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Exploring the Scalability of Deep Le...

Williams, Taylor.

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Exploring the Scalability of Deep Learning on GPU Clusters.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring the Scalability of Deep Learning on GPU Clusters./
Author:	Williams, Taylor.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	132 p.
Notes:	Source: Masters Abstracts International, Volume: 80-07.
Contained By:	Masters Abstracts International80-07.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13426089
ISBN:	9780438802285

Exploring the Scalability of Deep Learning on GPU Clusters.
Williams, Taylor.

Exploring the Scalability of Deep Learning on GPU Clusters. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 132 p.

Source: Masters Abstracts International, Volume: 80-07.

Thesis (M.S.)--Trent University (Canada), 2019.

This item must not be added to any third party search indexes.

In recent years, we have observed an unprecedented rise in popularity of AI-powered systems. They have become ubiquitous in modern life, being used by countless people every day. Many of these AI systems are powered, entirely or partially, by deep learning models. From language translation to image recognition, deep learning models are being used to build systems with unprecedented accuracy. The primary downside, is the significant time required to train the models. Fortunately, the time needed for training the models is reduced through the use of GPUs rather than CPUs. However, with model complexity ever increasing, training times even with GPUs are on the rise. One possible solution to ever-increasing training times is to use parallelization to enable the distributed training of models on GPU clusters. This thesis investigates how to utilise clusters of GPU-accelerated nodes to achieve the best scalability possible, thus minimising model training times.

ISBN: 9780438802285Subjects--Topical Terms:

523869
Computer science.

Exploring the Scalability of Deep Learning on GPU Clusters.
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500 $a Source: Masters Abstracts International, Volume: 80-07.
500 $a Publisher info.: Dissertation/Thesis.
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506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a In recent years, we have observed an unprecedented rise in popularity of AI-powered systems. They have become ubiquitous in modern life, being used by countless people every day. Many of these AI systems are powered, entirely or partially, by deep learning models. From language translation to image recognition, deep learning models are being used to build systems with unprecedented accuracy. The primary downside, is the significant time required to train the models. Fortunately, the time needed for training the models is reduced through the use of GPUs rather than CPUs. However, with model complexity ever increasing, training times even with GPUs are on the rise. One possible solution to ever-increasing training times is to use parallelization to enable the distributed training of models on GPU clusters. This thesis investigates how to utilise clusters of GPU-accelerated nodes to achieve the best scalability possible, thus minimising model training times.
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