With OpenACC it is possible to offload computations from the CPU to a GPU, see http://www.icl.utk.edu/~luszczek/teaching/courses/fall2016/cosc462/pdf/OpenACC_Fundamentals.pdf.

Example

In the following we show how to achieve this in the case of a reduction operation involving a large loop:

#include <iostream>
#include <cmath>
int main() {
 int n = 1000000000;
 double total = 0;
 int i;
#pragma acc parallel loop copy(total) copyin(n) reduction(+:total)
 for (i = 0; i < n; ++i) {
 total += exp(sin(M_PI * (double) i/12345.6789));
 }
 std::cout << "total is " << total << '\n';
}

Save the above code in file total.cxx.

Note the pragma

#pragma acc parallel loop copy(total) copyin(n) reduction(+:total)

which moves variables total and n to the GPU and creates teams of threads to compute the total sum in parallel. 

Compile

We'll use the Cray C++ compiler to build the executable but first we need to load a few modules:

module load craype-broadwell
module load cray-libsci_acc 
module load craype-accel-nvidia60 
module load PrgEnv-cray

(Ignore warning "cudatoolkit >= 8.0 is required"). Furthermore, you may need to load cuda/fft or cuda/blas

To compare the execution times between the CPU and GPU version, we build two executables:

CC -h noacc -o total total.cxx
CC -o totalAccGpu total.cxx

with executable total compiled with -h noacc, i.e. OpenACC turned off.

Run

The following commands will submit the runs to the Mahuika queue (note --partition=gpu --gres=gpu:1 in the case of the executable that offloads to the GPU):

time srun --ntasks=1 --cpus-per-task=1 ./total
time srun --ntasks=1 --cpus-per-task=1 --partition=gpu --gres=gpu:1 ./totalAccGpu

executable	time [s]
total	7.6
totalAccGpu	0.41

 

Check out https://support.nesi.org.nz/hc/en-gb/articles/360001127856-Offloading-to-GPU-with-OpenMP- to see how to offload computations onto a GPU using OpenMP.