Thrust/CUDA replicate an array multiple times combined with the values of another array












1















Let's say I have two arrays



A = {1, 2, 3}



and



B = {10,20,30,40,50}



I want to generate a new array which would have a size of



sizeof(A) * sizeof(B)



I want to replicate B sizeof(A) times, and on each repetition i, the resultant array should have A[i] added to it. So the result would be something like



{11,21,31,41,51,12,22,32,42,52,13,23,33,43,53}










share|improve this question



























    1















    Let's say I have two arrays



    A = {1, 2, 3}



    and



    B = {10,20,30,40,50}



    I want to generate a new array which would have a size of



    sizeof(A) * sizeof(B)



    I want to replicate B sizeof(A) times, and on each repetition i, the resultant array should have A[i] added to it. So the result would be something like



    {11,21,31,41,51,12,22,32,42,52,13,23,33,43,53}










    share|improve this question

























      1












      1








      1








      Let's say I have two arrays



      A = {1, 2, 3}



      and



      B = {10,20,30,40,50}



      I want to generate a new array which would have a size of



      sizeof(A) * sizeof(B)



      I want to replicate B sizeof(A) times, and on each repetition i, the resultant array should have A[i] added to it. So the result would be something like



      {11,21,31,41,51,12,22,32,42,52,13,23,33,43,53}










      share|improve this question














      Let's say I have two arrays



      A = {1, 2, 3}



      and



      B = {10,20,30,40,50}



      I want to generate a new array which would have a size of



      sizeof(A) * sizeof(B)



      I want to replicate B sizeof(A) times, and on each repetition i, the resultant array should have A[i] added to it. So the result would be something like



      {11,21,31,41,51,12,22,32,42,52,13,23,33,43,53}







      cuda thrust






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 14 '18 at 8:47









      strandedstranded

      1356




      1356
























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          This task can be interpreted as a 2-dimensional problem where the output array can be treated as a matrix of dimensions sizeof(A) times sizeof(B). In this way, we can use 2D CUDA indexing to achieve the desired functionality. A sample CUDA C++ code of this 2D implementation is shown below:



          #include <iostream>
          #include <cuda_runtime.h>
          #include <cassert>

          using namespace std;

          __global__ void kernel_replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          const int ai = blockIdx.x * blockDim.x + threadIdx.x;
          const int bi = blockIdx.y * blockDim.y + threadIdx.y;

          if(ai<alen && bi<blen)
          {
          const int ci = ai * blen + bi;
          c[ci] = a[ai] + b[bi];
          }
          }


          void replicate_device(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          dim3 block(16,16);
          dim3 grid;
          grid.x = (alen + block.x - 1) / block.x;
          grid.y = (blen + block.y - 1) / block.y;

          kernel_replicate<<<grid, block>>>(a,b,c,alen,blen,clen);

          assert(cudaSuccess == cudaDeviceSynchronize());
          }


          void replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          int *ad, *bd, *cd;

          size_t abytes = alen * sizeof(int);
          size_t bbytes = blen * sizeof(int);
          size_t cbytes = clen * sizeof(int);

          cudaMalloc(&ad, abytes);
          cudaMalloc(&bd, bbytes);
          cudaMalloc(&cd, cbytes);

          cudaMemcpy(ad,a, abytes, cudaMemcpyHostToDevice);
          cudaMemcpy(bd,b, bbytes, cudaMemcpyHostToDevice);

          replicate_device(ad,bd,cd, alen,blen,clen);

          cudaMemcpy(c,cd, cbytes, cudaMemcpyDeviceToHost);


          cudaFree(ad);
          cudaFree(bd);
          cudaFree(cd);
          }


          int main()
          {
          const int alen = 3;
          const int blen = 5;
          const int clen = alen * blen;

          int A[alen] = {1,2,3};
          int B[blen] = {10,20,30,40,50};
          int C[clen] = {0};

          replicate(A,B,C,alen, blen, clen);

          for(int i=0; i<alen; i++)
          {
          cout<<A[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<blen; i++)
          {
          cout<<B[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<clen; i++)
          {
          cout<<C[i]<<" ";
          }
          cout<<endl;


          return 0;
          }





          share|improve this answer
























          • This is exactly what I wanted, thanks!

            – stranded
            Nov 14 '18 at 10:04











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          1 Answer
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          1 Answer
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          active

          oldest

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          2














          This task can be interpreted as a 2-dimensional problem where the output array can be treated as a matrix of dimensions sizeof(A) times sizeof(B). In this way, we can use 2D CUDA indexing to achieve the desired functionality. A sample CUDA C++ code of this 2D implementation is shown below:



          #include <iostream>
          #include <cuda_runtime.h>
          #include <cassert>

          using namespace std;

          __global__ void kernel_replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          const int ai = blockIdx.x * blockDim.x + threadIdx.x;
          const int bi = blockIdx.y * blockDim.y + threadIdx.y;

          if(ai<alen && bi<blen)
          {
          const int ci = ai * blen + bi;
          c[ci] = a[ai] + b[bi];
          }
          }


          void replicate_device(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          dim3 block(16,16);
          dim3 grid;
          grid.x = (alen + block.x - 1) / block.x;
          grid.y = (blen + block.y - 1) / block.y;

          kernel_replicate<<<grid, block>>>(a,b,c,alen,blen,clen);

          assert(cudaSuccess == cudaDeviceSynchronize());
          }


          void replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          int *ad, *bd, *cd;

          size_t abytes = alen * sizeof(int);
          size_t bbytes = blen * sizeof(int);
          size_t cbytes = clen * sizeof(int);

          cudaMalloc(&ad, abytes);
          cudaMalloc(&bd, bbytes);
          cudaMalloc(&cd, cbytes);

          cudaMemcpy(ad,a, abytes, cudaMemcpyHostToDevice);
          cudaMemcpy(bd,b, bbytes, cudaMemcpyHostToDevice);

          replicate_device(ad,bd,cd, alen,blen,clen);

          cudaMemcpy(c,cd, cbytes, cudaMemcpyDeviceToHost);


          cudaFree(ad);
          cudaFree(bd);
          cudaFree(cd);
          }


          int main()
          {
          const int alen = 3;
          const int blen = 5;
          const int clen = alen * blen;

          int A[alen] = {1,2,3};
          int B[blen] = {10,20,30,40,50};
          int C[clen] = {0};

          replicate(A,B,C,alen, blen, clen);

          for(int i=0; i<alen; i++)
          {
          cout<<A[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<blen; i++)
          {
          cout<<B[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<clen; i++)
          {
          cout<<C[i]<<" ";
          }
          cout<<endl;


          return 0;
          }





          share|improve this answer
























          • This is exactly what I wanted, thanks!

            – stranded
            Nov 14 '18 at 10:04
















          2














          This task can be interpreted as a 2-dimensional problem where the output array can be treated as a matrix of dimensions sizeof(A) times sizeof(B). In this way, we can use 2D CUDA indexing to achieve the desired functionality. A sample CUDA C++ code of this 2D implementation is shown below:



          #include <iostream>
          #include <cuda_runtime.h>
          #include <cassert>

          using namespace std;

          __global__ void kernel_replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          const int ai = blockIdx.x * blockDim.x + threadIdx.x;
          const int bi = blockIdx.y * blockDim.y + threadIdx.y;

          if(ai<alen && bi<blen)
          {
          const int ci = ai * blen + bi;
          c[ci] = a[ai] + b[bi];
          }
          }


          void replicate_device(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          dim3 block(16,16);
          dim3 grid;
          grid.x = (alen + block.x - 1) / block.x;
          grid.y = (blen + block.y - 1) / block.y;

          kernel_replicate<<<grid, block>>>(a,b,c,alen,blen,clen);

          assert(cudaSuccess == cudaDeviceSynchronize());
          }


          void replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          int *ad, *bd, *cd;

          size_t abytes = alen * sizeof(int);
          size_t bbytes = blen * sizeof(int);
          size_t cbytes = clen * sizeof(int);

          cudaMalloc(&ad, abytes);
          cudaMalloc(&bd, bbytes);
          cudaMalloc(&cd, cbytes);

          cudaMemcpy(ad,a, abytes, cudaMemcpyHostToDevice);
          cudaMemcpy(bd,b, bbytes, cudaMemcpyHostToDevice);

          replicate_device(ad,bd,cd, alen,blen,clen);

          cudaMemcpy(c,cd, cbytes, cudaMemcpyDeviceToHost);


          cudaFree(ad);
          cudaFree(bd);
          cudaFree(cd);
          }


          int main()
          {
          const int alen = 3;
          const int blen = 5;
          const int clen = alen * blen;

          int A[alen] = {1,2,3};
          int B[blen] = {10,20,30,40,50};
          int C[clen] = {0};

          replicate(A,B,C,alen, blen, clen);

          for(int i=0; i<alen; i++)
          {
          cout<<A[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<blen; i++)
          {
          cout<<B[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<clen; i++)
          {
          cout<<C[i]<<" ";
          }
          cout<<endl;


          return 0;
          }





          share|improve this answer
























          • This is exactly what I wanted, thanks!

            – stranded
            Nov 14 '18 at 10:04














          2












          2








          2







          This task can be interpreted as a 2-dimensional problem where the output array can be treated as a matrix of dimensions sizeof(A) times sizeof(B). In this way, we can use 2D CUDA indexing to achieve the desired functionality. A sample CUDA C++ code of this 2D implementation is shown below:



          #include <iostream>
          #include <cuda_runtime.h>
          #include <cassert>

          using namespace std;

          __global__ void kernel_replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          const int ai = blockIdx.x * blockDim.x + threadIdx.x;
          const int bi = blockIdx.y * blockDim.y + threadIdx.y;

          if(ai<alen && bi<blen)
          {
          const int ci = ai * blen + bi;
          c[ci] = a[ai] + b[bi];
          }
          }


          void replicate_device(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          dim3 block(16,16);
          dim3 grid;
          grid.x = (alen + block.x - 1) / block.x;
          grid.y = (blen + block.y - 1) / block.y;

          kernel_replicate<<<grid, block>>>(a,b,c,alen,blen,clen);

          assert(cudaSuccess == cudaDeviceSynchronize());
          }


          void replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          int *ad, *bd, *cd;

          size_t abytes = alen * sizeof(int);
          size_t bbytes = blen * sizeof(int);
          size_t cbytes = clen * sizeof(int);

          cudaMalloc(&ad, abytes);
          cudaMalloc(&bd, bbytes);
          cudaMalloc(&cd, cbytes);

          cudaMemcpy(ad,a, abytes, cudaMemcpyHostToDevice);
          cudaMemcpy(bd,b, bbytes, cudaMemcpyHostToDevice);

          replicate_device(ad,bd,cd, alen,blen,clen);

          cudaMemcpy(c,cd, cbytes, cudaMemcpyDeviceToHost);


          cudaFree(ad);
          cudaFree(bd);
          cudaFree(cd);
          }


          int main()
          {
          const int alen = 3;
          const int blen = 5;
          const int clen = alen * blen;

          int A[alen] = {1,2,3};
          int B[blen] = {10,20,30,40,50};
          int C[clen] = {0};

          replicate(A,B,C,alen, blen, clen);

          for(int i=0; i<alen; i++)
          {
          cout<<A[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<blen; i++)
          {
          cout<<B[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<clen; i++)
          {
          cout<<C[i]<<" ";
          }
          cout<<endl;


          return 0;
          }





          share|improve this answer













          This task can be interpreted as a 2-dimensional problem where the output array can be treated as a matrix of dimensions sizeof(A) times sizeof(B). In this way, we can use 2D CUDA indexing to achieve the desired functionality. A sample CUDA C++ code of this 2D implementation is shown below:



          #include <iostream>
          #include <cuda_runtime.h>
          #include <cassert>

          using namespace std;

          __global__ void kernel_replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          const int ai = blockIdx.x * blockDim.x + threadIdx.x;
          const int bi = blockIdx.y * blockDim.y + threadIdx.y;

          if(ai<alen && bi<blen)
          {
          const int ci = ai * blen + bi;
          c[ci] = a[ai] + b[bi];
          }
          }


          void replicate_device(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          dim3 block(16,16);
          dim3 grid;
          grid.x = (alen + block.x - 1) / block.x;
          grid.y = (blen + block.y - 1) / block.y;

          kernel_replicate<<<grid, block>>>(a,b,c,alen,blen,clen);

          assert(cudaSuccess == cudaDeviceSynchronize());
          }


          void replicate(int* a, int* b, int* c, int alen, int blen, int clen)
          {
          int *ad, *bd, *cd;

          size_t abytes = alen * sizeof(int);
          size_t bbytes = blen * sizeof(int);
          size_t cbytes = clen * sizeof(int);

          cudaMalloc(&ad, abytes);
          cudaMalloc(&bd, bbytes);
          cudaMalloc(&cd, cbytes);

          cudaMemcpy(ad,a, abytes, cudaMemcpyHostToDevice);
          cudaMemcpy(bd,b, bbytes, cudaMemcpyHostToDevice);

          replicate_device(ad,bd,cd, alen,blen,clen);

          cudaMemcpy(c,cd, cbytes, cudaMemcpyDeviceToHost);


          cudaFree(ad);
          cudaFree(bd);
          cudaFree(cd);
          }


          int main()
          {
          const int alen = 3;
          const int blen = 5;
          const int clen = alen * blen;

          int A[alen] = {1,2,3};
          int B[blen] = {10,20,30,40,50};
          int C[clen] = {0};

          replicate(A,B,C,alen, blen, clen);

          for(int i=0; i<alen; i++)
          {
          cout<<A[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<blen; i++)
          {
          cout<<B[i]<<" ";
          }
          cout<<endl;

          for(int i=0; i<clen; i++)
          {
          cout<<C[i]<<" ";
          }
          cout<<endl;


          return 0;
          }






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 14 '18 at 9:41









          sgarizvisgarizvi

          12.5k84378




          12.5k84378













          • This is exactly what I wanted, thanks!

            – stranded
            Nov 14 '18 at 10:04



















          • This is exactly what I wanted, thanks!

            – stranded
            Nov 14 '18 at 10:04

















          This is exactly what I wanted, thanks!

          – stranded
          Nov 14 '18 at 10:04





          This is exactly what I wanted, thanks!

          – stranded
          Nov 14 '18 at 10:04




















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