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DTSTART;TZID=America/Chicago:20181111T143600
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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:Margaret Lawson (University of Illinois, Sandia National Labor
 atories)\n\nLarge-scale scientific simulations are an important tool for s
 cientific discovery. In recent years, there has been a rapid growth in the
  amount of data output by these simulations. Extended runs of simulations 
 such as XGC edge plasma fusion can easily generate datasets in the terabyt
 e to petabyte range. With such large datasets, it is no longer feasible fo
 r scientists to load entire simulation outputs in search of features of in
 terest. Scientists need an efficient, low-memory usage way of identifying 
 which simulations produce a phenomenon, when and where the phenomenon appe
 ars, and how the phenomenon changes over time. However, current I/O system
 s such as HDF, NetCDF, and ADIOS do not provide these metadata capabilitie
 s. While some alternative tools have been developed that are optimized for
  a single type of analysis (global, spatial or temporal), no system provid
 es an efficient way to perform all of these types of analysis. To fill thi
 s need, I have created EMPRESS, an RDBMS-based metadata management system 
 for extreme scale scientific simulations. EMPRESS offers users the ability
  to efficiently tag and search features of interest without having to read
  in the associated datasets. Users can then use this metadata to perform s
 patial, temporal or global analysis and make discoveries. EMPRESS has been
  tested using several of Sandia's capacity clusters. Testing has primarily
  involved 1000, 2000, and 4000 cores, but several 8000 core tests were per
 formed as well. Testing has proved that EMPRESS offers vastly better perfo
 rmance on these vital metadata functions than HDF5.\n\nTag: Diversity, Edu
 cation, Hot Topics\n\nRegistration Category: Workshop Reg Pass\n\nSession 
 Chairs: Toni Collis (Women in High Performance Computing); Weronika Filing
 er (Edinburgh Parallel Computing Centre (EPCC); University of Edinburgh, S
 cotland); and Misbah Mubarak (Amazon Web Services)\n\n
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