Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash

Understand

Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization.

• We address the instability of the large mini-batch training with batch-size control and label smoothing.
• We address the overhead of the gradient synchronization with 2D-Torus all-reduce.
• Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations.