Performance Evaluation of Fast Fourier Transforms on Emerging RISC-V Hardware with Vector Extension Support
Abstract
This manuscript presents a performance evaluation of Fast Fourier Transform (FFT) implementations on emerging processors supporting the RISC-V Vector Extension (RVV 1.0). By introducing juFFTe, a light-weight high-performance library for discrete Fourier transforms, it is demonstrated how effective vectorization of performance-critical FFT kernels can be achieved on RVV-enabled hardware. Comprehensive benchmarks on three RVV 1.0-ready processors, the SiFive X280, the X100 core of the SpacemiT K3 and the C920v2 core of the Sophon SG2044, reveal substantial performance improvements of juFFTe (https://github.com/FZJ-JSC/juFFTe) over the widely used FFTW3 library. Although RVV-enabled platforms show promising results at this stage of development, a comparison with AMD's Zen 5 architecture indicates that RISC-V needs further maturing to reach the performance of established micro-architectures.
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BibTeX
@misc{seibel2026performance,
title = {{Performance Evaluation of Fast Fourier Transforms on Emerging RISC-V Hardware with Vector Extension Support}},
author = {Daniel Seibel and Kaveh Haghighi Mood and Jayesh Badwaik and Prateek Chawla and Stepan Nassyr and Andreas Herten},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2608.28076},
eprint = {2608.28076},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28076},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Performance Evaluation of Fast Fourier Transforms on Emerging RISC-V Hardware with Vector Extension Support
AU - Daniel Seibel
AU - Kaveh Haghighi Mood
AU - Jayesh Badwaik
AU - Prateek Chawla
AU - Stepan Nassyr
AU - Andreas Herten
PY - 2026
DA - 2026/08/28
PB - arXiv
DO - 10.48550/arxiv.2608.28076
UR - https://arxiv.org/abs/2608.28076
AB - This manuscript presents a performance evaluation of Fast Fourier Transform (FFT) implementations on emerging processors supporting the RISC-V Vector Extension (RVV 1.0). By introducing juFFTe, a light-weight high-performance library for discrete Fourier transforms, it is demonstrated how effective vectorization of performance-critical FFT kernels can be achieved on RVV-enabled hardware. Comprehensive benchmarks on three RVV 1.0-ready processors, the SiFive X280, the X100 core of the SpacemiT K3 and the C920v2 core of the Sophon SG2044, reveal substantial performance improvements of juFFTe (https://github.com/FZJ-JSC/juFFTe) over the widely used FFTW3 library. Although RVV-enabled platforms show promising results at this stage of development, a comparison with AMD's Zen 5 architecture indicates that RISC-V needs further maturing to reach the performance of established micro-architectures.
ER -