Highly Effective GPU Realization of Discrete Wavelet Transform for Big-Data Problems

Abstract

Discrete wavelet transform (DWT) is widely used in the tasks of signal processing, analysis and recognition. Moreover it’s practical applications are not limited to the case of one-dimensional signals but also apply to images and multidimensional data. From the moment of introduction of the dedicated libraries that enable to use graphics processing units (GPUs) for mass-parallel general purpose calculations the development of effective GPU based implementations of one-dimensional DWT is an important field of scientific research. It is also important because with use of one-dimensional procedure we can calculate DWT in multidimensional case if only the transform’s separability is assumed. In this paper the authors propose a novel approach to calculation of one-dimensional DWT based on lattice structure which takes advantage of shared memory and registers in order to implement necessary inter-thread communication. The experimental analysis reveals high time-effectiveness of the proposed approach which can be even 5 times higher for Maxwell architecture, and up to 2 times for Turing family GPU cards, than the one characteristic for the convolution based approach in computational tasks that can be classified as big-data problems.