This paper explores the possibility of using program profiling to enhance the efficiency of value prediction. Value prediction attempts to eliminate true-data dependencies by predicting the outcome values of instructions at run-time and executing true-data dependent instructions based on that prediction. So far, all published techniques in this area have examined hardware-only value prediction mechanisms. In order to enhance the efficiency of value prediction, it is proposed that program profiling be employed to collect information that describes the tendency of instructions in a program to be value-predictable. The compiler that acts as a mediator passes this information to the value-prediction hardware mechanisms. Such information is exploited by the hardware in order to reduce mispredictions, better utilize the prediction table resources, distinguish between different value predictability patterns and still benefit from the advantages of value prediction to increase instruction-level parallelism. We show that the proposed method outperforms the hardware-only mechanisms in most of the examined benchmarks.

This article presents an experimental and analytical study of value prediction and its impact on speculative execution in superscalar microprocessors. Value prediction is a new paradigm that suggests predicting outcome values of operations (at run-time ) and using these predicted values to trigger the execution of true-data-dependent operations speculatively. As a result, stals to memory locations can be reduced and the amount of instruction-level parallelism can be extended beyond the limits of the program's dataflow graph. This article examines the characteristics of the value prediction concept from two perspectives: (1) the related phenomena that are reflected in the nature of computer programs and (2) the significance of these phenomena to boosting instruction-level parallelism of superscalar microprocessors that support speculative execution. In order to better understand these characteristics, our work combines both analytical and experimental studies.