{"id":{"repo_id":"qu-belfast","oai_identifier":"oai:pure.qub.ac.uk/portal:studenttheses/22a3cdcb-3773-4117-a06d-23f031539a36"},"canonical_url":"https://search.dev.ndltd.org/etd/qu-belfast/oai:pure.qub.ac.uk/portal:studenttheses/22a3cdcb-3773-4117-a06d-23f031539a36","repository":{"repo_id":"qu-belfast","name":"Queen's University Belfast","base_url":"https://pureadmin.qub.ac.uk/ws/oai"},"display":{"title":"Software-defined Significance-Driven Computing","abstract":"Approximate computing has been an emerging programming and system design paradigm that has been proposed as a way to overcome the
power-wall problem that hinders the scaling of the next generation of both high-end and mobile computing systems. Towards this
end, a lot of researchers have been studying the effects of approximation to applications and those hardware modifications that
allow increased power benefits for reduced reliability. In this work, we focus on runtime system modifications and task-based
programming models that enable software-controlled, user-driven approximate computing.
We employ a systematic methodology that allows us to evaluate the potential energy and performance benefits of approximate
computing using as building blocks unreliable hardware components. We present a set of extensions to OpenMP 4.0 that enable the
programmer to define computations suitable for approximation. We introduce task-significance, a novel concept that describes the
contribution of a task to the quality of the result. We use significance as a channel of communication from domain specific
knowledge about applications towards the runtime-system, where we can optimise approximate execution depending on user
constraints.
Finally, we show extensions to the Linux kernel that enable it to operate seamlessly on top of unreliable memory and provide a
user-space interface for memory allocation from the unreliable portion of the physical memory. Having this framework in place
allowed us to identify what we call the refresh-by-access property of applications that use dynamic random-access memory (DRAM).
We use this property to implement techniques for task-based applications that minimise the probability of errors when using
unreliable memory enabling increased quality and power efficiency when using unreliable DRAM.","abstract_html":"Approximate computing has been an emerging programming and system design paradigm that has been proposed as a way to overcome the <br/>power-wall problem that hinders the scaling of the next generation of both high-end and mobile computing systems. Towards this<br/>end, a lot of researchers have been studying the effects of approximation to applications and those hardware modifications that<br/>allow increased power benefits for reduced reliability. In this work, we focus on runtime system modifications and task-based<br/>programming models that enable software-controlled, user-driven approximate computing.<br/><br/>We employ a systematic methodology that allows us to evaluate the potential energy and performance benefits of approximate<br/>computing using as building blocks unreliable hardware components. We present a set of extensions to OpenMP 4.0 that enable the <br/>programmer to define computations suitable for approximation. We introduce task-significance, a novel concept that describes the <br/>contribution of a task to the quality of the result. We use significance as a channel of communication from domain specific<br/>knowledge about applications towards the runtime-system, where we can optimise approximate execution depending on user<br/>constraints.<br/><br/>Finally, we show extensions to the Linux kernel that enable it to operate seamlessly on top of unreliable memory and provide a<br/>user-space interface for memory allocation from the unreliable portion of the physical memory. Having this framework in place<br/>allowed us to identify what we call the refresh-by-access property of applications that use dynamic random-access memory (DRAM).<br/>We use this property to implement techniques for task-based applications that minimise the probability of errors when using<br/>unreliable memory enabling increased quality and power efficiency when using unreliable DRAM.","abstract_has_math":false,"creators":["Chalios, Charalambos"],"institution":"Queen's University Belfast","degree_name":"Doctor of Philosophy","degree_level":"Doctoral Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Vandierendonck, Hans"],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-12-11","date_published":"2017-12-11","updated_at":"2026-07-24T03:54:53Z","subjects":["approximate computing","significance-based computing","parallel computing","reliability","task-based programming","DRAM","scheduling","linux"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:pure.qub.ac.uk/portal:studenttheses/22a3cdcb-3773-4117-a06d-23f031539a36"],"render_values":[{"text":"oai:pure.qub.ac.uk/portal:studenttheses/22a3cdcb-3773-4117-a06d-23f031539a36","href":null,"code":true}]}]},"links":{"outbound_url":"https://pure.qub.ac.uk/en/studentTheses/22a3cdcb-3773-4117-a06d-23f031539a36","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Vandierendonck, Hans"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["European Commission","Northern Ireland Department for the Economy"]},{"key":"dc:creator","label":"Author","values":["Chalios, Charalambos"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-12-11"]},{"key":"dc:date.issued","label":"Date","values":["2017-12-11"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Electronics, Electrical Engineering and Computer Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Queen's University Belfast"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://pure.qub.ac.uk/en/studentTheses/22a3cdcb-3773-4117-a06d-23f031539a36"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral Thesis"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["approximate computing","significance-based computing","parallel computing","reliability","task-based programming","DRAM","scheduling","linux"]}]},{"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":["oai:pure.qub.ac.uk/portal:studenttheses/22a3cdcb-3773-4117-a06d-23f031539a36","https://pure.qub.ac.uk/en/studentTheses/22a3cdcb-3773-4117-a06d-23f031539a36"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://pure.qub.ac.uk/files/148227326/thesis_hardbound.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Approximate computing has been an emerging programming and system design paradigm that has been proposed as a way to overcome the
power-wall problem that hinders the scaling of the next generation of both high-end and mobile computing systems. Towards this
end, a lot of researchers have been studying the effects of approximation to applications and those hardware modifications that
allow increased power benefits for reduced reliability. In this work, we focus on runtime system modifications and task-based
programming models that enable software-controlled, user-driven approximate computing.
We employ a systematic methodology that allows us to evaluate the potential energy and performance benefits of approximate
computing using as building blocks unreliable hardware components. We present a set of extensions to OpenMP 4.0 that enable the
programmer to define computations suitable for approximation. We introduce task-significance, a novel concept that describes the
contribution of a task to the quality of the result. We use significance as a channel of communication from domain specific
knowledge about applications towards the runtime-system, where we can optimise approximate execution depending on user
constraints.
Finally, we show extensions to the Linux kernel that enable it to operate seamlessly on top of unreliable memory and provide a
user-space interface for memory allocation from the unreliable portion of the physical memory. Having this framework in place
allowed us to identify what we call the refresh-by-access property of applications that use dynamic random-access memory (DRAM).
We use this property to implement techniques for task-based applications that minimise the probability of errors when using
unreliable memory enabling increased quality and power efficiency when using unreliable DRAM."]},{"key":"dc:title","label":"Title","values":["Software-defined Significance-Driven Computing"]}]}],"canonical_facts":{"dc:contributor.advisor":["Vandierendonck, Hans"],"dc:contributor.sponsor":["European Commission","Northern Ireland Department for the Economy"],"dc:creator":["Chalios, Charalambos"],"dc:date":["2017-12-11"],"dc:date.issued":["2017-12-11"],"dc:description.abstract":["Approximate computing has been an emerging programming and system design paradigm that has been proposed as a way to overcome the
power-wall problem that hinders the scaling of the next generation of both high-end and mobile computing systems. Towards this
end, a lot of researchers have been studying the effects of approximation to applications and those hardware modifications that
allow increased power benefits for reduced reliability. In this work, we focus on runtime system modifications and task-based
programming models that enable software-controlled, user-driven approximate computing.
We employ a systematic methodology that allows us to evaluate the potential energy and performance benefits of approximate
computing using as building blocks unreliable hardware components. We present a set of extensions to OpenMP 4.0 that enable the
programmer to define computations suitable for approximation. We introduce task-significance, a novel concept that describes the
contribution of a task to the quality of the result. We use significance as a channel of communication from domain specific
knowledge about applications towards the runtime-system, where we can optimise approximate execution depending on user
constraints.
Finally, we show extensions to the Linux kernel that enable it to operate seamlessly on top of unreliable memory and provide a
user-space interface for memory allocation from the unreliable portion of the physical memory. Having this framework in place
allowed us to identify what we call the refresh-by-access property of applications that use dynamic random-access memory (DRAM).
We use this property to implement techniques for task-based applications that minimise the probability of errors when using
unreliable memory enabling increased quality and power efficiency when using unreliable DRAM."],"dc:identifier":["oai:pure.qub.ac.uk/portal:studenttheses/22a3cdcb-3773-4117-a06d-23f031539a36","https://pure.qub.ac.uk/en/studentTheses/22a3cdcb-3773-4117-a06d-23f031539a36"],"dc:identifier.uri":["https://pure.qub.ac.uk/files/148227326/thesis_hardbound.pdf"],"dc:language":["eng"],"dc:publisher.department":["School of Electronics, Electrical Engineering and Computer Science"],"dc:publisher.institution":["Queen's University Belfast"],"dc:relation.isreferencedby":["https://pure.qub.ac.uk/en/studentTheses/22a3cdcb-3773-4117-a06d-23f031539a36"],"dc:subject":["approximate computing","significance-based computing","parallel computing","reliability","task-based programming","DRAM","scheduling","linux"],"dc:title":["Software-defined Significance-Driven Computing"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral Thesis"],"dc:type.qualificationname":["Doctor of Philosophy"]},"updated_at":"2026-07-24T03:54:53Z"}