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High-Performance Computing Applied in Project UBEST

  • Ricardo MartinsEmail author
  • João RogeiroEmail author
  • Marta RodriguesEmail author
  • André B. FortunatoEmail author
  • Anabela OliveiraEmail author
  • Alberto AzevedoEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)

Abstract

UBEST aims at improving the global understanding of present and future biogeochemical buffering capacity of estuaries through the development of Observatories, computational web-portals that integrate field observation and real-time MPI (Message Passing Interface) numerical simulations. HPC (High-Performance Computing) is applied in Observatories to serve both on-the-fly frontend user requests for multiple spatial analyses and to speed up backend’s forecast hydrodynamic and ecological simulations based on unstructured grids. Backend simulations are performed using the open-source SCHISM (Semi-implicit Cross-scale Hydroscience Integrated System Model). Python programming language will be used in this project to automate the MPI simulations and the web-portal in Django.

Keywords

HPC Estuaries Numerical models Parallel computing Forecasts SCHISM UBEST 

Notes

Acknowledgements

The authors would like to thank Dr. Y. Zhang for making the models SCHISM and SELFE openly available. This work was developed in the scope of project UBEST (PTDC/AAG-MAA/6899/2014), funded by the Fundação para a Ciência e a Tecnologia (FCT). The third author was also funded by FCT through grant (SFRH/BPD/87512/2012).

References

  1. 1.
    Rodrigues, M., Fortunato, A.B., Freire, P.: Salinity evolution in the Tagus estuary relative to climate change. In: 4as Jornadas de Engenharia Hidrográfica, Lisbon, June 2016Google Scholar
  2. 2.
    Costa, M., Oliveira, A., Rodrigues, M., Azevedo, A.: Application of parallel, high-performance computing in coastal environmental modeling: circulation and ecological dynamics in the Portuguese coast. In: Proceedings da 3rd Iberian Grid Infrastructure, pp. 375–386 (2009)Google Scholar
  3. 3.
    Zhang, Y., Ye, F., Stanev, E.V., Grashorn, S.: Seamless cross-scale modeling with SCHISM. Ocean Model. 102, 64–81 (2016)CrossRefGoogle Scholar
  4. 4.
    Rogeiro, J., et al.: Running high resolution coastal models in forecast systems: moving from workstations and HPC cluster to cloud resources. Adv. Eng. Softw. 117, 70–79 (2017). ISSN 0965–9978CrossRefGoogle Scholar
  5. 5.
    Glenis, V., McGough, A.S., Kutija, V., Kilsby, C., Woodman, S.: Flood modelling for cities using cloud computing. J. Cloud Comput. 2(7), 1–14 (2013)Google Scholar
  6. 6.
    Zhang, Y., Witter, R.W., Priest, G.P.: Tsunami-tide interaction in 1964 Prince William Sound Tsunami. Ocean Model. 40, 246–259 (2011)CrossRefGoogle Scholar
  7. 7.
    Dietrich, J.C., et al.: Modeling hurricane waves and storm surge using integrally-coupled, scalable computations. Coast. Eng. 58(1), 45–65 (2011).  https://doi.org/10.1016/j.coastaleng.2010.08.001CrossRefGoogle Scholar
  8. 8.
    Hashioka, T., Yamanaka, Y.: Ecosystem change in the western North Pacific associated with global warming using 3D-NEMURO. Ecol. Model. 202, 95–104 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhang, Y., Baptista, A.M.: SELFE: a semi-implicit Eulerian-Lagrangian finite-element model for cross-scale ocean circulation. Ocean Model. 21(3–4), 71–96 (2008)CrossRefGoogle Scholar
  10. 10.
    Azevedo, A., et al.: An oil risk management system based on high-resolution hazard and vulnerability calculations. Ocean Coast. Manag. 136(1), 1–18 (2017).  https://doi.org/10.1016/j.ocecoaman.2016.11.014CrossRefGoogle Scholar
  11. 11.
    Rodrigues, M., Guerreiro, M., David, L.M., Oliveira, A., Menaia, J., Jacob, J.: Role of environmental forcings on fecal contamination behavior in a small, intermittent coastal stream: an integrated modelling approach. J. Environ. Eng. 142(5), 05016001 (2016)CrossRefGoogle Scholar
  12. 12.
    Rodrigues, M., Oliveira, A., Queiroga, H., Fortunato, A.B., Zhang, Y.J.: Three-dimensional modeling of the lower trophic levels in the Ria de Aveiro (Portugal). Ecol. Model. 220(9–10), 1274–1290 (2009). ISSN 0304-3800CrossRefGoogle Scholar
  13. 13.
    Fortunato, A.B., et al.: Operational forecast framework applied to extreme sea levels at regional and local scales. J. Oper. Oceanogr. 10(1), 1–15 (2017)CrossRefGoogle Scholar
  14. 14.
    Freire, P., et al.: A local-scale approach to estuarine flood risk management. Nat. Hazards 84(3), 1705–1739 (2016)CrossRefGoogle Scholar
  15. 15.
    Cholia, S., Skinner, D., Boverhof, J.: NEWT: a RESTful service for building high performance computing web applications. In: 2010 Gateway Computing Environments Workshop (GCE), New Orleans, LA, pp. 1–11 (2010).  https://doi.org/10.1109/gce.2010.5676125

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Hydraulics and Environment DepartmentLNEC – National Laboratory for Civil EngineeringLisbonPortugal

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