![]() SM allows more than one block to execute concurrently. That means that the process of scheduling the block to the SM is atomic. Also, since there might be synchronization between threads, once the block starts executing on the SM, all the threads in the block execute on the same SM at the same time (concurrency, not parallelism). Communication is not supported across SMs, thus, all threads in a block are executed on one SM. SM provides hardware resources required for communication and synchronization for threads in the same block. The hardware for block is SM (Streaming Multiprocessor). To coordinate the communication of the threads within the CTA, one can specify synchronization points where threads wait until all threads in the CTA have arrived. Threads within a CTA can communicate with each other. A cooperative thread array, or CTA, is an array of threads that execute a kernel concurrently or in parallel. The Parallel Thread Execution (PTX) programming model is explicitly parallel: a PTX program specifies the execution of a given thread of a parallel thread array. The block is also known as Cooperative Thread Arrays, you can also refer to the following information: Therefore, block_size must be an integer multiple of 32. The threads use the same hardware resources even if the number of active threads in the last warp is less than 32. These 32 threads execute the same instruction at a time, known as a SIMT. In a block, a warp is made up of 32 consecutive threads. Thus, the maximum value of block_size can be 1024. Features and Technical Specifications points out that Maximum number of threads per block and Maximum x- or y-dimension of a block are both 1024. Therefore, both values should be greater than 0. Grid_size and block_size represent the number of blocks and the number of threads in each block, respectively, for launching the kernel. We will discuss what values should be taken for Dg and Db next. For more specific descriptions of grid dim and block dim, refer to Programming Model. Both Dg and Db can be directly replaced by the numbers corresponding to the x dimension, as shown at the beginning of this article. If the type is one-dimensional structure, the values of the two dimensions y and z are both 1, except for x. Db represents the dimension of the block. S is of type cudaStream_t and specifies the associated stream S is an optional argument which defaults to 0.ĭg represents the dimension of the grid. Ns is of type size_t and specifies the number of bytes in shared memory that is dynamically allocated per block for this call in addition to the statically allocated memory this dynamically allocated memory is used by any of the variables declared as an external array as mentioned in shared Ns is an optional argument which defaults to 0 The execution configuration is specified by inserting an expression of the form > between the function name and the parenthesized argument list, where:ĭg is of type dim3 (see dim3) and specifies the dimension and size of the grid, such that Dg.x * Dg.y * Dg.z equals the number of blocks being launched ĭb is of type dim3 (see dim3) and specifies the dimension and size of each block, such that Db.x * Db.y * Db.z equals the number of threads per block You can refer to CUDA C++ Programming Guide (Hereinafter called Guide): As for >, it is an extension of CUDA to C++, known as Execution Configuration. Both of them have the same syntax as C++. 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.Cuda_kernel is the identifier of the global function, and within the (.) are the corresponding parameters that call cuda_kernel. To sumup, it does it matter if you use a dim3 structure. Int y = blockIdx.y * blockDim.y + threadIdx.y īecause blockIdx.y and threadIdx.y will be zero. 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. ![]() ![]() When defining a variable of type dim3, any component left unspecified is initialized to 1. 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. The way you arrange the data in memory is independently on how you would configure the threads of your kernel.
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