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Toward Decoupling the Selection of Compression Algorithms from Quality Constraints

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10524)

Abstract

Data intense scientific domains use data compression to reduce the storage space needed. Lossless data compression preserves the original information accurately but on the domain of climate data usually yields a compression factor of only 2:1. Lossy data compression can achieve much higher compression rates depending on the tolerable error/precision needed. Therefore, the field of lossy compression is still subject to active research. From the perspective of a scientist, the compression algorithm does not matter but the qualitative information about the implied loss of precision of data is a concern.

With the Scientific Compression Library (SCIL), we are developing a meta-compressor that allows users to set various quantities that define the acceptable error and the expected performance behavior. The ongoing work a preliminary stage for the design of an automatic compression algorithm selector. The task of this missing key component is the construction of appropriate chains of algorithms to yield the users requirements. This approach is a crucial step towards a scientifically safe use of much-needed lossy data compression, because it disentangles the tasks of determining scientific ground characteristics of tolerable noise, from the task of determining an optimal compression strategy given target noise levels and constraints. Future algorithms are used without change in the application code, once they are integrated into SCIL.

In this paper, we describe the user interfaces and quantities, two compression algorithms and evaluate SCIL’s ability for compressing climate data. This will show that the novel algorithms are competitive with state-of-the-art compressors ZFP and SZ and illustrate that the best algorithm depends on user settings and data properties.

Keywords

Compression Algorithm Lossy Compression Optimal Compression Scheme Target Noise Level AbsTol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported in part by the German Research Foundation (DFG) through the Priority Programme 1648 “Software for Exascale Computing” (SPPEXA) (GZ: LU 1353/11-1).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Deutsches KlimarechenzentrumHamburgGermany
  2. 2.Universität HamburgHamburgGermany

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