{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1616"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1616","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"A fault tolerant grid generation technique","abstract":"Automatic and parallel mesh generation has been highlighted as a bottleneck for large scale automated Computational Fluid Dynamics analysis. The desire for large scale automated CFD is driven by the growing computational capabilities in large scale supercomputers. Unfortunately, as compute clusters grow in size, they also suffer more failures. Left unchecked, the increased frequency of failures may stymie any efforts to fully utilize these machines. This work aims to tackle one component required for automated large scale engineering analysis by developing a fault tolerant mesh generator. The mesh generator uses a novel com- munication layer written using the transport layer ZeroMQ and is made fault tolerant through an integrated in-memory checkpoint and recovery strategy. Benefits of using in-memory checkpoints vs traditional in-disk checkpoints are discussed. By relying on in-memory checkpointing, it is demonstrated that the mesh generator to be capable of generating Cartesian meshes in parallel. The generator continues to operate even while the compute cluster it is running suffers failures. The generator is shown to be high performing, including being capable of generating an 8.6 billion element mesh in just over 1 minute while creating multiple in-memory checkpoints.","abstract_html":"Automatic and parallel mesh generation has been highlighted as a bottleneck for large scale automated Computational Fluid Dynamics analysis. The desire for large scale automated CFD is driven by the growing computational capabilities in large scale supercomputers. Unfortunately, as compute clusters grow in size, they also suffer more failures. Left unchecked, the increased frequency of failures may stymie any efforts to fully utilize these machines. This work aims to tackle one component required for automated large scale engineering analysis by developing a fault tolerant mesh generator. The mesh generator uses a novel com- munication layer written using the transport layer ZeroMQ and is made fault tolerant through an integrated in-memory checkpoint and recovery strategy. Benefits of using in-memory checkpoints vs traditional in-disk checkpoints are discussed. By relying on in-memory checkpointing, it is demonstrated that the mesh generator to be capable of generating Cartesian meshes in parallel. The generator continues to operate even while the compute cluster it is running suffers failures. The generator is shown to be high performing, including being capable of generating an 8.6 billion element mesh in just over 1 minute while creating multiple in-memory checkpoints.","abstract_has_math":false,"creators":["O'Connell, Matthew D."],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Karman, Steve L., Jr.","Newman, James C. III; Park, Michael A.; Tanis, Craig R.","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:46:28Z","subjects":["Numerical grid generation (Numerical analysis)"],"languages":["English","eng"],"rights":[],"rights_urls":["https://rightsstatements.org/page/InC/1.0/?language=en"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/466","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Karman, Steve L., Jr.","Newman, James C. 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D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."]},{"key":"dc:description.abstract","label":"Abstract","values":["Automatic and parallel mesh generation has been highlighted as a bottleneck for large scale automated Computational Fluid Dynamics analysis. The desire for large scale automated CFD is driven by the growing computational capabilities in large scale supercomputers. Unfortunately, as compute clusters grow in size, they also suffer more failures. Left unchecked, the increased frequency of failures may stymie any efforts to fully utilize these machines. This work aims to tackle one component required for automated large scale engineering analysis by developing a fault tolerant mesh generator. The mesh generator uses a novel com- munication layer written using the transport layer ZeroMQ and is made fault tolerant through an integrated in-memory checkpoint and recovery strategy. Benefits of using in-memory checkpoints vs traditional in-disk checkpoints are discussed. By relying on in-memory checkpointing, it is demonstrated that the mesh generator to be capable of generating Cartesian meshes in parallel. The generator continues to operate even while the compute cluster it is running suffers failures. The generator is shown to be high performing, including being capable of generating an 8.6 billion element mesh in just over 1 minute while creating multiple in-memory checkpoints."]},{"key":"dc:title","label":"Title","values":["A fault tolerant grid generation technique"]}]}],"canonical_facts":{"dc:contributor":["Karman, Steve L., Jr.","Newman, James C. III; Park, Michael A.; Tanis, Craig R.","College of Engineering and Computer Science"],"dc:creator":["O'Connell, Matthew D."],"dc:date":["2016-08-01T07:00:00Z"],"dc:description":["Dept. of Computational Engineering","Ph. D.; A dissertation submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Doctor of Philosophy."],"dc:description.abstract":["Automatic and parallel mesh generation has been highlighted as a bottleneck for large scale automated Computational Fluid Dynamics analysis. The desire for large scale automated CFD is driven by the growing computational capabilities in large scale supercomputers. Unfortunately, as compute clusters grow in size, they also suffer more failures. Left unchecked, the increased frequency of failures may stymie any efforts to fully utilize these machines. This work aims to tackle one component required for automated large scale engineering analysis by developing a fault tolerant mesh generator. The mesh generator uses a novel com- munication layer written using the transport layer ZeroMQ and is made fault tolerant through an integrated in-memory checkpoint and recovery strategy. Benefits of using in-memory checkpoints vs traditional in-disk checkpoints are discussed. By relying on in-memory checkpointing, it is demonstrated that the mesh generator to be capable of generating Cartesian meshes in parallel. The generator continues to operate even while the compute cluster it is running suffers failures. The generator is shown to be high performing, including being capable of generating an 8.6 billion element mesh in just over 1 minute while creating multiple in-memory checkpoints."],"dc:identifier":["https://scholar.utc.edu/theses/466"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["https://rightsstatements.org/page/InC/1.0/?language=en"],"dc:subject":["Numerical grid generation (Numerical analysis)"],"dc:title":["A fault tolerant grid generation technique"],"dc:type":["Doctoral dissertations","Text"]},"updated_at":"2026-07-24T05:46:28Z"}