Improved Parallel Implementation of 1D Discrete Wavelet Transform Using CPU-GPU

Abstract

This work describes a data-level parallelization strategy to accelerate the discrete wavelet transform (DWT) which was implemented and compared in two multi-threaded architectures, both with shared memory. The first considered architecture was a multi-core server and the second one was a graphics processing unit (GPU). The main goal of the research is to improve the computation times for popular DWT algorithms for representative modern GPU architectures. Comparisons were based on performance metrics (i.e., execution time, speedup, efficiency, and cost) for five decomposition levels of the DWT Daubechies db6 over random arrays of lengths 103, 104, 105, 106, 107, 108, and 109. The execution times in our proposed GPU strategy were around 1.2×10−5 s, compared to 3501×10−5 s for the sequential implementation. On the other hand, the maximum achievable speedup and efficiency were reached by our proposed multi-core strategy for a number of assigned threads equal to 32.