We implemented filtered up/downsampling as a single fused operation, and bias and activation as another one. This motivated us to optimize these operations using hand-written CUDA kernels. You are asked to calculate the index of this image pixels in the kernel function, but to run kernel the blocks are 16x16 sizes. In the StyleGAN2 paper, the team mentioned that they implemented custom CUDA kernels to speed up the training time. Dimensions of the block in number of threads dim3 blockIdx.
The only difference here is that we pass the multiBlockArray we created earlier as the argument to how many blocks we want to run, and then proceed as normal. dim3 grid(16, 16) dim3 block (16,16) search<<Once we got the space we need on our device, it’s time to launch our kernel and do the calculation needed from the GPU. HostArray, BLOCKS * BLOCKS * sizeof(int),
As you can see, we take care of a two dimensional array, using BLOCKS*BLOCKS when allocating:ĬudaMalloc( (void**)&deviceArray, BLOCKS * BLOCKS * sizeof(int) ) The following should work in this case dim3 blockSize (16, 16, 1) dim3 gridSize ( (width + blockSize.x - 1)/ blockSize.x, (height + blockSize.y - 1) / blockSize. Next, we allocate the memory needed for our array on the device. Then we define a 2d array, a pointer for copying to/from the GPU and our dim3 variable: So, why is it dim3? Well, in the future CUDA C might support 3d-arrays as well, but for now, it’s only reserved, so when you create the array, you specify the dimension of the X-axis, and the Y-axis, and then the 3rd axis automatically is set to 1.įirst of all, include stdio.h and define the size of our block array: How do we do this? First of all, we will need to use a keyword from the CUDA C library, and define our variable. Basically, it’s all the same as before, but we used multidimensional indexing. But since they are 2d, you can think of them as a coordinate system where you have blocks in the x- and y-axis. organize the computation into 2D blocks with TX threads in the x-direction. dim3 grid(1024, 768, 1) dim3 blockdim(16, 16, 1) dim3 blocks((grid.x + blockdim.x - 1) / blockdim.x, (grid.y + blockdim.y - 1) / blockdim.y, (grid.z + blockdim.z - 1) / blockdim. CUDA uses the vector type dim3 for the dimension variables, gridDim and. These types of blocks work just the same way as the other blocks we have seen so far in this tutorial. Given this, you can calculate the blocks. We will create the same program as in the last tutorial, but instead display a 2d-array of blocks, each displaying a calculated value.
#Dim3 grid calculation how to#
In this short tutorial, we will look at how to launch multidimensional blocks on the GPU (grids). The dtype argument takes Numba types.Welcome to part 5 of the Parallel Computing tutorial. The shape argument is similar as in NumPy API, with the requirement that it must contain a constant expression. The return value of is a NumPy-array-like object.
#define pos2d(Y, X, W) ((Y) * (W) + (X)) const unsigned int BPG = 50 const unsigned int TPB = 32 const unsigned int N = BPG * TPB _global_ void cuMatrixMul ( const float A, const float B, float C ) Write by the host and slower to write by the device. To write by the host and to read by the device, but slower to wc – a boolean flag to enable writecombined allocation which is faster.
#Dim3 grid calculation portable#
The following are special DeviceNDArray factories: numba.cuda. copy_to_host ( ary=None, stream=0 ) ¶Ĭopy self to ary or create a new numpy ndarray copy_to_host ( stream = stream ) DeviceNDArray.
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