{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101222"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101222","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Scalable asynchronous connected components detection based on a parallel union-find algorithm","abstract":"Connectivity in a graph is a well-studied problem. Various parallel algorithms to detect and label connected components exist, many of which are optimized for a shared-memory environment. However, scientific and engineering applications today process large-scale graphs that do not fit in a single compute node. This calls for a highly scalable solution to the connectivity problem. We propose a novel distributed-memory parallel algorithm based on the Union-Find data structure and asynchronous messaging. We strengthen the scalability of our approach by introducing several optimization techniques for parallel execution. The algorithm is implemented as a library using Charm++, a migratable object-based parallel programming model, allowing any Charm++ application to easily perform connected components detection. MPI applications may also use the library either via Adaptive MPI, or by using interoperability features of Charm++. In addition, the library will also support reading data from the disk. As a driving use case we utilize the library in ChaNGa, a cosmological simulation framework, to detect clusters of stars and classify galaxies. We evaluate the performance of our algorithm for real and synthetic graphs, computing connectivity on a probabilistic mesh benchmark with over 250 million edges in under 10 seconds using 4,096 cores of the Blue Waters (Cray XE) Supercomputer.","abstract_html":"Connectivity in a graph is a well-studied problem. Various parallel algorithms to detect and label connected components exist, many of which are optimized for a shared-memory environment. However, scientific and engineering applications today process large-scale graphs that do not fit in a single compute node. This calls for a highly scalable solution to the connectivity problem. We propose a novel distributed-memory parallel algorithm based on the Union-Find data structure and asynchronous messaging. We strengthen the scalability of our approach by introducing several optimization techniques for parallel execution. The algorithm is implemented as a library using Charm++, a migratable object-based parallel programming model, allowing any Charm++ application to easily perform connected components detection. MPI applications may also use the library either via Adaptive MPI, or by using interoperability features of Charm++. In addition, the library will also support reading data from the disk. As a driving use case we utilize the library in ChaNGa, a cosmological simulation framework, to detect clusters of stars and classify galaxies. We evaluate the performance of our algorithm for real and synthetic graphs, computing connectivity on a probabilistic mesh benchmark with over 250 million edges in under 10 seconds using 4,096 cores of the Blue Waters (Cray XE) Supercomputer.","abstract_has_math":false,"creators":["Senthil Kumar Karthik, -"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kale, Laxmikant"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:36:54Z","date_published":"2018-09-04T20:36:54Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Scalable Graph Connectivity","Parallel Union-Find","Graph Clustering","HPC","Charm++"],"languages":["en"],"rights":["Copyright 2018 - Senthil Kumar Karthik"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101222","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kale, Laxmikant"]},{"key":"dc:creator","label":"Author","values":["Senthil Kumar Karthik, -"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:36:54Z","2020-09-05T09:15:09Z","2018-04-24","2018-05"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Scalable Graph Connectivity","Parallel Union-Find","Graph Clustering","HPC","Charm++"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 - Senthil Kumar Karthik"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101222"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Connectivity in a graph is a well-studied problem. Various parallel algorithms to detect and label connected components exist, many of which are optimized for a shared-memory environment. However, scientific and engineering applications today process large-scale graphs that do not fit in a single compute node. This calls for a highly scalable solution to the connectivity problem. We propose a novel distributed-memory parallel algorithm based on the Union-Find data structure and asynchronous messaging. We strengthen the scalability of our approach by introducing several optimization techniques for parallel execution. The algorithm is implemented as a library using Charm++, a migratable object-based parallel programming model, allowing any Charm++ application to easily perform connected components detection. MPI applications may also use the library either via Adaptive MPI, or by using interoperability features of Charm++. In addition, the library will also support reading data from the disk. As a driving use case we utilize the library in ChaNGa, a cosmological simulation framework, to detect clusters of stars and classify galaxies. We evaluate the performance of our algorithm for real and synthetic graphs, computing connectivity on a probabilistic mesh benchmark with over 250 million edges in under 10 seconds using 4,096 cores of the Blue Waters (Cray XE) Supercomputer.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, - Senthil Kumar Karthik, accepted the attached license on 2018-04-24 at 16:01.","The student, - Senthil Kumar Karthik, submitted this Thesis for approval on 2018-04-24 at 16:17.","This Thesis was approved for publication on 2018-04-24 at 17:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12454 on 2018-08-31 at 17:21:26","Made available in DSpace on 2018-09-04T20:36:54Z (GMT). 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Various parallel algorithms to detect and label connected components exist, many of which are optimized for a shared-memory environment. However, scientific and engineering applications today process large-scale graphs that do not fit in a single compute node. This calls for a highly scalable solution to the connectivity problem. We propose a novel distributed-memory parallel algorithm based on the Union-Find data structure and asynchronous messaging. We strengthen the scalability of our approach by introducing several optimization techniques for parallel execution. The algorithm is implemented as a library using Charm++, a migratable object-based parallel programming model, allowing any Charm++ application to easily perform connected components detection. MPI applications may also use the library either via Adaptive MPI, or by using interoperability features of Charm++. In addition, the library will also support reading data from the disk. As a driving use case we utilize the library in ChaNGa, a cosmological simulation framework, to detect clusters of stars and classify galaxies. We evaluate the performance of our algorithm for real and synthetic graphs, computing connectivity on a probabilistic mesh benchmark with over 250 million edges in under 10 seconds using 4,096 cores of the Blue Waters (Cray XE) Supercomputer.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-05-01","The student, - Senthil Kumar Karthik, accepted the attached license on 2018-04-24 at 16:01.","The student, - Senthil Kumar Karthik, submitted this Thesis for approval on 2018-04-24 at 16:17.","This Thesis was approved for publication on 2018-04-24 at 17:09.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12454 on 2018-08-31 at 17:21:26","Made available in DSpace on 2018-09-04T20:36:54Z (GMT). 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