Problem
Research questions and friction points this paper is trying to address.
Fast Fourier Transform
RISC-V Vector Extension
Performance Evaluation
Performance Evaluation of Fast Fourier Transforms on Emerging RISC-V Hardware with Vector Extension Support
🤖 AI Summary
研究通过引入轻量级高性能库juFFTe，评估了RISC-V向量扩展硬件上快速傅里叶变换(FFT)的性能优化方法及效果。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Fast Fourier Transform
RISC-V Vector Extension
juFFTe
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