We examine the performance profile of Convolutional Neural Network training
on the current generation of NVIDIA Graphics Processing Units. We introduce two
new Fast Fourier Transform convolution implementations: one based on NVIDIA's
cuFFT library, and another based on a Facebook authored FFT implementation,
fbfft, that provides significant speedups over cuFFT (over 1.5x) for whole
CNNs. Both of these convolution implementations are available in open source,
and are faster than NVIDIA's cuDNN implementation for many common convolutional
layers (up to 23.5x for some synthetic kernel configurations). We discuss
different performance regimes of convolutions, comparing areas where
straightforward time domain convolutions outperform Fourier frequency domain
convolutions. Details on algorithmic applications of NVIDIA GPU hardware
specifics in the implementation of fbfft are also provided.

本文研究了卷积神经网络在当前 NVIDIA 图形处理器上的性能，介绍了两种新的快速傅立叶变换卷积实现，对于整个 CNN，fbfft 比 cuFFT 快 1.5 倍以上，并且对于许多普通的卷积层，速度也比 NVIDIA 的 cuDNN 实现 快（达到了一些合成内核配置的 23.5 倍）。