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DTSTAMP:20230124T171526Z
LOCATION:C141
DTSTART;TZID=America/Chicago:20221113T153000
DTEND;TZID=America/Chicago:20221113T160000
UID:submissions.supercomputing.org_SC22_sess421_ws_drbsd104@linklings.com
SUMMARY:Understanding Impact of Lossy Compression on Derivative-Related Me
 trics in Scientific Datasets
DESCRIPTION:Workshop\n\nUnderstanding Impact of Lossy Compression on Deriv
 ative-Related Metrics in Scientific Datasets\n\nSu, Di, Gok, Cheng, Cappel
 lo\n\nToday’s scientific simulations are producing extremely large amount 
 of data everyday, which induces grand challenges in transferring and stori
 ng 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 bec
 ome a priority. Derivative-related metrics are critical quantities of inte
 rest for many applications across domains. However, no prior research expl
 ored the impact of lossy compression on derivative-related metrics in part
 icular. In this paper, we focus on understanding the impact of various err
 or-controlled lossy compressors on multiple derivative-related metrics com
 monly concerned by users. We perform solid experiments that involve 5 stat
 e-of-the-art lossy compressors and 4 real-world application datasets. We s
 ummarize 5 valuable takeaways, which can shed some light in understanding 
 the impact of lossy compression on derivative-related metrics.\n\nSession 
 Format: Recorded\n\nRegistration Category: Workshop Reg Pass
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