cudaBayesreg: Bayesian Computation in CUDA

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.

Adelino Ferreira da Silva

CRAN packages used

cudaBayesreg, bayesm, cudaBayesregData, oro.nifti

CRAN Task Views implied by cited packages

MedicalImaging, Bayesian, HighPerformanceComputing, Cluster, Distributions, Econometrics, Multivariate


Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".


For attribution, please cite this work as

Silva, "cudaBayesreg: Bayesian Computation in CUDA", The R Journal, 2010

BibTeX citation

  author = {Silva, Adelino Ferreira da},
  title = {cudaBayesreg: Bayesian Computation in CUDA},
  journal = {The R Journal},
  year = {2010},
  note = {},
  doi = {10.32614/RJ-2010-015},
  volume = {2},
  issue = {2},
  issn = {2073-4859},
  pages = {48-55}