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Title:
accelerating viola-jones facce detection algorithm on gpus
Author: Jia Haipeng ; Zhang Yunquan ; Wang Weiyan ; Jia Haipeng ; Xu Jianliang
Source: Proceedings of the 14th IEEE International Conference on High Performance Computing and Communications, HPCC-2012 - 9th IEEE International Conference on Embedded Software and Systems, ICESS-2012
Conference Name: IEEE 14th International Conference on High Performance Computing and Communications (HPCC) / IEEE 9th International Conference on Embedded Software and Systems (ICESS)
Conference Date: JUN 25-27, 2012
Issued Date: 2012
Conference Place: Liverpool, ENGLAND
Keyword: Viola-Jones ; Imbalanced Computation ; Persistent Threads ; Local Queues ; Global Queues
Indexed Type: ISTP ; EI
ISBN: 978-0-7695-4749-7
Department: Jia Haipeng; Zhang Yunquan; Wang Weiyan Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Beijing Peoples R China.
Sponsorship: IEEE, IEEE Comp Soc, Univ Bradford, IEEE Tech Comm Scalable Comp (TCSC)
Abstract: The Viola-Jones face detection algorithm represents a class of parallel algorithms that both memory accesses and work distributions are irregular, thereby hard to obtain high performance on GPUs. Furthermore, conventional GPU programming wisdom usually guides us on how to optimize data parallel workloads with regular inputs and outputs. While how to efficiently write task-level parallelism programs with irregular workloads have not much material to reference. In this paper, we present an OpenCL-implementation of Viola-Jones face detection algorithm with high performance on both NVIDIA and AMD GPUs through five main techniques: warp size work granularity, persistent threads, Uberkernel, local and global queues. We also demonstrate the high performance of our implementation by comparing it with a well-optimized CPU version from OpenCV library. Experiment results show that the speedup reaches up to 5.193 similar to 35.08 times (16.91 on average) and 5.85 similar to 32.641 times (17.535 on average) on AMD and NVIDIA GPU respectively.
English Abstract: The Viola-Jones face detection algorithm represents a class of parallel algorithms that both memory accesses and work distributions are irregular, thereby hard to obtain high performance on GPUs. Furthermore, conventional GPU programming wisdom usually guides us on how to optimize data parallel workloads with regular inputs and outputs. While how to efficiently write task-level parallelism programs with irregular workloads have not much material to reference. In this paper, we present an OpenCL-implementation of Viola-Jones face detection algorithm with high performance on both NVIDIA and AMD GPUs through five main techniques: warp size work granularity, persistent threads, Uberkernel, local and global queues. We also demonstrate the high performance of our implementation by comparing it with a well-optimized CPU version from OpenCV library. Experiment results show that the speedup reaches up to 5.193 similar to 35.08 times (16.91 on average) and 5.85 similar to 32.641 times (17.535 on average) on AMD and NVIDIA GPU respectively.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15807
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Jia Haipeng,Zhang Yunquan,Wang Weiyan,et al. accelerating viola-jones facce detection algorithm on gpus[C]. 见:IEEE 14th International Conference on High Performance Computing and Communications (HPCC) / IEEE 9th International Conference on Embedded Software and Systems (ICESS). Liverpool, ENGLAND. JUN 25-27, 2012.
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