Table of Contents
International Scholarly Research Notices
Volume 2014, Article ID 368149, 11 pages
Research Article

A GPU-Based Gibbs Sampler for a Unidimensional IRT Model

1Educational Measurement and Statistics, Department of Educational Psychology & Special Education, Southern Illinois University, Carbondale, IL 62901, USA
2Department of Computer Science, Southern Illinois University, Carbondale, IL 62901, USA

Received 24 April 2014; Revised 11 July 2014; Accepted 21 July 2014; Published 30 October 2014

Academic Editor: Jussi Tohka

Copyright © 2014 Yanyan Sheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 computationally expensive due to the large number of iterations. This limits the use of the procedure in many applications. In an effort to overcome such restraint, previous studies focused on utilizing the message passing interface (MPI) in a distributed memory-based Linux cluster to achieve certain speedups. However, given the high data dependencies in a single Markov chain for IRT models, the communication overhead rapidly grows as the number of cluster nodes increases. This makes it difficult to further improve the performance under such a parallel framework. This study aims to tackle the problem using massive core-based graphic processing units (GPU), which is practical, cost-effective, and convenient in actual applications. The performance comparisons among serial CPU, MPI, and compute unified device architecture (CUDA) programs demonstrate that the CUDA GPU approach has many advantages over the CPU-based approach and therefore is preferred.