Dependence Graph Model for Accurate Critical Path Analysis on Out-of-Order Processors

The dependence graph model of out-of-order (OoO) instruction execution is a powerful representation used for the critical path analysis. However, most, if not all, of the previous models are out-of-date and lack enough detail to model modern OoO processors, or are too specific and complicated which limit their generality and applicability. In this paper, we propose an enhanced dependence graph model which remains simple but greatly improves the accuracy over prior models. The evaluation results using the gem5 simulator with configurations similar to Intel's Haswell and Silvermont architecture show that the proposed enhanced model achieves CPI errors of 2.1% and 4.4% which are 90.3% and 77.1% improvements from the state-of-the-art model.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.25(2017) (online)------------------------------The dependence graph model of out-of-order (OoO) instruction execution is a powerful representation used for the critical path analysis. However, most, if not all, of the previous models are out-of-date and lack enough detail to model modern OoO processors, or are too specific and complicated which limit their generality and applicability. In this paper, we propose an enhanced dependence graph model which remains simple but greatly improves the accuracy over prior models. The evaluation results using the gem5 simulator with configurations similar to Intel's Haswell and Silvermont architecture show that the proposed enhanced model achieves CPI errors of 2.1% and 4.4% which are 90.3% and 77.1% improvements from the state-of-the-art model.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.25(2017) (online)------------------------------