![]() ![]() Obviously, hardware constraints impose limits on the dimensions of the grid and the blocks. Int blocksPerGrid = ceil(n / ( float)threadsPerBlock) We can do this in 2 ways:ĭim3 gridDim(ceil(n / ( float)threadsPerBlock), 1, 1) gridDim) will vary with the size of the input vectors so that the grid will have enough threads to cover all vector elements. For example, suppose we would like to launch our vector addition kernel vecAdd with a set number of threads per block equal to 256. However, for convenience, CUDA C lets us use plain variables or direct mathematical expressions to specify ECPs for 1D grids. Each such parameter is of type dim3, which is a C struct with three unsigned integer fields: x, y, and z.įor 1D and 2D grids and blocks, the unused dimensions should be set to 1 for clarity. The second ECP specifies the dimensions of each block in number of threads. The first ECP specifies the dimensions of the grid in number of blocks. At kernel launch, we specify 2 parameters enclosed within triple signs >. We can choose to use fewer dimensions by setting unused dimensions to 1. In general, a grid is a 3D array of blocks, and each block is a 3D array of threads. the number of blocks in a grid) and the block size blockDim (i.e. The execution configuration parameters (ECPs) in a kernel launch specify the grid size gridDim (i.e. Thus, inside this two-level hierarchy, a thread has a tuple of unique coordinates (blockIdx, threadIdx). Additionally, each thread in a block has a unique index, accessed via threadIdx. Think of the kernel function as specifying the C statements that are executed by each individual thread at runtime.Īll threads in a block share the same block index, accessed via blockIdx. CUDA Thread OrganizationĪll CUDA threads in a grid execute the same kernel function and they rely on special variables to distinguish themselves from each other and to identify the appropriate portion of the data to process. 20, 2010.We'll be delving into the details of the organization, resource assignment, synchronization, and scheduling of threads in a grid. number of threads in a block in the x dimension) Full global thread ID in x dimension can be computed by: x = blockIdx.x * blockDim.x + threadIdx.x Įxample - x direction A 1-D grid and 1-D block 4 blocks, each having 8 threads Global ID 26 threadIdx.x threadIdx.x threadIdx.x threadIdx.x 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 blockIdx.x = 0 blockIdx.x = 1 blockIdx.x = 2 blockIdx.x = 3 gridDim = 4 x 1 blockDim = 8 x 1 Global thread ID = blockIdx.x * blockDim.x + threadIdx.x = 3 * = thread 26 with linear global addressing Derived from Jason Sanders, "Introduction to CUDA C" GPU technology conference, Sept. ThreadIdx.x - “thread index” within block in “x” dimension blockIdx.x - “block index” within grid in “x” dimension blockDim.x - “block dimension” in “x” dimension (i.e. If want a 1-D structure, can use a integer for B and T in: myKernel>(arg1, … ) B – An integer would define a 1D grid of that size T –An integer would define a 1D block of that size Example myKernel>(arg1, … ) ħ CUDA Built-in Variables for a 1-D grid and 1-D block T – a structure that defines the number of threads in a block in each dimension (1D, 2D, or 3D). Compute capability 1.0 Maximum number of threads per block = 512 Maximum sizes of x- and y- dimension of thread block = 512 Maximum size of each dimension of grid of thread blocks = 65535ĭefining Grid/Block Structure Need to provide each kernel call with values for two key structures: Number of blocks in each dimension Threads per block in each dimension myKernel>(arg1, … ) B – a structure that defines the number of blocks in grid in each dimension (1D or 2D). NVIDIA defines “compute capabilities”, 1.0, 1.1, … with these limits and features supported. Can be 1 or 2 dimensions Can be 1, 2 or 3 dimensions CUDA C programming guide, v 3.2, 2010, NVIDIAĤ Device characteristics - some limitations Linked to internal organization Threads in one block execute together. NVIDIA GPUs consist of an array of execution cores each of which can support a large number of threads, many more than the number of cores Threads grouped into “blocks” Blocks can be 1, 2, or 3 dimensional Each kernel call uses a “grid” of blocks Grids can be 1 or 2 dimensional Programmer will specify the grid/block organization on each kernel call, within limits set by the GPUĪllows flexibility and efficiency in processing 1D, 2-D, and 3-D data on GPU. These notes will introduce: One dimensional and multidimensional grids and blocks How the grid and block structures are defined in CUDA Predefined CUDA variables Adding vectors using one-dimensional structures Adding/multiplying arrays using 2-dimensional structures ITCS 6/8010 CUDA Programming, UNC-Charlotte, B. ![]()
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