Parallel and GPU Based Strategies for Selected CFD and Climate Modeling Models

  • Krzysztof KurowskiEmail author
  • Michał Kulczewski
  • Mikołaj Dobski
Part of the Environmental Science and Engineering book series (ESE, volume 3)


In recent years we have observed a huge increase in natural disasters, such as earthquakes, tornadoes and floods. Even Poland as a relatively stable region has experienced many big natural disasters, in particular three floods early this year with the estimated cost around 3 billion Euro. To address the dramatic changes in our climate, hydro-meteo scientists have to work together to share important data, tweak existing meteorological models, even couple them, ultimately achieving the goal of preventing such disasters in the future. The computing power and data capacity grow every day, and scientists are willing to run the hydro-meteo simulations at greater model size as well as use new archiving and back up services for historical analysis. However, to benefit from modern architectures, applications and data structures have to be adapted properly. In this chapter we discuss various ideas to improve the performance of Eulag - a numerical solver for all-scale geophysical flows using innovative computing technologies. We present preliminary results of applying the GPGPU and OpenMP shared memory model to Eulag, so that the application can be well scaled on cluster of GPUs or cluster of SMPs nodes respectively. Moreover, we present additional useful solutions to visualize the hydro-meteorological data at speed, as well as to share data in a secure way among end-users


Climate modelling Visualization Optimization Modern architectures 


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  1. 1.
  2. 2.
    Blażewicz M, Kurowski K, Ludwiczak B, Napierala K (2010) High performance computing on new accelerated hardware architectures. Computational Methods in Science And Technology Special Issue SEMI-ANNUAL:71-79Google Scholar
  3. 3.
    Succi S (2001) The Lattice Boltzmann Equation for Fluid Dynamics and Beyond. Oxford University Press, USAGoogle Scholar
  4. 4.
    Sukop M.C, Thorne D.T. (2006) Lattice Boltzmann Modeling. SpringerGoogle Scholar
  5. 5.
    Coheh I.M, Kundu P.K. (2007) Fluid Mechanics. Academic PressGoogle Scholar
  6. 6.
    Geller S, Krafczyk M, Tolke J, Turek S, Hron J (2006) Benchmark computations based on lattice-Boltzmann, finite element and finite volume methods for laminar flows, Computer & Fluids, Volume 35, Issues 8-9, 888-897Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Prusa J.M, Smolarkiewicz P.K, Wyszogrodzki A. (2008) Eulag, a computational model for multiscale flows, Computer & Fluids, Volume 37, 1193-1207Google Scholar
  10. 10.
    Piotrowski, Z.P, Kurowski, M.J, Rosa, B, Ziemiański, M.Z. (2010) EULAG Model for Multiscale Flows - Towards the Petascale Generation of Mesoscale Numerical Weather Prediction, Parallel Processing and Applied Mathematics, Lecture Notes in Computer Science, Volume 6068/2010, 380-387Google Scholar
  11. 11.
  12. 12.
    Wolfe M, Toepfer C. (2009) The PGI Accelerator Programming Model on NVIDIA GPUs Part3: Porting WRF.
  13. 13.
    Smolarkiewicz P.K. (1984) A Fully Multidimensional Positive Definite Advection Transport Algorithm with Small Implicit Diffusion. Journal of Computational Physics 54:325-362CrossRefGoogle Scholar
  14. 14.
    Russel M, Dziubecki P, Grabowski P, Krysiński P, Kuczyński T, Szejnfeld D, Tarnawczyk D, Wolniewicz G, Nabrzyski J (2008) The vine toolkit: A java framework for developing grid applications. Parallel Processing and Applied Mathematics: 331-340Google Scholar
  15. 15.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Krzysztof Kurowski
    • 1
    Email author
  • Michał Kulczewski
    • 1
  • Mikołaj Dobski
    • 1
  1. 1.Poznan Supercomputing and Networking CenterPoznanPoland

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