CUDA - process a single pixel buffer data (array) on multiple simultaneous kernels, is it possible?

Currently I have one pixel buffer and I process the data in it with a single kernel call:

dim3 threadsPerBlock(32, 32)
dim3 blocks(screenWidth / threadsPerBlock.x, screenHeight / threadsPerBlock.y);
kernel<<<blocks, threadsPerBlock>>>();

The pixel buffer contains all the pixels in a window with dimensions screenWidth x screenHeight.

My idea is to divide the window in 2 or 4 parts and to process the pixel data simultaneously.

Can this be done, and if it can - how ?

I've read little about streams but from what I understood two streams cannot work on a single piece of data (e.g. my pixelBuffer), or am I wrong ?

Edit: My graphics card is with compute capability 3.0

Edit 2: I use SDL to do the drawing and I have a single GPU, and I use user defined data array:

main.cu

 Color vfb_linear[VFB_MAX_SIZE * VFB_MAX_SIZE]; // array on the Host
 Color vfb[VFB_MAX_SIZE][VFB_MAX_SIZE] // 2D array used for SDL
 extern "C" void callKernels(Color* dev_vfb);

int main()
{
    Color* dev_vfb; // pixel array used on the GPU
    // allocate memory for dev_vfb on the GPU
    cudaMalloc((void**)&dev_vfb, sizeof(Color) * RES_X * RES_Y);
    // memcpy HostToDevice
    cudaMemcpy(dev_vfb, vfb_linear, sizeof(Color) * RES_X * RES_Y, cudaMemcpyHostToDevice);

    callKernels(dev_vfb); // wrapper function that calls the kernels

    // memcpy DeviceToHost
    cudaMemcpy(vfb_linear, dev_vfb, sizeof(Color) * RES_X * RES_Y, cudaMemcpyDeviceToHost);

    // convert vfb_linear into 2D array so it can be handled by SDL
    convertDeviceToHostBuffer();    

    display(vfb); // render pixels on screen with SDL

}

cudaRenderer.cu

__global__ void kernel(Color* dev_vfb)
{
    int x = threadIdx.x + blockIdx.x * blockDim.x;
    int y = threadIdx.y + blockIdx.y * blockDim.y;
    int offset = x + y * blockDim.x * gridDim.x;

    if (offset < RES_X * RES_Y)
    {
        dev_vfb[offset] = getColorForPixel();
    }
}

extern "C" callKernels(Color* dev_vfb)
{
    dim3 threadsPerBlock(32, 32)
    dim3 blocks(screenWidth / threadsPerBlock.x, screenHeight / threadsPerBlock.y);
    kernel<<<blocks, threadsPerBlock>>>(dev_vfb);
}

contents of display(vfb):

void display(Color vfb[VFB_MAX_SIZE][VFB_MAX_SIZE])
{
    // screen is pointer to SDL_Surface
    int rs = screen->format->Rshift;
    int gs = screen->format->Gshift;
    int bs = screen->format->Bshift;

    for (int y = 0; y < screen->h; ++y)
    {
        Uint32* row = (Uint32*) ((Uint8*) screen->pixels + y * screen->pitch);
        for (int x = 0; x < screen->w; ++x)
            row[x] = vfb[y][x].toRGB32(rs, gs, bs);
    }
    SDL_Flip(screen);
}

This is a simple example of what I am doing in my project. It is a raytracer and maybe SDL is the worst choice for interop with CUDA but I don't know if I will have time to change it.

Answers


There's nothing that prevents two streams from working on the same piece of data in global memory of one device.

As I said in the comments, I don't think this is a sensible approach to make things run faster. However, the modifications to your code would be something like this (coded in browser, not tested):

__global__ void kernel(Color* dev_vfb, int slices)
{
    int x = threadIdx.x + blockIdx.x * blockDim.x;
    int y = threadIdx.y + blockIdx.y * blockDim.y;
    int offset = x + y * blockDim.x * gridDim.x;

    if (offset < (RES_X * RES_Y/slices)
    {
        dev_vfb[offset] = getColorForPixel();
    }
}

extern "C" callKernels(Color* dev_vfb)
{
    int num_slices=2;
    cudaStream_t streams[num_slices];
    for (int i = 0; i < num_slices; i++)
      cudaStreamCreate(&(streams[i]));
    dim3 threadsPerBlock(32, 32)
    dim3 blocks(screenWidth / threadsPerBlock.x, screenHeight / (num_slices*threadsPerBlock.y));
    for (int i = 0; i < num_slices; i++){
      int off = i * (screenWidth*screenHeight/num_slices);
      kernel<<<blocks, threadsPerBlock, 0, streams[i]>>>(dev_vfb+off, num_slices); }
}

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