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MPFFT: An Auto-Tuning FFT Library for OpenCL GPUs |
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| Authors: | Yan Li Yun-Quan Zhang Yi-Qun Liu Guo-Ping Long Hai-Peng Jia |
| Affiliation: | 1. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;Graduate University of Chinese Academy of Sciences, Beijing 100049, China 2. Institute of Software, Chinese Academy of Sciences, Beijing 100190, China 3. School of Information Science and Engineering, Ocean University of China, Qingdao 266000, China |
| 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. |
| Keywords: | fast Fourier transform GPU OpenCL auto-tuning |
| 本文献已被 CNKI 万方数据 SpringerLink 等数据库收录！ | |
