1. In situ analysis and visualization of massively parallel computations
- Author
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Marc Buffat, Anne Cadiou, Christophe Pera, Lionel Le Penven, Laboratoire de Mecanique des Fluides et d'Acoustique (LMFA), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Computer science ,business.industry ,Computation ,Big data ,Process (computing) ,020207 software engineering ,02 engineering and technology ,Parallel computing ,Supercomputer ,01 natural sciences ,010305 fluids & plasmas ,Theoretical Computer Science ,Visualization ,Computational science ,[SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph] ,Resource (project management) ,Hardware and Architecture ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,business ,Massively parallel ,Computational steering ,Software ,ComputingMilieux_MISCELLANEOUS - Abstract
Massively parallel simulations generate increasing volumes of big data, whose exploitation requires increasingly large storage resources, efficient networking technologies and post-processing facilities. In the coming era of exascale supercomputing, there is an emerging need for new data analysis and visualization strategies. A promising solution consists of coupling analysis with simulation, so that both are performed simultaneously. This paper describes a client–server in situ analysis for massively parallel time-evolving computations. Its application to very large turbulent transition simulations using a spectral approximation is presented. It is shown to have a low impact on the computational time with a reasonable increase of resource usage, while enriching data exploration. Computational steering is performed with real-time adjustment of the simulation parameters, thereby getting closer to a numerical experiment process. This would not have been achieved with a classical work flow using off-line visualization.
- Published
- 2015
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