This is a quick tutorial of how to install the R package ‘gputools’ version 1.1 using R version 3.3.2 (2016-10-31) and cuda 8.0 on Ubuntu 16.04. Most of these versions are new so I did some search on the internet and I could not find a tutorial about that. However most of this tutorial is based on this page which is for ‘gputools’ version 0.28 and cuda 7.0 on Ubuntu 15.04. At the end I just changed a few lines.

I have tested it on a ASUS ROG G752VM with NVIDIA GeForce GTX 965M graphics card. The instruction assumes you have the necessary CUDA compatible hardware support. In my case I also installed the NVIDIA driver 367.57 first. My computer was new so I did not have any nvidia driver or compatibility issues. However I strongly recommend to look on the internet how to remove the old drivers first, before install the new ones (things like sudo apt-get purge nvidia-cuda*).

Installing CUDA 8.0

First, to install CUDA 8.0 we can do:

wget https://developer.nvidia.com/compute/cuda/8.0/prod/local_installers/cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64-deb
sudo dpkg -i cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64.deb
sudo apt-get update
sudo apt-get install cuda

Or you can download the CUDA repository package for Ubuntu 16.04 from the CUDA download site and follow the instructions according to your necessities.

Environment Variables

I tried to install the gputools package without adding the variables to the environment and i got an error related to nvcc. Thus, as part of the CUDA environment, we should add the nvcc compiler in the .bashrc file of your home folder.

export CUDA_HOME=/usr/local/cuda-8.0
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64

PATH=${CUDA_HOME}/bin:${PATH}
PATH=${CUDA_HOME}/bin/nvcc:${PATH}
export PATH

Installing gputools version 1.1

The fastest way to install gpuplots if you are using R version 3.3.2 is:

install.packages('gputools')

Now my tutorial differs a bit more from the tutorial I mentioned before. I received the message:

rinterface.cu:1:14: fatal error: R.h: No such file or directory #include

So we have to check where R header dir location is. First lets locate the file R.h:

locate \/R.h

## /usr/share/R/include/R.h

Then next step is to tell to gputools where the R.h is located. Thus it is necessary to change a line in the source package. First download and extract the source package:

wget http://cran.r-project.org/src/contrib/gputools_1.1.tar.gz
tar -zxvf gputools_1.1.tar.gz

Look into the folder you just extracted then open the file configure on your favourite Ubuntu editor to replace the string R_INCLUDE="${R_HOME}/include" for R_INCLUDE="/usr/share/R/include" (which is the location of my R.h file).

The two finals steps are compress the modified source code

tar -czvf gputools_1.1_new.tar.gz gputools

and install the modified package

install.packages("~/gputools_1.1_new.tar.gz", repos = NULL, type = "source")

I had lots of warning messages but no error.

Testing performance

Now we can try some simple benchmarks and see how much time the CPU and gpu time will spend. First a small matrix multiplication:

library(gputools)

magnitude <- 10
dimA <- 2*magnitude;dimB <- 3*magnitude;dimC <- 4*magnitude
matA <- matrix(runif(dimA*dimB), dimA, dimB)
matB <- matrix(runif(dimB*dimC), dimB, dimC)

system.time(matA%*%matB);
##    user  system elapsed 
##   0.000   0.000   0.001
system.time(gpuMatMult(matA, matB))
##    user  system elapsed 
##   0.076   0.140   0.215

then using larger matrices:

magnitude <- 1000
dimA <- 2*magnitude;dimB <- 3*magnitude;dimC <- 4*magnitude
matA <- matrix(runif(dimA*dimB), dimA, dimB)
matB <- matrix(runif(dimB*dimC), dimB, dimC)

system.time(matA%*%matB);
##    user  system elapsed 
##  15.552   0.028  15.579
system.time(gpuMatMult(matA, matB))
##    user  system elapsed 
##   0.792   0.124   0.914