@inproceedings{60bbc7ba31424468a37c93332f723c4c,
title = "GPU-Accelerated computing with gibbs sampler for the 2PNO IRT model",
abstract = "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.",
keywords = "Bayesian estimation, CUDA, High performance computing, Item response theory, MCMC, Optimization, Two-parameter IRT model",
author = "Yanyan Sheng and Welling, {William S.} and Zhu, {Michelle M.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 79th Annual International Meeting of the Psychometric Society, IMPS 2014 ; Conference date: 21-07-2014 Through 25-07-2014",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-19977-1_5",
language = "English",
isbn = "9783319199764",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer New York LLC",
pages = "59--73",
editor = "{van der Ark}, {L. Andries} and Wen-Chung Wang and Douglas, {Jeffrey A.} and Bolt, {Daniel M.} and Sy-Miin Chow",
booktitle = "Quantitative Psychology Research - The 79th Annual Meeting of the Psychometric Society, 2014",
}