Understanding Effectiveness of Multi-Error-Bounded Lossy Compression for Preserving Ranges of Interest in Scientific Analysis

Lossy compression frameworks have been proposed as a method to reduce the size of data produced by scientific simulations. However, they do so at the expense of precision and existing compressors apply a single error bound across the entire dataset. Varying the precision across user-specified ranges of scalar values appears to be a promising approach to further improve compression ratios while retaining precision in specific areas of interest. In this work, we investigate a specific compression method, based on the SZ framework, that can set multiple error bounds. We evaluate its effectiveness by applying it to real-world datasets which have concrete precision requirements. Our results show that the multi-error-bounded lossy compression can improve compression ration by 15 % with negligible overhead in compression time.