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DTSTAMP:20260522T150116Z
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:Franck Cappello (Argonne National Laboratory) and Peter Lindst
 rom (Lawrence Livermore National Laboratory)\n\nLarge-scale numerical simu
 lations and experiments generate very large datasets that are difficult to
  analyze, store, and transfer. This problem will be exacerbated for future
  generations of systems. Data reduction becomes a necessity in order to re
 duce as much as possible the time lost in data transfer and storage. Data 
 compression is an attractive and efficient reduction technique that is rat
 her agnostic to the application. This tutorial will introduce motivating e
 xamples, basic compression techniques, state of the art data transformatio
 n, prediction, quantization, and coding techniques; discuss in detail the 
 SZ and ZFP lossy compressors (including their latest developments); introd
 uce compression error assessment metrics; and show how lossy compression i
 mpacts visualization and other data analytics. The tutorial will also cove
 r Z-checker, a tool to characterize data sets and assess compression error
 . The tutorial will use examples of real world compressors and datasets co
 ming from simulations and instruments to illustrate the different compress
 ion techniques, their performance and their impact. From a user perspectiv
 e, the tutorial will detail how to use ZFP,  SZ and Z-checker as stand alo
 ne software/libraries and as modules integrated in parallel I/O libraries 
 (ADIOS, HDF5).\n\nTag: Data Management\n\nRegistration Category: Tutorial 
 Reg Pass\n\n
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