{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116244"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116244","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Programming systems for safe and accurate parallel programs in the face of uncertainty","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Fernando, Vimuth"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Misailovic, Sasa","Torrellas, Josep","Mitra, Sayan","Carbin, Micheal"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["distributed programming","wireless network-on-chip","reliability and accuracy","approximate programming"],"languages":["en","eng"],"rights":["Copyright 2022 Vimuth Fernando"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116244","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Misailovic, Sasa","Torrellas, Josep","Mitra, Sayan","Carbin, Micheal"]},{"key":"dc:creator","label":"Author","values":["Fernando, Vimuth"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-14"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["distributed programming","wireless network-on-chip","reliability and accuracy","approximate programming"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Vimuth Fernando"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116244"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Vimuth Fernando, accepted the attached license on 2022-07-14 at 13:00.","The student, Vimuth Fernando, submitted this Dissertation for approval on 2022-07-14 at 15:15.","This Dissertation was approved for publication on 2022-07-14 at 16:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18308 on 2022-11-15 at 18:21:05","Many emerging distributed applications operate on inherently noisy data or produce approximate results. Emerging application domains, including IoT, self-driving cars, and precision agriculture, routinely need to deal with noise from their sensors and unreliable communication mediums. Furthermore, increased volume of data, and the rise of highly parallel and often heterogeneous systems have brought forth new challenges in overcoming bottlenecks in both computation and communication between processing units. Many prominent systems adopted approximation in communication to address these challenges. Developing software in the presence of these novel architectures, optimizations, and approximations can be a challenging task. As these systems get deployed in safety critical situations, it is important to verify that they behave in a predictable and safe manner, even in situations where the outcomes are uncertain. Developers need to ensure that the programs operating with noisy data do not result in unexpected crashes and produce acceptable results with high reliability. In recent years, researchers have designed several analyses for verifying these program properties in the presence of uncertainty. These prior works had stayed away from parallel programming models, in part due to the complexities involved with reasoning about parallel interactions. This dissertation presents an ecosystem of several programming language tools and techniques across the computational stack that provides foundations for safety and accuracy analyses of parallel programs that deal with uncertainty. First, the dissertation will present a software infrastructure that enables simple and efficient use of a novel architecture that speeds up communication in a manycore processor using a wireless network. Next, the dissertation will show how to use programming language techniques to reduce the complexity of verifying the correctness of a subset of asynchronous message passing parallel programs in Parallely. We show how to lift many existing analyses that are designed for sequential programs to the domain of parallel programs. Next, the dissertation presents how to further extend verification to bigger programs and newer error models using runtime monitoring in Diamont. Finally, the dissertation presents several case studies that look at extending runtime verification to recovery mechanisms, algorithmic fairness analysis, and a novel architecture with potentially erroneous wireless communication."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Programming systems for safe and accurate parallel programs in the face of uncertainty"]}]}],"canonical_facts":{"dc:contributor":["Misailovic, Sasa","Torrellas, Josep","Mitra, Sayan","Carbin, Micheal"],"dc:creator":["Fernando, Vimuth"],"dc:date":["2022-08","2022-07-14"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Vimuth Fernando, accepted the attached license on 2022-07-14 at 13:00.","The student, Vimuth Fernando, submitted this Dissertation for approval on 2022-07-14 at 15:15.","This Dissertation was approved for publication on 2022-07-14 at 16:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18308 on 2022-11-15 at 18:21:05","Many emerging distributed applications operate on inherently noisy data or produce approximate results. Emerging application domains, including IoT, self-driving cars, and precision agriculture, routinely need to deal with noise from their sensors and unreliable communication mediums. Furthermore, increased volume of data, and the rise of highly parallel and often heterogeneous systems have brought forth new challenges in overcoming bottlenecks in both computation and communication between processing units. Many prominent systems adopted approximation in communication to address these challenges. Developing software in the presence of these novel architectures, optimizations, and approximations can be a challenging task. As these systems get deployed in safety critical situations, it is important to verify that they behave in a predictable and safe manner, even in situations where the outcomes are uncertain. Developers need to ensure that the programs operating with noisy data do not result in unexpected crashes and produce acceptable results with high reliability. In recent years, researchers have designed several analyses for verifying these program properties in the presence of uncertainty. These prior works had stayed away from parallel programming models, in part due to the complexities involved with reasoning about parallel interactions. This dissertation presents an ecosystem of several programming language tools and techniques across the computational stack that provides foundations for safety and accuracy analyses of parallel programs that deal with uncertainty. First, the dissertation will present a software infrastructure that enables simple and efficient use of a novel architecture that speeds up communication in a manycore processor using a wireless network. Next, the dissertation will show how to use programming language techniques to reduce the complexity of verifying the correctness of a subset of asynchronous message passing parallel programs in Parallely. We show how to lift many existing analyses that are designed for sequential programs to the domain of parallel programs. Next, the dissertation presents how to further extend verification to bigger programs and newer error models using runtime monitoring in Diamont. Finally, the dissertation presents several case studies that look at extending runtime verification to recovery mechanisms, algorithmic fairness analysis, and a novel architecture with potentially erroneous wireless communication."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116244"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Vimuth Fernando"],"dc:subject":["distributed programming","wireless network-on-chip","reliability and accuracy","approximate programming"],"dc:title":["Programming systems for safe and accurate parallel programs in the face of uncertainty"],"dc:type":["text","Thesis"],"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":"2026-07-22T22:24:55Z"}