Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TPDS.2010.115 | IEEE Trans. Parallel Distrib. Syst. |
Keywords | Field | DocType |
algorithm characteristic,execution time,image coding,image processing algorithms,computer graphic equipment,image processing,image processing algorithm,gpu,example application,multiview stereo matching,nonphotorealistic rendering,cuda,gpgpu.,jpeg2000 image encoding,feature extraction,massive parallel graphics,linear feature extraction,performance evaluation,proposed metrics,compute unified device architecture programming model,coprocessors,parallel implementation,openmp,diverse characteristic,massive parallel graphics processing units,concurrent computing,parallel programming,gpgpu,algorithm design and analysis,scattering,computer vision,parallel processing,computer architecture | CUDA,Computer science,Parallel computing,Image processing,Feature extraction,General-purpose computing on graphics processing units,Digital image processing,Graphics processing unit,Rendering (computer graphics),Speedup | Journal |
Volume | Issue | ISSN |
22 | 1 | 1045-9219 |
Citations | PageRank | References |
50 | 3.50 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| In Kyu Park | 1 | 316 | 35.97 |
| Nitin Singhal | 2 | 113 | 10.55 |
| Man Hee Lee | 3 | 77 | 8.18 |
| Sungdae Cho | 4 | 177 | 15.35 |
| Chris Kim | 5 | 85 | 7.72 |