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DTSTART:19700308T020000
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DTSTART:19701101T020000
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BEGIN:VEVENT
DTSTAMP:20211207T055410Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211114T163000
DTEND;TZID=America/Chicago:20211114T170000
UID:submissions.supercomputing.org_SC21_sess424_ws_drbsd104@linklings.com
SUMMARY:Understanding Effectiveness of Multi-Error-Bounded Lossy Compressi
 on for Preserving Ranges of Interest in Scientific Analysis
DESCRIPTION:Workshop\n\nUnderstanding Effectiveness of Multi-Error-Bounded
  Lossy Compression for Preserving Ranges of Interest in Scientific Analysi
 s\n\nLiu, Di, Zhao, Chard, Ding...\n\nMultiple lossy compression framework
 s have been proposed to address the vast volumes of data being produced by
  scientific simulations. Setting different precisions to different ranges 
 of data based on researchers' interests appears to be a promising approach
  to further improve the compression ratios of many scientific datasets. Ho
 wever, previous researches have not clearly demonstrated how to apply diff
 erent precisions to different ranges of data and not many real-world datas
 ets are evaluated to show the effectiveness of this idea. In this work, we
  investigate a specific compression method that can set multiple error bou
 nds based on the SZ framework. We carefully assess its effectiveness using
  real-world datasets which have concrete demands on multiple precisions. T
 he experimental results show that the multi-error-bounded lossy compressio
 n can achieve a 15% improvement in compression ratio, with negligible over
 head in compression time.\n\nTag: Online Only, Applications, Big Data, Dat
 a Analytics, Data Management\n\nRegistration Category: Workshop Reg Pass
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