A hybrid parallel Delaunay image-to-mesh conversion algorithm scalable on distributed-memory clusters
In this paper, we present a scalable three dimensional parallel Delaunay image-to-mesh conversion algorithm. A nested master–worker model is used to simultaneously explore process- and thread-level parallelization. The mesh generation includes two stages: coarse and fine meshing. First, a coarse mes...
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| Vydáno v: | Computer aided design Ročník 103; s. 34 - 46 |
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01.10.2018
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| ISSN: | 0010-4485, 1879-2685 |
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| Abstract | In this paper, we present a scalable three dimensional parallel Delaunay image-to-mesh conversion algorithm. A nested master–worker model is used to simultaneously explore process- and thread-level parallelization. The mesh generation includes two stages: coarse and fine meshing. First, a coarse mesh is constructed in parallel by the threads of the master process. Then the coarse mesh is partitioned. Finally, the fine mesh refinement procedure is executed until all the elements in the mesh satisfy the quality and fidelity criteria. The communication and computation are separated during the fine mesh refinement procedure. The master thread of each process that initializes the MPI environment is in charge of the inter-node MPI communication for data (submesh) movement while the worker threads of each process are responsible for the local mesh refinement within the node. We conducted a set of experiments to test the performance of the algorithm on distributed memory clusters and observed that the granularity of coarse level data decomposition, which affects the coarse level concurrency, has a significant influence on the performance of the algorithm. With the proper value of granularity, the algorithm is scalable to 45 distributed memory compute nodes (900 cores).
•The Hybrid MPI+Threads method is a hybrid programming image-to-mesh conversion algorithm.•The Hybrid MPI+Threads method creates meshes with quality and fidelity guarantees.•The Hybrid MPI+Threads method supports parallel non-uniform mesh generation for multi-material objects.•The Hybrid MPI+Threads method is a scalable parallel meshing algorithm on distributed multi-core node clusters. |
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| AbstractList | In this paper, we present a scalable three dimensional parallel Delaunay image-to-mesh conversion algorithm. A nested master–worker model is used to simultaneously explore process- and thread-level parallelization. The mesh generation includes two stages: coarse and fine meshing. First, a coarse mesh is constructed in parallel by the threads of the master process. Then the coarse mesh is partitioned. Finally, the fine mesh refinement procedure is executed until all the elements in the mesh satisfy the quality and fidelity criteria. The communication and computation are separated during the fine mesh refinement procedure. The master thread of each process that initializes the MPI environment is in charge of the inter-node MPI communication for data (submesh) movement while the worker threads of each process are responsible for the local mesh refinement within the node. We conducted a set of experiments to test the performance of the algorithm on distributed memory clusters and observed that the granularity of coarse level data decomposition, which affects the coarse level concurrency, has a significant influence on the performance of the algorithm. With the proper value of granularity, the algorithm is scalable to 45 distributed memory compute nodes (900 cores).
•The Hybrid MPI+Threads method is a hybrid programming image-to-mesh conversion algorithm.•The Hybrid MPI+Threads method creates meshes with quality and fidelity guarantees.•The Hybrid MPI+Threads method supports parallel non-uniform mesh generation for multi-material objects.•The Hybrid MPI+Threads method is a scalable parallel meshing algorithm on distributed multi-core node clusters. In this paper, we present a scalable three dimensional parallel Delaunay image-to-mesh conversion algorithm. A nested master–worker model is used to simultaneously explore process- and thread-level parallelization. The mesh generation includes two stages: coarse and fine meshing. First, a coarse mesh is constructed in parallel by the threads of the master process. Then the coarse mesh is partitioned. Finally, the fine mesh refinement procedure is executed until all the elements in the mesh satisfy the quality and fidelity criteria. The communication and computation are separated during the fine mesh refinement procedure. The master thread of each process that initializes the MPI environment is in charge of the inter-node MPI communication for data (submesh) movement while the worker threads of each process are responsible for the local mesh refinement within the node. We conducted a set of experiments to test the performance of the algorithm on distributed memory clusters and observed that the granularity of coarse level data decomposition, which affects the coarse level concurrency, has a significant influence on the performance of the algorithm. With the proper value of granularity, the algorithm is scalable to 45 distributed memory compute nodes (900 cores). |
| Author | Chrisochoides, Nikos P. Feng, Daming Chernikov, Andrey N. |
| Author_xml | – sequence: 1 givenname: Daming surname: Feng fullname: Feng, Daming email: dfeng@cs.odu.edu – sequence: 2 givenname: Andrey N. surname: Chernikov fullname: Chernikov, Andrey N. email: achernik@cs.odu.edu – sequence: 3 givenname: Nikos P. surname: Chrisochoides fullname: Chrisochoides, Nikos P. email: nikos@cs.odu.edu |
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| CitedBy_id | crossref_primary_10_1016_j_cma_2020_113634 crossref_primary_10_1016_j_parco_2020_102672 crossref_primary_10_1016_j_cma_2020_112881 crossref_primary_10_2514_1_J057657 crossref_primary_10_1007_s00366_024_01994_0 crossref_primary_10_1016_j_finel_2022_103723 crossref_primary_10_2514_1_J060270 |
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| Snippet | In this paper, we present a scalable three dimensional parallel Delaunay image-to-mesh conversion algorithm. A nested master–worker model is used to... |
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| SubjectTerms | 3-D technology Algorithms Cluster analysis Clusters Computer memory Concurrency Conversion Distributed memory Distributed shared memory Finite element method Grid refinement (mathematics) Hybrid programming Image-to-mesh conversion Mesh generation Parallel mesh generation Parallel processing Three dimensional models Two-level parallelization |
| Title | A hybrid parallel Delaunay image-to-mesh conversion algorithm scalable on distributed-memory clusters |
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