Accelerating Gossip SGD with Periodic Global Averaging

Yiming Chen, Kun Yuan, Yingya Zhang, Pan Pan, Yinghui Xu, Wotao Yin
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:1791-1802, 2021.

Abstract

Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient than the prevalent parallel SGD. However, its convergence rate is reversely proportional to quantity $1-\beta$ which measures the network connectivity. On large and sparse networks where $1-\beta \to 0$, Gossip SGD requires more iterations to converge, which offsets against its communication benefit. This paper introduces Gossip-PGA, which adds Periodic Global Averaging to accelerate Gossip SGD. Its transient stage, i.e., the iterations required to reach asymptotic linear speedup stage, improves from $\Omega(\beta^4 n^3/(1-\beta)^4)$ to $\Omega(\beta^4 n^3 H^4)$ for non-convex problems. The influence of network topology in Gossip-PGA can be controlled by the averaging period $H$. Its transient-stage complexity is also superior to local SGD which has order $\Omega(n^3 H^4)$. Empirical results of large-scale training on image classification (ResNet50) and language modeling (BERT) validate our theoretical findings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-chen21y, title = {Accelerating Gossip SGD with Periodic Global Averaging}, author = {Chen, Yiming and Yuan, Kun and Zhang, Yingya and Pan, Pan and Xu, Yinghui and Yin, Wotao}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {1791--1802}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/chen21y/chen21y.pdf}, url = {https://proceedings.mlr.press/v139/chen21y.html}, abstract = {Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient than the prevalent parallel SGD. However, its convergence rate is reversely proportional to quantity $1-\beta$ which measures the network connectivity. On large and sparse networks where $1-\beta \to 0$, Gossip SGD requires more iterations to converge, which offsets against its communication benefit. This paper introduces Gossip-PGA, which adds Periodic Global Averaging to accelerate Gossip SGD. Its transient stage, i.e., the iterations required to reach asymptotic linear speedup stage, improves from $\Omega(\beta^4 n^3/(1-\beta)^4)$ to $\Omega(\beta^4 n^3 H^4)$ for non-convex problems. The influence of network topology in Gossip-PGA can be controlled by the averaging period $H$. Its transient-stage complexity is also superior to local SGD which has order $\Omega(n^3 H^4)$. Empirical results of large-scale training on image classification (ResNet50) and language modeling (BERT) validate our theoretical findings.} }
Endnote
%0 Conference Paper %T Accelerating Gossip SGD with Periodic Global Averaging %A Yiming Chen %A Kun Yuan %A Yingya Zhang %A Pan Pan %A Yinghui Xu %A Wotao Yin %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-chen21y %I PMLR %P 1791--1802 %U https://proceedings.mlr.press/v139/chen21y.html %V 139 %X Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient than the prevalent parallel SGD. However, its convergence rate is reversely proportional to quantity $1-\beta$ which measures the network connectivity. On large and sparse networks where $1-\beta \to 0$, Gossip SGD requires more iterations to converge, which offsets against its communication benefit. This paper introduces Gossip-PGA, which adds Periodic Global Averaging to accelerate Gossip SGD. Its transient stage, i.e., the iterations required to reach asymptotic linear speedup stage, improves from $\Omega(\beta^4 n^3/(1-\beta)^4)$ to $\Omega(\beta^4 n^3 H^4)$ for non-convex problems. The influence of network topology in Gossip-PGA can be controlled by the averaging period $H$. Its transient-stage complexity is also superior to local SGD which has order $\Omega(n^3 H^4)$. Empirical results of large-scale training on image classification (ResNet50) and language modeling (BERT) validate our theoretical findings.
APA
Chen, Y., Yuan, K., Zhang, Y., Pan, P., Xu, Y. & Yin, W.. (2021). Accelerating Gossip SGD with Periodic Global Averaging. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:1791-1802 Available from https://proceedings.mlr.press/v139/chen21y.html.

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