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Memory-accelerated parallel method for multidimensional fast fourier implementation on GPU

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Abstract

Fast Fourier transform (FFT) is a well-known algorithm that calculates the discrete Fourier transform (DFT) of discrete data and is an essential tool in scientific and engineering computation. Due to the large amounts of data, parallelly executing FFT in graphics processing unit (GPU) can effectively optimize the performance. Following this approach, FFTW and some other FFT packages were designed, but the fixed computation pattern makes it hard to utilize the computing power of GPU. Additionally, the memory access pattern is not optimized to alleviate the bottleneck of data exchange. Motivated by these challenges, we propose an efficient GPU-accelerated multidimensional FFT library to achieve better performance in this paper. We present a detailed and clear implementation strategy and optimize FFT by having as few memory transfers as possible. The data will be reshuffled on the CPU, and the access mode is also optimized to coordinate with the GPU memory access pattern. Several optimizations are also demonstrated to enhance the performance of our approach for varying FFT sizes, and the evaluation shows that our approach consistently outperforms rocFFT with a speedup of about 25% to 250% on average in AMD Instinct MI100 GPU.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study, and source data are provided with the paper in Figs. 6, 7, 8, 9.

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Acknowledgements

This work was supported in part by the Major Project on the Integration of Industry, Education and Research of Zhongshan under Grant 210602103890051.

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Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510000, Guangdong, China

    Yichang Hu & Lu Lu

  2. Guangdong MeiWeiXian Flavoring Food Co., LTD., Zhongshan, 528437, Guangdong, China

    Cuixu Li

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  1. Yichang Hu
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Correspondence to Lu Lu.

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Hu, Y., Lu, L. & Li, C. Memory-accelerated parallel method for multidimensional fast fourier implementation on GPU. J Supercomput 78, 18189–18208 (2022). https://doi.org/10.1007/s11227-022-04570-9

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