Energy Optimization and Analysis with EAR

Corbalan, Julita; Alonso, Lluis; Aneas, Jordi; Brochard, Luigi

doi:10.1109/cluster49012.2020.00067

Cited by 8 publications

(8 citation statements)

References 18 publications

(13 reference statements)

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“…To implement and use the energy model in a real-world environment, we used Energy-Aware Runtime (EAR) [28]. EAR is a framework that provides an energy-efficient solution for HPC clusters.…”

Section: Job Power and Runtime Modelingmentioning

confidence: 99%

Energy hardware and workload aware job scheduling towards interconnected HPC environments

D'Amico¹,

Corbalan²

2024

IEEE Trans. Parallel Distrib. Syst.

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New HPC machines are getting close to the exascale. Power consumption for those machines has been increasing, and researchers are studying ways to reduce it. A second trend is HPC machines' growing complexity, with increasing heterogeneous hardware components and different clusters architectures cooperating in the same machine. We refer to these environments with the term heterogeneous multi-cluster environments. With the aim of optimizing performance and energy consumption in these environments, this paper proposes an Energy-Aware-Multi-Cluster (EAMC) job scheduling policy. EAMC-policy is able to optimize the scheduling and placement of jobs by predicting performance and energy consumption of arriving jobs for different hardware architectures and processor frequencies, reducing workload's energy consumption, makespan, and response time. The policy assigns a different priority to each job-resource combination so that the most efficient ones are favored, while less efficient ones are still considered on a variable degree, reducing response time and increasing cluster utilization. We implemented EAMC-policy in Slurm, and we evaluated a scenario in which two CPU clusters collaborate in the same machine. Simulations of workloads running applications modeled from real-world show a reduction of response time and makespan by up to 25% and 6% while saving up to 20% of total energy consumed when compared to policies minimizing runtime, and by 49%, 26%, and 6% compared to policies minimizing energy.

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“…To implement and use the energy model in a real-world environment, we used Energy-Aware Runtime (EAR) [28]. EAR is a framework that provides an energy-efficient solution for HPC clusters.…”

Section: Job Power and Runtime Modelingmentioning

confidence: 99%

Energy hardware and workload aware job scheduling towards interconnected HPC environments

D'Amico¹,

Corbalan²

2024

IEEE Trans. Parallel Distrib. Syst.

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“…EAR is a system software for energy management in data centres [2]. EAR offers four main services concerning energy management: Monitoring, Accounting, Control and Optimisation.…”

Section: Ear: Energy Management Frameworkmentioning

confidence: 99%

“…The energy model is a new one based on the default model used in [2]. The default model was proposed in [8] and [9].…”

Section: A Ear Avx512 Energy Modelmentioning

confidence: 99%

“…All the policies implements the same policy API, being relatively easy to compare energy and performance results with EAR and different policies. In the previous work presented in [2] DVFS applied to the CPU was the technique used by energy policies for energy optimisation, where UFS was enabled by hardware.…”

Section: Introductionmentioning

confidence: 99%

“…Computed using the accumulated energy 2. In this paper we have used Intel Node Manager for energy readings.…”

mentioning

confidence: 99%

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

Corbalan

Vidal

Alonso

et al. 2021

2021 IEEE International Conference on Cluster Computing (CLUSTER)

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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.

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