Parallel QR Factorization on a Block Data Flow Architecture

In this paper, we introduce a block dataflow architecture (BDFA) which is very efficient for some computational intensive multidimensional digital signal processing and matrix algorithms. We demonstrate the advantages of this architecture by using the Givens rotations of a dense matrix as an example. The BDFA eliminates the requirement for global data communications and significantly reduces the time required for hand shaking for data transmission by using block dataflow. In this paper, we discuss the implementation of the orthogonal transformation of a dense matrix using Givens rotations on the BDFA.