MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring

Qian Gong, Jieyang Chen, Ben Whitney, Xin Liang, Viktor Reshniak, Tania Banerjee, Jaemoon Lee, Anand Rangarajan, Lipeng Wan, Nicolas Vidal, Qing Liu, Ana Gainaru, Norbert Podhorszki, Richard Archibald, Sanjay Ranka, Scott Klasky

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capability and precise error control, MGARD addresses a wide range of requirements, including storage reduction, high-performance I/O, and in-situ data analysis. It features a unified application programming interface (API) that seamlessly operates across diverse computing architectures. MGARD has been optimized with highly-tuned GPU kernels and efficient memory and device management mechanisms, ensuring scalable and rapid operations.

Original languageEnglish
Article number101590
JournalSoftwareX
Volume24
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023

Keywords

  • Data refactoring
  • Derived quantities preservation
  • Error-controlled data compression
  • I/O acceleration

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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