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
Link To Document :
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