如何使用cudaMalloc / cudaMemcpy作为指向包含指针的结构的指针？ [英] How to use cudaMalloc / cudaMemcpy for a pointer to a structure containing pointers?

问题描述

我已经看过这个网站和其他人，没有什么工作。我正在为我的具体情况发布一个问题。

I've looked all around this site and others, and nothing has worked. I'm resorting to posting a question for my specific case.

我有一堆矩阵，目标是使用内核让GPU做同样的操作。我确定我可以得到内核工作，但我不能让cudaMalloc / cudaMemcpy工作。

I have a bunch of matrices, and the goal is to use a kernel to let the GPU to do the same operation on all of them. I'm pretty sure I can get the kernel to work, but I can't get cudaMalloc / cudaMemcpy to work.

我有一个指向Matrix结构的指针，它有一个名为elements的成员指向一些浮动。我可以做所有的非cuda malloc只是罚款。

I have a pointer to a Matrix structure, which has a member called elements that points to some floats. I can do all the non-cuda mallocs just fine.

感谢任何/所有的帮助。

Thanks for any/all help.

：

typedef struct {
    int width;
    int height;
    float* elements;
} Matrix;

int main void() {
    int rows, cols, numMat = 2; // These are actually determined at run-time
    Matrix* data = (Matrix*)malloc(numMat * sizeof(Matrix));

    // ... Successfully read from file into "data" ...

    Matrix* d_data;
    cudaMalloc(&d_data, numMat*sizeof(Matrix)); 
    for (int i=0; i<numMat; i++){
        // The next line doesn't work
        cudaMalloc(&(d_data[i].elements), rows*cols*sizeof(float));

        // Don't know if this works
        cudaMemcpy(d_data[i].elements, data[i].elements,  rows*cols*sizeof(float)), cudaMemcpyHostToDevice);
    }

    // ... Do other things ...
}

谢谢！

推荐答案

你必须知道你的内存在哪里。 malloc分配主机内存，cudaMalloc分配设备上的内存，并返回指向该内存的指针。但是，此指针仅在设备功能中有效。

You have to be aware where your memory resides. malloc allocates host memory, cudaMalloc allocates memory on the device and returns a pointer to that memory back. However, this pointer is only valid in device functions.

您想要什么可以如下所示：

What you want could be achived as followed:

typedef struct {
    int width;
    int height;
    float* elements;
} Matrix;

int main void() {
    int rows, cols, numMat = 2; // These are actually determined at run-time
    Matrix* data = (Matrix*)malloc(numMat * sizeof(Matrix));

    // ... Successfully read from file into "data" ...
    Matrix* h_data = (Matrix*)malloc(numMat * sizeof(Matrix));
    memcpy(h_data, data, numMat * sizeof(Matrix);

    for (int i=0; i<numMat; i++){

        cudaMalloc(&(h_data[i].elements), rows*cols*sizeof(float));
        cudaMemcpy(h_data[i].elements, data[i].elements,  rows*cols*sizeof(float)), cudaMemcpyHostToDevice);

     }// matrix data is now on the gpu, now copy the "meta" data to gpu
     Matrix* d_data;
     cudaMalloc(&d_data, numMat*sizeof(Matrix)); 
     cudaMemcpy(d_data, h_data, numMat*sizeof(Matrix));
     // ... Do other things ...
}

make something clear：
Matrix * data 包含主机上的数据。
Matrix * h_data 在元素中包含指向设备内存的指针，可以作为参数传递给内核。内存在GPU上。
Matrix * d_data 是完全在GPU上，可以像主机上的数据一样使用。

To make things clear: Matrix* data contains the data on the host. Matrix* h_data contains a pointer to the device memory in elements which can be passed to the kernels as parameters. The memory is on the GPU. Matrix* d_data is completly on the GPU and can be used like data on the host.

在你的内核代码中，kann现在可以访问矩阵值，例如

in your kernel code you kann now access the matrix values, e.g.,

__global__ void doThings(Matrix* matrices)
{
      matrices[i].elements[0] = 42;
}

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