Here is an example indexing scheme based on the mapping defined above. Thread index within the block (zero-based)Įach of the above are dim3 structures and can be read in the kernel to assign particular workloads to any thread. MappingĮvery thread in CUDA is associated with a particular index so that it can calculate and access memory locations in an array. One can initialise as many of the three coordinates as they like dim3 threads(256) // Initialise with x as 256, y and z will both be 1ĭim3 blocks(100, 100) // Initialise x and y, z will be 1ĭim3 anotherOne(10, 54, 32) // Initialises all three values, x will be 10, y gets 54 and z will be 32. Blocks can be organized into one- or two-dimensional grids (say up to 65,535 blocks) in each dimension.ĭim3 is a 3d structure or vector type with three integers,, and. The limitation on the number of threads in a block is actually imposed because the number of registers that can be allocated across all threads is limited. For example if the maximum, and dimensions of a block are 512, 512 and 64, it should be allocated such that 512, which is the maximum number of threads per block. When a kernel is launched the number of threads per thread block, and the number of thread blocks is specified, this, in turn, defines the total number of CUDA threads launched. The blocks in a grid must be able to be executed independently, as communication or cooperation between blocks in a grid is not possible. DimensionsĪs many parallel applications involve multidimensional data, it is convenient to organize thread blocks into 1D, 2D or 3D arrays of threads. In the chevrons we place the number of blocks and the number of threads per block.ġ00, 256 would launch 100 blocks of 256 threads each (total of 25600 threads).ĥ0, 1024 would launch 50 blocks of 1024 threads each (51200 threads in total). The host calls a kernel using a triple chevron.

Cuda dim3 block