Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3661-2025
https://doi.org/10.5194/gmd-18-3661-2025
Development and technical paper
 | 
20 Jun 2025
Development and technical paper |  | 20 Jun 2025

Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models

Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny

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Cited articles

Abdulsalam, S., Zong, Z., Gu, Q., and Qiu, M.: Using the Greenup, Powerup, and Speedup metrics to evaluate software energy efficiency, in: 2015 Sixth International Green and Sustainable Computing Conference (IGSC), 1–8, https://doi.org/10.1109/IGCC.2015.7393699, 2015. a, b
Acosta, M. C., Palomas, S., Paronuzzi Ticco, S. V., Utrera, G., Biercamp, J., Bretonniere, P.-A., Budich, R., Castrillo, M., Caubel, A., Doblas-Reyes, F., Epicoco, I., Fladrich, U., Joussaume, S., Kumar Gupta, A., Lawrence, B., Le Sager, P., Lister, G., Moine, M.-P., Rioual, J.-C., Valcke, S., Zadeh, N., and Balaji, V.: The computational and energy cost of simulation and storage for climate science: lessons from CMIP6, Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, 2024a. a, b, c, d
Acosta, M. C., Palomas, S., and Tourigny, E.: Balancing EC-Earth3 Improving the Performance of EC-Earth CMIP6 Configurations by Minimizing the Coupling Cost, Earth Space Sci., 10, e2023EA002912, https://doi.org/10.1029/2023EA002912, 2023b. a
Alexeev, Y., Mickelson, S., Leyffer, S., Jacob, R., and Craig, A.: The Heuristic Static Load-Balancing Algorithm Applied to the Community Earth System Model, in: 2014 IEEE International Parallel & Distributed Processing Symposium Workshops, 1581–1590, https://doi.org/10.1109/IPDPSW.2014.177, 2014. a
Autosubmit documentation and user guide: Autosubmit, Autosubmit [code], https://autosubmit.readthedocs.io/en/master/, last access: 18 November 2024. a
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Short summary
We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
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