In CUDA, kernel functions can be executed out of sync in a certain cudaStream. SquareDistance = firstPoint.squareDistance(bothPoint) īy default, if the stream number is not defined explicitly or if the 0 index is specified during execution of the kernel function (the fourth parameter in the triple angle brackets), all functions will be executed consecutively. If (both & (none || srcOnly & dstOnly) & !isFirstPoint) IsFirstPoint = !atomicCAS(&isFirstPointSet, false, true) synchronization after writing to CUDA shared memory Also, it’s possible to allocate an amount of shared memory and define the stream index for out-of-sync work. To call a kernel function, it’s necessary to define the number of blocks and threads that will be used for execution, set off by triple angle brackets (>). Threads and blocks can be of type int (as in the example above) or dim3 (for the multidimensional threadIdx and blockIdx variables). In the bounds of one block there’s a limit to the number of threads. Similarly to threads, blocks are identified with the help of the blockIdx variable. For the sake of convenience, the threadIdx variable is a three-component vector so threads can be identified using a one-, two-, or three-dimensional thread index. This identifier is accessible within the kernel via the built-in threadIdx variable. A unique identifier is assigned to each thread that executes a kernel function. In CUDA, threads are organized in blocks. At the same time, threads belonging to different warps within one block can be out of sync. Thus, threads belonging to one warp are synchronized at the CUDA hardware level. CPU time is allocated in such a way that at any given moment all cores of a multiprocessor can process only one warp. However, several scalar processor blocks can share the resources of one streaming multiprocessor. Threads are grouped into warps (each warp comprises 32 threads), which, in turn, are grouped into larger entities called blocks.Īll threads of a block are run on a single streaming multiprocessor, which is composed of scalar processors. A set of threads executed under one task is called a grid. CUDA parallel computing architecture allows one command to be executed by several more or less independent threads.
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