Résumé: Software application when executed on computer systems having disparate hardware features, result in dissimilar performance and power. Therefore, selecting systems with optimal performance and power values for application execution is an essential problem to address. However, access to many physical systems is required to collect performance power, which is a demanding task. Therefore, our first objective is to build an accurate prediction model for physical systems to achieve the second objective of computer system selection. To achieve the first objective, we propose a novel model, "cross performance and power prediction with scaling." We develop a cross prediction model for physical systems by training a decision tree machine learning algorithm on performance and power datasets obtained from a large number of simulation systems built in the Gem5 simulator using emulation mode. However, design differences between Gem5 systems and physical systems lead to large prediction inaccuracies. We determine the application-specific "scaling factor" to compensate for the prediction inaccuracies and apply it to the predicted values for accurate physical systems predictions. We evaluate our model on well-known applications from SD-VBS and MiBench benchmarks achieving errors of 10-25% and 6-40% for performance and power for general-purpose systems. With accurate predictions for physical systems from our model, we achieve the second goal of computer system selection.

Langue: Anglais