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DTSTART:19700308T020000
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DTSTAMP:20181221T160903Z
LOCATION:C141
DTSTART;TZID=America/Chicago:20181112T133000
DTEND;TZID=America/Chicago:20181112T170000
UID:submissions.supercomputing.org_SC18_sess233_tut109@linklings.com
SUMMARY:Compression for Scientific Data
DESCRIPTION:Tutorial\nData Management, Tutorial Reg Pass\n\nCompression fo
 r Scientific Data\n\nCappello, Lindstrom\n\nLarge-scale numerical simulati
 ons and experiments generate very large datasets that are difficult to ana
 lyze, store, and transfer. This problem will be exacerbated for future gen
 erations of systems. Data reduction becomes a necessity in order to reduce
  as much as possible the time lost in data transfer and storage. Data comp
 ression is an attractive and efficient reduction technique that is rather 
 agnostic to the application. This tutorial will introduce motivating examp
 les, basic compression techniques, state of the art data transformation, p
 rediction, quantization, and coding techniques; discuss in detail the SZ a
 nd ZFP lossy compressors (including their latest developments); introduce 
 compression error assessment metrics; and show how lossy compression impac
 ts visualization and other data analytics. The tutorial will also cover Z-
 checker, a tool to characterize data sets and assess compression error. Th
 e tutorial will use examples of real world compressors and datasets coming
  from simulations and instruments to illustrate the different compression 
 techniques, their performance and their impact. From a user perspective, t
 he tutorial will detail how to use ZFP,  SZ and Z-checker as stand alone s
 oftware/libraries and as modules integrated in parallel I/O libraries (ADI
 OS, HDF5).
URL:https://sc18.supercomputing.org/presentation/?id=tut109&sess=sess233
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