I would be clear where the configuration of the threads has been defined, and the 1D, 2D and 3D access pattern depends on how you are interpreting your data and also how you are accessing them by 1D, 2D and 3D blocks of threads. To sumup, it does it matter if you use a dim3 structure. As we are dealing with matrices now, we want to specify a second dimension (and, again, we can omit the third one). Int y = blockIdx.y * blockDim.y + threadIdx.y īecause blockIdx.y and threadIdx.y will be zero. This is because, if you only give a number to the kernel call as we did, it is assumed that you created a dim3 mono-dimensional variable, implying y 1 and z 1. So, in both cases: dim3 blockDims(512) and myKernel>(.) you will always have access to threadIdx.y and threadIdx.z.Īs the thread ids start at zero, you can calculate a memory position as a row major order using also the ydimension: int x = blockIdx.x * blockDim.x + threadIdx.x The same happens for the blocks and the grid. For a 1D grid, the index (given by the x attribute) is an integer spanning the range from 0 inclusive to exclusive. The block indices in the grid of threads launched a kernel. When defining a variable of type dim3, any component left unspecified is initialized to 1. This value is the same for all threads in a given kernel, even if they belong to different blocks (i.e. dim3can take up to 3 parameters, any unitialized parameters will default to 1 so in our example Db is an 8,8,1block (num threads in this block is 64 8 8 1) and Dg is a outwidth/blockDim.x, outheight/blockDim.y, 1grid where the number of blocks is (outwidth/blockDim.x outheight/blockDim. However, the access pattern depends on how you are interpreting your data and also how you are accessing them by 1D, 2D and 3D blocks of threads.ĭim3 is an integer vector type based on uint3 that is used to specify dimensions. The memory is always a 1D continuous space of bytes. Accelerated Computing GPU Teaching Kit The GPU Teaching Kit is licensed by NVIDIA and the University of. Covering a 62 H76Picture with 16 H16 Blocks Not all threads in a Block will follow the same control flow path. The way you arrange the data in memory is independently on how you would configure the threads of your kernel. Multidimensional Kernel Configuration Lecture 3.2 CUDA Parallelism Model.
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