• DocumentCode
    228712
  • Title

    The DRIHM Project: A Flexible Approach to Integrate HPC, Grid and Cloud Resources for Hydro-Meteorological Research

  • Author

    Dagostino, Daniele ; Clematis, Andrea ; Galizia, Antonella ; Quarati, Alfonso ; Danovaro, Emanuele ; Roverelli, Luca ; Zereik, Gabriele ; Kranzlmuller, Dieter ; Schiffers, Michael ; Gentschen Felde, Nils ; Straube, Christian ; Caumontz, Olivier ; Richard,

  • Author_Institution
    Inst. of Appl. Math. & Inf. Technol., Italy
  • fYear
    2014
  • fDate
    16-21 Nov. 2014
  • Firstpage
    536
  • Lastpage
    546
  • Abstract
    The distributed research infrastructure for hydrometeorology (DRIHM) project focuses on the development of an e-Science infrastructure to provide end-to-end hydro meteorological research (HMR) services (models, data, and post processing tools) by exploiting HPC, Grid and Cloud facilities. In particular, the DRIHM infrastructure supports the execution and analysis of high-resolution simulations through the definition of workflows composed by heterogeneous HMR models in a scalable and interoperable way, while hiding all the low level complexities. This contribution gives insights into best practices adopted to satisfy the requirements of an emerging multidisciplinary scientific community composed of earth and atmospheric scientists. To this end, DRIHM supplies innovative services leveraging high performance and distributed computing resources. Hydro meteorological requirements shape this IT infrastructure through an iterative "learning-by-doing" approach that permits tight interactions between the application community and computer scientists, leading to the development of a flexible, extensible, and interoperable framework.
  • Keywords
    cloud computing; geophysics computing; grid computing; hydrology; meteorology; parallel processing; DRIHM project; HPC; cloud resources; distributed computing resources; distributed research infrastructure for hydrometeorology project; e-science infrastructure; grid resources; heterogeneous HMR models; high performance computing resources; hydrometeorological research; iterative learning-by-doing approach; Atmospheric modeling; Biological system modeling; Computational modeling; Data models; Forecasting; Meteorology; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    High Performance Computing, Networking, Storage and Analysis, SC14: International Conference for
  • Conference_Location
    New Orleans, LA
  • Print_ISBN
    978-1-4799-5499-5
  • Type

    conf

  • DOI
    10.1109/SC.2014.49
  • Filename
    7013031