MGARD+: Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction
| Autor: | Lipeng Wan, David Pugmire, Xin Liang, Matthew Wolf, Dingwen Tao, Jieyang Chen, James Kress, Scott Klasky, Qing Liu, Norbert Podhorszki, Ben Whitney |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer science Lossy compression Theoretical Computer Science Data modeling Reduction (complexity) Computer Science - Distributed Parallel and Cluster Computing Computational Theory and Mathematics Computer engineering Hardware and Architecture Compression ratio Decomposition (computer science) Distributed Parallel and Cluster Computing (cs.DC) Decomposition method (constraint satisfaction) Error detection and correction Software Data compression |
| Zdroj: | IEEE Transactions on Computers. 71:1522-1536 |
| ISSN: | 2326-3814 0018-9340 |
| Popis: | Data management is becoming increasingly important in dealing with the large amounts of data produced by 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. |
| Databáze: | OpenAIRE |
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