Communication-efficient Distributed Learning for Large Batch Optimization

Rui Liu, Barzan Mozafari
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:13925-13946, 2022.

Abstract

Many communication-efficient methods have been proposed for distributed learning, whereby gradient compression is used to reduce the communication cost. However, given recent advances in large batch optimization (e.g., large batch SGD and its variant LARS with layerwise adaptive learning rates), the compute power of each machine is being fully utilized. This means, in modern distributed learning, the per-machine computation cost is no longer negligible compared to the communication cost. In this paper, we propose new gradient compression methods for large batch optimization, JointSpar and its variant JointSpar-LARS with layerwise adaptive learning rates, that jointly reduce both the computation and the communication cost. To achieve this, we take advantage of the redundancy in the gradient computation, unlike the existing methods compute all coordinates of the gradient vector, even if some coordinates are later dropped for communication efficiency. JointSpar and its variant further reduce the training time by avoiding the wasted computation on dropped coordinates. While computationally more efficient, we prove that JointSpar and its variant also maintain the same convergence rates as their respective baseline methods. Extensive experiments show that, by reducing the time per iteration, our methods converge faster than state-of-the-art compression methods in terms of wall-clock time.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-liu22n, title = {Communication-efficient Distributed Learning for Large Batch Optimization}, author = {Liu, Rui and Mozafari, Barzan}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {13925--13946}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/liu22n/liu22n.pdf}, url = {https://proceedings.mlr.press/v162/liu22n.html}, abstract = {Many communication-efficient methods have been proposed for distributed learning, whereby gradient compression is used to reduce the communication cost. However, given recent advances in large batch optimization (e.g., large batch SGD and its variant LARS with layerwise adaptive learning rates), the compute power of each machine is being fully utilized. This means, in modern distributed learning, the per-machine computation cost is no longer negligible compared to the communication cost. In this paper, we propose new gradient compression methods for large batch optimization, JointSpar and its variant JointSpar-LARS with layerwise adaptive learning rates, that jointly reduce both the computation and the communication cost. To achieve this, we take advantage of the redundancy in the gradient computation, unlike the existing methods compute all coordinates of the gradient vector, even if some coordinates are later dropped for communication efficiency. JointSpar and its variant further reduce the training time by avoiding the wasted computation on dropped coordinates. While computationally more efficient, we prove that JointSpar and its variant also maintain the same convergence rates as their respective baseline methods. Extensive experiments show that, by reducing the time per iteration, our methods converge faster than state-of-the-art compression methods in terms of wall-clock time.} }
Endnote
%0 Conference Paper %T Communication-efficient Distributed Learning for Large Batch Optimization %A Rui Liu %A Barzan Mozafari %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-liu22n %I PMLR %P 13925--13946 %U https://proceedings.mlr.press/v162/liu22n.html %V 162 %X Many communication-efficient methods have been proposed for distributed learning, whereby gradient compression is used to reduce the communication cost. However, given recent advances in large batch optimization (e.g., large batch SGD and its variant LARS with layerwise adaptive learning rates), the compute power of each machine is being fully utilized. This means, in modern distributed learning, the per-machine computation cost is no longer negligible compared to the communication cost. In this paper, we propose new gradient compression methods for large batch optimization, JointSpar and its variant JointSpar-LARS with layerwise adaptive learning rates, that jointly reduce both the computation and the communication cost. To achieve this, we take advantage of the redundancy in the gradient computation, unlike the existing methods compute all coordinates of the gradient vector, even if some coordinates are later dropped for communication efficiency. JointSpar and its variant further reduce the training time by avoiding the wasted computation on dropped coordinates. While computationally more efficient, we prove that JointSpar and its variant also maintain the same convergence rates as their respective baseline methods. Extensive experiments show that, by reducing the time per iteration, our methods converge faster than state-of-the-art compression methods in terms of wall-clock time.
APA
Liu, R. & Mozafari, B.. (2022). Communication-efficient Distributed Learning for Large Batch Optimization. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:13925-13946 Available from https://proceedings.mlr.press/v162/liu22n.html.

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