The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization

Ernesto E. Prudencio, Karl W. Schulz

Research output: Chapter in Book/Report/Conference proceedingChapter

52 Scopus citations

Abstract

QUESO is a collection of statistical algorithms and programming constructs supporting research into the uncertainty quantification (UQ) of models and their predictions. It has been designed with three objectives: it should (a) be sufficiently abstract in order to handle a large spectrum of models, (b) be algorithmically extensible, allowing an easy insertion of new and improved algorithms, and (c) take advantage of parallel computing, in order to handle realistic models. Such objectives demand a combination of an object-oriented design with robust software engineering practices. QUESO is written in C++, uses MPI, and leverages libraries already available to the scientific community. We describe some UQ concepts, present QUESO, and list planned enhancements.
Original languageEnglish (US)
Title of host publicationEuro-Par 2011: Parallel Processing Workshops
PublisherSpringer Nature
Pages398-407
Number of pages10
ISBN (Print)9783642297366
DOIs
StatePublished - 2012
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work has been supported by the National Nuclear Se-curity Administration, U.S D.O.E., under Award Number DE-FC52-08NA28615.The first author was also partially supported by Sandia National Laborato-ries, under Contracts1017123 and 1086312, and by King Abdullah University of Science and Technology (KAUST), under the Academic Excellence Allianceprogram. The authors would also like to thank fruitful discussions with manyresearchers, including P. Bauman, S. H. Cheung, M. Eldred, O. Ghattas, J.Martin, K. Miki, F. Nobile, T. Oliver, C. Simmons, L. Swiler, R. Tempone, G.Terejanu and L. Wilcox.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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