Automatic Generation of High-Performance FFT Kernels on Arm and X86 CPUs

doi:10.1109/TPDS.2020.2977629

	Automatic Generation of High-Performance FFT Kernels on Arm and X86 CPUs
	Li, Zhihao 1,2,3; Jia, Haipeng 1; Zhang, Yunquan 1; Chen, Tun 1; Yuan, Liang 1; Vuduc, Richard 4
	2020-08-01
发表期刊	IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
ISSN	1045-9219
卷号	31 期号:8 页码:1925-1941
摘要	This article presents AutoFFT, a template-based code generation framework that can automatically generate high-performance FFT kernels for all natural-number radices. AutoFFT is based on the Cooley-Tukey FFT algorithm, which exploits the symmetric and periodic properties of the DFT matrix, as the outer parallelization framework. Because butterflies are the core operations of the Cooley-Tukey algorithm, we explore additional symmetric and periodic properties of the DFT matrix and formulate multiple optimized calculation templates to further reduce the number of floating-point operations for butterflies of arbitrary natural numbers. To fully exploit hardware resources, we encapsulate a series of optimizations in an assembly template optimizer. Given any DFT problem, AutoFFT automatically generates C FFT kernels using these calculation templates and converts them into efficient assembly kernels using the template optimizer. Through a series of experiments on Arm, Intel, and AMD processors, we show that AutoFFT-generated kernels can outperform those in Fastest Fourier Transform in the West (FFTW), the Arm Performance Libraries (ARMPL), and the Intel Math Kernel Library (MKL).
关键词	AutoFFT FFT code generation template DFT
DOI	10.1109/TPDS.2020.2977629
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2107YFB0202105] ; National Key Research and Development Program of China[2016YFB0200803] ; National Key Research and Development Program of China[2017YFB0202302] ; National Natural Science Foundation of China[61602443] ; National Natural Science Foundation of China[61432018] ; National Natural Science Foundation of China[61521092] ; National Natural Science Foundation of China[61502450]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000561084300003
出版者	IEEE COMPUTER SOC
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15791
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Jia, Haipeng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, SKL Comp Architecture, Beijing 100864, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Georgia Inst Technol, Atlanta, GA 30332 USA 4.Georgia Inst Technol, Sch Computat Sci & Engn, Atlanta, GA 30332 USA
推荐引用方式 GB/T 7714	Li, Zhihao,Jia, Haipeng,Zhang, Yunquan,et al. Automatic Generation of High-Performance FFT Kernels on Arm and X86 CPUs[J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,2020,31(8):1925-1941.
APA	Li, Zhihao,Jia, Haipeng,Zhang, Yunquan,Chen, Tun,Yuan, Liang,&Vuduc, Richard.(2020).Automatic Generation of High-Performance FFT Kernels on Arm and X86 CPUs.IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS,31(8),1925-1941.
MLA	Li, Zhihao,et al."Automatic Generation of High-Performance FFT Kernels on Arm and X86 CPUs".IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 31.8(2020):1925-1941.