Graphical processing units are rapidly gaining maturity as powerful general parallel comput ing devices. The package cudaBayesreg uses GPU–oriented procedures to improve the performance of Bayesian computations. The paper motivates the need for devising high-performance computing strategies in the context of fMRI data analysis. Some features of the package for Bayesian analysis of brain fMRI data are illustrated. Comparative computing performance figures between sequential and parallel implementations are presented as well.
cudaBayesreg, bayesm, cudaBayesregData, oro.nifti
MedicalImaging, Bayesian, HighPerformanceComputing, Cluster, Distributions, Econometrics, Multivariate
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For attribution, please cite this work as
Silva, "The R Journal: cudaBayesreg: Bayesian Computation in CUDA", The R Journal, 2010
BibTeX citation
@article{RJ-2010-015,
author = {Silva, Adelino Ferreira da},
title = {The R Journal: cudaBayesreg: Bayesian Computation in CUDA},
journal = {The R Journal},
year = {2010},
note = {https://doi.org/10.32614/RJ-2010-015},
doi = {10.32614/RJ-2010-015},
volume = {2},
issue = {2},
issn = {2073-4859},
pages = {48-55}
}