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Online Mesh Refinement for Parallel Atmospheric Models

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Abstract

Forecast precisions of climatological models are limited by computing power and time available for the executions. As more and faster processors are used in the computation, the resolution of the mesh adopted to represent the Earth’s atmosphere can be increased, and consequently the numerical forecast is more accurate. However, a finer mesh resolution, able to include local phenomena in a global atmosphere integration, is still not possible due to the large number of data elements to compute in this case. To overcome this situation, different mesh refinement levels can be used at the same time for different areas of the domain. Thus, our paper evaluates how mesh refinement at run time (online) can improve performance for climatological models.The online mesh refinement (OMR) increases dynamically mesh resolution in parts of a domain,when special atmosphere conditions are registered during the execution. Experimental results show that the execution of a model improved by OMR provides better resolution for the meshes, without any significant increase of execution time. The parallel performance of the simulations is also increased through the creation of threads in order to explore different levels of parallelism.

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References

  1. Cera, M.C., Pezzi, G.P., Pilla, M., Maillard, N., Navaux, P.: Improving the dynamic creation of processes in MPI-2. In: Mohr, B., Träff, J., Worringen, J., Dongarra, J. (eds.) Recent Advances in Parallel Virtual Machine and Message Passing Interface. Lecture Notes in Computer Science, vol. 4192, pp. 247–255. Springer, Berlin (2006)

    Chapter  Google Scholar 

  2. Chandra, R.: Parallel Programming in OpenMP. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  3. Curtis-Maury, M., Ding, X., Antonopoulos, C.D., Nikolopoulos, D.S.: An evaluation of OpenMP on current and emerging multithreaded/multicore processors. In: Proceedings of the 2005 and 2006 International Conference on OpenMP Shared Memory Parallel Programming, IWOMP’05/IWOMP’06, pp. 133–144. Springer, Berlin (2008)

  4. Fazenda, A.L., Demerval, S.M., Enari, E.H., Panetta, J., Rodrigues, L.F.: First Time User Guide (BRAMS Version 4.2) (2011)

  5. Gropp, W., Ewing, L., Thakur, R.: Using MPI-2—Advanced Features of the Message-Passing Interface. The MIT Press, Cambridge (1999)

    Google Scholar 

  6. Gropp, W., Lusk, E., Doss, N., Skjellum, A.: High-Performance, Portable Implementation of the MPI Message Passing Interface Standard. Parallel Comput. 22(6), 789–828 (1996)

    Article  MATH  Google Scholar 

  7. Kirk, D.B., Hwu, W.W.M.: Programming Massively Parallel Processors: A Hands- on Approach. Morgan Kaufmann Publishers, San Francisco (2010)

    Google Scholar 

  8. MacNeice, P., Olson, K.M., Mobarry, C., de Fainchtein, R., Packer, C.: PARAMESH: A parallel adaptive mesh refinement community toolkit. Comput. Phys. Commun. 126(3), 330–354 (2000)

    Article  MATH  Google Scholar 

  9. Marshall, J., Adcroft, A., Hill, C., Perelman, L., Heisey, C.: A finite-volume incompressible Navier-Stokes model for studies of ocean on parallel computers. J. Geophys. Res. 102(C3), 5753–5756 (1997)

    Article  Google Scholar 

  10. Osthoff, C., Grunmann, P., Boito, F., Kassick, R., Pilla, L., Navaux, P., Schepke, C., Panetta, J., Maillard, N., Dias, P.L.S., Walko, R.: Improving performance on atmospheric models through a hybrid OpenMP/MPI implementation. In: The 9th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA 2011). IEEE Technical Committee on Scalable Computing, Busan, Korea (2011)

  11. Osthoff, C., Schepke, C., Panetta, J., Grunmann, P.J., Dias, P.L.S., Kassick, R.V., Boito, F.Z., Navaux, P.O.A., Lopes, P.P., Fabricio, Souto, R.P.: OpenMP for accelerators performance evaluation on atmosphere model’s application system. In: Proceedings of XXX Iberian-Latin-American Congress on Computational Methods in Engineering, 2011, Ouro Preto. Mecanica Computacional Vol. XXX, pp. -. Asociación Argentina de Mecánica Computacional (AMCA), Ouro Preto, Brazil (2011)

  12. Plewa, T., Linde, T., Weirs, V.G.: Adaptive Mesh Refinement—Theory and Applications. Springer, Berlin (2003)

    Google Scholar 

  13. Schepke, C., Maillard, N., Osthoff, C., Dias, P.: Performance evaluation of an atmospheric simulation model on multi-core environments. In: Proceedings of Conferencia Latino Americana de Computación de Alto Rendimiento, pp. 330–332. Instituto de Informática/UFRGS, Gramado, RS, Brazil (2010)

  14. Schepke, C., Maillard, N., Schneider, J., Heiss, H.U.: Online mesh refinement in parallel meteorological applications. In: Proceedings of Conferencia Latino Americana de Computación de Alto Rendimiento, Colima, Mexico (2011)

  15. Schepke, C., Maillard, N., Schneider, J., Heiss, H.U.: Why online dynamic mesh refinement is better for parallel climatological models. In: 23th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2011). IEEE, Vitória, Espírito Santo (2011)

  16. Schmidt, G.A., Ruedy, R., Hansen, J.E., Aleinov, I., Bell, N., Bauer, M., Bauer, S., Cairns, B., Canuto, V., Cheng, Y., et al.: Present-day atmospheric simulations using GISS model E: Comparison to in situ, satellite, and reanalysis data. J. Clim. 19(2), 153 (2006)

    Article  Google Scholar 

  17. Vasquez, T.: Weather Forecasting Red Book. Weather Graphics Technologies, Garland (2006)

    Google Scholar 

  18. Walko, R.L., Avissar, R.: The ocean-land-atmosphere model (OLAM). Part I: Shallow-water tests. Mon. Weather Rev. 136(11), 4033–4044 (2008)

    Article  Google Scholar 

  19. Washington, W.M., Parkinson, C.L.: An Introduction to Three Dimensional Climate Modeling, 2nd edn. University Science Books, Herndon (2005)

    Google Scholar 

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Acknowledgments

This work was supported by the Brazilian research foundation Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)—“National Counsel of Technological and Scientific Development”.

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Correspondence to Claudio Schepke.

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Schepke, C., Maillard, N., Schneider, J. et al. Online Mesh Refinement for Parallel Atmospheric Models. Int J Parallel Prog 41, 552–569 (2013). https://doi.org/10.1007/s10766-012-0235-4

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  • DOI: https://doi.org/10.1007/s10766-012-0235-4

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