Fast & Effective Lossy Compression Algorithms for Scientific Datasets

Jeremy Iverson, Chandrika Kamath, and George Karypis
Europar, 2012
Download Paper
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 epsilon. Each set of vertices is replaced by a constant value with reconstruction error bounded by epsilon. 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.
Research topics: Parallel processing | Scientific computing