{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81936"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81936","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimizing Memory-Resident Decision Support System Workloads for Cache Memories","abstract":"In the second part of this work cache optimizations are proposed for two database system components: algorithms and query optimizer. In the former, blocking and prefetching are applied to database algorithms. In the latter the first public domain cache-oriented query optimizer is proposed. This optimizer chooses the ordering of operations and implementation of those operations using the number of cache misses and the number of instructions as the metric. In an evaluation of the proposed optimizations using a real architecture, some complex queries show performance improvement over the existing optimizations. One query from a standard benchmark achieves 29% improvement while the average for five queries is 13%. While this improvement is moderate, these optimizations are implemented in the database system without any changes to the hardware. Therefore the proposed optimizations provide improvement at no additional cost. A sensitivity test showed that the improvement provided by the proposed optimizations is independent of changes to the cache configuration like cache size, line size, and miss penalty. Finally, the prefetching optimization doubles the performance improvement from 13% to 28% in average for all queries.","abstract_html":"In the second part of this work cache optimizations are proposed for two database system components: algorithms and query optimizer. In the former, blocking and prefetching are applied to database algorithms. In the latter the first public domain cache-oriented query optimizer is proposed. This optimizer chooses the ordering of operations and implementation of those operations using the number of cache misses and the number of instructions as the metric. In an evaluation of the proposed optimizations using a real architecture, some complex queries show performance improvement over the existing optimizations. One query from a standard benchmark achieves 29% improvement while the average for five queries is 13%. While this improvement is moderate, these optimizations are implemented in the database system without any changes to the hardware. Therefore the proposed optimizations provide improvement at no additional cost. A sensitivity test showed that the improvement provided by the proposed optimizations is independent of changes to the cache configuration like cache size, line size, and miss penalty. Finally, the prefetching optimization doubles the performance improvement from 13% to 28% in average for all queries.","abstract_has_math":false,"creators":["Trancoso, Pedro P.M."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Torrellas, Josep"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T20:21:05Z","date_published":"2015-09-25T20:21:05Z","updated_at":"2026-07-22T22:26:17Z","subjects":["Computer Science"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI9912399"],"render_values":[{"text":"(MiAaPQ)AAI9912399","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/81936","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Torrellas, Josep"]},{"key":"dc:creator","label":"Author","values":["Trancoso, Pedro P.M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T20:21:05Z","10000-01-01","1998"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/81936","(MiAaPQ)AAI9912399"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In the second part of this work cache optimizations are proposed for two database system components: algorithms and query optimizer. In the former, blocking and prefetching are applied to database algorithms. In the latter the first public domain cache-oriented query optimizer is proposed. This optimizer chooses the ordering of operations and implementation of those operations using the number of cache misses and the number of instructions as the metric. In an evaluation of the proposed optimizations using a real architecture, some complex queries show performance improvement over the existing optimizations. One query from a standard benchmark achieves 29% improvement while the average for five queries is 13%. While this improvement is moderate, these optimizations are implemented in the database system without any changes to the hardware. Therefore the proposed optimizations provide improvement at no additional cost. A sensitivity test showed that the improvement provided by the proposed optimizations is independent of changes to the cache configuration like cache size, line size, and miss penalty. Finally, the prefetching optimization doubles the performance improvement from 13% to 28% in average for all queries.","Made available in DSpace on 2015-09-25T20:21:05Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9912399.pdf: 6214552 bytes, checksum: c2398098af007f8aa4b5c6d5a9aeaeac (MD5) Previous issue date: 1998","Embargo set by: Seth Robbins for item 83217 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","133 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998."]},{"key":"dc:title","label":"Title","values":["Optimizing Memory-Resident Decision Support System Workloads for Cache Memories"]}]}],"canonical_facts":{"dc:contributor":["Torrellas, Josep"],"dc:creator":["Trancoso, Pedro P.M."],"dc:date":["2015-09-25T20:21:05Z","10000-01-01","1998"],"dc:description":["In the second part of this work cache optimizations are proposed for two database system components: algorithms and query optimizer. In the former, blocking and prefetching are applied to database algorithms. In the latter the first public domain cache-oriented query optimizer is proposed. This optimizer chooses the ordering of operations and implementation of those operations using the number of cache misses and the number of instructions as the metric. In an evaluation of the proposed optimizations using a real architecture, some complex queries show performance improvement over the existing optimizations. One query from a standard benchmark achieves 29% improvement while the average for five queries is 13%. While this improvement is moderate, these optimizations are implemented in the database system without any changes to the hardware. Therefore the proposed optimizations provide improvement at no additional cost. A sensitivity test showed that the improvement provided by the proposed optimizations is independent of changes to the cache configuration like cache size, line size, and miss penalty. Finally, the prefetching optimization doubles the performance improvement from 13% to 28% in average for all queries.","Made available in DSpace on 2015-09-25T20:21:05Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9912399.pdf: 6214552 bytes, checksum: c2398098af007f8aa4b5c6d5a9aeaeac (MD5) Previous issue date: 1998","Embargo set by: Seth Robbins for item 83217 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","133 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998."],"dc:identifier":["http://hdl.handle.net/2142/81936","(MiAaPQ)AAI9912399"],"dc:language":["eng"],"dc:subject":["Computer Science"],"dc:title":["Optimizing Memory-Resident Decision Support System Workloads for Cache Memories"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:17Z"}