Damaris/Viz: a Nonintrusive, Adaptable and User-Friendly In Situ Visualization Framework

Abstract : Reducing the amount of data stored by simulations will be of utmost importance for the next generation of large-scale computing. Accordingly, there is active research to shift analysis and visualization tasks to run in situ, that is, closer to the simulation via the sharing of some resources. This is beneficial as it can avoid the necessity of storing large amounts of data for post-processing. In this paper, we focus on the specific case of in situ visualization where analysis codes are collocated with the simulation's code and run on the same resources. It is important for an in situ technique to require minimum modifications to existing codes, be adaptable, and have a low impact on both run times and resource usage. We accomplish this through the Damaris/Viz framework, which provides in situ visualization support to the Damaris I/O middleware. The use of Damaris as a bridge to existing visualization packages allows us to (1) reduce code moditication to a minimum for existing simulations, (2) gather capabilities of several visualization tools to offer a unified data management interface, (3) use dedicated cores to hide the run time impact of in situ visualization and (4) efficiently use memory through a shared-memory-based communication model. Experiments are conducted on Blue Waters and Grid'5000 to visualize the CM1 atmospheric simulation and the Nek5000 CFD solver.
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Communication dans un congrès
LDAV - IEEE Symposium on Large-Scale Data Analysis and Visualization, Oct 2013, Atlanta, United States. 2013
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Contributeur : Matthieu Dorier <>
Soumis le : lundi 18 novembre 2013 - 17:55:20
Dernière modification le : mercredi 16 mai 2018 - 11:23:28
Document(s) archivé(s) le : mercredi 19 février 2014 - 02:55:14

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  • HAL Id : hal-00859603, version 1

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Matthieu Dorier, Robert Sisneros, Tom Peterka, Gabriel Antoniu, Dave Semeraro. Damaris/Viz: a Nonintrusive, Adaptable and User-Friendly In Situ Visualization Framework. LDAV - IEEE Symposium on Large-Scale Data Analysis and Visualization, Oct 2013, Atlanta, United States. 2013. 〈hal-00859603〉

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