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cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis

Adelino R. Ferreira da Silva
Universidade Nova de Lisboa
Journal of Statistical Software, Vol. 44, Issue 4, 2011

@article{FerreiradaSilva:2011:JSSOBK:v44i04,

   author={Adelino R. Ferreira da Silva},

   title={cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis},

   journal={Journal of Statistical Software},

   volume={44},

   number={4},

   pages={1–24},

   day={27},

   month={10},

   year={2011},

   CODEN={JSSOBK},

   ISSN={1548-7660},

   bibdate={2011-06-16},

   URL={http://www.jstatsoft.org/v44/i04},

   accepted={2011-06-16},

   submitted={2010-10-20}

}

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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.
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