Understanding and modeling lossy compression schemes on HPC scientific data

Tao Lu, Qing Liu, Xubin He, Huizhang Luo, Eric Suchyta, Jong Choi, Norbert Podhorszki, Scott Klasky, Mathew Wolf, Tong Liu, Zhenbo Qiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

Scientific simulations generate large amounts of floating-point data, which are often not very compressible using the traditional reduction schemes, such as deduplication or lossless compression. The emergence of lossy floating-point compression holds promise to satisfy the data reduction demand from HPC applications; however, lossy compression has not been widely adopted in science production. We believe a fundamental reason is that there is a lack of understanding of the benefits, pitfalls, and performance of lossy compression on scientific data. In this paper, we conduct a comprehensive study on state-of-The-Art lossy compression, including ZFP, SZ, and ISABELA, using real and representative HPC datasets. Our evaluation reveals the complex interplay between compressor design, data features and compression performance. The impact of reduced accuracy on data analytics is also examined through a case study of fusion blob detection, offering domain scientists with the insights of what to expect from fidelity loss. Furthermore, the trial and error approach to understanding compression performance involves substantial compute and storage overhead. To this end, we propose a sampling based estimation method that extrapolates the reduction ratio from data samples, to guide domain scientists to make more informed data reduction decisions.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages348-357
Number of pages10
ISBN (Print)9781538643686
DOIs
StatePublished - Aug 3 2018
Event32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018 - Vancouver, Canada
Duration: May 21 2018May 25 2018

Publication series

NameProceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018

Other

Other32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018
Country/TerritoryCanada
CityVancouver
Period5/21/185/25/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

Keywords

  • Compression
  • Data Reduction
  • High Performance Computing

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