{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129273"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129273","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Malleable parallel computing with Ray: a runtime framework for dynamic resource management in iterative solvers","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_has_math":false,"creators":["Yuan, Yue"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Kindratenko, Volodymyr"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-04-29","date_published":"2025-04-29","updated_at":"2026-07-22T22:25:04Z","subjects":["Malleable MPI","Ray","Cloud"],"languages":["en","eng"],"rights":["Copyright 2025 Yue Yuan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129273","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kindratenko, Volodymyr"]},{"key":"dc:creator","label":"Author","values":["Yuan, Yue"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-04-29","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Malleable MPI","Ray","Cloud"]}]},{"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 2025 Yue Yuan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129273"]}]},{"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 2025-10-19 without embargo terms","The student, Yue Yuan, accepted the attached license on 2025-04-29 at 07:53.","The student, Yue Yuan, submitted this Thesis for approval on 2025-04-29 at 08:14.","This Thesis was approved for publication on 2025-04-29 at 16:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22064 on 2025-10-19 at 18:11:13","With the increasing demand for scalable parallel computing, frameworks such as MPI have been widely used for scientific research. However, emerging distributed computing frameworks such as Ray provide new opportunities for more flexible and dynamic resource management. This thesis presents an MPI-like framework implemented in Ray which provides one way to migrate the MPI applications to the Ray platform. Our framework features a dynamic process allocation mechanism that empowers process migration by allowing the application’s processes to expand or shrink. This thesis also implements the V-cycle multigrid method, a widely used solver for large-scale linear systems, to evaluate the performance and scalability of our framework. Our experiments demonstrate the trade-offs between static and dynamic resource allocation, highlighting the impact on computational efficiency. We show that while MPI provides strong performance guarantees under fixed workloads, Ray’s flexibility in rank adaptation can significantly improve resource utilization in dynamic environments. Our results provide valuable insights into the feasibility of using Ray as an alternative to MPI for large-scale scientific computing, particularly in scenarios where dynamic load balancing and resource elasticity are crucial."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Malleable parallel computing with Ray: a runtime framework for dynamic resource management in iterative solvers"]}]}],"canonical_facts":{"dc:contributor":["Kindratenko, Volodymyr"],"dc:creator":["Yuan, Yue"],"dc:date":["2025-04-29","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Yue Yuan, accepted the attached license on 2025-04-29 at 07:53.","The student, Yue Yuan, submitted this Thesis for approval on 2025-04-29 at 08:14.","This Thesis was approved for publication on 2025-04-29 at 16:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22064 on 2025-10-19 at 18:11:13","With the increasing demand for scalable parallel computing, frameworks such as MPI have been widely used for scientific research. However, emerging distributed computing frameworks such as Ray provide new opportunities for more flexible and dynamic resource management. This thesis presents an MPI-like framework implemented in Ray which provides one way to migrate the MPI applications to the Ray platform. Our framework features a dynamic process allocation mechanism that empowers process migration by allowing the application’s processes to expand or shrink. This thesis also implements the V-cycle multigrid method, a widely used solver for large-scale linear systems, to evaluate the performance and scalability of our framework. Our experiments demonstrate the trade-offs between static and dynamic resource allocation, highlighting the impact on computational efficiency. We show that while MPI provides strong performance guarantees under fixed workloads, Ray’s flexibility in rank adaptation can significantly improve resource utilization in dynamic environments. Our results provide valuable insights into the feasibility of using Ray as an alternative to MPI for large-scale scientific computing, particularly in scenarios where dynamic load balancing and resource elasticity are crucial."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129273"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Yue Yuan"],"dc:subject":["Malleable MPI","Ray","Cloud"],"dc:title":["Malleable parallel computing with Ray: a runtime framework for dynamic resource management in iterative solvers"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}