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| mpfft: an auto-tuning fft library for opencl gpus | |
| Li Yan; Zhang Yun-Quan; Liu Yi-Qun; Long Guo-Ping; Jia Hai-Peng | |
| 2013 | |
| Source | Journal of Computer Science and Technology |
| Pages | 90-105 |
| Indexed Type | EI |
| ISSN | 1000-9000 |
| Department | (1) Institute of Software Chinese Academy of Sciences Beijing 100190 China; (2) Graduate University of Chinese Academy of Sciences Beijing 100049 China; (3) School of Information Science and Engineering Ocean University of China Qingdao 266000 China |
| English Abstract | Fourier methods have revolutionized many fields of science and engineering, such as astronomy, medical imaging, seismology and spectroscopy, and the fast Fourier transform (FFT) is a computationally efficient method of generating a Fourier transform. 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 software. However, the complexity of GPU programming poses a significant challenge to developers. In this paper, we propose an automatic performance tuning framework for FFT on various OpenCL GPUs, and implement a high performance library named MPFFT based on this framework. For power-of-two length FFTs, our library substantially outperforms the clAmdFft library on AMD GPUs and achieves comparable performance as the CUFFT library on NVIDIA GPUs. Furthermore, our library also supports non-power-of-two size. For 3D non-power-of-two FFTs, our library delivers 1.5x to 28x faster than FFTW with 4 threads and 20.01x average speedup over CUFFT 4.0 on Tesla C2050. © 2013 Springer Science+Business Media New York & Science Press, China.; Fourier methods have revolutionized many fields of science and engineering, such as astronomy, medical imaging, seismology and spectroscopy, and the fast Fourier transform (FFT) is a computationally efficient method of generating a Fourier transform. 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 software. However, the complexity of GPU programming poses a significant challenge to developers. In this paper, we propose an automatic performance tuning framework for FFT on various OpenCL GPUs, and implement a high performance library named MPFFT based on this framework. For power-of-two length FFTs, our library substantially outperforms the clAmdFft library on AMD GPUs and achieves comparable performance as the CUFFT library on NVIDIA GPUs. Furthermore, our library also supports non-power-of-two size. For 3D non-power-of-two FFTs, our library delivers 1.5x to 28x faster than FFTW with 4 threads and 20.01x average speedup over CUFFT 4.0 on Tesla C2050. © 2013 Springer Science+Business Media New York & Science Press, China. |
| Keyword | Medical Imaging Program Processors |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/15976 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Li Yan,Zhang Yun-Quan,Liu Yi-Qun,et al. mpfft: an auto-tuning fft library for opencl gpus[C],2013:90-105. |
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