Installing GPU Packages
For Windows users, in the R main console, you can select the menu item “Packages > Install package(s) from local zip files”. Then navigate to the extraction folder you have just created, and install the two binary packages rpud_0.5.0.zip and rpudplus_0.5.0.zip in turn. The rpud_0.5.0_src.zip package is for your reference only. It is not meant to be installed.
For Mac OS X users, in an R console, you should change your working directory to the extraction folder.
And you can install the binary packages in R.
You can optionally install the rpud source package in a terminal if you already have the GNU toolchain installed.
The R installation process is a little bit different on Linux. For Fedora users, enter the following in a terminal.
For Ubuntu users, use the following instead.
Ubuntu users should also follow the CRAN instruction for the latest R release.
Then you can open an R console and change working directory to the extraction folder.
And you can install the binary packages in turn.
You can optionally install the rpud source package in R.
If you need SVM and Bayesian inferences, you should meet their dependencies on coda and SparseM in R.
Now you may verify your rpudplus installation.
Copyright (C) 2010-2015 Chi Yau. All Rights Reserved.
Rpudplus is free for academic use only. There is absolutely NO warranty.
The rpudplus GPU package requires double precision arithmetic hardware support. In order to fully exploit its capabilities, you should ensure the compute capability of your CUDA GPU exceeds 1.3 or above according to the CUDA hardware page.
Furthermore, the Windows version of rpudplus prefers a Tesla GPU running in TCC mode. Since the GeForce hardware does not support TCC mode, the performance is suboptimal.