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

Abstract

Data management is becoming increasingly important in dealing with the large amounts of data produced by todays large-scale scientific simulations and instruments. 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 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 70X, and the proposed compression method can improve compression ratio by up to 2X compared with other state-of-the-art error-bounded lossy compressors under the same level of data distortion.

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Publication Status

Early Access

Comments

This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC

Keywords and Phrases

Arrays; Compressors; Computers; Data Models; Distortion; Error Control; High-Performance Computing; Lossy Compression; Multilevel Decomposition; Optimization; Quantization (Signal); Scientific Data

International Standard Serial Number (ISSN)

0018-9340; 1557-9956

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2021

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