Scalable Deep Learning on Supercomputers

Purpose

This project enables deep learning training on supercomputer scale without losing test accuracy.

Overview

In this project, we investigate supercomputers' capability of speeding up deep neural network (DNN) training. Our approach is to use large batch size, powered by the Layer-wise Adaptive Rate Scaling (LARS) algorithm, to make efficient use of massive computing resources. We investigate the generality of this approach using AlexNet and ResNet-50 using the ImageNet-1K dataset. With batch sizes that are larger than 16K, our approach shows significantly better test accuracy compared to the baseline in previous work. We are able to scale the 100-epoch AlexNet training to 2,048 Intel Xeon Platinum 8160 processors on the Stampede2 supercomputer and reduce training time from hours to 11 minutes. Similarly, we reduce the 90-epoch ResNet-50 training time to 20 minutes using 2,048 Intel Xeon Phi 7250 processors. Our implementation is open source and has been released in the Intel distribution of Caffe v1.0.7.

Impact

  • This research is one of the earliest works that showcases supercomputers' capability of speeding up deep learning training without losing test accuracy.
  • The corresponding paper, "ImageNet Training in Minutes" won the best paper in the 47th International Conference on Parallel Processing [1].
  • The work was featured on NSF News [2] and reported by media [3].

Funding Source(s)

Base funding

Paper References

[1] You, Yang, Zhao Zhang, Cho-Jui Hsieh, James Demmel, and Kurt Keutzer. "ImageNet training in minutes." In Proceedings of the 47th International Conference on Parallel Processing, p. 1. ACM, 2018.

Related links

Zhao Zhang

Research Associate
zzhang@tacc.utexas.edu