Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8501-2026
https://doi.org/10.5194/gmd-19-8501-2026
Development and technical paper
 | 
11 Sep 2026
Development and technical paper |  | 11 Sep 2026

Task aggregation as a strategy to optimize Earth System Model workflows in HPC: assessing real scenarios with EC-Earth

Pablo Goitia, Manuel G. Marciani, Miguel Castrillo, and Mario C. Acosta

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Cited articles

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, Geoscientific Model Development, 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, 2024. a, b, c
Ahn, D. H., Bass, N., Chu, A., Garlick, J., Grondona, M., Herbein, S., Ingólfsson, H. I., Koning, J., Patki, T., Scogland, T. R., Springmeyer, B., and Taufer, M.: Flux: Overcoming scheduling challenges for exascale workflows, Future Generat. Comput. Syst., 110, 202–213, https://doi.org/10.1016/j.future.2020.04.006, 2020. a
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015. a
Autosubmit Team: Autosubmit v3.15 Documentation, https://autosubmit.readthedocs.io/en/3.15.0-branch/ (last access: 24 August 2026), 2023. a
Babuji, Y., Woodard, A., Li, Z., Katz, D. S., Clifford, B., Kumar, R., Lacinski, L., Chard, R., Wozniak, J. M., Foster, I., Wilde, M., and Chard, K.: Parsl: Pervasive Parallel Programming in Python, in: HPDC '19, Association for Computing Machinery, New York, NY, USA, 25–36, ISBN 9781450366700, https://doi.org/10.1145/3307681.3325400, 2019. a
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Short summary
Earth System Model workflows commonly run on highly congested high-performance computing platforms, meaning that each individual workflow task potentially faces lengthy waiting times in the queues of the schedulers. In this work, we evaluate the task aggregation approach in EC-Earth3 workflows to reduce the queue times and, consequently, the total execution time. The results show up to 23% more simulated years over the same time span, with queuing times reduced by up to a factor of 12.33.
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