A Discrete-event-based Simulator for Distributed Deep Learning
This provides a tool for researchers and engineers to optimize distributed deep learning setups, though it is incremental as it builds on existing simulation concepts.
The authors tackled the lack of simulation tools for distributed deep learning environments by proposing sim4DistrDL, a discrete-event simulator that integrates deep learning and network modules, enabling evaluation of configurations and scalability for DNN-based applications.
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.