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Last edited by Julita Corbalan Jun 17, 2022
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Energy Aware Runtime (EAR) package provides an energy management framework for super computers. EAR contains different components, all together provide three main services:
  1. An easy-to-use and lightweight optimization service to automatically select the optimal CPU frequency according to the application and the node characteristics. This service is provided by two components: the EAR library (EARL) and the EAR daemon (EARD). EARL is a smart component which is loaded next to the application, intercepting MPI calls and selecting the CPU frequency based on the application behaviour on the fly. The library is loaded automatically through the EAR Loader (EARLO) and SLURM plugin (EARPLUG).
  2. A complete energy and performance accounting and monitoring system based on SQL database (MariaDB and PostgreSQL are supported). The energy accounting system is configurable in terms of application details and update frequency. The EAR database daemon (EARDBD) is used to cache those metrics prior to DB insertions.
  3. A global energy management to monitor and control the energy consumed in the system through the EAR global manager daemon (EARGMD). This control is configurable, it can dynamically adapt policy settings based on global energy limits or just offer global cluster monitoring.

Visit the architecture page for a detailed description of each of these components.

License

EAR is an open source software and it is licensed under both the BSD-3 license and EPL-1.0 license. Full text of both licenses can be found in COPYING.BSD and COPYING.EPL files.

Contact: ear-support@bsc.es

Publications

J. Corbalan, L. Alonso, J. Aneas and L. Brochard, "Energy Optimization and Analysis with EAR," 2020 IEEE International Conference on Cluster Computing (CLUSTER), 2020, pp. 464-472, doi: 10.1109/CLUSTER49012.2020.00067.

J. Corbalan, O. Vidal, L. Alonso and J. Aneas, "Explicit uncore frequency scaling for energy optimisation policies with EAR in Intel architectures," 2021 IEEE International Conference on Cluster Computing (CLUSTER), 2021, pp. 572-581, doi: 10.1109/Cluster48925.2021.00089.

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