(C) 2016 by Mathematics and Computer Science (MCS), Argonne National Laboratory. See COPYRIGHT in top-level directory.
- Major Authors: Sheng Di, Kai Zhao, Xin Liang
- Supervisor: Franck Cappello
- Other Contributors: Robert Underwood, Sihuan Li, Ali M. Gok
Kindly note: If you mention SZ in your paper, the most appropriate citation is including these three references (ICDE21, HPDC2020, Bigdata2018), because they cover the design and implementation of the latest version of SZ.
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SZ3 Algorithm: Kai Zhao, Sheng Di, Maxim Dmitriev, Thierry-Laurent D. Tonellot, Zizhong Chen, and Franck Cappello. "Optimizing Error-Bounded Lossy Compression for Scientific Data by Dynamic Spline Interpolation", Proceeding of the 37th IEEE International Conference on Data Engineering (ICDE 21), Chania, Crete, Greece, Apr 19 - 22, 2021.
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SZauto: Kai Zhao, Sheng Di, Xin Liang, Sihuan Li, Dingwen Tao, Zizhong Chen, and Franck Cappello. "Significantly Improving Lossy Compression for HPC Datasets with Second-Order Prediction and Parameter Optimization", Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing (HPDC 20), Stockholm, Sweden, 2020. (code: https://github.com/szcompressor/SZauto/)
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SZ 2.0+: Xin Liang, Sheng Di, Dingwen Tao, Zizhong Chen, Franck Cappello, "Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets", in IEEE International Conference on Big Data (Bigdata 2018), Seattle, WA, USA, 2018.
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SZ 1.4.0-1.4.13: Dingwen Tao, Sheng Di, Franck Cappello. "Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization", in IEEE International Parallel and Distributed Processing Symposium (IPDPS 2017), Orlando, Florida, USA, 2017.
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SZ 0.1-1.0: Sheng Di, Franck Cappello. "Fast Error-bounded Lossy HPC Data Compression with SZ", in IEEE International Parallel and Distributed Processing Symposium (IPDPS 2016), Chicago, IL, USA, 2016.
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Point-wise relative error bound mode (i.e., PW_REL): Xin Liang, Sheng Di, Dingwen Tao, Zizhong Chen, Franck Cappello, "An Efficient Transformation Scheme for Lossy Data Compression with Point-wise Relative Error Bound", in IEEE International Conference on Clustering Computing (CLUSTER 2018), Belfast, UK, 2018. (Best Paper)
- Zstandard (https://facebook.github.io/zstd/). Zstandard v1.3.5 is included and will be used if libzstd can be found by pkg-config.
- mkdir build && cd build
- cmake -DCMAKE_INSTALL_PREFIX:PATH=[INSTALL_DIR] ..
- make
- make install
Then, you'll find all the executables in [INSTALL_DIR]/bin and header files in [INSTALL_DIR]/include
You can use the executable 'sz' command to do the compression/decompression.
Please refer to 'include/SZ3/api/sz.hpp' for API and instructions.
Version New features
- SZ 3.0.0 SZ3 is the C++ version of SZ with modular and composable design.
- SZ 3.0.1 Improve the build process.
- SZ 3.0.2 Support point-wise relative error bound mode.
- SZ 3.1.0 The default algorithm is now interpolation+Lorenzo.