Hardware-validated CPU performance and energy modelling

Abstract

Full-system simulation frameworks such as gem5 are used extensively to evaluate research ideas and for design-space exploration. Moreover, energy-efficiency has become the key design constraint in recent years and many works use a separate power modelling framework to evaluate energy consumption. While such tools are convenient and flexible, they are known to contain sources of error which are often not fully understood and potentially impact the conclusions drawn from investigations. This work enables accurate, hardware-validated performance, power, and energy modelling of CPUs by first presenting a methodology to evaluate and identify sources of error in CPU performance models, and secondly developing empirical power models optimised for use with such performance models. Hierarchical clustering, correlation analysis, and regression techniques are used to identify sources of error without requiring detailed CPU specifications and enable existing models to be improved, new models to be developed, validation of simulator changes, and testing of model suitability for specific use-cases. Furthermore, the GemStone open-source software tool is presented, which automates the process of characterising hardware platforms, identifying sources of error in gem5 models, applying power analysis, and quantifying the effect of errors on the performance, power, and energy estimations. In addition, the mean percentage error in execution time was found to swing from −51% to +10% between two versions of the same gem5 model, underlining the need for an automated tool to validate models against reference hardware, ensuring accuracy and consistency.

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Identifiers

ORCID for Matthew Walker: orcid.org/0000-0001-6368-3644

ORCID for Geoff Merrett: orcid.org/0000-0003-4980-3894

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