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DTSTAMP:20260522T150118Z
LOCATION:D175
DTSTART;TZID=America/Chicago:20181111T164200
DTEND;TZID=America/Chicago:20181111T165800
UID:submissions.supercomputing.org_SC18_sess143_ws_drbsd113@linklings.com
SUMMARY:Synthetic Data Generation for Evaluating Parallel I/O Compression 
 Performance and Scalability
DESCRIPTION:Sean B. Ziegeler and Christopher P. Stone (US Department of De
 fense HPC Modernization Program, Engility Corporation)\n\nCompression is o
 ne of the most common forms of data reduction and is typically the least i
 nvasive. As compute capability continues to outpace I/O bandwidths, compre
 ssion becomes that much more attractive. This paper explores the scalable 
 performance of parallel compression and presents an in-depth analysis of a
  coherent noise algorithm to generate synthetic data that can be used to e
 asily evaluate parallel compression. The algorithm favors simplicity, ease
 -of-use, and scalability over high-fidelity reconstruction of real data, s
 o we go to lengths to show that the synthetic data generated is suitable a
 s a proxy for evaluating compression, especially in benchmarks and mini-ap
 ps.\n\nTag: Data Management, Hot Topics, Scientific Computing\n\nRegistrat
 ion Category: Workshop Reg Pass\n\n
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