tcFFT: Accelerating Half-Precision FFT through Tensor Cores

Binrui Li, Shenggan Cheng, James Lin
Shanghai Jiao Tong University, Shanghai, China
arXiv:2104.11471 [cs.DC], (23 Apr 2021)


   title={tcFFT: Accelerating Half-Precision FFT through Tensor Cores},

   author={Binrui Li and Shenggan Cheng and James Lin},






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Fast Fourier Transform (FFT) is an essential tool in scientific and engineering computation. The increasing demand for mixed-precision FFT has made it possible to utilize half-precision floating-point (FP16) arithmetic for faster speed and energy saving. Specializing in lower precision, NVIDIA Tensor Cores can deliver extremely high computation performance. However, the fixed computation pattern makes it hard to utilize the computing power of Tensor Cores in FFT. Therefore, we developed tcFFT to accelerate FFT with Tensor Cores. Our tcFFT supports batched 1D and 2D FFT of various sizes and it exploits a set of optimizations to achieve high performance: 1) single-element manipulation on Tensor Core fragments to support special operations needed by FFT; 2) fine-grained data arrangement design to coordinate with the GPU memory access pattern. We evaluated our tcFFT and the NVIDIA cuFFT in various sizes and dimensions on NVIDIA V100 and A100 GPUs. The results show that our tcFFT can outperform cuFFT 1.29x-3.24x and 1.10x-3.03x on the two GPUs, respectively. Our tcFFT has a great potential for mixed-precision scientific applications.
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