Accurate and stable empirical CPU power modelling for multi- and many-core systems

Modern processors must provide an increasing level of performance, and are therefore including higher numbers of Heterogeneous Multi-Processing (HMP) elements. Intelligent run-time control of performance and power consumption is required to extend battery-life in mobile systems, reduce energy and cooling costs in data centres, and increase peak performance while respecting thermal and power constraints. Accurate online power estimation is essential in guiding run-time power management mechanisms and energy-aware scheduling decisions. We present a statistically-rigorous methodology for developing accurate and stable run-time power models and we experimentally demonstrate their ability to perform more accurately across a wider range of workloads. We highlight significant shortcomings in existing techniques and present an improved model formulation that also accounts for thermal effects. Moreover, we present the Powmon software tools that automates our methodology, allowing power models to be developed for other platforms. Accurate performance and power modelling is also essential in full-system simulation. We present the GemStone open-source software tool, which automates the process of characterising hardware platforms; identifying sources of error in gem5 performance models using machine learning techniques; applying the empirical power models to simulation data; and quantifying the effect of simulation errors on the performance, power and energy estimations, including their scaling across Dynamic Voltage-Frequency Scaling (DVFS) levels and HMP core types. The presented work enables the development and implementation of smart run-time power management and energy-aware scheduling algorithms, as well as hardware-validated performance, power and energy simulation for design-space exploration and optimisation of future systems.