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In-Depth Exploration of Single-Snapshot Lossy Compression Techniques for N-Body Simulations
Dingwen Tao, Sheng Di, Zizhong Chen +1
In situ lossy compression allowing user-controlled data loss can significantly reduce the I/O burden. For large-scale N-body simulations where only one snapshot can be compressed a…
Exploration of Pattern-Matching Techniques for Lossy Compression on Cosmology Simulation Data Sets
Dingewn Tao, Sheng Di, Zizhong Chen +1
Because of the vast volume of data being produced by today's scientific simulations, lossy compression allowing user-controlled information loss can significantly reduce the data s…
Z-checker: A Framework for Assessing Lossy Compression of Scientific Data
Dingwen Tao, Sheng Di, Hanqi Guo +2
Because of vast volume of data being produced by today's scientific simulations and experiments, lossy data compressor allowing user-controlled loss of accuracy during the compress…
Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization
Dingwen Tao, Sheng Di, Zizhong Chen +1
Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific research. In this work, we…