Performance Evaluation of Fast Fourier Transforms on Emerging RISC-V Hardware with Vector Extension Support
Mathematical SoftwareDistributed, Parallel, and Cluster Computing
Summary
The authors tested how well Fast Fourier Transform (FFT) computations perform on new processors using the RISC-V Vector Extension (RVV 1.0). They created a lightweight software library called juFFTe that takes advantage of these processors' vector capabilities to speed up FFT calculations. Their tests on three different RVV processors showed juFFTe runs much faster than the common FFTW3 library. However, when compared to a more established processor like AMD's Zen 5, the RISC-V platforms still need improvements to match that performance level.
Authors
Daniel Seibel, Kaveh Haghighi Mood, Jayesh Badwaik, Prateek Chawla, Stepan Nassyr, Andreas Herten
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.