Thrust/CUDA replicate an array multiple times combined with the values of another array
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
add a comment |
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
add a comment |
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
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
cuda thrust
asked Nov 14 '18 at 8:47
strandedstranded
1356
1356
add a comment |
add a comment |
1 Answer
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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;
}
This is exactly what I wanted, thanks!
– stranded
Nov 14 '18 at 10:04
add a comment |
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1 Answer
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1 Answer
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active
oldest
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active
oldest
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active
oldest
votes
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;
}
This is exactly what I wanted, thanks!
– stranded
Nov 14 '18 at 10:04
add a comment |
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;
}
This is exactly what I wanted, thanks!
– stranded
Nov 14 '18 at 10:04
add a comment |
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;
}
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;
}
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
add a comment |
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
add a comment |
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