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DTSTAMP:20181221T160726Z
LOCATION:D175
DTSTART;TZID=America/Chicago:20181111T152400
DTEND;TZID=America/Chicago:20181111T154000
UID:submissions.supercomputing.org_SC18_sess143_ws_drbsd110@linklings.com
SUMMARY:Feature-Relevant Data Reduction for In Situ Workflows
DESCRIPTION:Workshop\nData Management, Hot Topics, Scientific Computing, W
 orkshop Reg Pass\n\nFeature-Relevant Data Reduction for In Situ Workflows\
 n\nFox, Wolf, Logan, Choi, Klasky...\n\nAs the amount of data produced by 
 HPC simulations continues to grow and I/O throughput fails to keep up, in 
 situ data reduction is becoming an increasingly necessary component of HPC
  workflows. Application scientists, however, prefer to avoid reduction in 
 order to preserve data fidelity for post-hoc analysis. In an attempt to co
 mpromise between data quality and data quantity, this work introduces the 
 concept of feature-relevant compression. We explore two scientific dataset
 s in an attempt to quantify the impacts of compression on features of inte
 rest by identifying such features and analyzing changes in their propertie
 s after compression. We find that it is indeed possible to compress simula
 tion data in a lossy manner while preserving desired properties within a p
 redetermined error rate. Additionally, we suggest that this error quantifi
 cation could be applied as part of an in situ workflow to dynamically tune
  compression parameters during simulation, compressing aggressively when f
 eatures are simple but preserving structure where data complexity increase
 s. Future work should focus on implementation, extension to additional com
 pression algorithms, and analysis of these techniques on quantities which 
 are derived from original simulation data.
URL:https://sc18.supercomputing.org/presentation/?id=ws_drbsd110&sess=sess
 143
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