Accelerating FFT Using NEC SX-Aurora Vector Engine

Novel architectures leveraging long and variable vector lengths like the NEC SX-Aurora or the vector extension of RISCV are appearing as promising solutions on the supercomputing market. These architectures often require re-coding of scientific kernels. For example, traditional implementations of algorithms for computing the fast Fourier transform (FFT) cannot take full advantage of vector architectures. In this paper, we present the implementation of FFT algorithms able to leverage these novel architectures. We evaluate these codes on NEC SX-Aurora, comparing them with the optimized NEC libraries. We present the benefits and limitations of two approaches of RADIX-2 FFT vector implementations. We show that our approach makes better use of the vector unit, reaching higher performance than the optimized NEC library for FFT sizes under 64k elements. More generally, we prove the importance of maximizing the vector length usage of the algorithm and that adapting the algorithm to replace memory instructions with register shuffling operations can boost the performance of FFT-like computational kernels.