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Distributed Out-of-Core Approach for In-Situ Volume Rendering of Massive Dataset

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High Performance Computing (ISC High Performance 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11887))

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Abstract

This paper proposes a method that allows a fluid remote interactive visualization of a terabytes volume on a conventional workstation co-located with the acquisition devices, leveraging remote high performance computing resources. We provide a study of the behavior of an out-of-core volume renderer, using a virtual addressing system with interactive data streaming, in a distributed environment. The method implements a sort-last volume renderer with a multi-resolution ray-guided approach to visualize very large volumes of data thanks to an hybrid multi-GPUs, multi-CPUs single node rendering server.

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Acknowledgments

This work is supported by the French national funds (PIA2’program “Intensive Computing and Numerical Simulation” call) under contract No. P112331-3422142 (3DNeuro Secure project). We would like to thank all the partners of the consortium led by Neoxia, the three French clusters (Cap Digital, Systematic and Medicen), Thierry Delzescaux and the Mircen team (CEA, France) for the two datasets as well as the Centre Image of the University of Reims for the VCA server used.

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Authors and Affiliations

  1. Université de Reims Champagne-Ardenne, CReSTIC, Reims, France

    Jonathan Sarton, Yannick Remion & Laurent Lucas

Authors
  1. Jonathan Sarton
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  2. Yannick Remion
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  3. Laurent Lucas
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Corresponding author

Correspondence to Laurent Lucas .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Michèle Weiland

  2. Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Sachsen, Germany

    Guido Juckeland

  3. Swiss National Supercomputing Centre, Lugano, Ticino, Switzerland

    Sadaf Alam

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Heike Jagode

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Sarton, J., Remion, Y., Lucas, L. (2019). Distributed Out-of-Core Approach for In-Situ Volume Rendering of Massive Dataset. In: Weiland, M., Juckeland, G., Alam, S., Jagode, H. (eds) High Performance Computing. ISC High Performance 2019. Lecture Notes in Computer Science(), vol 11887. Springer, Cham. https://doi.org/10.1007/978-3-030-34356-9_47

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  • DOI: https://doi.org/10.1007/978-3-030-34356-9_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34355-2

  • Online ISBN: 978-3-030-34356-9

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