{"648908":{"#nid":"648908","#data":{"type":"event","title":"PhD Defense by  Thaleia Dimitra Doudali","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003EAdding machine intelligence to hybrid memory management\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThaleia Dimitra Doudali\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESchool of Computer Science\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECollege of Computing\u003C\/p\u003E\r\n\r\n\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.cc.gatech.edu\/~tdoudali\/\u0022 title=\u0022https:\/\/www.cc.gatech.edu\/~tdoudali\/\u0022\u003Ehttps:\/\/www.cc.gatech.edu\/~tdoudali\/\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate:\u0026nbsp;\u003C\/strong\u003EWednesday July 21\u003Csup\u003Est\u003C\/sup\u003E 2021\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E\u0026nbsp;3:00 PM - 5:00 PM (EST)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation:\u0026nbsp;\u003C\/strong\u003E \u003Ca href=\u0022https:\/\/bluejeans.com\/567264791\/1482\u0022\u003Ehttps:\/\/bluejeans.com\/567264791\/1482\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ECommittee:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Ada Gavrilovska (Advisor, School of Computer Science, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Vivek Sarkar (School of Computer Science, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Alexey Tumanov (School of Computer Science, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Tushar Krishna (School of Electrical and Computer Engineering, Georgia Institute of Technology)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDr. Sudhanva Gurumurthi (Principal Member of the Technical Staff, AMD)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003Cbr \/\u003E\r\nComputing platforms increasingly incorporate heterogeneous memory hardware technologies, as a way to scale application performance, memory capacities and achieve cost effectiveness. However, this heterogeneity, along with the greater irregularity in the behavior of emerging workloads, render existing hybrid memory management approaches ineffective, calling for more intelligent methods. To this end, this thesis reveals new insights, develops novel methods and contributes system-level mechanisms towards the practical integration of machine learning to hybrid memory management, boosting application performance and system resource efficiency.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EFirst, this thesis builds Kleio; a hybrid memory page scheduler with machine intelligence. Kleio deploys Recurrent Neural Networks to learn memory access patterns at a page granularity and to improve upon the selection of dynamic page migrations across the memory hardware components. Kleio cleverly focuses the machine learning on the page subset whose timely movement will reveal most application performance improvement, while preserving history-based lightweight management for the rest of the pages. In this way, Kleio bridges on average 80% of the relative existing performance gap, while laying the grounds for practical machine intelligent data management with manageable learning overheads.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn addition, this thesis contributes three system-level mechanisms to further boost application performance and reduce the operational and learning overheads of machine learning-based hybrid memory management. First, this thesis builds Cori; a system-level solution for tuning the operational frequency of periodic page schedulers for hybrid memories. Cori leverages insights on data reuse times to fine tune the page migration frequency in a lightweight manner. Second, this thesis contributes Coeus; a page grouping mechanism for page schedulers like Kleio. Coeus leverages Cori\u0026rsquo;s data reuse insights to tune the granularity at which patterns are interpreted by the page scheduler and enable the training of a single Recurrent Neural Network per page cluster, reducing by 3x the model training times. The combined effects of Cori and Coeus provide 3x additional performance improvements to Kleio. Finally, this thesis proposes Cronus; an image-based page selector for page schedulers like Kleio. Cronus uses visualization to accelerate the process of selecting which page patterns should be managed with machine learning, reducing by 75x the operational overheads of Kleio. Cronus lays the foundations for future use of visualization and computer vision methods in memory management, such as image-based memory access pattern classification, recognition and prediction.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAdditional Meeting Details:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMeeting URL\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/bluejeans.com\/567264791\/1482?src=join_info\u0022\u003Ehttps:\/\/bluejeans.com\/567264791\/1482?src=join_info\u003C\/a\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EMeeting ID\u003C\/p\u003E\r\n\r\n\u003Cp\u003E567 264 791\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EParticipant Passcode\u003C\/p\u003E\r\n\r\n\u003Cp\u003E1482\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EWant to dial in from a phone?\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDial one of the following numbers:\u003C\/p\u003E\r\n\r\n\u003Cp\u003E+1.408.419.1715 (United States (San Jose))\u003C\/p\u003E\r\n\r\n\u003Cp\u003E+1.408.915.6290 (United States (San Jose))\u003C\/p\u003E\r\n\r\n\u003Cp\u003E(see all numbers - \u003Ca href=\u0022https:\/\/www.bluejeans.com\/numbers\u0022\u003Ehttps:\/\/www.bluejeans.com\/numbers\u003C\/a\u003E)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EEnter the meeting ID and passcode followed by #\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EConnecting from a room system?\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDial: bjn.vc or 199.48.152.152 and enter your meeting ID \u0026amp; passcode\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Adding machine intelligence to hybrid memory management "}],"uid":"27707","created_gmt":"2021-07-20 14:19:15","changed_gmt":"2021-07-20 14:19:15","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2021-07-21T16:00:00-04:00","event_time_end":"2021-07-21T18:00:00-04:00","event_time_end_last":"2021-07-21T18:00:00-04:00","gmt_time_start":"2021-07-21 20:00:00","gmt_time_end":"2021-07-21 22:00:00","gmt_time_end_last":"2021-07-21 22:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}