Empirical CPU power modelling and estimation in the gem5 simulator

Abstract

Power modelling is important for modern CPUs to inform power management approaches and allow design space exploration. Power simulators, combined with a full-system architectural simulator such as gem5, enable power-performance trade-offs to be investigated early in the design of a system with different configurations (e.g number of cores, cache size, etc.). However, the accuracy of existing power simulators, such as McPAT, is known to be low due to the abstraction and specification errors, and this can lead to incorrect research conclusions. In this paper, we present an accurate power model, built from measured data, integrated into gem5 for estimating the power consumption of a simulated quad-core ARM Cortex-A15. A power modelling methodology based on Performance Monitoring Counters (PMCs) is used to build and evaluate the integrated model in gem5. We first validate this methodology on the real hardware with 60 workloads at nine Dynamic Voltage and Frequency Scaling (DVFS) levels and four core mappings (2,160 samples), showing an average error between estimated and real measured power of less than 6%. Correlation between gem5 activity statistics and hardware PMCs is investigated to build a gem5 model representing a quad-core ARM Cortex-A15. Experimental validation with 15 workloads at four DVFS levels on real hardware and gem5 has been conducted to understand how the difference between the gem5 simulated activity statistics and the hardware PMCs affects the estimated power consumption.

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Identifiers

ORCID for Karunakar Reddy Basireddy: orcid.org/0000-0001-9755-1041

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

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

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