fbseqCUDA package is an internal backend of
fbseq package that runs the Markov chain Monte Carlo (MCMC) procedure behind the scenes. It is implemented with CUDA for acceleration with parallel computing. For installation, CUDA must be installed. To use
fbseqCUDA package in an MCMC,
fbseq package must be installed, and a CUDA-capable general-purpose graphics processing unit (GPU) must be installed on your machine.
- The R version and R packages listed in the "Depends", "Imports", and "Suggests" fields of the "package's DESCRIPTION file.
- A CUDA-capable NVIDIA graphics processing unit (GPU) with compute capability 2.0 or greater.
- CUDA version 6.0 or greater. More information about CUDA is available through NVIDIA.
- Optional: the code uses double precision values for computation, so GPUs that natively support double precision will be much faster than ones that do not.
Option 1: install a stable release (recommended).
Navigate to a list of stable releases on the project's GitHub page. Download the desired
tar.gz bundle, then install it either with
install.packages(..., repos = NULL, type="source") from within R
R CMD INSTALL from the Unix/Linux command line.
Option 2: use
install_github to install the development version.
For this option, you need the
devtools package, available from CRAN or GitHub. Open R and run
Option 3: build the development version from the source.
Open a command line program such as Terminal in Mac/Linux and enter the following commands.
git clone firstname.lastname@example.org:wlandau/fbseqCUDA.git R CMD build fbseqCUDA R CMD INSTALL ...
... is replaced by the name of the tarball produced by
R CMD build.
If CUDA is not found, open
fbseqCUDA/src/Makevars in a text editor. The top line reads
CUDA_HOME = /usr/local/cuda
but this may not be correct for your system. Replace
/usr/local/cuda with the correct path to the installation of CUDA on your computer.