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DTSTART;TZID=America/Chicago:20221116T164500
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UID:submissions.supercomputing.org_SC22_sess227_spostu110@linklings.com
SUMMARY:Statistical Prediction of Lossy Compression Ratios for 3D Scientif
 ic Data
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nStatistical Predic
 tion of Lossy Compression Ratios for 3D Scientific Data\n\nKrasowska\n\nIn
  the fields of science and engineering, lossy compression plays a growing 
 role in running scientific simulations, as output data is on the scale of 
 terabytes. Using error bounded lossy compression reduces the amount of sto
 rage for each simulation; however, there is no known bound for the upper l
 imit of lossy compressibility. Data correlation structures, compressors an
 d error bounds are factors allowing larger compression ratios and improved
  quality metrics. This provides one direction towards quantifying lossy co
 mpressibility. Our previous work explored 2D statistical methods to charac
 terize the data correlation structures and their relationships, through fu
 nctional models, to compression ratios and quality metrics for 2D scientif
 ic data. In this poster, we explore the expansion of our statistical metho
 ds to 3D scientific data. The method was comparable to 2D. Our work is the
  next step towards evaluating the theoretical limits of lossy compressibil
 ity used to predict compression performance and optimally adapt compressor
 s.\n\nSession Format: Recorded\n\nRegistration Category: Tech Program Reg 
 Pass
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