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| reduction algorithm optimization based on the opencl | |
| Yan Shen-Gen; Zhang Yun-Quan; Long Guo-Ping; Li Yan | |
| 2011 | |
| Source | Ruan Jian Xue Bao/Journal of Software
 |
| ISSN | 1000-9825 |
| Volume | 22Issue:UPPL. 2Pages:163-171 |
| English Abstract | Reduction algorithm has a wide range of applications in areas such as scientific computing and image processing. This paper systematically studies the reduction algorithm optimization on the GPU's cross-platform performance optimization based on the OpenCL framework. Previous research has generally focused on a single hardware architecture, however, this paper based on the OpenCL, studies various kinds of optimization methods, such as using vector, on-chip memory bank conflict, threads organization, instruction selection and so on. The research takes the minMax function for example, dilatationed each optimization method for develep the performance, and detailed the reason. The study tests the algorithm both on AMD GPU and NVIDIA GPU platforms. The test results show that the optimized algorithm on both platforms has achieved good performance. In the AMD ATI Radeon HD 5850 platform, Int and Float types of data bandwidth utilization up to 89%. In the NVIDIA GPU Tesla C2050 platform, the performance has reached 1.3 to 1.9 times compare to appropriate function version of CUDA. ©2011 Journal of Software.; Reduction algorithm has a wide range of applications in areas such as scientific computing and image processing. This paper systematically studies the reduction algorithm optimization on the GPU's cross-platform performance optimization based on the OpenCL framework. Previous research has generally focused on a single hardware architecture, however, this paper based on the OpenCL, studies various kinds of optimization methods, such as using vector, on-chip memory bank conflict, threads organization, instruction selection and so on. The research takes the minMax function for example, dilatationed each optimization method for develep the performance, and detailed the reason. The study tests the algorithm both on AMD GPU and NVIDIA GPU platforms. The test results show that the optimized algorithm on both platforms has achieved good performance. In the AMD ATI Radeon HD 5850 platform, Int and Float types of data bandwidth utilization up to 89%. In the NVIDIA GPU Tesla C2050 platform, the performance has reached 1.3 to 1.9 times compare to appropriate function version of CUDA. ©2011 Journal of Software. |
| Indexed Type | EI |
| Keyword | Image Processing Optimization |
| Department | (1) Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing 100190 China; (2) State Key Laboratory of Computing Science Institute of Software Chinese Academy of Sciences Beijing 100190 China; (3) Graduate University Chinese Academy of Sciences Beijing 100190 China |
| Language | 中文 |
| Content Type | 期刊论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16156 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Yan Shen-Gen,Zhang Yun-Quan,Long Guo-Ping,et al. reduction algorithm optimization based on the opencl[J]. Ruan Jian Xue Bao/Journal of Software,2011,22(UPPL. 2):163-171. |
| APA | Yan Shen-Gen,Zhang Yun-Quan,Long Guo-Ping,&Li Yan.(2011).reduction algorithm optimization based on the opencl.Ruan Jian Xue Bao/Journal of Software,22(UPPL. 2),163-171. |
| MLA | Yan Shen-Gen,et al."reduction algorithm optimization based on the opencl".Ruan Jian Xue Bao/Journal of Software 22.UPPL. 2(2011):163-171. |
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