Abstract
The emergence of RISC-V as a reduced instruction set architecture has brought several advantages such as openness, flexibility, scalability, and efficiency compared to other commercial ISAs. It has gained significant popularity, especially in the field of high-performance computing. However, there is a lack of high-performance implementations of numerical algorithms, including the Fast Fourier Transform (FFT) algorithm. To address this issue, the paper focuses on optimizing the butterfly network, butterfly kernel, and single instruction multiple data (SIMD) operations to achieve efficient calculations for FFT with a computation scale of \(2^n\) on a RISC-V architecture CPUs. The experimental results demonstrate a significant improvement in the performance of the FFT algorithm library implemented using the proposed optimizations compared to existing implementations like FFTW on RISC-V CPUs.
X. Zhang—This work is partly supported by Beijing Municipal Science and Technology Program under Grant No. Z211100004421002.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Asanovic, K., Patterson, D.A.: Instruction sets should be free: the case for RISC-V. https://people.eecs.berkeley.edu/krste/papers/EECS-2014-146.pdf. Accessed 23 Mar 2023
Cooley, J.W., Tukey, J.W.: An algorithm for the machine calculation of complex Fourier series. Math. Comput. 19(90), 297–301 (1965)
Chen, T., Jia, H., Li, Z., Li, C., Zhang, Y.: A transpose-free three-dimensional FFT algorithm on ARM CPUs. In: 2021 IEEE 23rd International Conference on High Performance Computing & Communications; 7th International Conference on Data Science & Systems; 19th International Conference on Smart City; 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), pp. 1–8. IEEE (2021)
Asanovic, K., Bodik, R., Catanzaro, B.C., et al.: The landscape of parallel computing research: a view from Berkeley (2006)
Frigo, M., Johnson, S.G.: The design and implementation of FFTW3. Proc. IEEE 93(2), 216–231 (2005)
Li, Z., et al.: AutoFFT: a template-based FFT codes auto-generation framework for ARM and X86 CPUs. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–15 (2019)
Rao, K.R., Kim, D.N., Hwang, J.J.: Fast Fourier Transform: Algorithms and Applications, vol. 32. Springer, Dordrecht (2010). https://doi.org/10.1007/978-1-4020-6629-0
Cooley, J.W., Lewis, P.A., Welch, P.D.: The fast Fourier transform and its applications. IEEE Trans. Educ. 12(1), 27–34 (1969)
Swarztrauber, P.N.: FFT algorithms for vector computers. Parallel Comput. 1(1), 45–63 (1984)
Li, Z., Jia, H., Zhang, Y., Chen, T., Yuan, L., Vuduc, R.: Automatic generation of high-performance FFT kernels on arm and x86 CPUs. IEEE Trans. Parallel Distrib. Syst. 31(8), 1925–1941 (2020)
Patterson, D., Waterman, A.: The RISC-V Reader: An Open Architecture Atlas. Strawberry Canyon (2017)
Patsidis, K., Nicopoulos, C., Sirakoulis, G.C., Dimitrakopoulos, G.: RISC-V 2: a scalable RISC-V vector processor. In: 2020 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE (2020)
Waterman, A.S.: Design of the RISC-V Instruction Set Architecture. University of California, Berkeley (2016)
Frigo, M., Johnson, S.G.: The benchmarking methodology of benchFFT (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, X., Zhang, X., Zhang, Y. (2023). Optimization of the FFT Algorithm on RISC-V CPUs. In: Bienz, A., Weiland, M., Baboulin, M., Kruse, C. (eds) High Performance Computing. ISC High Performance 2023. Lecture Notes in Computer Science, vol 13999. Springer, Cham. https://doi.org/10.1007/978-3-031-40843-4_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-40843-4_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40842-7
Online ISBN: 978-3-031-40843-4
eBook Packages: Computer ScienceComputer Science (R0)