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DTSTAMP:20260522T150126Z
LOCATION:D168
DTSTART;TZID=America/Chicago:20181112T112000
DTEND;TZID=America/Chicago:20181112T114500
UID:submissions.supercomputing.org_SC18_sess140_ws_isav109@linklings.com
SUMMARY:In Situ Data-Driven Adaptive Sampling for Large-Scale Simulation D
 ata Summarization
DESCRIPTION:Ayan Biswas, Soumya Dutta, Jesus Pulido, and James Ahrens (Los
  Alamos National Laboratory)\n\nRecent advancements in the high-performanc
 e computing have enabled the scientists to model various scientific phenom
 ena in great detail. However, the analysis and visualization of the output
  data from such large-scale simulations are posing significant challenges 
 due to the excessive size of output data and disk I/O bottleneck. One viab
 le solution to this problem is to create a sub-sampled dataset which is ab
 le to preserve the important information of the data and also is significa
 ntly smaller in size compared to the raw data. Creating an in situ workflo
 w for generating such intelligently sub-sampled datasets is of prime impor
 tance for such simulations. In this work, we propose an information-driven
  data sampling technique and compare it with two well-known sampling metho
 ds to demonstrate the superiority of the proposed method. The in situ perf
 ormance of the proposed method is evaluated by applying the sampling techn
 iques to the Nyx Cosmology simulation. We compare and contrast the perform
 ances of these various sampling algorithms and provide a holistic view of 
 all the methods so that the scientists can choose appropriate sampling sch
 emes based on their analysis requirements.\n\nTag: Data Analytics, Data Ma
 nagement, Visualization\n\nRegistration Category: Workshop Reg Pass\n\nSes
 sion Chairs: Earl P.N. Duque (Intelligent Light); Nicola Ferrier (Argonne 
 National Laboratory (ANL), University of Chicago); Kenneth Moreland (Oak R
 idge National Laboratory (ORNL)); and Matthew Wolf (Oak Ridge National Lab
 oratory (ORNL))\n\n
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