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<img src="./images/logo.png" align="right" width="440">
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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:
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<img align="right" src="./images/logo.png">
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Energy Aware Runtime (EAR) package provides an energy management framework for super computers.
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EAR contains different components, all together provide three main services:
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1. An **easy-to-use** and **lightweight** optimization service to automatically
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select the optimal CPU, memory and GPU frequency according to the application and the node characteristics.
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This service is provided by two components: the EAR Library (EARL) and the EAR Node Manager (EARD).
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EARL is a smart component which is loaded next to the application and offers
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application metrics monitoring and it can select the frequencies based on the
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application behaviour on the fly.
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The Library is loaded automatically through the EAR Loader (EARLO) and it can be easly integrated with different system batch schedulers (e.g., SLURM).
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2. A complete **energy and performance accounting and monitoring system** mainly based
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on relational SQL databases (MariaDB and PostgreSQL are currently supported).
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The energy accounting system is configurable in terms of application details and
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update frequency.
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The EAR Database (EARDBD) is used to cache those metrics prior to DB insertions and optimize
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the connectivity with the DB server.
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Current EAR version already includes several report plugins for non-relational Databases such as EXAMON.
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3. A **cluster energy manager** to monitor and control the energy consumed in
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the system through the EAR Global Manager (EARGMD).
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This control is configurable, it can dynamically adapt policy settings based on
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global energy limits or just offer global cluster monitoring.
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Visit the [architecture page](Architecture) for a detailed description of each
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of these components.
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The [user guide](User-guide) contains information about how to user EAR as an end
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user in a production environment.
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The [admin guide](Admin-guide) has all the information related to the installation
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and setting up, as well as all core components details.
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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**).
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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.
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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.
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Visit [the architecture page](Architecture) for a detailed description of each of these components.
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## License
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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.
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EAR is a 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.
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Contact: [ear-support@bsc.es](mailto:ear-support@bsc.es)
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| ... | ... | @@ -18,3 +43,8 @@ Contact: [ear-support@bsc.es](mailto:ear-support@bsc.es) |
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[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.](https://ieeexplore.ieee.org/document/9229570)
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[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.](https://ieeexplore.ieee.org/document/9555970)
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[J. Corbalan, L. Alonso, C. Navarrete and C. Guillen, "Soft Cluster Powercap at SuperMUC-NG with EAR," 2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC), Pittsburgh, PA, USA, 2022, pp. 1-8, doi: 10.1109/IGSC55832.2022.9969360](https://ieeexplore.ieee.org/document/9969360)
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## Current version
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This wiki corresponds has been updated to correspond to EAR version 4.2. |
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\ No newline at end of file |
