启动GPU内核以对3D数据集进行计算的最佳方法是什么？

Question

I am using CUDA to do calculations on a potentially large 3D data set. I think it is best to see a short code snippet first:

void launch_kernel(/*arguments . . . */){
    int bx = xend-xstart, by = yend-ystart, bz = zend-zstart;

    dim3 blocks(/*dimensions*/);
    dim3 threads(/*dimensions*/);
    kernel<<blocks, threads>>();
}

I have a 3D set of cells and I need to launch a kernel to compute each one. The problem is that the input size may exceed the capabilities of the GPU, specifically the threads. So code like this:

void launch_kernel(/*arguments . . . */){
       int bx = xend-xstart, by = yend-ystart, bz = zend-zstart;

       dim3 blocks(bx,by,1);
       dim3 threads(bz);
       kernel<<blocks, threads>>();
   }

... doesn't work well. Because what if the dimensions are 1000x1000x1000? - I can't launch 1000 threads per block. Or even better, what if the dimensions are 5x5x1000? - Now I am barely launching any blocks, but the kernel would need to be launched 5x5x512 b/c of the hardware and each thread would do 2 calculations. I also can't just mash up all my dimensions, putting some of the z's in the blocks and some in the threads b/c I need to be able to identify the dimensions in the kernel. Currently:

__global__ void kernel(/*arguments*/){
    int x = xstart + blockIdx.x;
    int y = ystart + blockIdx.y;
    int z = zstart + threadIdx.x;
    if(x < xend && y < yend && z < zend){
        //calculate
    }
}

I need a solid, efficient way to figure out these variables:

the block x dimension, block y dimensions, thread x (and y? and z?), the x,y,z once I am in the kernel through the blockIdx and threadIdx, and, if the input exceeds hardware, the amount of a "step" I take for each dimension in a for loop inside my kernel calculation.

If you have a questions, please ask. This is a difficult question, and it has been troubling me (especially since the amount of blocks/threads I launch is a major component of performance). This code needs to be automated in its decisions for different data sets, and I am not sure how to do that efficiently. Thank you in advance.

Answer 1

I think you are vastly over complicating things here. The basic problem seems to be that you need to run a kernel on a 1000 x 1000 x 1000 computational domain. So you require 1000000000 threads, which is well within the capabilities of all CUDA compatible hardware. So just use a standard 2D CUDA execution grid with at least the number of threads needed to do the computation (if you don't understand how to do that leave a comment and I will add it to the answer) and then inside your kernel call a little setup function something like this:

__device__ dim3 thread3d(const int dimx, const int dimxy)
{
    // The dimensions of the logical computational domain are (dimx,dimy,dimz)
    // and dimxy = dimx * dimy
    int tidx = threadIdx.x + blockIdx.x * blockDim.x;
    int tidy = threadIdx.y + blockIdx.y * blockDim.y;
    int tidxy = tidx + gridDim.x * tidy;

    dim3 id3d;
    id3d.z = tidxy / dimxy;
    id3d.y = tidxy / (id3d.z * dimxy);
    id3d.x = tidxy - (id3d.z * dimxy - id3d.y * dimx);

    return id3d;
}

[disclaimer: written in browser, never compiled, never run, never tested. Use at own risk].

This function will return "logical" thread coordinates in the 3D domain (dimx,dimy,dimz) from a CUDA 2D execution grid. Call it at the beginning of the kernel something like this:

__global__ void kernel(arglist, const int dimx, const int dimxy)
{
    dim3 tid = thread3d(dimx, dimxy);

    // tid.{xyx} now contain unique 3D coordinates on the (dimx,dimy,dimz) domain
    .....
}

Note that there is a lot of integer computational overhead in getting that grid set up, so you might want to think about why you really need a 3D grid. You would be surprised at the number of times it isn't actually necessary and much of that set up overhead can be avoided.

Answer 2

I would first use cudaGetDeviceProperties() to find the compute capability of your GPU so you know exactly how many threads per block are allowed for your GPU (if your program needs to be generalized such that it can run on any CUDA capable device).

Then, using that number, I would make a big nested if statement testing the dimensions of your input. If all of the dimensions are sufficiently small, you can have one block of (bx,by,bz) threads (unlikely). If that doesn't work, then find the largest dimension (or two dimensions) that can fit into one block and partition according to that. If that doesn't work, then you'll have to partition the smallest dimension such that some chunk of it fits into one block - such as (MAX_NUMBER_THREADS_PER_BLOCK,1,1) threads and (bx/MAX_NUMBER)THREADS_PER_BLOCK,by,bz) blocks assuming bx<by<bz and bx>MAX_NUMBER_THREADS_PER_BLOCK .

You'll need different kernels for each case, which is a bit of a pain but at the end of the day its a doable job.