MGARD+: Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction
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.
X. Liang et al., "MGARD+: Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction," IEEE Transactions on Computers, Institute of Electrical and Electronics Engineers (IEEE), Jan 2021.
The definitive version is available at https://doi.org/10.1109/TC.2021.3092201
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)
Article - Journal
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01 Jan 2021