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DTSTART:19700308T020000
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DTSTAMP:20181221T160727Z
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:Workshop\nData Management, Hot Topics, Scientific Computing, W
 orkshop Reg Pass\n\nSynthetic Data Generation for Evaluating Parallel I/O 
 Compression Performance and Scalability\n\nZiegeler, Stone\n\nCompression 
 is one of the most common forms of data reduction and is typically the lea
 st invasive. As compute capability continues to outpace I/O bandwidths, co
 mpression becomes that much more attractive. This paper explores the scala
 ble performance of parallel compression and presents an in-depth analysis 
 of a coherent noise algorithm to generate synthetic data that can be used 
 to easily evaluate parallel compression. The algorithm favors simplicity, 
 ease-of-use, and scalability over high-fidelity reconstruction of real dat
 a, so we go to lengths to show that the synthetic data generated is suitab
 le as a proxy for evaluating compression, especially in benchmarks and min
 i-apps.
URL:https://sc18.supercomputing.org/presentation/?id=ws_drbsd113&sess=sess
 143
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