Abstract

Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. This article surveys and presents experimental results of currently identified use cases of generic lossy compression to address the different limitations of scientific computing systems. The article shows from a collection of experiments run on parallel systems of a leadership facility that lossy data compression not only can reduce the footprint of scientific data sets on storage but also can reduce I/O and checkpoint/restart times, accelerate computation, and even allow significantly larger problems to be run than without lossy compression. These results suggest that lossy compression will become an important technology in many aspects of high performance scientific computing. Because the constraints for each use case are different and often conflicting, this collection of results also indicates the need for more specialization of the compression pipelines.

Department(s)

Computer Science

Comments

This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC

Keywords and Phrases

Applications; Floating-Point Data; Lossy Compression; Scientific Data Set; Use Cases

International Standard Serial Number (ISSN)

1094-3420; 1741-2846

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2019 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Publication Date

01 Nov 2019

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