{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/130707"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/130707","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Investigating system resilience in distributed evolutionary GAN training","abstract":"General Adverserial Networks (GANs) provide a useful approach to new data generation with a few common problems of mode collapsing and oscillating behavior. Lipizzaner improves the performance of distributed GAN training with the use of a spatially distributed coevolutionary algorithm and gradient-based optimizers. However, in its current state the use of Lipizzaner is limited by its vulnerabilities on systems that encounter frequent node failures. When faced with a single node failure, Lipizzaner's entire experiment comes to a halt and must be restarted. We see a need for increasing Lipizzaner's resilience to such failures and do the following. We apply a combination of uncoordinated checkpointing, attempted reconnecting, and restarting nodes to form a simple and efficient solution for system resilience in Lipizzaner. We find that checkpointing and reconnecting are essential and simple solutions to failure recovery in Lipizzaner, while restarting nodes requires a more nuanced approach that shows promising results when used correctly to address node failures.","abstract_html":"General Adverserial Networks (GANs) provide a useful approach to new data generation with a few common problems of mode collapsing and oscillating behavior. Lipizzaner improves the performance of distributed GAN training with the use of a spatially distributed coevolutionary algorithm and gradient-based optimizers. However, in its current state the use of Lipizzaner is limited by its vulnerabilities on systems that encounter frequent node failures. When faced with a single node failure, Lipizzaner's entire experiment comes to a halt and must be restarted. We see a need for increasing Lipizzaner's resilience to such failures and do the following. We apply a combination of uncoordinated checkpointing, attempted reconnecting, and restarting nodes to form a simple and efficient solution for system resilience in Lipizzaner. We find that checkpointing and reconnecting are essential and simple solutions to failure recovery in Lipizzaner, while restarting nodes requires a more nuanced approach that shows promising results when used correctly to address node failures.","abstract_has_math":false,"creators":["Mustafi, Urmi."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Lipizzaner improves the performance of distributed GAN training with the use of a spatially distributed coevolutionary algorithm and gradient-based optimizers. However, in its current state the use of Lipizzaner is limited by its vulnerabilities on systems that encounter frequent node failures. When faced with a single node failure, Lipizzaner's entire experiment comes to a halt and must be restarted. We see a need for increasing Lipizzaner's resilience to such failures and do the following. We apply a combination of uncoordinated checkpointing, attempted reconnecting, and restarting nodes to form a simple and efficient solution for system resilience in Lipizzaner. We find that checkpointing and reconnecting are essential and simple solutions to failure recovery in Lipizzaner, while restarting nodes requires a more nuanced approach that shows promising results when used correctly to address node failures."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Investigating system resilience in distributed evolutionary GAN training"]}]}],"canonical_facts":{"dc:contributor.advisor":["Erik Hemberg and Jamal Toutouh."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Mustafi, Urmi."],"dc:date.accessioned":["2021-05-24T19:52:31Z"],"dc:date.available":["2021-05-24T19:52:31Z"],"dc:date.issued":["2021"],"dc:description":["Thesis: M. 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We apply a combination of uncoordinated checkpointing, attempted reconnecting, and restarting nodes to form a simple and efficient solution for system resilience in Lipizzaner. We find that checkpointing and reconnecting are essential and simple solutions to failure recovery in Lipizzaner, while restarting nodes requires a more nuanced approach that shows promising results when used correctly to address node failures."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/130707"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Investigating system resilience in distributed evolutionary GAN training"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:21:16Z"}