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

Because of the computational power of today’s GPUs, they are starting to be harnessed more and more to help out CPUs on high-performance computing. In addition, an increasing number of today’s state-of-the-art supercomputers include commodity GPUs to bring us unprecedented levels of performance in t...

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Bibliographic Details
Published in:Journal of real-time image processing 2012-09, Vol.7 (3), p.145-152
Main Authors: Franco, Joaquín, Bernabé, Gregorio, Fernández, Juan, Ujaldón, Manuel
Format: Article
Language:English
Subjects:
Algorithms
Central processing units
Computation
Computer Graphics
Computer Science
CPUs
Decomposition
Electromagnetic wave filters
Graphics processing units
Image processing
Image Processing and Computer Vision
Multimedia Information Systems
Pattern Recognition
Quadratures
Signal,Image and Speech Processing
Special Issue
Wavelet transforms
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Summary:Because of the computational power of today’s GPUs, they are starting to be harnessed more and more to help out CPUs on high-performance computing. In addition, an increasing number of today’s state-of-the-art supercomputers include commodity GPUs to bring us unprecedented levels of performance in terms of raw GFLOPS and GFLOPS/cost. In this work, we present a GPU implementation of an image processing application of growing popularity: The 2D fast wavelet transform (2D-FWT). Based on a pair of Quadrature Mirror Filters, a complete set of application-specific optimizations are developed from a CUDA perspective to achieve outstanding factor gains over a highly optimized version of 2D-FWT run in the CPU. An alternative approach based on the Lifting Scheme is also described in Franco et al. (Acceleration of the 2D wavelet transform for CUDA-enabled Devices, 2010 ). Then, we investigate hardware improvements like multicores on the CPU side, and exploit them at thread-level parallelism using the OpenMP API and pthreads . Overall, the GPU exhibits better scalability and parallel performance on large-scale images to become a solid alternative for computing the 2D-FWT versus those thread-level methods run on emerging multicore architectures.
ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-011-0224-7