![]() ![]() To compute the index of row r in terms of CUDA threadIdx and blockIdx, we can take blockIdx.y and multiply it with blockDim.y to get the total number of threads up to blockIdx.y number of blocks. In our case rows are indexed in the y-dimension. CUDA provides a simple indexing mechanism to obtain the thread-ID within a thread-block ( threadIdx.x, threadIdx.y and threadIdx.z) and block-ID within a grid ( blockIdx.x, blockIdx.y and blockIdx.z). Threads are arranged in 2-D thread-blocks in a 2-D grid. Because each thread is computing an element in the C matrix first we must calculate the row and column of this element. There will be P×Q number of threads executing this code. Cuda vector add dim3 code#To understand this code first you need to know that each CUDA thread will be executing this code independently. template _global_ void naive_matrix_multiply(const T *A, const T *B, T* C, int width, int P, int Q) Therefore we need values P and Q (dimensions of C matrix) to check if a given thread computes a valid element in the output matrix. Because we take the ceiling of q/32 and p/32 CUDA kernel launcher will launch more threads than we need. We also need width which is the length of our vector-vector multiplication each threads have to do. elements in a row will be placed in consecutive memory locations). Assume that all of our matrices are arranged in row-major order (i.e. First, what are the arguments we need for the kernel? We need A matrix, B matrix and result C matrix. Now let’s move on to our matrix multiplication kernel. dim3 dim_grid(ceilf(P/(float)BLOCK_SIZE), ceilf(Q/(float)BLOCK_SIZE), 1) dim3 dim_block(BLOCK_SIZE, BLOCK_SIZE, 1) ![]() So x-dimension of the grid will have ⌈q/32⌉ blocks. Here I assumed that columns in the matrix are indexed in x-dimension and rows in y-dimension. So block and grid dimension can be specified as follows using CUDA. 2).įig.2 : Thread-block and grid organization for simple matrix multiplication Now how should we arrange our grid? Since the output matrix is p × q, we need to have at least ⌈p/32⌉ number of thread-blocks in y-dimension and ⌈q/32⌉ number of thread-blocks in x-dimension (Fig. Therefore we can use a 32 x 32 2-D thread-block (Let’s assume that our thread-block size is BLOCK_SIZE x BLOCK_SIZE from here). In most modern NVIDIA GPUs one thread-block can have a maximum of 1024 threads. Sine we are multiplying 2-D matrices it only makes sense to arrange the thread-blocks and grid in 2-D. A thread-block or grid can be arranged in 1-D, 2-D or 3-D. If you are unfamiliar with thread-blocks and grid, refer to this. Thread-block is the smallest group of threads allowed by the programming model and grid is an arrangement of multiple thread-blocks. In CUDA programming model threads are organized into thread-blocks and grids. each element in C matrix will be calculated by a separate CUDA thread. Obvious way to implement our parallel matrix multiplication in CUDA is to let each thread do a vector-vector multiplication i.e. 1: What happens in matrix multiplication? for all intents and purposes, it is lost).Fig. Whatever data those pointers previously referenced is no longer accessible from those pointers (i.e. They are creating a new allocation for h_A, h_B and h_C. These lines of code: cudaHostAlloc((void **)
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