Chaco

Chaco: Software for Partitioning Graphs. Before a calculation can be performed on a parallel computer, it must first be decomposed into tasks which are assigned to different processors. Efficient use of the machine requires that each processor have about the same amount of work to do and that the quantity of interprocessor communication is kept small. Finding an optimal decomposition is provably hard, but due to its practical importance, a great deal of effort has been devoted to developing heuristics for this problem. The decomposition problem can be addressed in terms of graph partitioning. Rob Leland and I have developed a variety of algorithms for graph partitioning and implemented them into a package we call Chaco. The code is being used at most of the major parallel computing centers around the world to simplify the development of parallel applications, and to ensure that high performance is obtained. Chaco has contributed to a wide variety of computational studies including investigation of the molecular structure of liquid crystals, evaluating the design of a chemical vapor deposition reactor and modeling automobile collisions. ...


References in zbMATH (referenced in 117 articles )

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  1. Selvitopi, Oguz; Acer, Seher; Manguoğlu, Murat; Aykanat, Cevdet: The effect of various sparsity structures on parallelism and algorithms to reveal those structures (2020)
  2. Song, Qi; Yang, Ren; Chen, Pu: An improvement to exact reanalysis algorithm for local non-topological structural modifications (2020)
  3. Herrmann, Julien; Özkaya, M. Yusuf; Uçar, Bora; Kaya, Kamer; Çatalyürek, ÜMit V.: Multilevel algorithms for acyclic partitioning of directed acyclic graphs (2019)
  4. Liang, Bowen; Nagarajan, Anand; Soghrati, Soheil: Scalable parallel implementation of CISAMR: a non-iterative mesh generation algorithm (2019)
  5. Uh Zapata, Miguel; Zhang, Wei; Pham Van Bang, Damien; Nguyen, Kim Dan: A parallel second-order unstructured finite volume method for 3D free-surface flows using a (\sigma) coordinate (2019)
  6. Agreste, Santa; Ricciardello, Angela: An overlapping domain decomposition method for large-scale problems (2018)
  7. Christian Schulz, Jesper Larsson Traeff: VieM v1.00 -- Vienna Mapping and Sparse Quadratic Assignment User Guide (2017) arXiv
  8. Li, Ruipeng; Saad, Yousef: Low-rank correction methods for algebraic domain decomposition preconditioners (2017)
  9. Tan, Tunzi; Gao, Suixiang; Mesa, Juan A.: An exact algorithm for min-max hyperstructure equipartition with a connected constraint (2017)
  10. Duan, Ran; Pettie, Seth: Linear-time approximation for maximum weight matching (2014)
  11. Magoulès, Frédéric; Laurent-Gengoux, Pascal; Pruvost, Florent: Preconditioners for Schwarz relaxation methods applied to differential algebraic equations (2014)
  12. Vecharynski, Eugene; Saad, Yousef; Sosonkina, Masha: Graph partitioning using matrix values for preconditioning symmetric positive definite systems (2014)
  13. Hager, William W.; Phan, Dzung T.; Zhang, Hongchao: An exact algorithm for graph partitioning (2013)
  14. Mészáros, Csaba: On sparse matrix orderings in interior point methods (2013)
  15. Nakshatrala, P. B.; Tortorelli, D. A.; Nakshatrala, K. B.: Nonlinear structural design using multiscale topology optimization. Part I: Static formulation (2013)
  16. Wu, Qinghua; Hao, Jin-Kao: Memetic search for the max-bisection problem (2013)
  17. De La Asunción, Marc; Mantas, José M.; Castro, Manuel J.; Fernández-Nieto, E. D.: An MPI-CUDA implementation of an improved roe method for two-layer shallow water systems (2012) ioport
  18. Ahusborde, E.; Glockner, S.: A 2D block-structured mesh partitioner for accurate flow simulations on non-rectangular geometries (2011)
  19. Bender, Michael A.; Kuszmaul, Bradley C.; Teng, Shang-Hua; Wang, Kebin: Optimal cache-oblivious mesh layouts (2011)
  20. Benlic, Una; Hao, Jin-Kao: An effective multilevel tabu search approach for balanced graph partitioning (2011)

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