Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

European Conference on Parallel Processing

Euro-Par 2011: Euro-Par 2011: Parallel Processing Workshops pp 398–407Cite as

  1. Home
  2. Euro-Par 2011: Parallel Processing Workshops
  3. Conference paper
The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization

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

  • Ernesto E. Prudencio30 &
  • Karl W. Schulz30 
  • Conference paper
  • 1568 Accesses

  • 20 Citations

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7155)

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.

Keywords

  • Software Design
  • Uncertainty Quantification
  • Parallel MCMC

Download conference paper PDF

References

  1. Babuška, I., Nobile, F., Tempone, R.: A stochastic collocation method for elliptic partial differential equations with random input data. SIAM J. Num. Anal. (2007)

    Google Scholar 

  2. Beck, J.L., Katafygiotis, L.S.: Updating of a model and its uncertainties utilizing dynamic test data. In: Proc. 1st International Conference on Computational Stochastic Mechanics, pp. 125–136 (1991)

    Google Scholar 

  3. Beck, J.L., Yuen, K.V.: Model selection using response measurements: A Bayesian probabilistic approach. ASCE Journal of Eng. Mechanics 130, 192–203 (2004)

    CrossRef  Google Scholar 

  4. Cheung, S.H., Beck, J.L.: New Bayesian updating methodology for model validation and robust predictions of a target system based on hierarchical subsystem tests. CMAME (2010) (accepted for publication)

    Google Scholar 

  5. Cheung, S.H., Oliver, T.A., Prudencio, E.E., Prudhomme, S., Moser, R.D.: Bayesian uncertainty analysis with applications to turbulence modeling. Reliability Engineering & System Safety (2011) (in press)

    Google Scholar 

  6. Eldred, M.S., et al.: DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (1994-2009), http://www.cs.sandia.gov/DAKOTA/

  7. Galassi, M., et al.: GNU Scientific Library (1996-2009), http://www.gnu.org/software/gsl/

  8. Haario, H., Laine, M., Mira, A., Saksman, E.: DRAM: Efficient adaptive MCMC. Stat. Comput. 16, 339–354 (2006)

    CrossRef  MathSciNet  Google Scholar 

  9. Hastings, W.K.: Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57(1), 97–109 (1970)

    CrossRef  MATH  Google Scholar 

  10. Heroux, M.: Trilinos (2009), http://www.trilinos.gov/

  11. Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T.: Bayesian model averaging: a tutorial (with discussion). Statistical Science 14, 382–417 (1999)

    CrossRef  MathSciNet  MATH  Google Scholar 

  12. Kaipio, J., Somersalo, E.: Statistical and Computational Inverse Problems, Applied Mathematical Sciences, vol. 160. Springer (2005)

    Google Scholar 

  13. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equations of state calculations by fast computing machines. Journal of Chemical Physics 21(6), 1087–1092 (1953)

    CrossRef  Google Scholar 

  14. Prudencio, E.E., Cai, X.C.: Parallel multilevel restricted Schwarz preconditioners with pollution removing for PDE-constrained optimization. SIAM J. Sci. Comp. 29, 964–985 (2007)

    CrossRef  MathSciNet  MATH  Google Scholar 

  15. Prudencio, E.E., Cheung, S.H.: Parallel adaptive multilevel sampling algorithms for the Bayesian analysis of mathematical models (2011) (submitted)

    Google Scholar 

  16. Robert, C.: The Bayesian Choice, 2nd edn. Springer (2004)

    Google Scholar 

  17. Smith, B.: PETSc (2009), http://www.mcs.anl.gov/petsc/

  18. TACC: Texas advanced computing center (2008), http://www.tacc.utexas.edu/

Download references

Author information

Authors and Affiliations

  1. Institute for Computational Engineering and Sciences (ICES), The University of Texas, Austin, USA

    Ernesto E. Prudencio & Karl W. Schulz

Authors
  1. Ernesto E. Prudencio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karl W. Schulz
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Scilytics, Koellnerhofgasse 3/15A, 1010, Vienna, Austria

    Michael Alexander

  2. ICAR-CNR, Via P. Castellino, 111, 80131, Napoli, Italy

    Pasqua D’Ambra

  3. University of Amsterdam, 1090, Amsterdam, Netherlands

    Adam Belloum

  4. Innovative Computing Laboratory, The University of Tennessee, USA

    George Bosilca

  5. Department of Experimental Medicine and Clinic, University Magna Græcia, 88100, Catanzaro, Italy

    Mario Cannataro

  6. Computer Science Department, University of Pisa, Italy

    Marco Danelutto

  7. Second University of Naples, Italy

    Beniamino Di Martino

  8. TU München, Boltzmannstr. 3, 85748, Garching, Germany

    Michael Gerndt

  9. Equipe Runtime, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Emmanuel Jeannot & Raymond Namyst & 

  10. Equipe HIEPACS, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Jean Roman

  11. Oak Ridge National Laboratory, Computer Science and Mathematics Division, 37831-6164, Oak Ridge, TN, USA

    Stephen L. Scott

  12. Department of Scientific Computing, University of Vienna, Nordbergstr. 15/3C, 1090, Vienna, Austrial

    Jesper Larsson Traff

  13. Computer Science and Mathematics Division, Oak Ridge National Laboratory, 37831, Oak Ridge, TN, USA

    Geoffroy Vallée

  14. Technische Universität München, Germany

    Josef Weidendorfer

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Prudencio, E.E., Schulz, K.W. (2012). The Parallel C++ Statistical Library ‘QUESO’: Quantification of Uncertainty for Estimation, Simulation and Optimization. In: Alexander, M., et al. Euro-Par 2011: Parallel Processing Workshops. Euro-Par 2011. Lecture Notes in Computer Science, vol 7155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29737-3_44

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-29737-3_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29736-6

  • Online ISBN: 978-3-642-29737-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature