high performance computing on graphics processing units: hgpu.org

The discrete wavelet Transform (DWT) has been studied and developed in various scientific and engineering fields. Its multi-resolution and locality nature facilitates application required for progressiveness in capturing high-frequency details. However, when dealing with enormous data volume, the performance may drastically reduce. The multi-resolution sub-band encoding provided by DWT enables for higher compression ratios, and progressive transformation of signals. The widespread usage of the DWT has motivated the development of fast DWT algorithms and their tuning on all sorts of computer systems. However, this transformation comes at the expense of additional computational complexity. Achieving real-time or interactive compression/de-compression speed, therefore, requires a fast implementation of DWT that leverages emerging parallel hardware systems. The recent advancement in the consumer level multicore hardware is equipped with Single Instruction and Multiple Data (SIMD) power.In this study, Parallel Discrete Wavelet Transform has been developed with novel Adaptive Load Balancing Algorithm (ALBA). The DWT is parallelized, partitioned, mapped and scheduled on single core and Multicore. The Parallel DWT is developed in C# for single and Intel Quad cores as well as the combination of C and CUDA is implemented on GPU. This brings the significant performance on a consumer level PC without extra cost.