Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-23-2025
https://doi.org/10.5194/sp-5-opsr-23-2025
02 Jun 2025
 | OPSR | Chapter 8.4
 | 02 Jun 2025 | OPSR | Chapter 8.4

Unlocking the power of parallel computing: GPU technologies for ocean forecasting

Andrew R. Porter and Patrick Heimbach

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Cited articles

Adams, S. V., Ford, R. W., Hambley, M., Hobson, J. M., Kavcic, I., Maynard, C. M., Melvin, T., Mueller, E. H., Mullerworth, S., Porter, A. R., Rezny, M., Shipway, B. J., and Wong, R.: LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models, J. Parallel Distr. Com., 132, 383–396, https://doi.org/10.1016/j.jpdc.2019.02.007, 2019. 
Alnæs, M. S., Logg, A., Ølgaard, K. B., Rognes, M. E., and Wells, G. N.: Unified Form Language: A Domain-Specific Language for Weak Formulations of Partial Differential Equations, ACM Trans. Math. Softw., 40, 1–37, https://doi.org/10.1145/2566630, 2014.​​​​​​​ 
Balaji, V.: Climbing down Charney's ladder: machine learning and the post-Dennard era of computational climate science, Philos. T. Roy. Soc. A, 379, 20200085, https://doi.org/10.1098/rsta.2020.0085, 2021. 
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Short summary
Numerical ocean forecasting is a key part of accurate models of the Earth system. However, they require powerful computing resources, and the architectures of the necessary computers are evolving rapidly. Unfortunately, this is a disruptive change – an ocean model must be modified to enable it to make use of this new computing hardware. This paper reviews what has been done in this area and identifies solutions to enable operational ocean forecasts to make use of the new computing hardware.
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