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DTSTART;TZID=America/Chicago:20181111T162600
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UID:submissions.supercomputing.org_SC18_sess143_ws_drbsd108@linklings.com
SUMMARY:Exploring Best Lossy Compression Strategy By Combining SZ with Spa
 tiotemporal Decimation
DESCRIPTION:Xin Liang (University of California, Riverside); Sheng Di (Arg
 onne National Laboratory); Sihuan Li (University of California, Riverside)
 ; Dingwen Tao (University of Alabama); Zizhong Chen (University of Califor
 nia, Riverside); and Franck Cappello (Argonne National Laboratory)\n\nIn t
 oday's extreme-scale scientific simulations, vast volumes of data are bein
 g produced such that the data cannot be accommodated by the parallel file 
 system or the data writing/reading performance will be fairly low because 
 of limited I/O bandwidth. In the past decade, many snapshot-based (or spac
 e-based) lossy compressors have been developed,  most of which rely on the
  smoothness of the data in space. However, the simulation data may get mor
 e 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 th
 e SZ compression model. The contribution is twofold. (1) We explore severa
 l strategies of combining the decimation method with the SZ lossy compress
 ion model in both the space dimension and time dimension during the simula
 tion. (2) We investigate the best-fit combined strategy upon different dem
 ands based on a couple of typical real-world simulations with multiple fie
 lds. Experiments show that either the space-based SZ or time-based SZ lead
 s to the best rate distortion. Decimation methods have very high compressi
 on rate with low rate distortion though, and SZ combined with temporal dec
 imation is a good tradeoff.\n\nTag: Data Management, Hot Topics, Scientifi
 c Computing\n\nRegistration Category: Workshop Reg Pass\n\n
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