Dynamic Load Balancing with the Parallel Partitioning Tool GridSpiderPar
Dynamic adaptive meshes are often used in high-performance computing. A mesh is locally refined or derefined in spots of interest or where high gradients of an objective function arise. To balance the load of processors, it is required to periodically repartition it. Dynamic load balancing algorithm...
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| Vydáno v: | Mathematical models and computer simulations Ročník 14; číslo 6; s. 910 - 917 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Moscow
Pleiades Publishing
01.12.2022
Springer Nature B.V |
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| ISSN: | 2070-0482, 2070-0490 |
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| Abstract | Dynamic adaptive meshes are often used in high-performance computing. A mesh is locally refined or derefined in spots of interest or where high gradients of an objective function arise. To balance the load of processors, it is required to periodically repartition it. Dynamic load balancing algorithms are developed based on the parallel geometric algorithm of mesh partitioning and the parallel incremental algorithm of graph partitioning using the partitioning tool GridSpiderPar. The initial partition of a mesh with a local refinement (6.7 × 10
6
hexahedrons) is compared with the results of repartitioning using the devised algorithms. A comparison of the results shows the advantages of the parallel geometric algorithm on this mesh and the features of using the parallel incremental algorithm. |
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| AbstractList | Dynamic adaptive meshes are often used in high-performance computing. A mesh is locally refined or derefined in spots of interest or where high gradients of an objective function arise. To balance the load of processors, it is required to periodically repartition it. Dynamic load balancing algorithms are developed based on the parallel geometric algorithm of mesh partitioning and the parallel incremental algorithm of graph partitioning using the partitioning tool GridSpiderPar. The initial partition of a mesh with a local refinement (6.7 × 106 hexahedrons) is compared with the results of repartitioning using the devised algorithms. A comparison of the results shows the advantages of the parallel geometric algorithm on this mesh and the features of using the parallel incremental algorithm. Dynamic adaptive meshes are often used in high-performance computing. A mesh is locally refined or derefined in spots of interest or where high gradients of an objective function arise. To balance the load of processors, it is required to periodically repartition it. Dynamic load balancing algorithms are developed based on the parallel geometric algorithm of mesh partitioning and the parallel incremental algorithm of graph partitioning using the partitioning tool GridSpiderPar. The initial partition of a mesh with a local refinement (6.7 × 10 6 hexahedrons) is compared with the results of repartitioning using the devised algorithms. A comparison of the results shows the advantages of the parallel geometric algorithm on this mesh and the features of using the parallel incremental algorithm. |
| Author | Golovchenko, E. N. |
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| Cites_doi | 10.26089/NumMet.v16r448 10.1016/S0167-8191(00)00048-X 10.1006/jpdc.1997.1407 10.1109/71.926167 10.1109/IPDPS.2007.370258 10.1109/SC.2000.1003 10.1109/71.780863 |
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| Copyright | Pleiades Publishing, Ltd. 2022. ISSN 2070-0482, Mathematical Models and Computer Simulations, 2022, Vol. 14, No. 6, pp. 910–917. © Pleiades Publishing, Ltd., 2022. Russian Text © The Author(s), 2022, published in Matematicheskoe Modelirovanie, 2022, Vol. 34, No. 4, pp. 59–69. |
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| Keywords | graph partitioning mesh partitioning high-performance computing |
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| References | SchloegelK.KarypisG.KumarV.Wavefront diffusion and LMSR: algorithms for dynamic repartitioning of adaptive meshesIEEE Trans. Parallel Distrib. Syst.20011245146610.1109/71.926167 E. N. Golovchenko, M. A. Kornilina, and M. V. Yakobovskiy, “Algorithms in the parallel partitioning tool GridSpiderPar for large mesh decomposition,” in Proc. 3rd Int. Conf. on Exascale Applications and Software (EASC 2015), Edinburgh, UK, April 21–23,2015, pp. 120–125. E. N. Golovchenko, “Decomposition of computational grids for solving continuum problems on high-performance computing systems,” Candidate’s Dissertation in Mathematics and Physics (Keldysh Inst. of Applied Mathematics, Russ. Acad. Sci., Moscow, 2014) [in Russian]. CatalyurekU. V.AykanatC.Hypergraph-partitioning based decomposition for parallel sparse-matrix vector multiplicationIEEE Trans. Parallel Distrib. Syst.19991067369310.1109/71.780863 U. V. Catalyurek, E. G. Boman, K. D. Devine, D. Bozdag, R. Heaphy, L. A. Riesen, “Hypergraph-based dynamic load balancing for adaptive scientific scientific computations,” in Proc. 21st International Parallel and Distributed Processing Symposium (IPDPS 2007), Long Beach, CA, March 26–30,2007, pp. 1–11. https://doi.org/10.1109/IPDPS.2007.370258 K. Schloegel, G. Karypis, and V. Kumar, “A unified algorithm for load-balancing adaptive scientific simulation,” in Proc. 2000 ACM / IEEE Conference on Supercomputing (SC’00), Dallas, TX, November 4–10,2000, pp. 1–11. https://doi.org/10.1109/SC.2000.1003 HendricksonB.KoldaT. G.Graph partitioning models for parallel computingParallel Comput.20002615191534178693810.1016/S0167-8191(00)00048-X0948.68130 E. N. Golovchenko and M. V. Yakobovskii, “Parallel partitioning tool GridSpiderPar for large mesh decomposition,” Vychisl. Metody Program. 16 (4), 507–517 (2015). WalshawC.CrossM.EverettM. G.“Parallel dynamic graph-partitioning for unstructured meshes,” Mathematics Research Report 97/IM/20 (Centre for Numerical Modelling and Process Analysis, University of Greenwich, UK, 1997), pp. 1–10J. Parallel Distrib. Comput.19974710210810.1006/jpdc.1997.1407 4392_CR1 B. Hendrickson (4392_CR6) 2000; 26 K. Schloegel (4392_CR2) 2001; 12 4392_CR4 U. V. Catalyurek (4392_CR5) 1999; 10 4392_CR9 4392_CR8 4392_CR7 C. Walshaw (4392_CR3) 1997; 47 |
| References_xml | – reference: HendricksonB.KoldaT. G.Graph partitioning models for parallel computingParallel Comput.20002615191534178693810.1016/S0167-8191(00)00048-X0948.68130 – reference: K. Schloegel, G. Karypis, and V. Kumar, “A unified algorithm for load-balancing adaptive scientific simulation,” in Proc. 2000 ACM / IEEE Conference on Supercomputing (SC’00), Dallas, TX, November 4–10,2000, pp. 1–11. https://doi.org/10.1109/SC.2000.1003 – reference: WalshawC.CrossM.EverettM. G.“Parallel dynamic graph-partitioning for unstructured meshes,” Mathematics Research Report 97/IM/20 (Centre for Numerical Modelling and Process Analysis, University of Greenwich, UK, 1997), pp. 1–10J. Parallel Distrib. Comput.19974710210810.1006/jpdc.1997.1407 – reference: U. V. Catalyurek, E. G. Boman, K. D. Devine, D. Bozdag, R. Heaphy, L. A. Riesen, “Hypergraph-based dynamic load balancing for adaptive scientific scientific computations,” in Proc. 21st International Parallel and Distributed Processing Symposium (IPDPS 2007), Long Beach, CA, March 26–30,2007, pp. 1–11. https://doi.org/10.1109/IPDPS.2007.370258 – reference: SchloegelK.KarypisG.KumarV.Wavefront diffusion and LMSR: algorithms for dynamic repartitioning of adaptive meshesIEEE Trans. Parallel Distrib. Syst.20011245146610.1109/71.926167 – reference: CatalyurekU. V.AykanatC.Hypergraph-partitioning based decomposition for parallel sparse-matrix vector multiplicationIEEE Trans. Parallel Distrib. Syst.19991067369310.1109/71.780863 – reference: E. N. Golovchenko, M. A. Kornilina, and M. V. Yakobovskiy, “Algorithms in the parallel partitioning tool GridSpiderPar for large mesh decomposition,” in Proc. 3rd Int. Conf. on Exascale Applications and Software (EASC 2015), Edinburgh, UK, April 21–23,2015, pp. 120–125. – reference: E. N. Golovchenko and M. V. Yakobovskii, “Parallel partitioning tool GridSpiderPar for large mesh decomposition,” Vychisl. Metody Program. 16 (4), 507–517 (2015). – reference: E. N. Golovchenko, “Decomposition of computational grids for solving continuum problems on high-performance computing systems,” Candidate’s Dissertation in Mathematics and Physics (Keldysh Inst. of Applied Mathematics, Russ. Acad. Sci., Moscow, 2014) [in Russian]. – ident: 4392_CR9 – ident: 4392_CR8 doi: 10.26089/NumMet.v16r448 – volume: 26 start-page: 1519 year: 2000 ident: 4392_CR6 publication-title: Parallel Comput. doi: 10.1016/S0167-8191(00)00048-X – volume: 47 start-page: 102 year: 1997 ident: 4392_CR3 publication-title: J. Parallel Distrib. Comput. doi: 10.1006/jpdc.1997.1407 – ident: 4392_CR7 – volume: 12 start-page: 451 year: 2001 ident: 4392_CR2 publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/71.926167 – ident: 4392_CR4 doi: 10.1109/IPDPS.2007.370258 – ident: 4392_CR1 doi: 10.1109/SC.2000.1003 – volume: 10 start-page: 673 year: 1999 ident: 4392_CR5 publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/71.780863 |
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| SubjectTerms | Dynamic loads Geometric algorithms Load balancing Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Mesh partitioning Partitioning Simulation and Modeling |
| Title | Dynamic Load Balancing with the Parallel Partitioning Tool GridSpiderPar |
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