Accelerating neural network training with distributed asynchronous and selective optimization (DASO)

Journal of Big Data

Fabrice von der Lehr¹  Martin Siggel¹  Markus Götz²  Achim Streit²  Charlotte Debus²  James Kahn²  Daniel Coquelin² 

[1] German Aerospace Center, Linder Höhe, 51147, Cologne, Germany;Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany;

关键词: Machine learning;    Neural networks;    Data parallel training;    Multi-node;    Multi-GPU;    Stale gradients;   

DOI  :  10.1186/s40537-021-00556-1

来源: Springer

【 摘 要 】

With increasing data and model complexities, the time required to train neural networks has become prohibitively large. To address the exponential rise in training time, users are turning to data parallel neural networks (DPNN) and large-scale distributed resources on computer clusters. Current DPNN approaches implement the network parameter updates by synchronizing and averaging gradients across all processes with blocking communication operations after each forward-backward pass. This synchronization is the central algorithmic bottleneck. We introduce the distributed asynchronous and selective optimization (DASO) method, which leverages multi-GPU compute node architectures to accelerate network training while maintaining accuracy. DASO uses a hierarchical and asynchronous communication scheme comprised of node-local and global networks while adjusting the global synchronization rate during the learning process. We show that DASO yields a reduction in training time of up to 34% on classical and state-of-the-art networks, as compared to current optimized data parallel training methods.

【 授权许可】

CC BY