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
- Towards Green Scientific Data Compression Through High-Level I/O Interfaces (Yevhen Alforov, Anastasiia Novikova, Michael Kuhn, Julian Kunkel, Thomas Ludwig), 2019-02-21 BibTeX DOI PDF
- Towards Decoupling the Selection of Compression Algorithms from Quality Constraints – an Investigation of Lossy Compression Efficiency (Julian Kunkel, Anastasiia Novikova, Eugen Betke), 2017-12 BibTeX URL DOI PDF
- Poster: Toward Decoupling the Selection of Compression Algorithms from Quality Constraints (Julian Kunkel, Anastasia Novikova, Eugen Betke), 2017-11-14 BibTeX PDF
- Toward Decoupling the Selection of Compression Algorithms from Quality Constraints (Julian Kunkel, Anastasiia Novikova, Eugen Betke, Armin Schaare), 2017 BibTeX DOI PDF
- Data Compression for Climate Data (Michael Kuhn, Julian Kunkel, Thomas Ludwig), 2016-06 BibTeX URL DOI PDF
Talks
- Scientific Data Compression with SCIL (Dr. Julian Kunkel), SPPEXA Final Symposium, Dresden, Germany, 2019-10-23 Presentation
- Decoupling the Selection of Compression Algorithms from Required Precision with the Scientific Compression Library (SCIL) (Dr. Julian Kunkel), ISC HPC, Poster session, Frankfurt, Germany, 2017-06-20 Presentation