Performance Evaluation of Different Data Value PredictionSchemes

Data value prediction has been widely accepted as aneffective mechanism to break data hazards for high performanceprocessor design. Several works have reported promising performancepotential. However, there is hardly enough information that ispresented in a clear way about performance comparison of theseprediction mechanisms. This paper investigates the performance impactof four previously proposed value predictors, namely last valuepredictor, stride value predictor, two-level value predictor and hybrid(stride+two-level) predictor. The impact of misprediction penalty,which has been frequently ignored, is discussed in detail. Severalother implementation issues, including instruction window size, issuewidth and branch predictor are also addressed and simulated. Simulationresults indicate that data value predictors act differently underdifferent configurations. In some cases, simpler schemes may be morebeneficial than complicated ones. In some particular cases, valueprediction may have negative impact on performance.