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Toward a Multi-method Approach: Lossy Data Compression for Climate Simulation Data

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High Performance Computing (ISC High Performance 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10524))

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Abstract

Earth System Model (ESM) simulations are increasingly constrained by the amount of data that they generate rather than by computational resources. The use of lossy data compression on model output can reduce storage costs and data transmission overheads, but care must be taken to ensure that science results are not impacted. Choosing appropriate compression algorithms and parameters is not trivial given the diversity of data produced by ESMs and requires an understanding of both the attributes of the data and the properties of the chosen compression methods. Here we discuss the properties of two distinct approaches for lossy compression in the context of a well-known ESM, demonstrating the different strengths of each, to motivate the development of an automated multi-method approach for compression of climate model output.

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Authors and Affiliations

  1. The National Center for Atmospheric Research, Boulder, CO, 80305, USA

    Allison H. Baker, Haiying Xu, Dorit M. Hammerling, Shaomeng Li & John P. Clyne

Authors
  1. Allison H. Baker
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  2. Haiying Xu
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  3. Dorit M. Hammerling
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  4. Shaomeng Li
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  5. John P. Clyne
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Corresponding author

Correspondence to Allison H. Baker .

Editor information

Editors and Affiliations

  1. Deutsches Klimarechenzentrum (DKRZ), Hamburg, Hamburg, Germany

    Julian M. Kunkel

  2. TITECH, Tokyo, Japan

    Rio Yokota

  3. Department of Computer Science, University of Delaware, Newark, Delaware, USA

    Michela Taufer

  4. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    John Shalf

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Baker, A.H., Xu, H., Hammerling, D.M., Li, S., Clyne, J.P. (2017). Toward a Multi-method Approach: Lossy Data Compression for Climate Simulation Data. In: Kunkel, J., Yokota, R., Taufer, M., Shalf, J. (eds) High Performance Computing. ISC High Performance 2017. Lecture Notes in Computer Science(), vol 10524. Springer, Cham. https://doi.org/10.1007/978-3-319-67630-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-67630-2_3

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  • Online ISBN: 978-3-319-67630-2

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