GPU-Accelerated computing with gibbs sampler for the 2PNO IRT model

Yanyan Sheng, William S. Welling, Michelle M. Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Item response theory (IRT) is a popular approach used for addressing large-scale statistical problems in psychometrics as well as in other fields. The fully Bayesian approach for estimating IRT models is usually memory and computational expensive due to the large number of iterations. This limits the use of the procedure in many applications. In an effort to overcome such restrictions, previous studies proposed to tackle the problem using massive core-based graphic processing units (GPU), and demonstrated the advantage of this approach over the message passing interface (MPI) by showing that a single GPU card could achieve a speedup of up to 50×. Given that GPU is practical, cost-effective, and convenient, this study aims to seek further improvements using a single GPU card.

Original languageEnglish
Title of host publicationQuantitative Psychology Research - The 79th Annual Meeting of the Psychometric Society, 2014
EditorsL. Andries van der Ark, Wen-Chung Wang, Jeffrey A. Douglas, Daniel M. Bolt, Sy-Miin Chow
PublisherSpringer New York LLC
Number of pages15
ISBN (Print)9783319199764
StatePublished - 1 Jan 2015
Event79th Annual International Meeting of the Psychometric Society, IMPS 2014 - Madison, United States
Duration: 21 Jul 201425 Jul 2014

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Other79th Annual International Meeting of the Psychometric Society, IMPS 2014
Country/TerritoryUnited States


  • Bayesian estimation
  • CUDA
  • High performance computing
  • Item response theory
  • MCMC
  • Optimization
  • Two-parameter IRT model


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