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arXiv · 1609.06870

Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability

Janis Keuper·Franz-Josef Pfreundt
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Abstract

This paper presents a theoretical analysis and practical evaluation of the main bottlenecks towards a scalable distributed solution for the training of Deep Neuronal Networks (DNNs). The presented results show, that the current state of the art approach, using data-parallelized Stochastic Gradient Descent (SGD), is quickly turning into a vastly communication bound problem. In addition, we present simple but fixed theoretic constraints, preventing effective scaling of DNN training beyond only a few dozen nodes. This leads to poor scalability of DNN training in most practical scenarios.

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Janis Keuper, Franz-Josef Pfreundt. 2016-09-22. Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability. https://arxiv.org/abs/1609.06870

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