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Authors: Adelino R. Ferreira da Silva
Title: [download]
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cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis
Reference: Vol. 44, Issue 4, Oct 2011
Submitted 2010-10-20, Accepted 2011-06-16
Type: Article
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.

Paper: [download]
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cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis
(application/pdf, 1.3 MB)
Supplements: [download]
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cudaBayesreg_0.3-13.tar.gz: R source package
(application/x-gzip, 162.9 KB)
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cudaBayesregData_0.3-10.tar.gz: R source package (for data)
(application/x-gzip, 31.8 MB)
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v44i04.R: R example code from the paper
(application/octet-stream, 11.8 KB)
Resources: BibTeX | OAI
Creative Commons License
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3)
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