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:20211207T054811Z
LOCATION:227-228
DTSTART;TZID=America/Chicago:20211118T113000
DTEND;TZID=America/Chicago:20211118T120000
UID:submissions.supercomputing.org_SC21_sess181_pap174@linklings.com
SUMMARY:High Performance Uncertainty Quantification with Parallelized Mult
 ilevel Markov Chain Monte Carlo
DESCRIPTION:Paper\n\nHigh Performance Uncertainty Quantification with Para
 llelized Multilevel Markov Chain Monte Carlo\n\nSeelinger, Reinarz, Rannab
 auer, Bader, Bastian...\n\nNumerical models of complex real-world phenomen
 a often necessitate high-performance computing (HPC). Uncertainties increa
 se problem dimensionality further and pose even greater challenges.\n\nWe 
 present a parallelization strategy for multilevel Markov chain Monte Carlo
 , a state-of-the-art, algorithmically scalable uncertainty quantification 
 (UQ) algorithm for Bayesian inverse problems, and a new software framework
  allowing for large-scale parallelism across forward model evaluations and
  the UQ algorithms themselves. The main scalability challenge presents its
 elf in the form of strong data dependencies introduced by the MLMCMC metho
 d, prohibiting trivial parallelization.\n\nOur software is released as par
 t of the modular and open-source MIT Uncertainty Quantification Library (M
 UQ), and can easily be coupled with arbitrary user codes. We demonstrate i
 t using the Distributed and Unified Numerics Environment (DUNE) and the Ex
 aHyPE Engine. The latter provides a realistic, large-scale tsunami model i
 n which we identify the source of a tsunami from buoy-elevation data.\n\nT
 ag: Reproducibility Badge, Algorithms, Applications, Machine Learning and 
 Artificial Intelligence, Numerical Algorithms\n\nRegistration Category: Te
 ch Program Reg Pass\n\nReproducibility Badges: Artifact Available
END:VEVENT
END:VCALENDAR