我在从设备到主机的内核上计算后返回二维结构。从设备到主机的cudaMemcpy错误
HANDLE_ERROR(cudaMemcpy(Pixel,Pixel_gpu,img_wd*img_ht*sizeof(pixel),cudaMemcpyDeviceToHost));
像素声明主机,Pixel_gpu是如下分配的设备:
**Pixel_gpu;
HANDLE_ERROR(cudaMalloc(&Pixel_gpu,img_wd*img_ht*sizeof(pixel)));
pixel **Pixel = (pixel**)malloc((img_ht)*sizeof(pixel*));
for(int i=0;i<(img_ht);i++)
Pixel[i]=(pixel*)malloc((img_wd)*sizeof(pixel));
使用这个我最终得到非法的内存访问错误。
尝试一个类似的内存对齐结果,也没有帮助。
pixel *Pixel_res = (pixel*)malloc(img_wd*img_ht*sizeof(pixel));
HANDLE_ERROR(cudaMemcpy(Pixel_res,Pixel_gpu,img_wd*img_ht*sizeof(pixel),cudaMemcpyDeviceToHost));
内核中启动:
cudaDeviceProp prop;
HANDLE_ERROR(cudaGetDeviceProperties(&prop, 0));
int thread_block=sqrt(prop.maxThreadsPerBlock);
dim3 DimGrid(ceil(img_wd/thread_block),ceil(img_ht/thread_block),1);
dim3 DimBlock(sqrt(prop.maxThreadsPerBlock),sqrt(prop.maxThreadsPerBlock),1);
//allocating gpu memory
pixel **Pixel_tmp_gpu, **Pixel_gpu;
HANDLE_ERROR(cudaMalloc(&Pixel_tmp_gpu,img_wd*img_ht*sizeof(pixel)));
HANDLE_ERROR(cudaMalloc(&Pixel_gpu,img_wd*img_ht*sizeof(pixel)));
float **kernel0_gpu, **kernel1_gpu;
HANDLE_ERROR(cudaMalloc(&kernel0_gpu,k*1*sizeof(float)));
HANDLE_ERROR(cudaMalloc(&kernel1_gpu,1*k*sizeof(float)));
cout<<"memory allocated"<<endl;
//copying needed data
HANDLE_ERROR(cudaMemcpy(Pixel_tmp_gpu,Pixel_tmp,img_wd*img_ht*sizeof(pixel),cudaMemcpyHostToDevice));
HANDLE_ERROR(cudaMemcpy(Pixel_gpu,Pixel,img_wd*img_ht*sizeof(pixel),cudaMemcpyHostToDevice));
HANDLE_ERROR(cudaMemcpy(kernel0_gpu,kernel0,k*1*sizeof(float),cudaMemcpyHostToDevice));
HANDLE_ERROR(cudaMemcpy(kernel1_gpu,kernel1,1*k*sizeof(float),cudaMemcpyHostToDevice));
cout<<"memory transfers done"<<endl;
vertical_conv<<<DimGrid,DimBlock>>>(Pixel_gpu, Pixel_tmp_gpu,img_wd, img_ht,kernel0_gpu,k);
time_t vertical_convolution=time(NULL);
cout<<" vertical_convolution time: "<<double(vertical_convolution - reading_file)<<"sec"<<endl;
horizontal_conv<<<DimGrid,DimBlock>>>(Pixel_tmp_gpu, Pixel_gpu, img_wd, img_ht, kernel1_gpu, k);
time_t horizontal_convolution=time(NULL);
cout<<" horizontal convolution time:" <<double(horizontal_convolution-vertical_convolution)<<" sec"<<endl;
pixel *Pixel_res = (pixel*)malloc(img_wd*img_ht*sizeof(pixel));
HANDLE_ERROR(cudaMemcpy(Pixel_res,Pixel_gpu,img_wd*img_ht*sizeof(pixel),cudaMemcpyDeviceToHost));
使用的函数:
struct pixel //to store RGB values
{
unsigned char r;
unsigned char g;
unsigned char b;
};
static void HandleError(cudaError_t err, const char *file, int line) {
if (err != cudaSuccess) {
cout<<cudaGetErrorString(err)<<" in "<< file <<" at line "<< line<<endl;
}
}
#define HANDLE_ERROR(err) (HandleError(err, __FILE__, __LINE__))
__device__ void padding(pixel** Pixel_val, int x_coord, int y_coord, int img_width, int img_height, pixel Px) //padding the image,depending on pixel coordinates, can be replaced by reflect for better result //currently zero padding
{
if(x_coord<img_width && y_coord<img_height && x_coord>=0 && y_coord>=0)
Px=Pixel_val[y_coord][x_coord];
}
垂直卷积:
__global__ void vertical_conv(pixel** Pixel_in, pixel** Pixel_out,int img_wd, int img_ht, float** kernel, int k)
{
float tmp_r, tmp_g, tmp_b;
pixel pix_val;
pix_val.r=0;pix_val.g=0;pix_val.b=0;
int row=blockIdx.y*blockDim.y + threadIdx.y;
int col = blockIdx.x*blockDim.x + threadIdx.x;
if(row<img_ht && col<img_wd){
tmp_r=0, tmp_g=0, tmp_b=0;
for(int l=0;l<k;l++)
{
padding(Pixel_in, col, row+l-(k-1)/2, img_wd, img_ht, pix_val);
tmp_r+=pix_val.r * kernel[l][0];
tmp_b+=pix_val.b * kernel[l][0];
tmp_g+=pix_val.g * kernel[l][0];
}
Pixel_out[row][col].r=tmp_r;
Pixel_out[row][col].g=tmp_g;
Pixel_out[row][col].b=tmp_b;
}
}
水平卷积:
__global__ void horizontal_conv(pixel** Pixel_in, pixel** Pixel_out, int img_wd, int img_ht, float** kernel, int k)
{
float tmp_r, tmp_b, tmp_g;
pixel pix_val;
pix_val.r=0;pix_val.g=0;pix_val.b=0;
//horizontal convolution
int row=blockIdx.y*blockDim.y + threadIdx.y;
int col = blockIdx.x*blockDim.x + threadIdx.x;
tmp_r=0, tmp_g=0, tmp_b=0;
if(row<img_ht && col<img_wd)
{
for(int l=0; l<k;l++)
{
padding(Pixel_in, col+l-(k-1)/2, row, img_wd, img_ht, pix_val);
tmp_r+=pix_val.r * kernel[0][l];
tmp_g+=pix_val.g * kernel[0][l];
tmp_b+=pix_val.b * kernel[0][l];
}
Pixel_out[row][col].r=tmp_r;
Pixel_out[row][col].g=tmp_g;
Pixel_out[row][col].b=tmp_b;
}
}
有人可以帮助我知道这里可能是错的吗?
你不能传递一个扁平的单指针('*')分配给内核,并期望把它用作双指针('**')数组。在主机和设备之间传递双指针数组需要特殊的编码,这是你缺少的。这是一个经常被误解的话题,所以有很多问题在讨论。您可以搜索'cuda二维数组'或'cuda'标签信息页面,以链接到讨论如何处理二维数组的标准问题。我相信这仍然不是[mcve]。它应该是别人可以编译和运行的东西,不需要大量的组装工作。 –
如果你只是将所有东西都转换成单指针数组,并且模拟2D访问(即[[row * width + col]'),那么你将会大大简化你的工作。 –
我会研究二维数组处理cuda,感谢您的建议。 –