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This paper studies the feasibility of parallelizin a ref cursive adaptive filtering algorithm? known as t e Recursive Least Squares al orithm, using cyclic reduction f techniques. Recursive a aptive filtering algorithms like the RLS algorithm, are inherently sequential and do not exhibit much concurrency in their operations. Much research has been performed in the parallelization of individual matrix operations. However, the RLS al orithm ! itself has not been examined before for paralle ization among the matrix operations. In this paper, we parallelize two recurrence cycles of the RLS algorithm using cyclic reduction. We show that though more complex than the serial RLS equations, the resultant parallel RLS equations exhibit a great deal of concurrency and potential for speedup if partitioned properly. A methodology and set of guidelines are provided for partitioning the parallel recurrence equations to achieve significant concurrency. The adaptive principles of the RLS algorithm are fundamental to many problem domains especially in si ! nal processing and control theory. For example the se f learning techniques of the RLS algorithm also exist in higher order signal processing adaptive filters as well as neural network applications. Therefore, the results of this study are applicable to a variety of scientific areas in applied computing.

Parallelization and analysis of a linear adaptive filtering algorithm