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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20210402T160553Z
LOCATION:Track 1
DTSTART;TZID=America/New_York:20201112T115500
DTEND;TZID=America/New_York:20201112T123000
UID:submissions.supercomputing.org_SC20_sess207_ws_drbsd101@linklings.com
SUMMARY:Combining Spatial and Temporal Properties for Improvements in Data
  Reduction
DESCRIPTION:Workshop\n\nCombining Spatial and Temporal Properties for Impr
 ovements in Data Reduction\n\nHickman Fulp, Biswas, Calhoun\n\nDue to I/O 
 bandwidth limitations, intelligent in situ data reduction methods are need
 ed to enable post-hoc workflows. Current state-of-the-art sampling methods
  save data points if their region is deemed spatially or temporally import
 ant. By analyzing the properties of the data values at each time-step, two
  consecutive steps may be found to be very similar. This research follows 
 the notion that if neighboring time-steps are very similar, samples from b
 oth are unnecessary, which leaves storage for more useful samples to be ch
 osen. Here, we present an investigation of the combination of spatial and 
 temporal sampling to drastically reduce data size without loss of valuable
  information.  We demonstrate that by reusing samples, our reconstructed d
 ataset is able to reduce the overall data size while achieving a higher po
 st-reconstruction quality over other reduction methods.\n\nRegistration Ca
 tegory: Workshop Reg Pass
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