AI 中文总结
该研究评估了支持RVV 1.0的三款处理器上juFFTe库的FFT性能，发现其优于FFTW3，但RISC-V性能仍需提升以比肩AMD Zen 5架构。
AI 中文摘要
本手稿对支持RISC-V向量扩展（RVV 1.0）的新型处理器上的快速傅里叶变换（FFT）实现进行了性能评估。通过推出juFFTe（一款轻量级高性能离散傅里叶变换库），展示了如何在支持RVV的硬件上实现对性能关键型FFT内核的高效向量化。对三款支持RVV 1.0的处理器（SiFive X280、SpacemiT K3的X100核心、算能SG2044的C920v2核心）进行的全面基准测试显示，juFFTe（网址：this https URL）相较于广泛使用的FFTW3库实现了显著的性能提升。尽管支持RVV的平台在当前开发阶段展现出良好前景，但与AMD的Zen 5架构对比表明，RISC-V仍需进一步成熟才能达到成熟微架构的性能水平。
英文摘要
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.
CommentsAccepted for International workshop on RISC-V for HPC at ISC26
