IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v29y2014i5p1301-1320.html
   My bibliography  Save this article

Using RngStreams for parallel random number generation in C++ and R

Author

Listed:
  • Andrew Karl
  • Randy Eubank
  • Jelena Milovanovic
  • Mark Reiser
  • Dennis Young

Abstract

The RngStreams software package provides one viable solution to the problem of creating independent random number streams for simulations in parallel processing environments. Techniques are presented for effectively using RngStreams with C++ programs that are parallelized via OpenMP or MPI. Ways to access the backbone generator from RngStreams in R through the parallel and rstream packages are also described. The ideas in the paper are illustrated with both a simple running example and a Monte Carlo integration application. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Andrew Karl & Randy Eubank & Jelena Milovanovic & Mark Reiser & Dennis Young, 2014. "Using RngStreams for parallel random number generation in C++ and R," Computational Statistics, Springer, vol. 29(5), pages 1301-1320, October.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:5:p:1301-1320
    DOI: 10.1007/s00180-014-0492-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-014-0492-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00180-014-0492-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rizopoulos, Dimitris, 2006. "ltm: An R Package for Latent Variable Modeling and Item Response Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i05).
    2. Schmidberger, Markus & Morgan, Martin & Eddelbuettel, Dirk & Yu, Hao & Tierney, Luke & Mansmann, Ulrich, 2009. "State of the Art in Parallel Computing with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i01).
    3. Manuel Eugster & Jochen Knaus & Christine Porzelius & Markus Schmidberger & Esmeralda Vicedo, 2011. "Hands-on tutorial for parallel computing with R," Computational Statistics, Springer, vol. 26(2), pages 219-239, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. L’Ecuyer, Pierre & Munger, David & Oreshkin, Boris & Simard, Richard, 2017. "Random numbers for parallel computers: Requirements and methods, with emphasis on GPUs," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 135(C), pages 3-17.
    2. Eric C. Ni & Dragos F. Ciocan & Shane G. Henderson & Susan R. Hunter, 2017. "Efficient Ranking and Selection in Parallel Computing Environments," Operations Research, INFORMS, vol. 65(3), pages 821-836, June.
    3. Araújo, Artur & Meira-Machado, Luís & Roca-Pardiñas, Javier, 2014. "TPmsm: Estimation of the Transition Probabilities in 3-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i04).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nick Bailey, 2020. "Measuring Poverty Efficiently Using Adaptive Deprivation Scales," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 891-910, June.
    2. Stefanie Hieke & Harald Binder & Alexandra Nieters & Martin Schumacher, 2014. "minPtest: a resampling based gene region-level testing procedure for genetic case-control studies," Computational Statistics, Springer, vol. 29(1), pages 51-63, February.
    3. W. W. Koczkodaj & T. Kakiashvili & A. Szymańska & J. Montero-Marin & R. Araya & J. Garcia-Campayo & K. Rutkowski & D. Strzałka, 2017. "How to reduce the number of rating scale items without predictability loss?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 581-593, May.
    4. Bivand, Roger, 2010. "Exploiting Parallelization in Spatial Statistics: an Applied Survey using R," Discussion Paper Series in Economics 25/2010, Norwegian School of Economics, Department of Economics.
    5. Yang Yixin & Lü Xin & Ma Jian & Qiao Han, 2014. "A Robust Factor Analysis Model for Dichotomous Data," Journal of Systems Science and Information, De Gruyter, vol. 2(5), pages 437-450, October.
    6. Arulmani Thiyagarajan & Tyler G. James & Roy Rillera Marzo, 2022. "Psychometric properties of the 21-item Depression, Anxiety, and Stress Scale (DASS-21) among Malaysians during COVID-19: a methodological study," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.
    7. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
    8. Frick, Hannah & Strobl, Carolin & Leisch, Friedrich & Zeileis, Achim, 2012. "Flexible Rasch Mixture Models with Package psychomix," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i07).
    9. Daniel L. Oberski, 2016. "A Review of Latent Variable Modeling With R," Journal of Educational and Behavioral Statistics, , vol. 41(2), pages 226-233, April.
    10. repec:jss:jstsof:39:c01 is not listed on IDEAS
    11. Cervantes, Víctor H., 2017. "DFIT: An R Package for Raju's Differential Functioning of Items and Tests Framework," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i05).
    12. repec:jss:jstsof:35:i03 is not listed on IDEAS
    13. Sora Lee & Daniel M. Bolt, 2018. "Asymmetric Item Characteristic Curves and Item Complexity: Insights from Simulation and Real Data Analyses," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 453-475, June.
    14. Jochen Ranger & Kay Brauer, 2022. "On the Generalized S − X 2 –Test of Item Fit: Some Variants, Residuals, and a Graphical Visualization," Journal of Educational and Behavioral Statistics, , vol. 47(2), pages 202-230, April.
    15. Nick Bailey & Anne-Catherine Guio, 2022. "Adaptive Deprivation Scales in a Multi-National Context: The European Child Deprivation Indicators," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 15(6), pages 2335-2362, December.
    16. Fuchs, Lisa Elena & Orero, Levi & Apondi, Victoria Atieno & Kipkorir, Lang'at, 2021. "How to stop wasting money in international development: Using a structured group selection approach to counter procedural inefficiency," World Development Perspectives, Elsevier, vol. 24(C).
    17. Arnond Sakworawich & Howard Wainer, 2020. "Scoring Tests With Contaminated Response Vectors," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 209-226, April.
    18. Tormod Bøe & Mari Hysing & Jens Christoffer Skogen & Kyrre Breivik, 2016. "The Strengths and Difficulties Questionnaire (SDQ): Factor Structure and Gender Equivalence in Norwegian Adolescents," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-15, May.
    19. Dawit G. Ayele & Temesgen Zewotir & Henry Mwambi, 2014. "Using Rasch Modeling to Re-Evaluate Rapid Malaria Diagnosis Test Analyses," IJERPH, MDPI, vol. 11(7), pages 1-11, June.
    20. Paula Fariña & Jorge González & Ernesto San Martín, 2019. "The Use of an Identifiability-Based Strategy for the Interpretation of Parameters in the 1PL-G and Rasch Models," Psychometrika, Springer;The Psychometric Society, vol. 84(2), pages 511-528, June.
    21. Matthijs J. Warrens & Alexandra de Raadt, 2015. "Ordering Properties of the First Eigenvector of Certain Similarity Matrices," Journal of Mathematics, Hindawi, vol. 2015, pages 1-5, November.
    22. Mark Alfano & Kathryn Iurino & Paul Stey & Brian Robinson & Markus Christen & Feng Yu & Daniel Lapsley, 2017. "Development and validation of a multi-dimensional measure of intellectual humility," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-28, August.

    More about this item

    Keywords

    OpenMP; MPI; Multicore; Rstream;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:29:y:2014:i:5:p:1301-1320. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.