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

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.This work has been funded by the BSC-Lenovo collaboration agreement.Peer ReviewedPostprint (author's final draft

Energy optimization and analysis with EAR

EAR is an energy management framework which offers three main services: energy accounting, energy control, and energy optimization. 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. EARL optimises energy by selecting the optimal CPU frequency, based on the energy policy selected and application runtime characteristics without any application modification or user input. Currently EARL only works for MPI applications but EAR itself can still operate for non-MPI applications. It automatically (and transparently) identifies iterative regions (loops) and computes a set of metrics per iteration, application signature, and, together with the system signature, applies energy models to estimate the execution time and power for the CPU frequencies available. System signature is a set of coefficients per-node computed during EAR installation via a learning phase. Given time and power projections, EARL selects the best frequency based on policy settings. This papers shows how to optimize energy using the EAR library with min_time_to_solution energy policy and how to analyse applications through EAR framework. Evaluation includes eight applications with different sizes and application signatures. Results show how EARL computes each application signature on the fly and applies the CPU frequency selected by the min_time_to_solution policy.This work has been mostly funded by the BSC-Lenovo collaboration agreement and partially by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through PID2019-107255GB project, by the Generalitat de Catalunya (2017- SGR-1414)Peer ReviewedPostprint (author's final draft