Scotch

Scotch 3.1 User’s Guide. The efficient execution of a parallel program on a parallel machine requires good placement of the communicating processes of the program onto the processors of the machine. When both the program and the machine are modeled in terms of weighted unoriented graphs, this problem amounts to static graph mapping. This document describes the capabilities and operations of Scotch, a software package devoted to graph mapping, based on the Dual Recursive Bipartitioning algorithm. Predefined mapping strategies allow for recursive application of any of several graph bipartitioning methods, including Fiduccia-Mattheyses, Gibbs-Poole-Stockmeyer, and multi-level methods. Scotch can map any weighted process graph onto any weighted target graph, whether they are connected or not. We give brief descriptions of the algorithm and bipartitioning methods, detail the input/output formats, instructions for use, and installation procedures, and provide a number of examples.


References in zbMATH (referenced in 65 articles )

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  1. Creech, Angus C. W.; Jackson, Adrian; Maddison, James R.: Adapting and optimising fluidity for high-fidelity coastal modelling (2018)
  2. Breedveld, Sebastiaan; van den Berg, Bas; Heijmen, Ben: An interior-point implementation developed and tuned for radiation therapy treatment planning (2017)
  3. Fu, Lin; Hu, Xiangyu Y.; Adams, Nikolaus A.: A physics-motivated centroidal Voronoi particle domain decomposition method (2017)
  4. Haddar, Houssem; Jiang, Zixian; Riahi, Mohamed Kamel: A robust inversion method for quantitative 3D shape reconstruction from coaxial eddy current measurements (2017)
  5. Li, Ruipeng; Saad, Yousef: Low-rank correction methods for algebraic domain decomposition preconditioners (2017)
  6. Pichon, Gregoire; Faverge, Mathieu; Ramet, Pierre; Roman, Jean: Reordering strategy for blocking optimization in sparse linear solvers (2017)
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  8. Rietmann, Max; Grote, Marcus; Peter, Daniel; Schenk, Olaf: Newmark local time stepping on high-performance computing architectures (2017)
  9. Xin, Zixing; Xia, Jianlin; de Hoop, Maarten V.; Cauley, Stephen; Balakrishnan, Venkataramanan: A distributed-memory randomized structured multifrontal method for sparse direct solutions (2017)
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  11. Aminfar, AmirHossein; Ambikasaran, Sivaram; Darve, Eric: A fast block low-rank dense solver with applications to finite-element matrices (2016)
  12. Ghysels, Pieter; Li, Xiaoye S.; Rouet, François-Henry; Williams, Samuel; Napov, Artem: An efficient multicore implementation of a novel HSS-structured multifrontal solver using randomized sampling (2016)
  13. Kalantzis, Vassilis; Li, Ruipeng; Saad, Yousef: Spectral Schur complement techniques for symmetric eigenvalue problems (2016)
  14. Lee, J.; Cookson, A.; Roy, I.; Kerfoot, E.; Asner, L.; Vigueras, G.; Sochi, T.; Deparis, S.; Michler, C.; Smith, N. P.; Nordsletten, D. A.: Multiphysics computational modeling in $\mathcalC\boldHeart$ (2016)
  15. Marras, Simone; Kelly, James F.; Moragues, Margarida; Müller, Andreas; Kopera, Michal A.; Vázquez, Mariano; Giraldo, Francis X.; Houzeaux, Guillaume; Jorba, Oriol: A review of element-based Galerkin methods for numerical weather prediction: finite elements, spectral elements, and discontinuous Galerkin (2016)
  16. Mathias Jacquelin, Yili Zheng, Esmond Ng, Katherine Yelick: An Asynchronous Task-based Fan-Both Sparse Cholesky Solver (2016) arXiv
  17. Meyerhenke, Henning; Sanders, Peter; Schulz, Christian: Partitioning (hierarchically clustered) complex networks via size-constrained graph clustering (2016)
  18. Napov, Artem; Li, Xiaoye S.: An algebraic multifrontal preconditioner that exploits the low-rank property. (2016)
  19. Amestoy, Patrick; Ashcraft, Cleve; Boiteau, Olivier; Buttari, Alfredo; L’Excellent, Jean-Yves; Weisbecker, Clément: Improving multifrontal methods by means of block low-rank representations (2015)
  20. Delling, Daniel; Fleischman, Daniel; Goldberg, Andrew V.; Razenshteyn, Ilya; Werneck, Renato F.: An exact combinatorial algorithm for minimum graph bisection (2015)

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