License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.6.9.59
URN: urn:nbn:de:0030-drops-69158
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2017/6915/
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Heuveline, Vincent ; Schick, Michael ; Webster, Clayton ; Zaspel, Peter
Weitere Beteiligte (Hrsg. etc.): Vincent Heuveline and Michael Schick and Clayton Webster and Peter Zaspel

Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372)

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dagrep_v006_i009_p059_s16372.pdf (1 MB)

Abstract

High performance computing is a key technology to solve large-scale real-world simulation problems on parallel computers. Simulations for a fixed, deterministic set of parameters are current state of the art. However, there is a growing demand in methods to appropriately cope with uncertainties in those input parameters. This is addressed in the developing research field of uncertainty quantification. Here, Monte-Carlo methods are easy to parallelize and thus fit well for parallel computing. However, their weak approximation capabilities lead to inaccurate results. The Dagstuhl Seminar 16372 "Uncertainty Quantification and High Performance Computing" brought together experts in the fields of uncertainty quantification and high performance computing. Discussions on the latest numerical techniques beyond pure Monte-Carlo and with strong approximation capabilities were fostered. This has been put in context of real-world problems on parallel computers.

BibTeX - Entry

@Article{heuveline_et_al:DR:2017:6915,
  author =	{Vincent Heuveline and Michael Schick and Clayton Webster and Peter Zaspel},
  title =	{{Uncertainty Quantification and High Performance Computing (Dagstuhl Seminar 16372)}},
  pages =	{59--73},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{6},
  number =	{9},
  editor =	{Vincent Heuveline and Michael Schick and Clayton Webster and Peter Zaspel},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/6915},
  URN =		{urn:nbn:de:0030-drops-69158},
  doi =		{10.4230/DagRep.6.9.59},
  annote =	{Keywords: high performance computing, parallelization, stochastic modeling, uncertainty quantification}
}

Keywords: high performance computing, parallelization, stochastic modeling, uncertainty quantification
Collection: Dagstuhl Reports, Volume 6, Issue 9
Issue Date: 2017
Date of publication: 13.01.2017

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