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In Situ Wavelet Compression on Supercomputers for Post Hoc Exploration

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Book cover In Situ Visualization for Computational Science

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Wavelet compression is a popular approach for reducing data size while maintaining high data integrity. This chapter considers how wavelet compression can be used for data visualization and post hoc exploration on supercomputers. There are three major parts in this chapter. The first part describes the basics of wavelet transforms, which are essential signal transformations in a wavelet compression pipeline, and how their properties can be used for data compression. The second part analyzes the efficacy of wavelet compression on scientific data, with a focus on analyses involving scientific visualizations. The third part evaluates how well wavelet compression fits in an in situ workflow on supercomputers. After reading this chapter, readers should have a high-level understanding of how wavelet compression works, as well as its efficacy for in situ compression and post hoc exploration.

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Acknowledgements

This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL). This work was also supported by the DOE Early Career Award for Hank Childs, Contract No. DE-SC0010652, Program Manager Lucy Nowell.

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Authors and Affiliations

  1. National Center for Atmospheric Research, Boulder, CO, USA

    Shaomeng Li & John Clyne

  2. University of Oregon, Eugene, OR, USA

    Hank Childs

Authors
  1. Shaomeng Li
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  2. John Clyne
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  3. Hank Childs
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Correspondence to Shaomeng Li .

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Editors and Affiliations

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

    Hank Childs

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

    Janine C. Bennett

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

    Christoph Garth

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Li, S., Clyne, J., Childs, H. (2022). In Situ Wavelet Compression on Supercomputers for Post Hoc Exploration. 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_3

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