CUDA cudaMemcpy数组结构

Question

I'd like to clean up the parameters of CUDA kernels in my project.

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Now, a kernel needs 3 uint32_t arrays, which leads to pretty ugly code: (id means the global thread id and valX is some arbitrary value)

__global__ void some_kernel(uint32_t * arr1, uint32_t * arr2, uint32_t * arr3){arr1[id] = val1; arr2[id] = val2; arr3[id] = val3;}

I'd like to sorround all those arrays with a struct:

typedef struct S{uint_32_t arr1, uint_32_t arr2, uint_32_t arr3, uint32_t size} S;

where size denotes the length of every arrX inside the struct.

What I would like to have, is something like:

__global__ void some_kernel(S * s){s->arr1[id] = val1; s->arr2[id] = val2; s->arr3[id] = val3;}

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What would a corresponding cudaMalloc and cudaMemcpy would look like for a struct like this? Are there any performance drawbacks from this, which I'm not seeing yet?

Thanks in advance!

Answer 1

You have at least two options. One excellent choice was already given by talonmies, but I'll introduce you to the "learn the hard way" approach.

First, your struct definition:

typedef struct S {
    uint32_t *arr1;
    uint32_t *arr2;
    uint32_t *arr3; 
    uint32_t size;
} S;

...and kernel definition (with some global variable, but you don't need to follow with that pattern):

const int size = 10000;

__global__ void some_kernel(S *s)
{
    int id = blockIdx.x * blockDim.x + threadIdx.x;
    if (id < size)
    {
        s->arr1[id] = 1; // val1
        s->arr2[id] = 2; // val2
        s->arr3[id] = 3; // val3
    }
}

Notice that if protects you from running out-of-bounds.

Next, we come with some function that prepares data, executes kernel and prints some result. Part one is data allocation:

uint32_t *host_arr1, *host_arr2, *host_arr3;
uint32_t *dev_arr1, *dev_arr2, *dev_arr3;

// Allocate and fill host data
host_arr1 = new uint32_t[size]();
host_arr2 = new uint32_t[size]();
host_arr3 = new uint32_t[size]();

// Allocate device data   
cudaMalloc((void **) &dev_arr1, size * sizeof(*dev_arr1));
cudaMalloc((void **) &dev_arr2, size * sizeof(*dev_arr2));
cudaMalloc((void **) &dev_arr3, size * sizeof(*dev_arr3));

// Allocate helper struct on the device
S *dev_s;
cudaMalloc((void **) &dev_s, sizeof(*dev_s));

It's nothing special, you just allocate three arrays and struct. What looks more interesting is how to handle copying of such data into device:

// Copy data from host to device
cudaMemcpy(dev_arr1, host_arr1, size * sizeof(*dev_arr1), cudaMemcpyHostToDevice);
cudaMemcpy(dev_arr2, host_arr2, size * sizeof(*dev_arr2), cudaMemcpyHostToDevice);
cudaMemcpy(dev_arr3, host_arr3, size * sizeof(*dev_arr3), cudaMemcpyHostToDevice);

// NOTE: Binding pointers with dev_s
cudaMemcpy(&(dev_s->arr1), &dev_arr1, sizeof(dev_s->arr1), cudaMemcpyHostToDevice);
cudaMemcpy(&(dev_s->arr2), &dev_arr2, sizeof(dev_s->arr2), cudaMemcpyHostToDevice);
cudaMemcpy(&(dev_s->arr3), &dev_arr3, sizeof(dev_s->arr3), cudaMemcpyHostToDevice);

Beside ordinary copy of array you noticed, that it's also neccessary to "bind" them with the struct. For that you need to pass an address of pointer. As result, only these pointers are copied.

Next kernel call, copy data back again to host and printing results:

// Call kernel
some_kernel<<<10000/256 + 1, 256>>>(dev_s); // block size need to be a multiply of 256

// Copy result to host:
cudaMemcpy(host_arr1, dev_arr1, size * sizeof(*host_arr1), cudaMemcpyDeviceToHost);
cudaMemcpy(host_arr2, dev_arr2, size * sizeof(*host_arr2), cudaMemcpyDeviceToHost);
cudaMemcpy(host_arr3, dev_arr3, size * sizeof(*host_arr3), cudaMemcpyDeviceToHost);

// Print some result
std::cout << host_arr1[size-1] << std::endl;
std::cout << host_arr2[size-1] << std::endl;
std::cout << host_arr3[size-1] << std::endl;

Keep in mind that in any serious code you should always check for errors from CUDA API calls.