BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20181221T160727Z
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:Workshop\nData Analytics, Data Management, Visualization, Work
 shop Reg Pass\n\nIn Situ Data-Driven Adaptive Sampling for Large-Scale Sim
 ulation Data Summarization\n\nBiswas, Dutta, Pulido, Ahrens\n\nRecent adva
 ncements in the high-performance computing have enabled the scientists to 
 model various scientific phenomena 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 an
 d disk I/O bottleneck. One viable solution to this problem is to create a 
 sub-sampled dataset which is able to preserve the important information of
  the data and also is significantly smaller in size compared to the raw da
 ta. Creating an in situ workflow for generating such intelligently sub-sam
 pled datasets is of prime importance for such simulations. In this work, w
 e propose an information-driven data sampling technique and compare it wit
 h two well-known sampling methods to demonstrate the superiority of the pr
 oposed method. The in situ performance of the proposed method is evaluated
  by applying the sampling techniques to the Nyx Cosmology simulation. We c
 ompare and contrast the performances of these various sampling algorithms 
 and provide a holistic view of all the methods so that the scientists can 
 choose appropriate sampling schemes based on their analysis requirements.
URL:https://sc18.supercomputing.org/presentation/?id=ws_isav109&sess=sess1
 40
END:VEVENT
END:VCALENDAR