MGARD+: Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction

Xin Liang, Ben Whitney, Jieyang Chen, Lipeng Wan, Qing Liu, Dingwen Tao, James Kress, David Pugmire, Matthew Wolf, Norbert Podhorszki, Scott Klasky

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale, but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which treats the multilevel decomposition as a preconditioner and terminates the decomposition process at an appropriate level. 3) We leverage a set of algorithmic optimization strategies to significantly improve the performance of multilevel decomposition/recomposition. 4) We evaluate our proposed method using four real-world scientific datasets and compare with several state-of-the-art lossy compressors. Experiments demonstrate that our optimizations improve the decomposition/recomposition performance of the existing multilevel method by up to $70 \times$70×, and the proposed compression method can improve compression ratio by up to $2 \times$2× compared with other state-of-the-art error-bounded lossy compressors under the same level of data distortion.

Original languageEnglish
Pages (from-to)1522-1536
Number of pages15
JournalIEEE Transactions on Computers
Issue number7
StatePublished - Jul 1 2022

Bibliographical note

Publisher Copyright:
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  • High-performance computing
  • error control
  • lossy compression
  • multilevel decomposition
  • scientific data

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


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