{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/114021"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/114021","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Optimizing Systems for Deep Learning Applications","abstract":"Modern systems for Machine Learning (ML) workloads support heterogeneous workloads and resources. However, existing resource managers in these systems do not differentiate between heterogeneous GPU resources. Moreover, users are usually unaware of the sufficient and appropriate type and amount of GPU resources to request for their ML jobs. In this thesis, we analyze the performance of ML training and inference jobs and identify ML model and GPU characteristics that impact this performance. 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In this dissertation, we present optimizations to improve the performance of these artificial intelligence applications while at the same time improving the performance and the utilization of the systems and the heterogeneous resources they run on. We propose utilizing machine learning models, that learn from historical data of application performance as well as application and resource characteristics, to predict the necessary and sufficient resource requirements for these applications to ensure the optimal performance for the application and the underlying system."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Optimizing Systems for Deep Learning Applications"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Butt, Ali R."],"dc:contributor.committeemember":["Anwar, Ali","Chantem, Thidapat","Min, Chang Woo","Tilevich, Eli"],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Albahar, Hadeel Ahmad"],"dc:date.accessioned":["2023-03-02T09:00:08Z"],"dc:date.available":["2023-03-02T09:00:08Z"],"dc:date.issued":["2023-03-01"],"dc:description.abstract":["Modern systems for Machine Learning (ML) workloads support heterogeneous workloads and resources. 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