cublasSgemv

cublasSgemv is a function within the NVIDIA CUDA Basic Linear Algebra Subprogram (cuBLAS) library. It is designed to perform matrix-vector multiplication for single-precision floating-point numbers. Specifically, cublasSgemv computes a matrix-vector product of the form y = alpha * A * x + beta * y, where A is a matrix, x is a vector, y is a vector, alpha is a scalar, and beta is a scalar. The function supports different matrix layouts, including row-major and column-major storage, and can handle transpose operations on the matrix A. This allows for flexibility in how matrices are represented and used in calculations. The function takes several arguments, including pointers to the matrix A, vectors x and y, scalar values alpha and beta, and information about the dimensions of the matrix and vectors, as well as the leading dimension of the matrix. cublasSgemv is an essential tool for scientific computing, deep learning, and other applications that rely on efficient matrix operations on NVIDIA GPUs. Its implementation leverages the parallel processing capabilities of CUDA to achieve high performance.