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

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  • Ferreira da Silva, Adelino R.

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.

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  • Ferreira da Silva, Adelino R., 2011. "cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 44(i04).
    • Handle: RePEc:jss:jstsof:v:044:i04
    DOI: http://hdl.handle.net/10.18637/jss.v044.i04
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    Cited by:

    1. Mahani, Alireza S. & Sharabiani, Mansour T.A., 2015. "SIMD parallel MCMC sampling with applications for big-data Bayesian analytics," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 75-99.

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