Articles | Volume 5-opsr
https://doi.org/10.5194/sp-5-opsr-23-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/sp-5-opsr-23-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
CORRESPONDING AUTHOR
Science and Technology Facilities Council, Daresbury Laboratory, Hartree Centre, Daresbury, UK
Patrick Heimbach
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA
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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.
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.
Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O., Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., Held, I. M., Jansen, M. F., John, J. G., Krasting, J. P., Langenhorst, A. R., Legg, S., Liang, Z., McHugh, C., Radhakrishnan, A., Reichl, B. G., Rosati, T., Samuels, B. L., Shao, A., Stouffer, R., Winton, M., Wittenberg, A. T., Xiang, B., Zadeh, N., and Zhang, R.: The GFDL Global Ocean and Sea Ice Model OM4.0: Model Description and Simulation Features, J. Adv. Model. Earth Sy., 11, 3167–3211, https://doi.org/10.1029/2019ms001726, 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.
Besard, T., Foket, C., and Sutter, B. D.: Effective Extensible Programming: Unleashing Julia on GPUs, IEEE T. Parall. Distr., 30, 827–841, https://doi.org/10.1109/tpds.2018.2872064, 2017.
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B.: Julia: A Fresh Approach to Numerical Computing, SIAM Rev., 59, 65–98, https://doi.org/10.1137/141000671, 2017.
Bishnu, S., Strauss, R. R., and Petersen, M. R.: Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1), Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, 2023.
Bouallègue, Z. B., Clare, M. C. A., Magnusson, L., Gascón, E., Maier-Gerber, M., Janoušek, M., Rodwell, M., Pinault, F., Dramsch, J. S., Lang, S. T. K., Raoult, B., Rabier, F., Chevallier, M., Sandu, I., Dueben, P., Chantry, M., and Pappenberger, F.: The Rise of Data-Driven Weather Forecasting: A First Statistical Assessment of Machine Learning–Based Weather Forecasts in an Operational-Like Context, B. Am. Meteorol. Soc., 105, E864–E883, https://doi.org/10.1175/bams-d-23-0162.1, 2024.
Carter Edwards, H., Trott, C. R., and Sunderland, D.: Kokkos: enabling manycore performance portability through polymorphic memory access patterns, J. Parallel Distr. Com., 74, 3202–3216, https://www.osti.gov/servlets/purl/1106586 (last access: 14 April 2025), 2014.
Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J., Halliwell, G. R., Bleck, R., Baraille, R., Wallcraft, A. J., Lozano, C., Tolman, H. L., Srinivasan, A., Hankin, S., Cornillon, P., Weisberg, R., Barth, A., He, R., Werner, F., and Wilkin, J.: U.S. GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM), Oceanography, 22, 64–75, https://doi.org/10.5670/oceanog.2009.39, 2009.
Churavy, V., Godoy, W. F., Bauer, C., Ranocha, H., Schlottke-Lakemper, M., Räss, L., Blaschke, J., Giordano, M., Schnetter, E., Omlin, S., Vetter, J. S., and Edelman, A.: Bridging HPC Communities through the Julia Programming Language, arXiv [preprint], https://doi.org/10.48550/arxiv.2211.02740, 4 November 2022.
Dennard, R. H., Gaensslen, F., Yu, H., Rideout, L., Bassous, E., and LeBlanc, A.: Design of ion-implanted MOSFET's with very small physical dimensions, IEEE J. Solid-St. Circ., SC-9, 256–268, 1974.
Draeger, E. W. and Siegel, A.: Exascale Was Not Inevitable; Neither Is What Comes Next, Comput. Sci. Eng., 25, 79–83, https://doi.org/10.1109/mcse.2023.3311375, 2023.
Freytag, G., Lima, J. V. F., Rech, P., and Navaux, P. O. A.: Impact of Reduced and Mixed-Precision on the Efficiency of a Multi-GPU Platform on CFD Applications, Lect. Notes Comput. Sc., 13380, 570–587, https://doi.org/10.1007/978-3-031-10542-5_39, 2022.
Häfner, D., Nuterman, R., and Jochum, M.: Fast, cheap, and turbulent – Global ocean modeling with GPU acceleration in Python, J. Adv. Model. Earth Sy., 13, e2021MS002717, https://doi.org/10.1029/2021MS002717, 2021.
Heimbach, P., O'Donncha, F., Smith, T., Garcia-Valdecasas, J. M., Arnaud, A., and Wan, L.: Crafting the Future: Machine Learning for Ocean Forecasting, in: Ocean prediction: present status and state of the art (OPSR), edited by: Álvarez Fanjul, E., Ciliberti, S. A., Pearlman, J., Wilmer-Becker, K., and Behera, S., Copernicus Publications, State Planet, 5-opsr, 22, https://doi.org/10.5194/sp-5-opsr-22-2025, 2025.
Kärnä, T., Kramer, S. C., Mitchell, L., Ham, D. A., Piggott, M. D., and Baptista, A. M.: Thetis coastal ocean model: discontinuous Galerkin discretization for the three-dimensional hydrostatic equations, Geosci. Model Dev., 11, 4359–4382, https://doi.org/10.5194/gmd-11-4359-2018, 2018.
Kimpson, T., Paxton, E. A., Chantry, M., and Palmer, T.: Climate-change modelling at reduced floating-point precision with stochastic rounding. Q. J. Roy. Meteor. Soc., 149, 843–855, https://doi.org/10.1002/qj.4435, 2023.
Klöwer, M., Hatfield, S., Croci, M., Düben, P. D., and Palmer, T. N.: Fluid Simulations Accelerated With 16 Bits: Approaching 4x Speedup on A64FX by Squeezing ShallowWaters.jl Into Float16, J. Adv. Model. Earth Sy., 14, e2021MS002684, https://doi.org/10.1029/2021ms002684, 2022.
Korn, P.: Formulation of an unstructured grid model for global ocean dynamics, J. Comput. Phys., 339, 525–552, https://doi.org/10.1016/j.jcp.2017.03.009, 2017.
Lawrence, B. N., Rezny, M., Budich, R., Bauer, P., Behrens, J., Carter, M., Deconinck, W., Ford, R., Maynard, C., Mullerworth, S., Osuna, C., Porter, A., Serradell, K., Valcke, S., Wedi, N., and Wilson, S.: Crossing the chasm: how to develop weather and climate models for next generation computers?, Geosci. Model Dev., 11, 1799–1821, https://doi.org/10.5194/gmd-11-1799-2018, 2018.
Liu, T., Zhuang, Y., Tian, M., Pan, J., Zeng, Y., Guo, Y., and Yang, M.: Parallel Implementation and Optimization of Regional Ocean Modeling System (ROMS) Based on Sunway SW26010 Many-Core Processor, IEEE Access, 7, 146170–146182, https://doi.org/10.1109/ACCESS.2019.2944922, 2019.
Loft, R.: Earth System Modeling Must Become More Energy Efficient, Eos, Transactions American Geophysical Union, 101, https://doi.org/10.1029/2020eo147051, 2020.
Madec, G., Bell, M., Benshila, R., Blaker, A., Boudrallé-Badie, R., Bricaud, C., Bruciaferri, D., Carneiro, D., Castrillo, M., Calvert, D., Chanut, J., Clementi, E., Coward, A., de, C., Dobricic, S., Epicoco, I., Éthé, C., Fiedler, E., Ford, D., Furner, R., Ganderton, J., Graham, T., Harle, J., Hutchinson, K., Iovino, D., King, R., Lea, D., Levy, C., Lovato, T., Maisonnave, E., Mak, J., Manuel, J., Martin, M., Martin, N., Martins, D., Masson, S., Mathiot, P., Mele, F., Mocavero, S., Moulin, A., Müller, S., Nurser, G., Oddo, P., Paronuzzi, S., Paul, J., Peltier, M., Person, R., Rousset, C., Rynders, S., Samson, G., Schroeder, D., Storkey, D., Storto, A., Téchené, S., Vancoppenolle, M., and Wilson, C.: NEMO Ocean Engine Reference Manual (v5.0), Zenodo [documentation], https://doi.org/10.5281/zenodo.14515373, 2024.
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers. J. Geophys. Res., 102, 5753–5766, https://doi.org/10.1029/96JC02775, 1997.
Paxton, E. A., Chantry, M., Klöwer, M., Saffin, L., and Palmer, T.: Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding, J. Climate, 35, 1215–1229, https://doi.org/10.1175/jcli-d-21-0343.1, 2022.
Perkel, J. M.: Julia: come for the syntax, stay for the speed, Springer Nature, 141–142, http://www.nature.com/articles/d41586-019-02310-3 (last access: 14 April 2025), 2019.
Ramadhan, A., Wagner, G., Hill, C., Campin, J.-M., Churavy, V., Besard, T., Souza, A., Edelman, A., Ferrari, R., and Marshall, J.: Oceananigans.jl: Fast and friendly geophysical fluid dynamics on GPUs, Journal of Open Source Software, 5, 2018, https://doi.org/10.21105/joss.02018, 2020.
Rasp, S., Hoyer, S., Merose, A., Langmore, I., Battaglia, P., Russell, T., Sanchez-Gonzalez, A., Yang, V., Carver, R., Agrawal, S., Chantry, M., Bouallegue, Z. B., Dueben, P., Bromberg, C., Sisk, J., Barrington, L., Bell, A., and Sha, F.: WeatherBench 2: A Benchmark for the Next Generation of Data-Driven Global Weather Models, J. Adv. Model. Earth Sy., 16, e2023MS004019, https://doi.org/10.1029/2023ms004019, 2024.
Ringler, T. Petersen, M., Higdon, R. L., Jacobsen, D., Jones, P. W., and Maltrud, M.: A multi-resolution approach to global ocean modeling, Ocean Model., 69, 211–232, https://doi.org/10.1016/j.ocemod.2013.04.010, 2013.
Rupp, K.: Microprocessor Trend Data, GitHub [data set], https://github.com/karlrupp/microprocessor-trend-data (last access: 12 September 2024), 2022.
Shchepetkin, A. F. and McWilliams, J. C.: A method for computing horizontal pressure-gradient force in an oceanic model with a nonaligned vertical coordinate, J. Geophys. Res., 108, 3090, https://doi.org/10.1029/2001JC001047, 2003.
Silvestri, S., Wagner, G. L., Constantinou, N. C., Hill, C. N., Campin, J.-M., Souza, A. N., Bishnu, S., Churavy, V., Marshall, J. C., and Ferrari, R.: A GPU-based ocean dynamical core for routine mesoscale-resolving climate simulations, ESSOAr [preprint], https://doi.org/10.22541/essoar.171708158.82342448/v1, 2024.
Sridhar, A., Tissaoui, Y., Marras, S., Shen, Z., Kawczynski, C., Byrne, S., Pamnany, K., Waruszewski, M., Gibson, T. H., Kozdon, J. E., Churavy, V., Wilcox, L. C., Giraldo, F. X., and Schneider, T.: Large-eddy simulations with ClimateMachine v0.2.0: a new open-source code for atmospheric simulations on GPUs and CPUs, Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022, 2022.
Strohmaier, E., Dongarra, J., Simon, H., Meuer, M., and Meuer, H.: The Top 500, PROMETEUS Professor Meuer Technologieberatung und -Services GmbH, https://www.top500.org/lists/top500/list/2024/11/ (last access: 14 April 2025), 2024.
Tsujino, H., Motoi, T., Ishikawa, I., Hirabara, M., Nakano, H., Yamanaka, G., Yasuda, T., and Ishizaki, H.: Reference manual for the Meteorological Research Institute Community Ocean Model (MRI.COM) version 3. Technical Reports of the Meteorological Research Institute, report no. 59, https://doi.org/10.11483/mritechrepo.59, 2010.
Voosen, P.: Climate modelers grapple with their own emissions, Science, 384, 494–495, https://doi.org/10.1126/science.adq1772, 2024.
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.
Numerical ocean forecasting is a key part of accurate models of the Earth system. However, they...
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