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In Situ Visualization for Computational Science pp 353–374Cite as

Scalable CPU Ray Tracing for In Situ Visualization Using OSPRay

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Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

In situ visualization increasingly involves rendering large numbers of images for post hoc exploration. As both the number of images to be rendered and the data being rendered are large, the scalability of the rendering component is of key concern. Furthermore, the renderer must be able to support a wide range of data distributions, simulation configurations, and HPC systems to provide the flexibility required for a portable, general purpose in situ rendering package. In this chapter, we discuss recent developments in OSPRay’s support for MPI-parallel applications to provide a flexible and scalable rendering API, with a focus on how these developments can be applied to enable scalable, high-quality in situ visualization.

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Notes

  1. 1.

    https://github.com/Twinklebear/osp-icet.

  2. 2.

    https://github.com/Twinklebear/ospray_senpai.

  3. 3.

    https://github.com/Twinklebear/mini-cinema.

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Acknowledgements

The Miranda data set is courtesy Andrew W. Cook, William Cabot, and Paul L. Miller, the Richtmyer-Meshkov is courtesy Ronald H. Cohen, William P. Dannevik, Andris M. Dimits, Donald E. Eliason, Arthur A. Mirin, and Ye Zhou. Both data sets were made available through the Open Scientific Visualization Datasets repository. This work is supported in part by the Intel Graphics and Visualization Institute of eXcellence at the Scientific Computing and Imaging Institute, University of Utah. This work is supported in part by NSF: CGV Award: 1314896, NSF:IIP Award: 1602127, NSF:ACI Award: 1649923, DOE/SciDAC DESC0007446, CCMSC DE-NA0002375 and NSF:OAC Award: 1842042. The authors acknowledge the Texas Advanced Computing Center (TACC) at The University of Texas at Austin for providing HPC resources that have contributed to the research results reported in this paper.

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Authors and Affiliations

  1. Intel Corporation, Mountain View, USA

    Will Usher, Johannes Günther, Aaron Knoll, Gregory P. Johnson, Carson Brownlee, Alok Hota, Bruce Cherniak & Jim Jeffers

  2. Intel, Now with Nvidia, Santa Clara, USA

    Jefferson Amstutz

  3. Intel, Now with Google, Mountain View, USA

    Tim Rowley

  4. SCI Institute, University of Utah, Salt Lake City, USA

    Valerio Pascucci

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  1. Will Usher
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Correspondence to Will Usher .

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Editors and Affiliations

  1. Computer and Information Science, University of Oregon, Eugene, OR, USA

    Prof. Hank Childs

  2. Extreme-Scale Data Science and Analytics, Sandia National Laboratories, Livermore, CA, USA

    Prof. Janine C. Bennett

  3. Scientific Visualization Lab, University of Kaiserslautern, Kaiserslautern, Germany

    Dr. Christoph Garth

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Usher, W. et al. (2022). Scalable CPU Ray Tracing for In Situ Visualization Using OSPRay. In: Childs, H., Bennett, J.C., Garth, C. (eds) In Situ Visualization for Computational Science. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-81627-8_16

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