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Scalability of Global 0.25° Ocean Simulations Using MOM

  • Marshall Ward
  • Yuanyuan Zhang
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 448)

Abstract

We investigate the scalability of global 0.25° resolution ocean-sea ice simulations using the Modular Ocean Model (MOM). We focus on two major platforms, hosted at the National Computational Infrastructure (NCI) National Facility: an x86-based PRIMERGY cluster with InfiniBand interconnects, and a SPARC-based FX10 system using the Tofu interconnect. We show that such models produce efficient, scalable results on both platforms up to 960 CPUs. Speeds are notably faster on Raijin when either hyperthreading or fewer cores per node are used. We also show that the ocean submodel scales up to 1920 CPUs with negligible loss of efficiency, but the sea ice and coupler components quickly become inefficient and represent substantial bottlenecks in future scalability. Our results show that both platforms offer sufficient performance for future scientific research, and highlight to the challenges for future scalability and optimization.

Keywords

ocean modeling performance profiling high performance computing parallel computing 

References

  1. 1.
    Taylor, K.E., Stouffer, R.J., Meehl, G.A.: An Overview of CMIP5 and the Experiment Design. Bull. Amer. Meteor. Soc. 93, 485–498 (2012)CrossRefGoogle Scholar
  2. 2.
    Chelton, D.B., de Szoeke, R.A., Schlax, M.G., El Naggar, K., Siwertz, N.: Geographical variability of the first baroclinicRossby radius of deformation. J. Phys. Oceanogr. 28, 433–459 (1998)CrossRefGoogle Scholar
  3. 3.
    Farneti, R., Delworth, T.L., Rosati, A.J., Griffies, S.M., Zeng, F.: The Role of Mesoscale Eddies in the Rectification of the Southern Ocean Response to Climate Change. J. Phys. Oceanogr. 40, 1539–1557 (2010)CrossRefGoogle Scholar
  4. 4.
    Spence, P., Griffies, S.M., England, M.H., Hogg, A., Mc, C., Saenko, O.A., Jourdain, N.C.: Rapid subsurface warming and circulation changes of Antarctic coastal waters by poleward shifting winds. Geophys. Res. Lett. 41, 4601–4610 (2014)Google Scholar
  5. 5.
    Delworth, T.L., Rosati, A., Anderson, W.G., Adcroft, A., Balaji, V., Benson, R., Dixon, K.W., Griffies, S.M., Lee, H.C., Pacanowski, R.C., Vecchi, G.A., Wittenberg, A.T., Zeng, F., Zhang, R.: Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. Journal of Climate 25(8) (2012)Google Scholar
  6. 6.
    Griffies, S.M.: Elements of the Modular Ocean Model (MOM), GFDL Ocean Group Technical Report No. 7. NOAA/Geophysical Fluid Dynamics Laboratory. 618 + xiii pages (2012 release)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Marshall Ward
    • 1
  • Yuanyuan Zhang
    • 2
  1. 1.National Computational InfrastructureCanberraAustralia
  2. 2.Fujitsu Australia LimitedCanberraAustralia

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