using Multiple Predictors and Filters

Value prediction [1], [2] has the potential to break through the performance limitations imposed by true data dependencies. Aggressive value predictors can deliver significant performance improvements, but usually require large hardware budgets. While predicting values of all instruction types is possible, prior work has shown that predicting just load values is most effective with a modest hardware budget (e.g., 8KB of prediction state [3], [4]). However, with hardware budget constraints and high prediction accuracy requirements (99%), prior work has struggled to increase the fraction of predicted loads (coverage) beyond the low 30s. In this paper, we analyzed four state-of-the-art load value predictors, and found that they complement each other. Based on that finding, we evaluated a new composite predictor that combines all four component predictors. Our results show that the composite predictor, combined with several optimizations we proposed, improve the benefit of load value prediction by 54%-74% depending on the total predictor budget. Moreover, our composite predictor delivers more than twice the coverage of first championship value prediction winner predictor (EVES [4]), and substantially increases the delivered speedup by more than 50%.