Towards Smart Visualization Framework for Climate Simulations

Abstract : The increasing gap between computational power and I/O performance in new supercomputers has started to drive a shift from an offline approach to data analysis to an inline approach, termed in situ visualization (ISV). While most visualization software now provides ISV, they typically visualize large dumps of unstructured data, by rendering everything at the highest possible resolution. This often negatively impacts the performance of simulations that support ISV, in particular when ISV is performed interactively, as in situ visualization requires synchronization with the simulation. In this work, we advocate for a smarter method of performing ISV. Our approach is data-driven: it aims to detect potentially interesting regions in the generated dataset in order to feed ISV frameworks with “the interesting” subset of the data produced by the simulation. While this method mitigates the load on ISV frameworks by making them more efficient and more interactive, it also helps scientists focus on the relevant part of their data. We investigate smart ISV in the context of a climate simulation, with a set of generic filters derived from information theory, statistics and image processing, and show the tradeoff between performance and quality of visualization.
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Contributeur : Lokman Rahmani <>
Soumis le : jeudi 17 mars 2016 - 19:44:34
Dernière modification le : mardi 17 avril 2018 - 09:08:51
Document(s) archivé(s) le : samedi 18 juin 2016 - 12:02:12


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  • HAL Id : hal-01290268, version 1


Lokman Rahmani, Matthieu Dorier, Luc Bougé, Gabriel Antoniu, Robert Sisneros, et al.. Towards Smart Visualization Framework for Climate Simulations. 2016. 〈hal-01290268〉



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