Use cases of lossy compression for floating-point data in scientific data sets

Franck Cappello, Sheng Di, Sihuan Li, Xin Liang, Ali Murat Gok, Dingwen Tao, Chun Hong Yoon, Xin Chuan Wu, Yuri Alexeev, Frederic T. Chong

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1201-1220
Number of pages20
JournalInternational Journal of High Performance Computing Applications
Issue number6
StatePublished - Nov 1 2019


  • applications
  • floating-point data
  • Lossy compression
  • scientific data set
  • use cases

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture


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