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Title:
MPFFT: An Auto-Tuning FFT Library for OpenCL GPUs
Author: Li, Yan ; Zhang, Yun-Quan ; Liu, Yi-Qun ; Long, Guo-Ping ; Jia, Hai-Peng
Keyword: fast Fourier transform ; GPU ; OpenCL ; auto-tuning
Source: JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
Issued Date: 2013
Volume: 28, Issue:1, Pages:90-105
Indexed Type: SCI
Department: [Li, Yan; Zhang, Yun-Quan; Liu, Yi-Qun; Long, Guo-Ping] Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China. [Li, Yan; Liu, Yi-Qun] Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China. [Jia, Hai-Peng] Ocean Univ China, Sch Informat Sci & Engn, Qingdao 266000, Peoples R 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 CPU, 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.
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 CPU, 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.
Language: 英语
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16959
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Li, Yan,Zhang, Yun-Quan,Liu, Yi-Qun,et al. MPFFT: An Auto-Tuning FFT Library for OpenCL GPUs[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2013-01-01,28(1):90-105.
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