Parallel 3D fast wavelet transform on manycore GPUs and multicore CPUs

Abstract

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.

The 2D wavelet transform on emerging architectures: GPUs and multicores

“…A comparison between different platforms will be showed to determine the best version. The work presented in this paper is a major revision and an extension of two previous papers published by the authors in [9] and [10].…”

Compression of Image Sequences in Interactive Medical Teleconsultations

“…Methods devised for general image compression include parallelized variants of JPEG 2000 [18], LZ77 [2], LZSS [26], and 3D fast wavelets [5]. Multiple parallel algorithms targeted for medical images are available as well.…”

“…Publicly-available datasets [25] (10 CT and 10 MR datasets) were employed. Several block sizes (16×16, 32×32, 64×64, 128×128) as well as several batch lengths (3,5,7,9,11) were tested with the given datasets. Tables 2 and 3 present the results for compression efficiency (expressed in averaged bpp) for the CT and MR datasets, respectively.…”

Massively parallel non-stationary EEG data processing on GPGPU platforms with Morlet continuous wavelet transform

“…Similarly, in [28] authors also explore the implementation of a fast 2D-DWT with Filter Bank Scheme (FBS) and Lifting Scheme (LS) using Cg on the same GPU. With NVIDIA's CUDA library [6], people have implemented 2D-DWT variants [10,18] and a 3D-DWT on GPUs [11]. In contrast to these methods, we aimed to significantly promote a CWT approach with the latest GPGPU technologies to better cater for the needs of processing massive non-stationary EEG data.…”