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DTSTAMP:20260422T000605Z
LOCATION:DEF Concourse
DTSTART;TZID=America/Denver:20231116T100000
DTEND;TZID=America/Denver:20231116T170000
UID:submissions.supercomputing.org_SC23_sess300_spostg123@linklings.com
SUMMARY:Sensitivity of Black-Box Statistical Prediction of Lossy Compressi
 on Ratios for 3D Scientific Data
DESCRIPTION:Alexandra Poulos (Clemson University)\n\nCompression ratio est
 imation is an important optimization of I/O workflows processing terabytes
  of data. Applications such as compression auto-tuning or lossy compressor
  selection require a high-throughput, accurate estimation. Prior works tha
 t utilize sampling are fast but inaccurate, while approaches which do not 
 use sampling are highly accurate but slow. Through sensitivity analysis we
  show that sampling a small number of moderately sized data blocks maintai
 ns fast data transfer and yields superior estimation accuracy compared to 
 existing sampling approaches, and we use this to construct a new fast and 
 accurate sampling method. In relation to non-sampling techniques, our meth
 od results in less than 10% degradation in estimation accuracy, while stil
 l maintaining the high throughput of the less accurate sampling methods.\n
 \nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass\n\n
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