MULTIPROCESSOR IMPLEMENTATION OF DIGITAL FILTERING ALGORITHMS USING A PARALLEL BLOCK PROCESSING METHOD

An efficient real-time implementation of digital filtering algorithms using a multiprocessor system in a ring network is investigated. This method is based on a parallel block processing approach, where a continuously supplied input data is divided into blocks, and the blocks are processed concurrently by being assigned to each processor in the system. This approach not only requires a simple interconnection network but also reduces significantly the number of communications among the processors. Therefore, the system is easily expandable and shows a high efficiency even in a large scale multiprocessing environment. In addition, various digital signal processing algorithms can be implemented on the same multiprocessor system. The data dependency of the blocks to be processed concurrently brings on dependency problems between the processors in the system. A systematic scheduling method has been developed by using a precedence graph for the analysis of the dependency relation. Methods for solving the dependency problems between the processors are also investigated to increase the number of processors which can be used efficiently. Implementation procedures and results for FIR, recursive, and adaptive filtering algorithms are illustrated. This method has been successfully tested on the SABRINA (Santa Barbara Ring Network Architecture) multiprocessor system developed in the University of California, Santa Barbara.