GPU based implementation of recursive digital filtering algorithms

Abstract

Recursive filtering is widely used for many signal processing applications. Speeding-up the computation of recursive filtering using many processing elements is difficult because of the dependency problem. In this paper, massively parallel computation of recursive filtering algorithms using GPGPUs (General Purpose Graphics Processing Units) is studied. The proposed method uses the multi-block parallel processing algorithm, where each thread executes one block of data as independently as possible. To resolve the dependency among threads, we develop a fast look-ahead method that shows high efficiency even when thousands of threads are used. The developed method has been implemented using Nvidia GTX 285 GPU and shows over 15 times of speed-up when compared to sequential CPU based implementations.