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DTSTART;TZID=America/Chicago:20221115T083000
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UID:submissions.supercomputing.org_SC22_sess223_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\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass
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