Todays exa-scale scientific applications or advanced instruments are producing vast volumes of data, which need to be shared/transferred through the network/devices with relatively low bandwidth (e.g., data sharing on WAN or transferring from edge devices to supercomputers). Lossy compression is one of the candidate strategies to address the big data issue. However, little work was done to make it resilient against silent errors, which may happen during the stage of compression or data transferring. In this paper, we propose a resilient error-bounded lossy compressor based on the SZ compression framework. Specifically, we design a new independentblock-wise model that decomposes the entire dataset into many independent sub-blocks to compress then, we design and implement a series of error detection/correction strategies elaboratively for each stage of SZ. Our method is arguably the first algorithmbased fault tolerance (ABFT) solution for lossy compression. Our proposed solution incurs negligible execution overhead in the faultfree situation. Upon soft errors happening, it ensures decompressed data strictly bounded within users requirement with a very limited degradation of compression ratio and low overhead.

Meeting Name

International Conference for High Performance Computing, Networking, Storage and Analysis, SC'21 (2021: Nov. 14-19, St. Louis, MO)


Computer Science


This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations - the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation’s exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract DE-AC02-06CH11357, and supported by the National Science Foundation under Grant No. 1617488, Grant No. 1619253 and Grant No. 2003709.

Keywords and Phrases

Algorithm Based Fault Tolerance; Data transfer; Lossy compression

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)

2167-4337; 2167-4329

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2021 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

19 Nov 2021