Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Adelino R. Ferreira da Silva
Title: cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis
Abstract: Graphic processing units (GPUs) are rapidly gaining maturity as powerful general parallel computing devices. A key feature in the development of modern GPUs has been the advancement of the programming model and programming tools. Compute Unified Device Architecture (CUDA) is a software platform for massively parallel high-performance computing on Nvidia many-core GPUs. In functional magnetic resonance imaging (fMRI), the volume of the data to be processed, and the type of statistical analysis to perform call for high-performance computing strategies. In this work, we present the main features of the R-CUDA package cudaBayesreg which implements in CUDA the core of a Bayesian multilevel model for the analysis of brain fMRI data. The statistical model implements a Gibbs sampler for multilevel/hierarchical linear models with a normal prior. The main contribution for the increased performance comes from the use of separate threads for fitting the linear regression model at each voxel in parallel. The R-CUDA implementation of the Bayesian model proposed here has been able to reduce significantly the run-time processing of Markov chain Monte Carlo (MCMC) simulations used in Bayesian fMRI data analyses. Presently, cudaBayesreg is only configured for Linux systems with Nvidia CUDA support.

Page views:: 5259. Submitted: 2010-10-20. Published: 2011-10-27.
Paper: cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis     Download PDF (Downloads: 5258)
cudaBayesreg_0.3-13.tar.gz: R source package Download (Downloads: 863; 162KB)
cudaBayesregData_0.3-10.tar.gz: R source package (for data) Download (Downloads: 1465; 31MB)
v44i04.R: R example code from the paper Download (Downloads: 891; 11KB)

DOI: 10.18637/jss.v044.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.