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A Survey on Error-Bounded Lossy Compression for Scientific Datasets

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arxiv 2404.02840 v3 pith:MZNB76MT submitted 2024-04-03 cs.DC

Sheng Di , Jinyang Liu , Kai Zhao , Xin Liang , Robert Underwood , Zhaorui Zhang , Milan Shah , Yafan Huang
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Jiajun Huang Xiaodong Yu Congrong Ren Hanqi Guo Grant Wilkins Dingwen Tao Jiannan Tian Sian Jin Zizhe Jian Daoce Wang MD Hasanur Rahman Boyuan Zhang Shihui Song Jon C. Calhoun Guanpeng Li Kazutomo Yoshii Khalid Ayed Alharthi Franck Cappello
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classification cs.DC
keywords compressionlossycompressorserror-boundedsurveydatadesignedscientific
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Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of parallel and distributed use cases for years. They are designed with distinct compression models and principles, such that each of them features particular pros and cons. In this paper we provide a comprehensive survey of emerging error-bounded lossy compression techniques. The key contribution is fourfold. (1) We summarize a novel taxonomy of lossy compression into 6 classic models. (2) We provide a comprehensive survey of 10 commonly used compression components/modules. (3) We summarized pros and cons of 46 state-of-the-art lossy compressors and present how state-of-the-art compressors are designed based on different compression techniques. (4) We discuss how customized compressors are designed for specific scientific applications and use-cases. We believe this survey is useful to multiple communities including scientific applications, high-performance computing, lossy compression, and big data.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. STZ: A High Quality and High Speed Streaming Lossy Compression Framework for Scientific Data

    cs.DC 2025-09 conditional novelty 6.0 of 10

    A streaming lossy compressor that supports both progressive and random-access decompression at quality near SZ3 and up to 6.7x lower decompression time.

  2. Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics

    cs.CE 2025-07 conditional novelty 5.0 of 10

    iPIC3D, an implicit kinetic plasma code, scales to 32,768 AMD MI300A APUs and projects 22.4 PFLOP/s sustained, reaching simulation domains of hundreds of ion skin depths.

  3. FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression

    cs.DC 2025-07 reject novelty 5.0 of 10

    The paper proposes a scalable ASIC architecture for neural-hybrid scientific lossy compression and claims large speedups, but the evaluation compares against baselines that produce much lower compression ratios.

  4. TriADA: Massively Parallel Trilinear Matrix-by-Tensor Multiply-Add Algorithm and Device Architecture for the Acceleration of 3D Discrete Transformations

    cs.DC 2025-06 conditional novelty 4.0 of 10

    The paper proposes a triple-stage outer-product algorithm and an isomorphic 3D mesh architecture that computes separable 3D orthogonal transforms in N1+N2+N3 time steps.

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