Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9709-2025
https://doi.org/10.5194/gmd-18-9709-2025
Development and technical paper
 | 
05 Dec 2025
Development and technical paper |  | 05 Dec 2025

Evaluating the impact of task aggregation in workflows with shared resource environments: use case for the MONARCH application

Manuel G. Marciani, Miguel Castrillo, Gladys Utrera, Mario C. Acosta, Bruno P. Kinoshita, and Francisco Doblas-Reyes

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This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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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, Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, 2024. a
Balaji, V., Maisonnave, E., Zadeh, N., Lawrence, B. N., Biercamp, J., Fladrich, U., Aloisio, G., Benson, R., Caubel, A., Durachta, J., Foujols, M.-A., Lister, G., Mocavero, S., Underwood, S., and Wright, G.: CPMIP: measurements of real computational performance of Earth system models in CMIP6, Geosci. Model Dev., 10, 19–34, https://doi.org/10.5194/gmd-10-19-2017, 2017. a
Brucker, P.: Scheduling Algorithms, in: 5th Edn., Springer, Berlin, Germany, https://doi.org/10.1007/978-3-540-69516-5, 2007. a
BSC-CNS: Blog post, https://www.bsc.es/marenostrum/marenostrum (last access: 29 September 2023), 2023. a
Cirne, W. and Berman, F.: Adaptive selection of partition size for supercomputer requests, in: Job Scheduling Strategies for Parallel Processing: IPDPS 2000 Workshop, Proceedings 6, 1 May 2000, Cancun, Mexico, Springer, 187–207, ISBN 3540411208, 2000. a
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Short summary
Earth System Model simulations are typically run on large, highly congested flagship computers using workflows. These workflows can consist of thousands of tasks. If these tasks are queued individually, the wait time can add up, resulting in a long response time. In this paper, we explore a technique for aggregating tasks into a single submission. We found that this simple technique reduced the time spent in the queue by up to 7 %.
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