Parallel Computation of Adaptive Filtering Algorithms on Multi-Core Systems

The performance of recent CPUs has been rapidly increasing with the help of parallel architectural supports, such as SIMD (Single Instruction Multiple Data) extensions and multi-core architecture. However, efficient use of such parallel supports for adaptive filtering is difficult due to feedback loops that induce the data dependency problem. In this paper, efficient parallel computation of adaptive filters is studied for multi-core architecture with SIMD arithmetic support. Control- and data-level parallel computation methods are considered, where the former finds parallelism in the evaluation of one output sample, while the latter processes multiple output samples at a time to increase the degree of parallelism. The control-level parallel approach frequently utilizes the pipelining technique to uncover the parallelism, whereas the data-level approach employs a parallel computation method for linear recurrence equations to resolve the dependency. Not only adaptive transversal LMS (Least Mean Square) but also gradient adaptive lattice (GAL) and QR-decomposition based least-square lattice (QRD-LSL) filters are implemented on a PC that employs both SIMD and multi-core architecture.