Transform coding on programmable stream processors
The Journal of Supercomputing
Wavelet-Based Adaptive Solvers on Multi-core Architectures for the Simulation of Complex Systems
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
High-performance signal processing on emerging many-core architectures using CUDA
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
SIAM Journal on Scientific Computing
Fast wavelet transform utilizing a multicore-aware framework
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume 2
The split-and-merge method in general purpose computation on GPUs
Parallel Computing
Interactive Editing of GigaSample Terrain Fields
Computer Graphics Forum
Algorithms and architectures for 2D discrete wavelet transform
The Journal of Supercomputing
Fast 3D wavelet transform on multicore and many-core computing platforms
The Journal of Supercomputing
Hi-index | 0.00 |
The widespread usage of the DiscreteWaveletTransform (DWT) has motivated the development of fastDWT algorithms and their tuning on all sorts of computersystems. Several studies have compared the performanceof the most popular schemes, known as Filter Bank(FBS) and Lifting (LS), and have always concluded thatLifting is the most efficient option. However, there isno such study on streaming processors such as modernGraphic Processing Units (GPUs). Current trends havetransformed these devices into powerful stream processorswith enough flexibility to perform intensive and complexfloating-point calculations. The opportunities opened upby these platforms, as well as the growing popularityof the DWT within the computer graphics field, make anew performance comparison of great practical interest.Our study indicates that FBS outperforms LS in currentgeneration GPUs. In our experiments, the actual FBS gainsrange between 10% and 140%, depending on the problemsize and the type and length of the wavelet filter. Moreover,design trends suggest higher gains in future generationGPUs.