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DTSTART:19700308T020000
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DTSTAMP:20230124T171521Z
LOCATION:C141
DTSTART;TZID=America/Chicago:20221113T163000
DTEND;TZID=America/Chicago:20221113T170000
UID:submissions.supercomputing.org_SC22_sess421_ws_drbsd109@linklings.com
SUMMARY:Characterization of Transform-Based Lossy Compression for HPC Data
 sets
DESCRIPTION:Workshop\n\nCharacterization of Transform-Based Lossy Compress
 ion for HPC Datasets\n\nMoon, Chen, Son, Kim\n\nAs the scale and complexit
 y of HPC systems keep growing, data compression techniques are often adopt
 ed to reduce the data movement bottleneck. While lossy compression becomes
  preferable to a lossless one because of the potential of generating high 
 compression ratios, it would lose its worth the effort without finding an 
 optimal balance between volume reduction and information loss. The insight
  of this paper is that quantifying dominant coefficients at the block leve
 l reveals the right balance, potentially impacting overall compression rat
 ios. Motivated by this, we characterize three transformation-based lossy c
 ompression mechanisms at the block level, using the statistical features t
 hat capture data characteristics. We build several prediction models using
  the collected features and the characteristics of dominant coefficients a
 nd evaluate the effectiveness of each model using six HPC datasets. Our re
 sults demonstrate that the random forest classifier captures the behavior 
 of dominant coefficients precisely, achieving nearly 99% of prediction acc
 uracy.\n\nSession Format: Recorded\n\nRegistration Category: Workshop Reg 
 Pass
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