CODEWITHSUNDEEP
cudathrust
lodhb
lodhb

Reputation: 959

Using CUDA Thrust algorithms sequentially on the host

I wish to compare a Thrust algorithm's runtime when executed sequentially on a single CPU core versus a parallel execution on a GPU.

Thrust specifies the thrust::seq execution policy, but how can I explicity target the host backend system? I wish to avoid executing the algorithm sequentially on the GPU.

Upvotes: 3

Views: 690

Answers (1)

Vitality
Vitality

Reputation: 21515

CUDA Thrust is architecture agnostic. Accordingly, consider the code I provided as an answer to

Cumulative summation in CUDA

In that code, MatingProbability and CumulativeProbability were thrust::device_vectors. thrust::transform and thrust::inclusive_scan were automatically able to recognize that and operate accordingly on the GPU.

Below, I'm providing the same code by changing thrust::device_vector to thrust::host_vector. Again, thrust::transform and thrust::inclusive_scan are able to automatically recognize that the vectors to operate on reside on the CPU and to operate accordingly.

#include <thrust/host_vector.h>
#include <thrust/transform.h>
#include <thrust/functional.h>
#include <thrust/iterator/counting_iterator.h>
#include <thrust/iterator/constant_iterator.h>
#include <cstdio>

template <class T>
struct scaling {
    const T _a;
    scaling(T a) : _a(a) { }
    __host__ __device__ T operator()(const T &x) const { return _a * x; }
};

void main()
{
   const int N = 20;

   double a = -(double)N;
   double b = 0.;

   double Dx = -1./(0.5*N*(N+1));

   thrust::host_vector<double> MatingProbability(N);
   thrust::host_vector<double> CumulativeProbability(N+1, 0.);

   thrust::transform(thrust::make_counting_iterator(a), thrust::make_counting_iterator(b), MatingProbability.begin(), scaling<double>(Dx));

   thrust::inclusive_scan(MatingProbability.begin(), MatingProbability.end(), CumulativeProbability.begin() + 1);

   for(int i=0; i<N+1; i++) 
   {
      double val = CumulativeProbability[i];
      printf("%d %3.15f\n", i, val);
   }

}

Upvotes: 3

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