Scientific Compression Library

The Scientific Compression Library (SCIL) is a meta-compressor that decouples definition of user requirements from the selection of the compression algorithm. In detail, it allows users to set various quantities that define the acceptable error and the expected performance behavior. The library then aims to choose the appropriate chain 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 identifying tolerable error bounds and performance behavior from the selection and configuration of the algorithms.

Key Information

Contact Dr. Julian Kunkel
Repository Public on GitHub
URL Also developed in the project: AIMES

Publications

  • \myPub{2019}{Towards Green Scientific Data Compression Through High-Level I/O Interfaces}{Yevhen Alforov, Anastasiia Novikova, Michael Kuhn, Julian Kunkel, Thomas Ludwig}{In 30th International Symposium on Computer Architecture and High Performance Computing, pp. 209–216, IEEE Computer Society, SBAC-PAD 2018, Lyon, France, ISBN: 978-1-5386-7769-8, ISSN: 1550-6533}
  • \myPub{2017}{Towards Decoupling the Selection of Compression Algorithms from Quality Constraints – an Investigation of Lossy Compression Efficiency}{Julian Kunkel, Anastasiia Novikova, Eugen Betke}{In Supercomputing Frontiers and Innovations, Series: Volume 4, Number 4, pp. 17–33}
  • \myPub{}{Toward Decoupling the Selection of Compression Algorithms from Quality Constraints}{Julian Kunkel, Anastasia Novikova, Eugen Betke}{Poster, SC17, Denver, CO, USA}
  • \myPub{}{Toward Decoupling the Selection of Compression Algorithms from Quality Constraints}{Julian Kunkel, Anastasiia Novikova, Eugen Betke, Armin Schaare}{In High Performance Computing: ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Lecture Notes in Computer Science (10524), pp. 1–12, Springer, ISC High Performance, Frankfurt, Germany, ISBN: 978-3-319-67629-6}
  • \myPub{2016}{Data Compression for Climate Data}{Michael Kuhn, Julian Kunkel, Thomas Ludwig}{In Supercomputing Frontiers and Innovations, Series: Volume 3, Number 1, pp. 75–94}

Talks

  • \myPub{2019}{Scientific Data Compression with SCIL}{SPPEXA Final Symposium}{Dresden, Germany}
  • \myPub{2017}{Decoupling the Selection of Compression Algorithms from Required Precision with the Scientific Compression Library (SCIL)}{ISC HPC, Poster session}{Frankfurt, Germany}