我有一个图像特征矩阵 A 是 n*m*31 矩阵 填充用于过滤，我有 B 作为对象过滤器 k*l*31。我想获得一个输出矩阵C是p * r * 31，图像A的大小没有填充。我尝试编写一个 CUDA 代码来在 A 上运行过滤器 B 并获得 C。
我假设 A 上的每个过滤操作和过滤器 B 都被一个线程 block 占用，因此每个线程 block 内都会有 k*l 操作。并且每个移位的过滤操作将在不同的线程 block 上完成。对于 A(0,0)，过滤将在 thread_block(0,0) 上，对于 A(0,1)，将在 thread_block(1,0) 上，依此类推。我也有第三个维度为 31。第三个维度的每个空间都将自行计算。因此，通过对矩阵进行正确的 3d 索引，我可能能够以非常并行的形式进行所有操作。
所以操作是
A n*m*31 X B k*l*31 = C p*r*31
如何有效地为操作进行内核索引？
最佳答案
这是我编写的一些代码，大致按照您的描述进行。
它创建一个 3D 数据集(称为 cell，但它与您的数组 A 相当)，用随机数据填充它，然后计算结果 3D基于 A 中的数据的输出数组(称为 node，但它与您的数组 C 相当)。 A 的数据大小大于 C 的大小(您称之为“填充”)以允许传递函数 B A 的边界元素。在我的例子中，函数 B 只是在 A 的 3D 立方体积中找到与 B 的大小相关联的最小值(我称之为 WSIZE，创建一个 3D 区域WSIZE x WSIZE x WSIZE) 并将结果存储在 C 中。
此特定代码试图通过将输入 A 的某个区域复制到每个 block 的共享内存中来利用数据重用。每个 block 计算多个输出点(即它多次计算 B 以填充 C 的区域)，以利用 B 的相邻计算的数据重用机会。
这可能有助于您入门。您显然必须用您想要的 B 函数替换 B (我的最小查找代码)。此外，在我的情况下，您需要将 B 域从立方域修改为与您的 B 尺寸相对应的任何类型的矩形棱镜。这也会影响共享内存操作，因此您可能希望在第一次迭代中放弃共享内存，只是为了让事情在功能上正确，然后添加共享内存优化，看看您可以获得什么好处。
#include <stdio.h>
#include <stdlib.h>
// these are just for timing measurments
#include <time.h>
// Computes minimum in a 3D volume, at each output point
// To compile it with nvcc execute: nvcc -O2 -o grid3d grid3d.cu
//define the window size (cubic volume) and the data set size
#define WSIZE 6
#define DATAXSIZE 100
#define DATAYSIZE 100
#define DATAZSIZE 20
//define the chunk sizes that each threadblock will work on
#define BLKXSIZE 8
#define BLKYSIZE 8
#define BLKZSIZE 8
// for cuda error checking
#define cudaCheckErrors(msg) \
do { \
cudaError_t __err = cudaGetLastError(); \
if (__err != cudaSuccess) { \
fprintf(stderr, "Fatal error: %s (%s at %s:%d)\n", \
msg, cudaGetErrorString(__err), \
__FILE__, __LINE__); \
fprintf(stderr, "*** FAILED - ABORTING\n"); \
return 1; \
} \
} while (0)
// device function to compute 3D volume minimum at each output point
__global__ void cmp_win(int knode[][DATAYSIZE][DATAXSIZE], const int kcell[][DATAYSIZE+(WSIZE-1)][DATAXSIZE+(WSIZE-1)])
{
__shared__ int smem[(BLKZSIZE + (WSIZE-1))][(BLKYSIZE + (WSIZE-1))][(BLKXSIZE + (WSIZE-1))];
int tempnode, i, j, k;
int idx = blockIdx.x*blockDim.x + threadIdx.x;
int idy = blockIdx.y*blockDim.y + threadIdx.y;
int idz = blockIdx.z*blockDim.z + threadIdx.z;
if ((idx < (DATAXSIZE+WSIZE-1)) && (idy < (DATAYSIZE+WSIZE-1)) && (idz < (DATAZSIZE+WSIZE-1))){
smem[threadIdx.z][threadIdx.y][threadIdx.x]=kcell[idz][idy][idx];
if ((threadIdx.z > (BLKZSIZE - WSIZE)) && (idz < DATAZSIZE))
smem[threadIdx.z + (WSIZE-1)][threadIdx.y][threadIdx.x] = kcell[idz + (WSIZE-1)][idy][idx];
if ((threadIdx.y > (BLKYSIZE - WSIZE)) && (idy < DATAYSIZE))
smem[threadIdx.z][threadIdx.y + (WSIZE-1)][threadIdx.x] = kcell[idz][idy+(WSIZE-1)][idx];
if ((threadIdx.x > (BLKXSIZE - WSIZE)) && (idx < DATAXSIZE))
smem[threadIdx.z][threadIdx.y][threadIdx.x + (WSIZE-1)] = kcell[idz][idy][idx+(WSIZE-1)];
if ((threadIdx.z > (BLKZSIZE - WSIZE)) && (threadIdx.y > (BLKYSIZE - WSIZE)) && (idz < DATAZSIZE) && (idy < DATAYSIZE))
smem[threadIdx.z + (WSIZE-1)][threadIdx.y + (WSIZE-1)][threadIdx.x] = kcell[idz+(WSIZE-1)][idy+(WSIZE-1)][idx];
if ((threadIdx.z > (BLKZSIZE - WSIZE)) && (threadIdx.x > (BLKXSIZE - WSIZE)) && (idz < DATAZSIZE) && (idx < DATAXSIZE))
smem[threadIdx.z + (WSIZE-1)][threadIdx.y][threadIdx.x + (WSIZE-1)] = kcell[idz+(WSIZE-1)][idy][idx+(WSIZE-1)];
if ((threadIdx.y > (BLKYSIZE - WSIZE)) && (threadIdx.x > (BLKXSIZE - WSIZE)) && (idy < DATAYSIZE) && (idx < DATAXSIZE))
smem[threadIdx.z][threadIdx.y + (WSIZE-1)][threadIdx.x + (WSIZE-1)] = kcell[idz][idy+(WSIZE-1)][idx+(WSIZE-1)];
if ((threadIdx.z > (BLKZSIZE - WSIZE)) && (threadIdx.y > (BLKYSIZE - WSIZE)) && (threadIdx.x > (BLKXSIZE - WSIZE)) && (idz < DATAZSIZE) && (idy < DATAYSIZE) && (idx < DATAXSIZE))
smem[threadIdx.z+(WSIZE-1)][threadIdx.y+(WSIZE-1)][threadIdx.x+(WSIZE-1)] = kcell[idz+(WSIZE-1)][idy+(WSIZE-1)][idx+(WSIZE-1)];
}
__syncthreads();
if ((idx < DATAXSIZE) && (idy < DATAYSIZE) && (idz < DATAZSIZE)){
tempnode = knode[idz][idy][idx];
for (i=0; i<WSIZE; i++)
for (j=0; j<WSIZE; j++)
for (k=0; k<WSIZE; k++)
if (smem[threadIdx.z + i][threadIdx.y + j][threadIdx.x + k] < tempnode)
tempnode = smem[threadIdx.z + i][threadIdx.y + j][threadIdx.x + k];
knode[idz][idy][idx] = tempnode;
}
}
int main(int argc, char *argv[])
{
typedef int cRarray[DATAYSIZE+WSIZE-1][DATAXSIZE+WSIZE-1];
typedef int nRarray[DATAYSIZE][DATAXSIZE];
int i, j, k, u, v, w, temphnode;
const dim3 blockSize(BLKXSIZE, BLKYSIZE, BLKZSIZE);
const dim3 gridSize(((DATAXSIZE+BLKXSIZE-1)/BLKXSIZE), ((DATAYSIZE+BLKYSIZE-1)/BLKYSIZE), ((DATAZSIZE+BLKZSIZE-1)/BLKZSIZE));
// these are just for timing
clock_t t0, t1, t2, t3;
double t1sum=0.0f;
double t2sum=0.0f;
double t3sum=0.0f;
// overall data set sizes
const int nx = DATAXSIZE;
const int ny = DATAYSIZE;
const int nz = DATAZSIZE;
// window (cubic minimization volume) dimensions
const int wx = WSIZE;
const int wy = WSIZE;
const int wz = WSIZE;
// pointers for data set storage via malloc
nRarray *hnode; // storage for result computed on host
nRarray *node, *d_node; // storage for result computed on device
cRarray *cell, *d_cell; // storage for input
// start timing
t0 = clock();
// allocate storage for data set
if ((cell = (cRarray *)malloc(((nx+(wx-1))*(ny+(wy-1))*(nz+(wz-1)))*sizeof(int))) == 0) {fprintf(stderr,"malloc Fail \n"); return 1;}
if ((node = (nRarray *)malloc((nx*ny*nz)*sizeof(int))) == 0) {fprintf(stderr,"malloc Fail \n"); return 1; }
if ((hnode = (nRarray *)malloc((nx*ny*nz)*sizeof(int))) == 0) {fprintf(stderr, "malloc Fail \n"); return 1; }
// synthesize data
for(i=0; i<(nz+(wz-1)); i++)
for(j=0; j<(ny+(wy-1)); j++)
for (k=0; k<(nx+(wx-1)); k++){
cell[i][j][k] = rand(); // unless we use a seed this will produce the same sequence all the time
if ((i<nz) && (j<ny) && (k<nx)) {
node[i][j][k] = RAND_MAX;
hnode[i][j][k] = RAND_MAX;
}
}
t1 = clock();
t1sum = ((double)(t1-t0))/CLOCKS_PER_SEC;
printf("Init took %3.2f seconds. Begin compute\n", t1sum);
// allocate GPU device buffers
cudaMalloc((void **) &d_cell, (((nx+(wx-1))*(ny+(wy-1))*(nz+(wz-1)))*sizeof(int)));
cudaCheckErrors("Failed to allocate device buffer");
cudaMalloc((void **) &d_node, ((nx*ny*nz)*sizeof(int)));
cudaCheckErrors("Failed to allocate device buffer2");
// copy data to GPU
cudaMemcpy(d_node, node, ((nx*ny*nz)*sizeof(int)), cudaMemcpyHostToDevice);
cudaCheckErrors("CUDA memcpy failure");
cudaMemcpy(d_cell, cell, (((nx+(wx-1))*(ny+(wy-1))*(nz+(wz-1)))*sizeof(int)), cudaMemcpyHostToDevice);
cudaCheckErrors("CUDA memcpy2 failure");
cmp_win<<<gridSize,blockSize>>>(d_node, d_cell);
cudaCheckErrors("Kernel launch failure");
// copy output data back to host
cudaMemcpy(node, d_node, ((nx*ny*nz)*sizeof(int)), cudaMemcpyDeviceToHost);
cudaCheckErrors("CUDA memcpy3 failure");
t2 = clock();
t2sum = ((double)(t2-t1))/CLOCKS_PER_SEC;
printf(" Device compute took %3.2f seconds. Beginning host compute.\n", t2sum);
// now compute the same result on the host
for (u=0; u<nz; u++)
for (v=0; v<ny; v++)
for (w=0; w<nx; w++){
temphnode = hnode[u][v][w];
for (i=0; i<wz; i++)
for (j=0; j<wy; j++)
for (k=0; k<wx; k++)
if (temphnode > cell[i+u][j+v][k+w]) temphnode = cell[i+u][j+v][k+w];
hnode[u][v][w] = temphnode;
}
t3 = clock();
t3sum = ((double)(t3-t2))/CLOCKS_PER_SEC;
printf(" Host compute took %3.2f seconds. Comparing results.\n", t3sum);
// and compare for accuracy
for (i=0; i<nz; i++)
for (j=0; j<ny; j++)
for (k=0; k<nx; k++)
if (hnode[i][j][k] != node[i][j][k]) {
printf("Mismatch at x= %d, y= %d, z= %d Host= %d, Device = %d\n", i, j, k, hnode[i][j][k], node[i][j][k]);
return 1;
}
printf("Results match!\n");
free(cell);
free(node);
cudaFree(d_cell);
cudaCheckErrors("cudaFree fail");
cudaFree(d_node);
cudaCheckErrors("cudaFree fail");
return 0;
}
关于image-processing - 用于图像过滤的 3d CUDA 内核索引？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/14920931/