Transparent Model-Driven Provisioning of Computing Resources for Numerically Intensive Simulations

2018 | book part. A publication with affiliation to the University of Göttingen.

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​Transparent Model-Driven Provisioning of Computing Resources for Numerically Intensive Simulations​
Korte, F. ; Bufe, A.; Kohler, C. W.; Brenner, G.; Grabowski, J. ; Wieder, P.  & Schöbel, A. ​ (2018)
In:​Baum, M.; Brenner, G.; Grabowski, J.; Hanschke, T.; Hartmann, S.​ (Eds.), Simulation Science. SimScience 2017. pp. 176​-192.  DOI: https://doi.org/10.1007/978-3-319-96271-9_11 

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Authors
Korte, Fabian ; Bufe, Alexander; Kohler, Christian W.; Brenner, Gunther; Grabowski, Jens ; Wieder, Philipp ; Schöbel, A. 
Editors
Baum, M. ; Brenner, G.; Grabowski, J. ; Hanschke, T.; Hartmann, S.
Abstract
Many simulations require large amounts of computing power to be executed. Traditionally, the computing power is provided by large high performance computing clusters that are solely built for this purpose. However, modern data centers do not only provide access to these high performance computing systems, but also offer other types of computing resources e.g., cloud systems, grid systems, or access to specialized computing resources, such as clusters equipped with accelerator hardware. Hence, the researcher is confronted with the choice of picking a suitable computing resource type for his simulation and acquiring the knowledge on how to access and manage his simulation on the resource type of choice. This is a time consuming and cumbersome process and could greatly benefit from supportive tooling. In this paper, we introduce a framework that allows to describe the simulation application in a resource-independent manner. It furthermore helps to select a suitable resource type according to the requirements of the simulation application and to automatically provision the required computing resources. We demonstrate the feasibility of the approach by providing a case study from the area of fluid mechanics.
Issue Date
2018
Organization
Gesellschaft für wissenschaftliche Datenverarbeitung 
ISBN
978-3-319-96270-2
978-3-319-96271-9
Language
English

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