Quantifying instruction criticality

Information about instruction criticality can be used to control the application of microarchitectural resources efficiently. To this end, several groups have proposed methods to predict critical instructions. This paper presents a framework that allows us to directly measure the criticality of individual dynamic instructions. This allows us to (1) measure the accuracy of proposed critical path predictors, (2) quantify the amount of slack present in noncritical instructions, and (3) provide a new metric, called tautness, which ranks critical instructions by their dominance on the critical path. This research investigates methods for improving critical path predictor accuracy, and studies the distribution of slack and tautness in programs. It shows that instruction criticality changes dynamically, and that criticality history patterns can be used to significantly improve predictor accuracy.