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An FFT Performance Model for Optimizing General-Purpose Processor Architecture

  • Ling LiEmail author
  • Yun-Ji ChenEmail author
  • Dao-Fu LiuEmail author
  • Cheng QianEmail author
  • Wei-Wu HuEmail author
Article

Abstract

General-purpose processor (GPP) is an important platform for fast Fourier transform (FFT), due to its flexibility, reliability and practicality. FFT is a representative application intensive in both computation and memory access, optimizing the FFT performance of a GPP also benefits the performances of many other applications. To facilitate the analysis of FFT, this paper proposes a theoretical model of the FFT processing. The model gives out a tight lower bound of the runtime of FFT on a GPP, and guides the architecture optimization for GPP as well. Based on the model, two theorems on optimization of architecture parameters are deduced, which refer to the lower bounds of register number and memory bandwidth. Experimental results on different processor architectures (including Intel Core i7 and Godson-3B) validate the performance model.

The above investigations were adopted in the development of Godson-3B, which is an industrial GPP. The optimization techniques deduced from our performance model improve the FFT performance by about 40%, while incurring only 0:8% additional area cost. Consequently, Godson-3B solves the 1024-point single-precision complex FFT in 0:368 μs with about 40Watt power consumption, and has the highest performance-per-watt in complex FFT among processors as far as we know. This work could benefit optimization of other GPPs as well.

Keywords

fast Fourier transform (FFT) general-purpose processor (GPP) performance prediction model vector unit DMA 

Supplementary material

11390_2011_186_MOESM1_ESM.pdf (123 kb)
(PDF 123 KB)

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Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2011

Authors and Affiliations

  1. 1.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Loongson Technologies Corporation LimitedBeijingChina

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