Design and Performance Evaluation of Image Processing Algorithms on GPUs

In this paper, we construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitati...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 22; H. 1; S. 91 - 104
Hauptverfasser: In Kyu Park, Singhal, Nitin, Man Hee Lee, Sungdae Cho, Kim, Chris W
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and analysis
Algorithms
Computer architecture
Computer vision
Concurrent computing
CUDA
Design engineering
Document management
GPGPU
GPU
Graphics processing unit
Image processing
Mathematical models
parallel implementation
Parallel processing
Parallel programming
Performance evaluation
Programming
Rendering
Scattering
Studies
ISSN:1045-9219, 1558-2183
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Zusammenfassung:In this paper, we construe key factors in design and evaluation of image processing algorithms on the massive parallel graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming model. A set of metrics, customized for image processing, is proposed to quantitatively evaluate algorithm characteristics. In addition, we show that a range of image processing algorithms map readily to CUDA using multiview stereo matching, linear feature extraction, JPEG2000 image encoding, and nonphotorealistic rendering (NPR) as our example applications. The algorithms are carefully selected from major domains of image processing, so they inherently contain a variety of subalgorithms with diverse characteristics when implemented on the GPU. Performance is evaluated in terms of execution time and is compared to the fastest host-only version implemented using OpenMP. It is shown that the observed speedup varies extensively depending on the characteristics of each algorithm. Intensive analysis is conducted to show the appropriateness of the proposed metrics in predicting the effectiveness of an application for parallel implementation.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2010.115