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Computing and Visualization in Science

, Volume 19, Issue 5–6, pp 65–76 | Cite as

Multilevel techniques for compression and reduction of scientific data—the univariate case

  • Mark Ainsworth
  • Ozan Tugluk
  • Ben Whitney
  • Scott Klasky
Original Article
  • 56 Downloads

Abstract

We present a multilevel technique for the compression and reduction of univariate data and give an optimal complexity algorithm for its implementation. A hierarchical scheme offers the flexibility to produce multiple levels of partial decompression of the data so that each user can work with a reduced representation that requires minimal storage whilst achieving the required level of tolerance. The algorithm is applied to the case of turbulence modelling in which the datasets are traditionally not only extremely large but inherently non-smooth and, as such, rather resistant to compression. We decompress the data for a range of relative errors, carry out the usual analysis procedures for turbulent data, and compare the results of the analysis on the reduced datasets to the results that would be obtained on the full dataset. The results obtained demonstrate the promise of multilevel compression techniques for the reduction of data arising from large scale simulations of complex phenomena such as turbulence modelling.

Keywords

Data compression Data reduction Lossy compression Multilevel compression Error-controlled compression 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mark Ainsworth
    • 1
    • 2
  • Ozan Tugluk
    • 1
    • 3
  • Ben Whitney
    • 1
  • Scott Klasky
    • 2
  1. 1.Division of Applied MathematicsBrown UniversityProvidenceUSA
  2. 2.Computer Science and Mathematics DivisionOak Ridge National LaboratoryOak RidgeUSA
  3. 3.Department of Astronautical EngineeringUniversity of Turkish Aeronautical AssociationAnkaraTurkey

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