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Toward Accurate Simulation of Edge-Based Distributed Deep Neural Network Training.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Toward Accurate Simulation of Edge-Based Distributed Deep Neural Network Training./
Author:	Daley, Joshua.
Description:	1 online resource (37 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798383098349

Toward Accurate Simulation of Edge-Based Distributed Deep Neural Network Training.
Daley, Joshua.

Toward Accurate Simulation of Edge-Based Distributed Deep Neural Network Training. - 1 online resource (37 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--State University of New York at Binghamton, 2024.

Includes bibliographical references

In distributed machine learning systems, topology and configuration form a broad domain. Especially in an edge scenario with many network-connected devices to coordinate for training, real systems are expensive and time-consuming to set up. We present ETSim, a simulator for distributed deep learning at the edge that allows us to quickly examine the effects of different system topologies and configurations on training. In an evaluation experiment, ETSim's results were at least 92% accurate to those of the real system. We use ETSim to conduct case studies and present various findings regarding topology and configuration in a parameter server architecture.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798383098349Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

EdgeIndex Terms--Genre/Form:

554714
Electronic books.

Toward Accurate Simulation of Edge-Based Distributed Deep Neural Network Training.
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502 $a Thesis (M.S.)--State University of New York at Binghamton, 2024.
504 $a Includes bibliographical references
520 $a In distributed machine learning systems, topology and configuration form a broad domain. Especially in an edge scenario with many network-connected devices to coordinate for training, real systems are expensive and time-consuming to set up. We present ETSim, a simulator for distributed deep learning at the edge that allows us to quickly examine the effects of different system topologies and configurations on training. In an evaluation experiment, ETSim's results were at least 92% accurate to those of the real system. We use ETSim to conduct case studies and present various findings regarding topology and configuration in a parameter server architecture.
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653 $a Machine learning
653 $a Server architecture
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