Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners

Artificial Neural Networks (ANNs) have been established as one of the most important algorithmic tools in the Machine Learning (ML) toolbox over the past few decades. ANNs' recent rise to widespread acceptance can be attributed to two developments: (1) the availability of large-scale training and testing datasets; and (2) the availability of new computer architectures for which ANN implementations are orders of magnitude more efficient. In this thesis, I present research on two aspects of the second development. First, I present a portable, open source implementation of ANNs in OpenCL and MPI. Second, I present performance and scaling models for ANN algorithms on state-of-the-art Graphics Processing Unit (GPU) based parallel compute clusters.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2018

Author dc:creator

• Sun, Chang

Chair dc:contributor.committeechair

• Plassmann, Paul E.

Committee members dc:contributor.committeemember

• Patterson, Cameron D.
• Jones, Mark T.

• artificial neural networks
• Machine learning
• heterogenous computing
• parallel computing

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:17090

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/85020