Understanding Impact of Lossy Compression on Derivative-Related Metrics in Scientific Datasets
DescriptionToday’s scientific simulations are producing extremely large amount of data everyday, which induces grand challenges in transferring and storing the data efficiently. Error-bounded lossy compression has been thought of as the most promising solution to the bigdata issue, however, it would cause data distortion that has to be controlled carefully for user’s post-hoc analysis. Recently, the preservation of quantities of interest has become a priority. Derivative-related metrics are critical quantities of interest for many applications across domains. However, no prior research explored the impact of lossy compression on derivative-related metrics in particular. In this paper, we focus on understanding the impact of various error-controlled lossy compressors on multiple derivative-related metrics commonly concerned by users. We perform solid experiments that involve 5 state-of-the-art lossy compressors and 4 real-world application datasets. We summarize 5 valuable takeaways, which can shed some light in understanding the impact of lossy compression on derivative-related metrics.
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TimeSunday, 13 November 20223:30pm - 4pm CST
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