Parallel Implementation of the 2D Discrete Wavelet Transform on Graphics Processing Units: Filter Bank versus Lifting

The widespread usage of the discrete wavelet transform (DWT) has motivated the development of fast DWT algorithms and their tuning on all sorts of computer systems. Several studies have compared the performance of the most popular schemes, known as filter bank scheme (FBS) and lifting scheme (LS), a...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 19; H. 3; S. 299 - 310
Hauptverfasser: Tenllado, C., Setoain, J., Prieto, M., Pinuel, L., Tirado, F.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2008
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Application software
Arithmetic
Computer architecture
Computer graphics
Computer Society
Discrete Wavelet Transform
Discrete wavelet transforms
Filter bank
Filter banks
Floating point arithmetic
Gain
Graphics processors
Hoisting
Optimization
Parallel algorithms
Parallel processing
Paralleland vector implementations
Parallelprocessing
Processors
SIMD processors
Streaming media
Studies
Trends
Wavelet transforms
Wavelets and fractals
Paralleland vector implementations
Wavelets and fractals
Parallelprocessing
Graphics processors
Parallel algorithms
Optimization
SIMD processors
ISSN:1045-9219, 1558-2183
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:The widespread usage of the discrete wavelet transform (DWT) has motivated the development of fast DWT algorithms and their tuning on all sorts of computer systems. Several studies have compared the performance of the most popular schemes, known as filter bank scheme (FBS) and lifting scheme (LS), and have always concluded that LS is the most efficient option. However, there is no such study on streaming processors such as modern Graphics Processing Units (GPUs). Current trends have transformed these devices into powerful stream processors with enough flexibility to perform intensive and complex floating-point calculations. The opportunities opened up by these platforms, as well as the growing popularity of the DWT within the computer graphics field, make a new performance comparison of great practical interest. Our study indicates that FBS outperforms LS in current-generation GPUs. In our experiments, the actual FBS gains range between 10 percent and 140 percent, depending on the problem size and the type and length of the wavelet filter. Moreover, design trends suggest higher gains in future-generation GPUs.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2007.70716