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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Published inIEEE transactions on parallel and distributed systems Vol. 22; no. 1; pp. 91 - 104
Main Authors In Kyu Park, Singhal, Nitin, Man Hee Lee, Sungdae Cho, Kim, Chris W
Format Journal Article
LanguageEnglish
Published New York IEEE 01.01.2011
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
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
Online AccessGet full text
ISSN1045-9219
1558-2183
DOI10.1109/TPDS.2010.115

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Abstract 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.
AbstractList 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.
Author Man Hee Lee
In Kyu Park
Kim, Chris W
Sungdae Cho
Singhal, Nitin
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  givenname: Chris W
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  organization: NVIDIA Corp., Seoul, South Korea
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SubjectTerms 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
Title Design and Performance Evaluation of Image Processing Algorithms on GPUs
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Volume 22
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