A Discrete-event-based Simulator for Distributed Deep Learning

Document Type

Conference Proceeding

Publication Date

1-1-2022

Abstract

New intelligence applications are driving increasing interest in deploying deep neural networks (DNN) in a distributed way. To set up distributed deep learning involves alterations of a great number of the parameter configurations of network/edge devices and DNN models, which are crucial to achieve best performances. Simulations measure scalability of intelligence applications in the early stage, as well as to determine the effects of different configurations, thus highly desired. However, work on simulating the distributed intelligence environment is still in its infancy. The existing simulation frameworks, such as NS-3, etc., cannot extended in a straightforward way to support simulations of distributed learning. In this paper, we propose a novel discrete event simulator, sim4DistrDL, which includes a deep learning module and a network simulation module to facilitate simulation of DNN-based distributed applications. Specifically, we give the design and implementation of the proposed learning simulator and present an illustrative use case.

Identifier

85141146465 (Scopus)

ISBN

[9781665497923]

Publication Title

Proceedings IEEE Symposium on Computers and Communications

External Full Text Location

https://doi.org/10.1109/ISCC55528.2022.9912919

ISSN

15301346

Volume

2022-June

Fund Ref

Beijing University of Posts and Telecommunications

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