Explicit uncore frequency scaling for energy optimisation policies with EAR in Intel architectures

Abstract

EAR is an energy management framework which offers three main services: energy accounting, energy control and energy optimisation. The latter is done through the EAR runtime library (EARL). EARL is a dynamic, transparent, and lightweight runtime library that provides energy optimisation and control. It implements energy optimisation policies that selects the optimal CPU frequency based on runtime application characteristics and policy settings. Given that EARL defines a policy API and a plugin mechanism, different policies can be easily evaluated.In this paper we propose and evaluate the utilisation of explicit Uncore Frequency Scaling (explicit UFS) in Intel architectures to increase the energy savings opportunities in the cases where the hardware cannot select the optimal frequency for the Integrated Memory Controller (IMC). We extended the min energy to solution policy to select the CPU and IMC frequencies and we executed and evaluated it with some kernels and six real applications. Results showed an average energy saving of 9% with an average time penalty of 3%. On some use cases, the impact of explicit UFS compared with HW UFS was up to 8% of extra energy savings.

Imitation Learning-Based Performance-Power Trade-Off Uncore Frequency Scaling Policy for Multicore System

“…The literature [ 12 , 15 , 16 , 17 ] uses a feedback-based single-step tuning-type algorithm for the design, where [ 12 , 15 , 17 ] decides the next action based on the impact of the current time-step uncore tuning action on the processor performance and power consumption. The literature [ 16 ] similarly evaluates the actual impact brought by the current action and decides on the subsequent action based on the feedback state. This single-step feedback algorithm is simple and easy to understand, but the adjustment action is incremental.…”