Deep Learning is an increasingly important subdomain of arti cial intelligence.
Deep Learning architectures, arti cial neural networks characterized by having both
a large breadth of neurons and a large depth of layers, bene ts from training on Big
Data. The size and complexity of the model combined with the size of the training
data makes the training procedure very computationally and temporally expensive.
Accelerating the training procedure of Deep Learning using cluster computers faces
many challenges ranging from distributed optimizers to the large communication overhead
speci c to a system with o the shelf networking components. In this thesis, we
present a novel synchronous data parallel distributed Deep Learning implementation
on HPCC Systems, a cluster computer system. We discuss research that has been
conducted on the distribution and parallelization of Deep Learning, as well as the
concerns relating to cluster environments. Additionally, we provide case studies that
evaluate and validate our implementation. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_40736 |
| Contributors | Kennedy, Robert Kwan Lee (author), Khoshgoftaar, Taghi M. (Thesis advisor), Florida Atlantic University (Degree grantor), College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 83 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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