GPUs have recently attracted our attention as accelerators on a wide variety of algorithms, including assorted examples within the image analysis field. Among them, wavelets are gaining popularity as solid tools for data mining and video compression, though this comes at the expense of a high computational cost. After proving the effectiveness of the GPU for accelerating the 2D Fast Wavelet Transform [1], we present in this paper a novel implementation on manycore GPUs and multicore CPUs for a high performance computation of the 3D Fast Wavelet Transform (3D-FWT). This algorithm poses a challenging access pattern on matrix operators demanding high sustainable bandwidth, as well as mathematical functions with remarkable arithmetic intensity on ALUs and FPUs. On the GPU side, we focus on CUDA programming to develop methods for an efficient mapping on manycores and to fully exploit the memory hierarchy, whose management is explicit by the programmer. On multicore CPUs, OpenMP and Pthreads are used as counterparts to maximize parallelism, and renowned techniques like tiling and blocking are exploited to optimize the use of memory. Experimental results on an Nvidia Tesla C870 GPU and an Intel Core 2 Quad Q6700 CPU indicate that our implementation runs three times faster on the Tesla and up to fifteen times faster when communications are neglected, which enables the GPU for processing real-time videos in many applications where the 3D-FWT is involved. (C) 2010 Published by Elsevier Ltd.
