Trent University Graduate Thesis Collection

Item Description
Identifier
tula:etd
Type
Collection
Language
eng
Extent
1 item
Rights
Copyright for all items in the Trent University Graduate Thesis Collection is held by the author, with all rights reserved, unless otherwise noted.
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Exploring the Scalability of Deep Learning on GPU Clusters

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Year: 2019, 2019
Member of: Trent University Graduate Thesis Collection
Name(s): Creator (cre): Williams, Taylor Alan, Thesis advisor (ths): McConnell, Sabine, Degree committee member (dgc): Hurley, Richard, Degree granting institution (dgg): Trent University
Abstract: <p>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… more
Full Text: Exploring the Scalability of Deep Learning on GPU Clusters A Thesis Submitted to the Committee on Graduate Studies in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Faculty of Arts and Science Peterborough, Ontario, …

Cloud Versus Bare Metal: A comparison of a high performance computing cluster running in a commercial cloud and on a traditional hardware cluster using OpenMP and OpenMPI

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Year: 2019, 2019
Member of: Trent University Graduate Thesis Collection
Name(s): Creator (cre): Bilaniuk, Vicky, Thesis advisor (ths): McConnell, Sabine, Degree committee member (dgc): Hurley, Richard, Degree granting institution (dgg): Trent University
Abstract: <p>A comparison of two high performance computing clusters running on AWS and Sharcnet was done to determine which scenarios yield the best performance. Algorithm complexity ranged from O (n) to O (n3). Data sizes ranged from 195 KB to 2 GB. The Sharcnet hardware consisted of Intel E5-2683 and Intel E7-4850 processors with memory sizes ranging from 256 GB to 3072 GB. On AWS, C4.8xlarge… more
Full Text: CLOUD VERSUS BARE METAL A comparison of a high performance computing cluster running in a commercial cloud and on a traditional hardware cluster using OpenMP and OpenMPI A Thesis Submitted to the Committee on Graduate Studies in Partial Fulfillment of the …

Support Vector Machines for Automated Galaxy Classification

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Year: 2019, 2019
Member of: Trent University Graduate Thesis Collection
Name(s): Creator (cre): Chambers, Cameron Darrin, Thesis advisor (ths): McConnell, Sabine, Thesis advisor (ths): Hurley, Richard, Degree granting institution (dgg): Trent University
Abstract: <p>Support Vector Machines (SVMs) are a deterministic, supervised machine learning algorithm that have been successfully applied to many areas of research. They are heavily grounded in mathematical theory and are effective at processing high-dimensional data. This thesis models a variety of galaxy classification tasks using SVMs and data from the Galaxy Zoo 2 project. SVM parameters were… more
Full Text: Support Vector Machines for Automated Galaxy Classification A Thesis Submitted to the Committee on Graduate Studies in Partial Fulfillment of the Requirements for the Degree of Master of Science in the Faculty of Arts and Science TRENT UNIVERSITY …