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DTSTAMP:20181221T160727Z
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DTSTART;TZID=America/Chicago:20181111T162600
DTEND;TZID=America/Chicago:20181111T164200
UID:submissions.supercomputing.org_SC18_sess143_ws_drbsd108@linklings.com
SUMMARY:Exploring Best Lossy Compression Strategy By Combining SZ with Spa
 tiotemporal Decimation
DESCRIPTION:Workshop\nData Management, Hot Topics, Scientific Computing, W
 orkshop Reg Pass\n\nExploring Best Lossy Compression Strategy By Combining
  SZ with Spatiotemporal Decimation\n\nLiang, Di, Li, Tao, Chen...\n\nIn to
 day's extreme-scale scientific simulations, vast volumes of data are being
  produced such that the data cannot be accommodated by the parallel file s
 ystem or the data writing/reading performance will be fairly low because o
 f limited I/O bandwidth. In the past decade, many snapshot-based (or space
 -based) lossy compressors have been developed,  most of which rely on the 
 smoothness of the data in space. However, the simulation data may get more
  and more complicated in space over time steps, such that the compression 
 ratios  decrease significantly. In this paper, we propose a novel, hybrid 
 lossy compression method by leveraging spatiotemporal decimation under the
  SZ compression model. The contribution is twofold. (1) We explore several
  strategies of combining the decimation method with the SZ lossy compressi
 on model in both the space dimension and time dimension during the simulat
 ion. (2) We investigate the best-fit combined strategy upon different dema
 nds based on a couple of typical real-world simulations with multiple fiel
 ds. Experiments show that either the space-based SZ or time-based SZ leads
  to the best rate distortion. Decimation methods have very high compressio
 n rate with low rate distortion though, and SZ combined with temporal deci
 mation is a good tradeoff.
URL:https://sc18.supercomputing.org/presentation/?id=ws_drbsd108&sess=sess
 143
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