{"id":{"repo_id":"rice","oai_identifier":"oai:repository.rice.edu:1911/17126"},"canonical_url":"https://search.dev.ndltd.org/etd/rice/oai:repository.rice.edu:1911/17126","repository":{"repo_id":"rice","name":"Rice University","base_url":"https://repository.rice.edu/server/oai/request"},"display":{"title":"Automatic data aggregation for software distributed shared memory systems","abstract":"Software Distributed Shared Memory (DSM) provides a shared-memory abstraction on distributed memory hardware, making a parallel programmer's task easier. Unfortunately, software DSM is less efficient than the direct use of the underlying message-passing hardware. The chief reason for this is that hand-coded and compiler-generated message-passing programs typically achieve better data aggregation in their messages than programs using software DSM. Software DSM has poorer data aggregation because the system lacks the knowledge of the application's behavior that a programmer or compiler analysis can provide. We propose four new techniques to perform automatic data aggregation in software DSM. Our techniques use run-time analysis of past data-fetch accesses made by a processor, to aggregate data movement for future accesses. They do not need any additional compiler support. We implemented our techniques in the TreadMarks software DSM system. We used a test suite of four applications--3D-FFT, Barnes-Hut, Ilink and Shallow. For these applications we obtained 40% to 66% reduction in message counts which resulted in 6% to 19% improvement in execution times.","abstract_html":"Software Distributed Shared Memory (DSM) provides a shared-memory abstraction on distributed memory hardware, making a parallel programmer's task easier. Unfortunately, software DSM is less efficient than the direct use of the underlying message-passing hardware. The chief reason for this is that hand-coded and compiler-generated message-passing programs typically achieve better data aggregation in their messages than programs using software DSM. Software DSM has poorer data aggregation because the system lacks the knowledge of the application's behavior that a programmer or compiler analysis can provide. We propose four new techniques to perform automatic data aggregation in software DSM. Our techniques use run-time analysis of past data-fetch accesses made by a processor, to aggregate data movement for future accesses. They do not need any additional compiler support. We implemented our techniques in the TreadMarks software DSM system. We used a test suite of four applications--3D-FFT, Barnes-Hut, Ilink and Shallow. For these applications we obtained 40% to 66% reduction in message counts which resulted in 6% to 19% improvement in execution times.","abstract_has_math":false,"creators":["Rajamani, Karthick"],"institution":"Rice University","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Zwaenepoel, Willy"],"committee_chairs":[],"committee_members":[],"year":1997,"date_issued":"1997","date_published":"1997","updated_at":"2026-07-24T04:10:39Z","subjects":["Computer science"],"languages":["eng"],"rights":["Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1911/17126","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zwaenepoel, Willy"]},{"key":"dc:creator","label":"Author","values":["Rajamani, Karthick"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2009-06-04T08:04:52Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2009-06-04T08:04:52Z"]},{"key":"dc:date.issued","label":"Date","values":["1997"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Rice University"]}]},{"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.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright is held by the author, unless otherwise indicated. 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Software DSM has poorer data aggregation because the system lacks the knowledge of the application's behavior that a programmer or compiler analysis can provide. We propose four new techniques to perform automatic data aggregation in software DSM. Our techniques use run-time analysis of past data-fetch accesses made by a processor, to aggregate data movement for future accesses. They do not need any additional compiler support. We implemented our techniques in the TreadMarks software DSM system. We used a test suite of four applications--3D-FFT, Barnes-Hut, Ilink and Shallow. For these applications we obtained 40% to 66% reduction in message counts which resulted in 6% to 19% improvement in execution times."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Automatic data aggregation for software distributed shared memory systems"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zwaenepoel, Willy"],"dc:creator":["Rajamani, Karthick"],"dc:date.accessioned":["2009-06-04T08:04:52Z"],"dc:date.available":["2009-06-04T08:04:52Z"],"dc:date.issued":["1997"],"dc:description.abstract":["Software Distributed Shared Memory (DSM) provides a shared-memory abstraction on distributed memory hardware, making a parallel programmer's task easier. Unfortunately, software DSM is less efficient than the direct use of the underlying message-passing hardware. The chief reason for this is that hand-coded and compiler-generated message-passing programs typically achieve better data aggregation in their messages than programs using software DSM. Software DSM has poorer data aggregation because the system lacks the knowledge of the application's behavior that a programmer or compiler analysis can provide. We propose four new techniques to perform automatic data aggregation in software DSM. Our techniques use run-time analysis of past data-fetch accesses made by a processor, to aggregate data movement for future accesses. They do not need any additional compiler support. We implemented our techniques in the TreadMarks software DSM system. We used a test suite of four applications--3D-FFT, Barnes-Hut, Ilink and Shallow. For these applications we obtained 40% to 66% reduction in message counts which resulted in 6% to 19% improvement in execution times."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/1911/17126"],"dc:language.iso":["eng"],"dc:rights":["Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder."],"dc:subject":["Computer science"],"dc:title":["Automatic data aggregation for software distributed shared memory systems"],"dc:type":["Thesis"],"thesis:degree_discipline":["Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Rice University"]},"updated_at":"2026-07-24T04:10:39Z"}