Using High-Performance Computing to Scale Generative Adversarial Networks

Generative adversarial networks(GANs) are methods that can be used for data augmentation, which helps in creating better detection models for rare or imbalanced datasets. They can be difficult to train due to issues such as mode collapse. We aim to improve the performance and accuracy of the Lipizzaner GAN framework by taking advantage of its distributed nature and running it at very large scales. Lipizzaner was implemented for robustness, but has not been tested at scale in high performance computing(HPC) systems. We believe that by utilizing HPC technologies, we can scale up Lipizzaner and observe performance enhancements. This thesis achieves this scale up, using Oak Ridge National Labs’ Summit Supercomputer. We observed improvements in the performance of Lipizzaner, especially when run with poorer network architectures, which implies Lipizzaner is able to overcome network limitations through scale.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Flores, Diana J.

Advisors dc:contributor.advisor

• Hemberg, Erik
• Toutouh, Jamal
• O’Reilly, Una-May

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139311

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139311