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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Fast and Effective Lossy Compression Algorithms for Scientific Datasets
verfasst von : Jeremy Iverson, Chandrika Kamath, George Karypis
Erschienen in: Euro-Par 2012 Parallel Processing
Verlag: Springer Berlin Heidelberg
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This paper focuses on developing effective and efficient algorithms for compressing scientific simulation data computed on structured and unstructured grids. A paradigm for lossy compression of this data is proposed in which the data computed on the grid is modeled as a graph, which gets decomposed into sets of vertices which satisfy a user defined error constraint
ε
. Each set of vertices is replaced by a constant value with reconstruction error bounded by
ε
. A comprehensive set of experiments is conducted by comparing these algorithms and other state-of-the-art scientific data compression methods. Over our benchmark suite, our methods obtained compression of 1% of the original size with average PSNR of 43.00 and 3% of the original size with average PSNR of 63.30. In addition, our schemes outperform other state-of-the-art lossy compression approaches and require on the average 25% of the space required by them for similar or better PSNR levels.