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Toward Accurate Simulation of Edge-B...

Daley, Joshua.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Toward Accurate Simulation of Edge-Based Distributed Deep Neural Network Training./
作者:	Daley, Joshua.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	37 p.
附註:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31296232
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. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 37 p.

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

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

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.

ISBN: 9798383098349Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Edge

Toward Accurate Simulation of Edge-Based Distributed Deep Neural Network Training.
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300 $a 37 p.
500 $a Source: Masters Abstracts International, Volume: 85-12.
500 $a Advisor: Lander, Leslie;Zhang, Yifan.
502 $a Thesis (M.S.)--State University of New York at Binghamton, 2024.
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.
590 $a School code: 0792.
650 4 $a Computer science. $3 523869
650 4 $a Systems science. $3 3168411
653 $a Edge
653 $a Topology
653 $a Training
653 $a Machine learning
653 $a Server architecture
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710 2 $a State University of New York at Binghamton. $b Computer Science. $3 1058053
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793 $a English
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