Comparison and Validation of Two Parallelization Approaches of FullSWOF_2D Software on a Real Case

  • Olivier DelestreEmail author
  • Morgan Abily
  • Florian Cordier
  • Philippe Gourbesville
  • Hélène Coullon
Part of the Springer Water book series (SPWA)


FullSWOF_2D (Full Shallow Water equation for Overland Flow in two dimensions) is a free software designed for shallow water flow simulations. The shallow water equations are solved thanks to a well-balanced finite volume scheme (based on the hydrostatic reconstruction), which is adapted to the properties of the model considered (in particular conservative laws, hyperbolic system, and steady states). The sources of this software (in C++) are available from This software has been validated on several analytical test cases integrated in SWASHES library and on rainfall overland flow simulations. Because of the simulations on big data necessity, this software has been parallelized with two different strategies (MPI and SKELGIS) in the framework of the CEMRACS 2012. Our purpose is to continue the comparison and the validation of these two versions of FullSWOF_Paral on realistic test cases. Our methodology will consist in comparing these two approaches on classical test cases such as Malpasset’s dam break with 2D hydraulic softwares such as MIKE 21, MIKE 21 FM, and TELEMAC 2D. The two strategies are presented in this paper. As this work is still in progress, only results from MPI version are presented here. More results will be given in future works.


Shallow water equations Fullswof MIKE 21 TELEMAC Malpasset dam break Validation Parallel computing 



This work was granted access to the HPC resources of Aix-Marseille Université financed by the project Equip@Meso (ANR-10-EQPX-29-01) of the program “Investissements d’Avenir” supervised by the Agence Nationale pour la Recherche.


  1. 1.
    Delestre, O. (2010). Simulation du ruissellement d’eau de pluie sur des surfaces agricoles. PhD thesis University of Orléans, in french.
  2. 2.
    Delestre, O., Cordier, S., Darboux, F., Du, M., James, F., Laguerre, C., et al. (2014). FullSWOF: A software for overland flow simulation. In P. Gourbesville, J. Cunge, & G. Caignaert (Eds.), Advances in Hydroinformatics (pp. 221–231). Springer Hydrogeology.Google Scholar
  3. 3.
    Esteves, M., Faucher, X., Galle, S., & Vauclin, M. (2000). Overland flow and infiltration modelling for small plots during unsteady rain: Numerical results versus observed values. Journal of Hydrology, 228, 265–282.CrossRefGoogle Scholar
  4. 4.
    Tatard, L., Planchon, O., Wainwright, J., Nord, G., Favis-Mortlock, D., Silvera, N., et al. (2008). Measurement and modelling of high-resolution flow-velocity data under simulated rainfall on a low-slope sandy soil. Journal of Hydrology, 348(1–2), 1–12.CrossRefGoogle Scholar
  5. 5.
    Goutal, N., & Maurel, F. (2002). A finite volume solver for 1D shallow-water equations applied to an actual river. International Journal for Numerical Methods in Fluids, 38, 1–19.CrossRefzbMATHGoogle Scholar
  6. 6.
    Caleffi, V., Valiani, A., & Zanni, A. (2003). Finite volume method for simulating extreme flood events in natural flood events in natural channels. Journal of Hydraulic Research, 41(2), 167–177.CrossRefGoogle Scholar
  7. 7.
    Alcrudo, F., & Gil, E. (1999). The Malpasset dam break case study. In The 4th CADAM Workshop, Zaragoza (pp. 95–109).Google Scholar
  8. 8.
    Valiani, A., Caleffi, V., & Zanni, A. (2002). Case study: Malpasset dam-break simulation using a two-dimensional finite volume methods. Journal of Hydraulic Engineering, 128(5), 460–472.CrossRefGoogle Scholar
  9. 9.
    Popinet, S. (2011). Quadtree-adaptive tsunami modelling. Ocean Dynamics, 61(9), 1261–1285.CrossRefGoogle Scholar
  10. 10.
    Andres, L. (2012). L’apport de la donnée topographique pour la modélisation 3D fine et classifiée d’un territoire, in french, Revue XYZ (Vol. 133, 4th trimester, pp. 24–30).Google Scholar
  11. 11.
    Abily, M., Bertrand, N., Delestre, O., Richet, Y., Duluc, C.-M., & Gourbesville, P. (2014). Global sensitivity analysis with 2D hydraulic codes: Application on uncertainties related to high resolution topographic data. In Proceeding of SimHydro 2014: Modelling of rapid transitory flows. Sophia Antipolis, France, June 11–13, 2014.Google Scholar
  12. 12.
    Aackermann, P., Pedersen, P., Engsig-Karup, A., Clausen, T., & Grooss, J. (2013). Development of a GPU-accelerated mike 21 solver for water wave dynamics. In R. Keller, D. Kramer, & J.P. Weiss (Eds.), Facing the Multicore-Challenge III, Springer Berlin Heidelberg (Vol. 7686, pp. 129–130).Google Scholar
  13. 13.
    Brodtkorb, A. R., Saetra, M. L., & Altinakar, M. (2012). Efficient shallow water simulations on GPUs: Implementation, visualization, verification, and validation. Computers and Fluids, 55, 1–12.CrossRefMathSciNetzbMATHGoogle Scholar
  14. 14.
    DHI. (2007). MIKE 21 FLOW MODEL (p. 58). Hydrodynamic module: Scientific documentation. Danish Hydraulics Institute.Google Scholar
  15. 15.
    DHI. (2007). MIKE 21 and MIKE 3 FLOW MODEL FM (p. 50). Hydrodynamic and transport module: Scientific documentation. Danish Hydraulics Institute.Google Scholar
  16. 16.
    Hervouet, J.-M. (1999). TELEMAC, a hydroinformatic system/Télémac, un système hydroinformatique. La Houille Blanche, 3–4, 21–28.CrossRefGoogle Scholar
  17. 17.
    Hervouet, J.-M., & Petitjean, A. (1999). Malpasset dam-break revisited with two-dimensional computations. Journal of Hydraulic Research, 37(6), 777–788.CrossRefGoogle Scholar
  18. 18.
    Hervouet, J.-M. (2000). A high resolution 2-D dam-break model using parallelization. Hydrological Processes, 14, 2211–2230.CrossRefGoogle Scholar
  19. 19.
    Hervouet, J.-M. (2007). Hydrodynamics of free surface flows: Modelling with the finite element. West Sussex: Wiley.Google Scholar
  20. 20.
    Delestre, O., Darboux, F., James, F., Lucas, C., Laguerre, C. & Cordier, S. (submitted). FullSWOF: A free software package for the simulation of shallow water flows. Scholar
  21. 21.
    Cordier, S., Coullon, H., Delestre, O., Laguerre, C., Le, M. H., Pierre, D., et al. (2013). FullSWOF_Paral: Comparison of two parallelization strategies (MPI and SKELGIS) on a software designed for hydrology applications, ESAIM: Proc., 43, 59–79.Google Scholar
  22. 22.
    Brugeas, L. (1996). Utilisation de MPI en décomposition de domaine. CNRS-IDRIS., p. 27.
  23. 23.
    EM Karniadakis, G., & Kirby II, R. M. (2003). Parallel scientific computing in C++ and MPI. Cambridge: Cambridge University Press.Google Scholar
  24. 24.
    Coullon, H., Le, M. -H., & Limet, S. (2013). Parallelization of shallow-water equations with the algorithmic skeleton library skelgis. International Conference of Computational Science. Barcelona Spain. Elsevier Procedia Computer Science, 18, 591–600.Google Scholar
  25. 25.
    Coullon, H., Limet, S. (2013). Algorithmic skeleton library for scientific simulations: SkelGIS. International Conference on High Performance Computing and Simulation. Helsinki Finland. IEEE HPCS 2013 (pp. 429–436).Google Scholar
  26. 26.
    Abily, M., Delestre, O., Amossé, L., Bertrand, N., Richet, Y., Duluc, C.-M., et al. (submitted). Uncertainty related to high resolution classified topographic data use for flood event modeling over urban areas: a sensitivity analysis based approach.Google Scholar
  27. 27.
    Abily, M., Delestre, O., Amosse, L., Bertrand, N., Laguerre, C., Duluc, C.-M. et al. (2014). Use of 3D classified topographic data with FullSWOF for High Resolution simulations of river flood event over a dense urban area. 3rd IAHR Europe Congress, Book of Proceedings, 2014, Porto, Portugal.Google Scholar
  28. 28.
    Delestre, O., Lucas, C., Ksinant, P.-A., Darboux, F., Laguerre, C., Vo, T. N. T., et al. (2013). SWASHES: a compilation of Shallow-Water analytic solutions for hydraulic and environmental studies. International Journal for Numerical Methods in Fluids, 72, 269–300. doi: 10.1002/fld.3741.CrossRefMathSciNetGoogle Scholar
  29. 29.
    Delestre, O., Lucas, C., Ksinant, P.-A., Darboux, F., Laguerre, C., James, F., et al. (2014). SWASHES: A library for benchmarking in hydraulic. In Gourbesville, P., Cunge, J., & Caignaert, G., (Eds.), Advances in Hydroinformatics, Springer Hydrogeology (pp. 233–243).Google Scholar
  30. 30.
    Caleffi, V., Valiani, A., & Zanni, A. (2003). Finite volume method for simulating extreme flood events in natural channels. Journal of Hydraulic Research, 41, 167–177.CrossRefGoogle Scholar
  31. 31.
    Valiani, A., Caleffi, V., & Zanni, A. (1999) Finite volume scheme for 2D Shallow-Water equations: Application to a flood event in the Toce river. The 4th CADAM Workshop, Zaragoza, Spain (pp. 185–206).Google Scholar
  32. 32.
    Berger, M. J., George, D. L., LeVeque, R. J., & Mandli, K. T. (2011). The GeoClaw software for depth-averaged flows with adaptive refinement. Advances in Water Resources, 34, 1195–1206.CrossRefGoogle Scholar
  33. 33.
    Duran, A., Liang, Q., & Marche, F. (2013). On the well-balanced numerical discretization of shallow water equations on unstructured meshes. Journal of Computational Physics, 235, 565–586.CrossRefMathSciNetzbMATHGoogle Scholar
  34. 34.
    Malleron, N., Zaoui, F., Goutal, N., & Morel, T. (2011). On the use of a high-performance framework for efficient model coupling in hydroinformatics. Environmental Modelling and Software, 26, 1747–1758.CrossRefGoogle Scholar
  35. 35.
    Singh, J., Altinakar, M. S., & Ding, Y. (2011). Two-dimensional modeling of dam-break flows over natural terrain using a central explicit scheme. Advances in Water Resources, 34, 1366–1375.CrossRefGoogle Scholar
  36. 36.
    Mulder, T., Zaragosi, S., Jouanneau, J.-M., Bellaiche, G., Guérinaud, S., & Querneau, J. (2009). Deposits related to the failure of the Malpasset Dam in 1959 An analogue for hyperpycnal deposits from jokulhlaups. Marine Geology, 260, 81–89.CrossRefGoogle Scholar
  37. 37.
    Benoist, G. (1989). Les études d’ondes de submersion des grands barrages d’EDF. La Houille Blanche, 1, 43–54.CrossRefGoogle Scholar
  38. 38.
    Valiani, A., Caleffi, V., & Zanni, A. (1999). Finite volume scheme for 2D shallow-water equations. Application to Malpasset dam-break. In The 4th CADAM Workshop, Zaragoza (pp. 63–94).Google Scholar
  39. 39.
    Audusse, E., Bouchut, F., Bristeau, M.-O., Klein, R., & Perthame, B. (2004). A fast and stable well-balanced scheme with hydrostatic reconstruction for shallow water flows. SIAM Journal on Scientific Computing, 25(6), 2050–2065.CrossRefMathSciNetzbMATHGoogle Scholar
  40. 40.
    Coullon, H., Fullana, J.-M., Lagrée, P.-Y., Limet, S., & Wang, X. (2014). blood flow arterial network simulation with the implicit parallelism library SkelGIS. International Conference of Computational Science. Cairns Australia (On press).Google Scholar
  41. 41.
    Mudalige, G. R., Giles, M. B., Reguly, I., Bertolli, C., & Kelly, P. H. J. (2012). Op2: An active library framework for solving unstructured mesh-based applications on multi-core and many-core architectures. IEEE innovative Parallel Computing (InPar) (pp. 1–12).Google Scholar
  42. 42.
    de Vito, Z., Joubert, N., Palacios, F., Oakley, S., Medina, M., Barrientos, M., Elsen, F. H., Aiken, A., Duraisamy, K., Darve, E., Alonso, J., & Hanrahan, P. (2011). Liszt: A domain specific language for building portable mesh-based PDE solvers. In Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC’11, pp. 1–12. ACM.Google Scholar
  43. 43.
    McCowan, A. D., Rasmussen, E. B., & Berg, P. (2001). Improving the performance of a two-dimensional hydraulic model for floodplain applications, In Hydraulics in Civil Engineering, (p. 11) T.I.o. Engineers (Ed.), Hobart, Australia.Google Scholar
  44. 44.
    DHI. (2007). mike 21 and mike 3 flow model fm (p. 50). Hydrodynamic and transport module: Scientific documentation. Danish Hydraulics Institute.Google Scholar
  45. 45.
    Aackermann, P., Pedersen, P., Engsig-Karup, A., Clausen, T. & Grooss, J. (2013). Development of a GPU-accelerated mike 21 solver for water wave dynamics. In R. Keller, D. Kramer, & J.-P. Weiss (Eds.), Facing the Multicore-Challenge III, Springer Berlin Heidelberg (Vol. 7686, pp. 129–130).Google Scholar
  46. 46.
    Sørensen, O. R., Sørensen, L. S., & Carlson, J. (2010). Parallelization of the flexible mesh modeling systems with MPI. In International MIKE by DHI Conference 2010 (pp. 30.1–30.8). Copenhagen, Denmark,Google Scholar
  47. 47.
    Karypis, G. & Kumar, V. (1998). METIS: family of multilevel partitioning algorithm.
  48. 48.
    Audouin, Y., Moulinec, C., Barber, R. W., Sunderland, A. G., Gu, X. -J. & Emerson, D. R. (2011). Preparing TELEMAC-2D for extremely large simulations. In Proceedings of the XVIIIth Telemac and Mascaret User Club 19–21 October 2011. Chatou (France): EDF R&D.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Olivier Delestre
    • 1
    Email author
  • Morgan Abily
    • 1
  • Florian Cordier
    • 1
  • Philippe Gourbesville
    • 1
  • Hélène Coullon
    • 2
  1. 1.University of Nice Sophia Antipolis/Polytech’Nice-Sophia/URE 005 I-CiTySophia Antipolis CedexFrance
  2. 2.LIFOUniversity of Orléans and Géo-Hyd, bâtiment IIIAOrléans Cedex 2France

Personalised recommendations