Institutional Repository
| parallelization and performance optimization on face detection algorithm with opencl: a case study | |
| Wang Weiyan; Zhang Yunquan; Yan Shengen; Zhang Ying; Jia Haipeng | |
| 2012 | |
| Source | Tsinghua Science and Technology
 |
| ISSN | 1007-0214 |
| Volume | 17Issue:3Pages:287-295 |
| English Abstract | Face detect application has a real time need in nature. Although Viola-Jones algorithm can handle it elegantly, today's bigger and bigger high quality images and videos still bring in the new challenge of real time needs. It is a good idea to parallel the Viola-Jones algorithm with OpenCL to achieve high performance across both AMD and NVidia GPU platforms without bringing up new algorithms. This paper presents the bottleneck of this application and discusses how to optimize the face detection step by step from a very nave implementation. Some brilliant tricks and methods like CPU execution time hidden, stubbles usage of local memory as high speed scratchpad and manual cache, and variable granularity were used to improve the performance. Those technologies result in 4-13 times speedup varying with the image size. Furthermore, those ideas may throw on some light on the way to parallel applications efficiently with OpenCL. Taking face detection as an example, this paper also summarizes some universal advice on how to optimize OpenCL program, trying to help other applications do better on GPU. © 2012 Tsinghua University Press.; Face detect application has a real time need in nature. Although Viola-Jones algorithm can handle it elegantly, today's bigger and bigger high quality images and videos still bring in the new challenge of real time needs. It is a good idea to parallel the Viola-Jones algorithm with OpenCL to achieve high performance across both AMD and NVidia GPU platforms without bringing up new algorithms. This paper presents the bottleneck of this application and discusses how to optimize the face detection step by step from a very nave implementation. Some brilliant tricks and methods like CPU execution time hidden, stubbles usage of local memory as high speed scratchpad and manual cache, and variable granularity were used to improve the performance. Those technologies result in 4-13 times speedup varying with the image size. Furthermore, those ideas may throw on some light on the way to parallel applications efficiently with OpenCL. Taking face detection as an example, this paper also summarizes some universal advice on how to optimize OpenCL program, trying to help other applications do better on GPU. © 2012 Tsinghua University Press. |
| Indexed Type | EI |
| Keyword | Algorithms Optimization |
| Department | (1) Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Science Beijing 100190 China; (2) State Key Laboratory of Computer Science Institute of Software Chinese Academy of Science Beijing 100190 China; (3) Graduate University Chinese Academy of Sciences Beijing 100190 China; (4) Ocean University of China Qingdao 2661 China |
| Language | 英语 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15016 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Wang Weiyan,Zhang Yunquan,Yan Shengen,et al. parallelization and performance optimization on face detection algorithm with opencl: a case study[J]. Tsinghua Science and Technology,2012,17(3):287-295. |
| APA | Wang Weiyan,Zhang Yunquan,Yan Shengen,Zhang Ying,&Jia Haipeng.(2012).parallelization and performance optimization on face detection algorithm with opencl: a case study.Tsinghua Science and Technology,17(3),287-295. |
| MLA | Wang Weiyan,et al."parallelization and performance optimization on face detection algorithm with opencl: a case study".Tsinghua Science and Technology 17.3(2012):287-295. |
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