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DTSTAMP:20211207T055400Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211118T083000
DTEND;TZID=America/Chicago:20211118T170000
UID:submissions.supercomputing.org_SC21_sess243_spostg105@linklings.com
SUMMARY:In-Situ Data Reduction for AMR-Based Cosmology Simulations
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nIn-Situ Data Reduc
 tion for AMR-Based Cosmology Simulations\n\nWang\n\nLarge-scale cosmology 
 simulations generate large amounts of data for post-analysis, resulting in
  I/O and storage bottlenecks. This work investigates an effective in-situ 
 error-bounded lossy compression for Nyx, an adaptive mesh refinement (AMR)
  based cosmology application. Our contribution is threefold: (1) We explor
 e the best-fit in-situ error-bounded lossy compressor (including SZ and TT
 HRESH) for Nyx considering both compression ratio and post-analysis qualit
 y. (2) We propose an approach to adaptively optimize the compressor for di
 fferent AMR levels based on our developed metric and data characteristics.
  (3) Our evaluation shows that our approach can improve the compression ra
 tio by 1.7X over the baseline (i.e., from 66 to 116) with the same post-an
 alysis quality.\n\nTag: In-Person Only\n\nRegistration Category: Tech Prog
 ram Reg Pass, Exhibit Hall Only
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