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Evaluating the performance of FFT library implementations on modern hybrid computing systems

Abstract

Fast Fourier transform is widely used to solve numerous scientific and engineering problems. In particular, this transform is behind the software dealing with speech and image recognition, signal analysis, modeling of properties of new materials and substances, etc. Newly emerging high-performance hybrid computing systems, as well as systems with alternative architectures, require research on discrete Fourier transform computation efficiency on these new platforms. The results of such research allow assessing the feasibility of certain solutions for building modern computing and data processing centers. This paper presents the results of such research covering modern hybrid computing systems based on the IBM POWER and Intel Xeon processors, as well as on NVIDIA Tesla co-processors. The analysis is carried out, and conclusions are presented on their performance when executing fast Fourier transforms. The impact of the existing architectural aspects of the hardware (CPU simultaneous multithreading mode, GPU data transfer bus, etc.) on the transform performance efficiency is assessed. The obtained results are used to provide recommendations on the optimal operation modes and settings of the considered mathematical libraries.

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Acknowledgements

This study used the computing resources and systems of the Shared Services Center ”Data Center of FEB RAS” (Khabarovsk) [27] and the Informatics Center of the Federal Research Center ”Computer Science and Control” of Russian Academy of Sciences (Moscow) [28].

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Correspondence to Sergey P. Korolev.

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This research was partly funded by Russian Foundation for Basic Research (RFBR), Project Number 18-29-03196.

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Malkovsky, S.I., Sorokin, A.A., Tsoy, G.I. et al. Evaluating the performance of FFT library implementations on modern hybrid computing systems. J Supercomput 77, 8326–8354 (2021). https://doi.org/10.1007/s11227-020-03591-6

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Keywords

  • Hybrid computing systems
  • IBM POWER
  • Intel Xeon
  • NVIDIA Tesla
  • FFT
  • IBM ESSL
  • FFTW
  • cuFFT
  • cuFFTW
  • Intel MKL