A Systematic Methodology to Generate Decomposable and Responsive Power Models for CMPs

Abstract

Power modeling based on performance monitoring counters (PMCs) attracted the interest of researchers since it became a quick approach to understand the power behavior of real systems. Consequently, several power-aware policies use models to guide their decisions. Hence, the presence of power models that are informative, accurate, and capable of detecting power phases is critical to improve the success of power-saving techniques. Additionally, the design of current processors varied considerably with the appearance of CMPs (multiple cores sharing resources). Thus, PMC-based power models warrant further investigation on current energy-efficient multicore processors. In this paper, we present a systematic methodology to produce decomposable PMC-based power models on current multicore architectures. Apart from being able to estimate the power consumption accurately, the models provide per component power consumption, supplying extra insights about power behavior. Moreover, we study their responsiveness -the capacity to detect power phases-. Specifically, we produce power models for an Intel Core 2 Duo with one and two cores enabled for all the DVFS configurations. The models are empirically validated using the SPECcpu2006, NAS and LMBENCH benchmarks. Finally, we compare the models against existing approaches concluding that the proposed methodology produces more accurate, responsive, and informative models.