YARCC has a number of NVidia GPGPU nodes which are documented in /wiki/spaces/RHPC/pages/25101677.

Access to the GPGPU cards is easily done through the 'gputools' package. You need to load the module "cuda" to use the package.

Package 'gputools'

gputools provides R interfaces to a number of common functions implemented using the NVidia CUDA language and toolkit.

function	equivalent R function	description
chooseGpu		select GPU device to perform calculations
cpuMatMult		perform matrix multiplication using R's BLAS library
getGpuId		queries the GPU driver for the ID of the device being usedfor the computation
gpuCor	cor with use="pairwisecomplete"	calculation correlation coefficients
gpuCrossprod		matrix cross-product
gpuDist	dist	compute distances between vectors
gpuDistClust	hClust	compute distances and hierarchical clustering
gpuGlm	glm, lm, loglin	fitting generalised linear models using QR decomposition
gpuGranger		Granger Causality Tests
gpuHclust	hclust	clustering on a set ofpoints
gpuLm		fitting linear models
gpuLm.defaultTol		switch tolerance depending on precision
gpuLm.fit		fitter function for linear models
gpuLs.fit		least sqares fit using QR decomposition
gpuMatMult		perform matrix multiplication
gpuMi		B spline based mutual information
gpuQr		estimate the QR decomposition for a matrix
gpuSolve		estimate the solution to a matrix vector equation
gpuTcrossprod		perform matrix transposed cross-product
gpuTtest		T-Test Estimator

More detailed information on using the package can be found here: gputools documentation

Example YARCC jobs using 'gputools'

Example GPGPU R script

require(gputools)

test.gpuCrossprod <- function(x, y)
{
  matA <- matrix(runif(x*y), x, y);
  matB <- matrix(runif(x*y), x, y);
  print(sprintf("Using GPU: %s for gpuCrossprod test", getGpuId()));
  system.time(gpuCrossprod(matA, matB), TRUE);
}
test.cpuCrossprod <- function(x, y)
{
  matA <- matrix(runif(x*y), x, y);
  matB <- matrix(runif(x*y), x, y);
  print("Using CPU for cpuCrossprod test");
  system.time(crossprod(matA, matB), TRUE);
}
print("Testing GPU")
test.gpuCrossprod(3000,4000)
test.cpuCrossprod(3000,4000)

GPGPU Job Script

#$ -cwd -V
#$ -l h_rt=0:15:00
#$ -o logs
#$ -e logs
#$ -N gpu_test
#$ -l nvidia_k40=1

echo `date`: executing gputools R module on host ${HOSTNAME} with ${NSLOTS} slots
/usr/bin/nvidia-smi --list-gpus
R CMD BATCH --no-save gpu-test.R output/gpu-test.Rout

Performance of GPGPU test

> print("Testing GPU")
[1] "Testing GPU"
>
> test.gpuCrossprod(3000,4000)
[1] "Using GPU: 0 for gpuCrossprod test"
   user  system elapsed
  0.573   0.458   1.048
> test.cpuCrossprod(3000,4000)
[1] "Using CPU for cpuCrossprod test"
   user  system elapsed
131.222   0.092 131.170
>
>
> proc.time()
   user  system elapsed
135.548   0.901 136.414

Research Computing Support

5) GPGPU Computing

Package 'gputools'

Example YARCC jobs using 'gputools'