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DTSTAMP:20181221T160726Z
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DTSTART;TZID=America/Chicago:20181111T143600
DTEND;TZID=America/Chicago:20181111T143900
UID:submissions.supercomputing.org_SC18_sess160_ws_whpc110@linklings.com
SUMMARY:Using a Robust Metadata Management System to Accelerate Scientific
  Discovery at Extreme Scales
DESCRIPTION:Workshop\nDiversity, Education, Hot Topics, Workshop Reg Pass\
 n\nUsing a Robust Metadata Management System to Accelerate Scientific Disc
 overy at Extreme Scales\n\nLawson\n\nLarge-scale scientific simulations ar
 e an important tool for scientific discovery. In recent years, there has b
 een a rapid growth in the amount of data output by these simulations. Exte
 nded runs of simulations such as XGC edge plasma fusion can easily generat
 e datasets in the terabyte to petabyte range. With such large datasets, it
  is no longer feasible for scientists to load entire simulation outputs in
  search of features of interest. Scientists need an efficient, low-memory 
 usage way of identifying which simulations produce a phenomenon, when and 
 where the phenomenon appears, and how the phenomenon changes over time. Ho
 wever, current I/O systems such as HDF, NetCDF, and ADIOS do not provide t
 hese metadata capabilities. While some alternative tools have been develop
 ed that are optimized for a single type of analysis (global, spatial or te
 mporal), no system provides an efficient way to perform all of these types
  of analysis. To fill this need, I have created EMPRESS, an RDBMS-based me
 tadata management system for extreme scale scientific simulations. EMPRESS
  offers users the ability to efficiently tag and search features of intere
 st without having to read in the associated datasets. Users can then use t
 his metadata to perform spatial, temporal or global analysis and make disc
 overies. EMPRESS has been tested using several of Sandia's capacity cluste
 rs. Testing has primarily involved 1000, 2000, and 4000 cores, but several
  8000 core tests were performed as well. Testing has proved that EMPRESS o
 ffers vastly better performance on these vital metadata functions than HDF
 5.
URL:https://sc18.supercomputing.org/presentation/?id=ws_whpc110&sess=sess1
 60
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