parallelization and performance optimization on face detection algorithm with opencl: a case study

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	parallelization and performance optimization on face detection algorithm with opencl: a case study
	Wang Weiyan ; Zhang Yunquan ; Yan Shengen ; Zhang Ying ; Jia Haipeng
刊名	Tsinghua Science and Technology
	2012
卷号	17 期号:3 页码:287-295
关键词	Algorithms Optimization
ISSN号	1007-0214
中文摘要	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.
英文摘要	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.
收录类别	EI
语种	英语
公开日期	2013-09-17
内容类型	期刊论文
源URL	[http://ir.iscas.ac.cn/handle/311060/15016]
专题	软件研究所_软件所图书馆_期刊论文
推荐引用方式 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.