{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/119460"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/119460","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Utilization-adaptive Memory Architectures","abstract":"DRAM contributes significantly to a server system's cost and global warming potential. To make matters worse, DRAM density scaling has not kept up with the scaling in logic and storage technologies. An effective way to reduce DRAM's monetary and environmental cost is to increase its effective utilization and extract the best possible performance in all utilization scenarios. To this end, this dissertation proposes Utilization-adaptive Memory Architectures that enhance the memory controller with the ability to adapt to current memory utilization and implement techniques to boost system performance. These techniques fall under two categories: (i) The techniques under Utilization-adaptive Hardware Memory Replication target the scenario where memory is underutilized and aim to boost performance versus a conventional system without replication, and (ii) The techniques under Utilization-adaptive Hardware Memory Compression target the scenario where memory utilization is high and aim to significantly increase memory capacity while closing the performance gap versus a conventional system that has sufficient memory and does not require compression.","abstract_html":"DRAM contributes significantly to a server system's cost and global warming potential. To make matters worse, DRAM density scaling has not kept up with the scaling in logic and storage technologies. An effective way to reduce DRAM's monetary and environmental cost is to increase its effective utilization and extract the best possible performance in all utilization scenarios. To this end, this dissertation proposes Utilization-adaptive Memory Architectures that enhance the memory controller with the ability to adapt to current memory utilization and implement techniques to boost system performance. These techniques fall under two categories: (i) The techniques under Utilization-adaptive Hardware Memory Replication target the scenario where memory is underutilized and aim to boost performance versus a conventional system without replication, and (ii) The techniques under Utilization-adaptive Hardware Memory Compression target the scenario where memory utilization is high and aim to significantly increase memory capacity while closing the performance gap versus a conventional system that has sufficient memory and does not require compression.","abstract_has_math":false,"creators":["Panwar, Gagandeep"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Butt, Ali R.","Jian, Xun"],"committee_members":["Kotra, Jagadish B.","Jia, Xiaoting","Patterson, Cameron D."],"year":2024,"date_issued":"2024-06-14","date_published":"2024-06-14","updated_at":"2026-07-22T22:20:37Z","subjects":["Memory","DRAM","Utilization","Replication","Compression","HPC","Cloud"],"languages":["en"],"rights":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:40361"],"render_values":[{"text":"vt_gsexam:40361","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/119460","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Butt, Ali R.","Jian, Xun"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kotra, Jagadish B.","Jia, Xiaoting","Patterson, Cameron D."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Panwar, Gagandeep"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-06-15T08:01:32Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-06-15T08:01:32Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-06-14"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Memory","DRAM","Utilization","Replication","Compression","HPC","Cloud"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:40361"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/119460"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["DRAM contributes significantly to a server system's cost and global warming potential. To make matters worse, DRAM density scaling has not kept up with the scaling in logic and storage technologies. An effective way to reduce DRAM's monetary and environmental cost is to increase its effective utilization and extract the best possible performance in all utilization scenarios. To this end, this dissertation proposes Utilization-adaptive Memory Architectures that enhance the memory controller with the ability to adapt to current memory utilization and implement techniques to boost system performance. These techniques fall under two categories: (i) The techniques under Utilization-adaptive Hardware Memory Replication target the scenario where memory is underutilized and aim to boost performance versus a conventional system without replication, and (ii) The techniques under Utilization-adaptive Hardware Memory Compression target the scenario where memory utilization is high and aim to significantly increase memory capacity while closing the performance gap versus a conventional system that has sufficient memory and does not require compression."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["A computer system's memory stores information for the system's immediate use (e.g., data and instructions for in-use programs). The performance and capacity of the dominant memory technology – Dynamic Random Access Memory (DRAM) – has not kept up with advancements in computing devices such as CPUs. Furthermore, DRAM significantly contributes to a server's carbon footprint because a server can have over a thousand DRAM chips – substantially more than any other type of chip. DRAM's manufacturing cycle and lifetime energy use make it the most carbon-unfriendly component on today's servers. To reduce the environmental impact of DRAM, an intuitive way is to increase its utilization. To this end, this dissertation explores Utilization-adaptive Memory Architectures which enable the memory controller to adapt to the system's current memory through a variety of techniques such as: (i) Utilization-adaptive Hardware Memory Replication which copies in-use data to free memory and uses the extra copy to improve performance, and (ii) Utilization-adaptive Hardware Memory Compression which uses dense representation for data to save memory and allows the system to run applications that require more memory than the physically installed memory. Compared to conventional systems that do not feature these techniques, these techniques improve performance for different memory utilization scenarios ranging from low to high."]},{"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":["Utilization-adaptive Memory Architectures"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Butt, Ali R.","Jian, Xun"],"dc:contributor.committeemember":["Kotra, Jagadish B.","Jia, Xiaoting","Patterson, Cameron D."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Panwar, Gagandeep"],"dc:date.accessioned":["2024-06-15T08:01:32Z"],"dc:date.available":["2024-06-15T08:01:32Z"],"dc:date.issued":["2024-06-14"],"dc:description.abstract":["DRAM contributes significantly to a server system's cost and global warming potential. To make matters worse, DRAM density scaling has not kept up with the scaling in logic and storage technologies. An effective way to reduce DRAM's monetary and environmental cost is to increase its effective utilization and extract the best possible performance in all utilization scenarios. To this end, this dissertation proposes Utilization-adaptive Memory Architectures that enhance the memory controller with the ability to adapt to current memory utilization and implement techniques to boost system performance. These techniques fall under two categories: (i) The techniques under Utilization-adaptive Hardware Memory Replication target the scenario where memory is underutilized and aim to boost performance versus a conventional system without replication, and (ii) The techniques under Utilization-adaptive Hardware Memory Compression target the scenario where memory utilization is high and aim to significantly increase memory capacity while closing the performance gap versus a conventional system that has sufficient memory and does not require compression."],"dc:description.abstractgeneral":["A computer system's memory stores information for the system's immediate use (e.g., data and instructions for in-use programs). The performance and capacity of the dominant memory technology – Dynamic Random Access Memory (DRAM) – has not kept up with advancements in computing devices such as CPUs. Furthermore, DRAM significantly contributes to a server's carbon footprint because a server can have over a thousand DRAM chips – substantially more than any other type of chip. DRAM's manufacturing cycle and lifetime energy use make it the most carbon-unfriendly component on today's servers. To reduce the environmental impact of DRAM, an intuitive way is to increase its utilization. To this end, this dissertation explores Utilization-adaptive Memory Architectures which enable the memory controller to adapt to the system's current memory through a variety of techniques such as: (i) Utilization-adaptive Hardware Memory Replication which copies in-use data to free memory and uses the extra copy to improve performance, and (ii) Utilization-adaptive Hardware Memory Compression which uses dense representation for data to save memory and allows the system to run applications that require more memory than the physically installed memory. Compared to conventional systems that do not feature these techniques, these techniques improve performance for different memory utilization scenarios ranging from low to high."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:40361"],"dc:identifier.uri":["https://hdl.handle.net/10919/119460"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["Memory","DRAM","Utilization","Replication","Compression","HPC","Cloud"],"dc:title":["Utilization-adaptive Memory Architectures"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Computer Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:37Z"}