Institutional Repository
| automatic fft performance tuning on opencl gpus | |
| Li Yan; Zhang Yunquan; Jia Haipeng; Long Guoping; Wang Ke | |
| 2011 | |
| Conference Name | 2011 17th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2011 |
| Source | Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS |
| Pages | 228-235 |
| Conference Date | December 7, 2011 - December 9, 2011 |
| Conference Place | Tainan, Taiwan |
| Indexed Type | EI |
| ISSN | 1521-9097 |
| ISBN | 9780769545769 |
| Department | (1) Laboratory of Parallel Software and Computational Science Institute of Software Chinese Academy of Sciences Beijing China; (2) State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China; (3) Chinese Academy of Sciences Graduate University Beijing China; (4) School of Information Science and Engineering Ocean University Qingdao China |
| English Abstract | School of Information Science and Engineering, Ocean University of China, Qingdao, China Many fields of science and engineering, such as astronomy, medical imaging, seismology and spectroscopy, have been revolutionized by Fourier methods. The fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier transform (DFT) and its inverse. The emerging class of high performance computing architectures, such as GPU, seeks to achieve much higher performance and efficiency by exposing a hierarchy of distinct memories to programmers. However, the complexity of GPU programming poses a significant challenge for programmers. In this paper, based on the Kronecker product form multi-dimensional FFTs, we propose an automatic performance tuning framework for various OpenCL GPUs. Several key techniques of GPU programming on AMD and NVIDIA GPUs are also identified. Our OpenCL FFT library achieves up to 1.5 to 4 times, 1.5 to 40 times and 1.4 times the performance of clAmdFft 1.0 for 1D, 2D and 3D FFT respectively on an AMD GPU, and the overall performance is within 90% of CUFFT 4.0 on two NVIDIA GPUs. © 2011 IEEE.; School of Information Science and Engineering, Ocean University of China, Qingdao, China Many fields of science and engineering, such as astronomy, medical imaging, seismology and spectroscopy, have been revolutionized by Fourier methods. The fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier transform (DFT) and its inverse. The emerging class of high performance computing architectures, such as GPU, seeks to achieve much higher performance and efficiency by exposing a hierarchy of distinct memories to programmers. However, the complexity of GPU programming poses a significant challenge for programmers. In this paper, based on the Kronecker product form multi-dimensional FFTs, we propose an automatic performance tuning framework for various OpenCL GPUs. Several key techniques of GPU programming on AMD and NVIDIA GPUs are also identified. Our OpenCL FFT library achieves up to 1.5 to 4 times, 1.5 to 40 times and 1.4 times the performance of clAmdFft 1.0 for 1D, 2D and 3D FFT respectively on an AMD GPU, and the overall performance is within 90% of CUFFT 4.0 on two NVIDIA GPUs. © 2011 IEEE. |
| Keyword | Algorithms Computer Systems Discrete Fourier Transforms Fast Fourier Transforms Medical Imaging |
| Sponsorship | National Cheng Kung University; National Science Council; Ministry of Education; Academia Sinica; National Center for High Performance Computing |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16294 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Li Yan,Zhang Yunquan,Jia Haipeng,et al. automatic fft performance tuning on opencl gpus[C],2011:228-235. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment