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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