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Factorization-Based Graph Repartitionings

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5910))

Abstract

The paper deals with the parallel computation of matrix factorization using graph partitioning-based domain decomposition. It is well-known that the partitioned graph may have both a small separator and well-balanced domains but sparse matrix decompositions on domains can be completely unbalanced.

In this paper we propose to enhance the iterative strategy for balancing the decompositions from [13] by graph-theoretical tools. We propose the whole framework for the graph repartitioning. In particular, new global and local reordering strategies for domains are discussed in more detail. We present both theoretical results for structured grids and experimental results for unstructured large-scale problems.

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References

  1. Catalyürek, U.V., Aykanat, C.: Hypergraph-partitioning based decomposition for parallel sparse-matrix vector multiplication. IEEE Transactions on Parallel and Distributed Systems 20, 673–693 (1999)

    Article  Google Scholar 

  2. Chen, Y., Davis, T.A., Hager, W.W., Rajamanickam, S.: Algorithm 887: CHOLMOD, Supernodal sparse Cholesky factorization and update/downdate. ACM Trans. Math. Softw. 35, 22:1–22:14 (2008)

    Google Scholar 

  3. Davis, T.A.: Direct Methods for Sparse Linear Systems. SIAM, Philadelphia (2006)

    MATH  Google Scholar 

  4. Hendrickson, B.: Graph partitioning and parallel solvers: Has the emperor no clothes? In: Ferreira, A., Rolim, J.D.P., Teng, S.-H. (eds.) IRREGULAR 1998. LNCS, vol. 1457, pp. 218–225. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  5. Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20, 359–392 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  6. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. The Bell System Technical Journal 49, 291–307 (1970)

    Google Scholar 

  7. Kumar, V., Grama, A., Gupta, A., Karypis, G.: Introduction to Parallel Computing. Benjamin-Cummings (1994)

    Google Scholar 

  8. Liu, J.W.H.: A tree model for sparse symmetric indefinite matrix factorization. SIAM J. Matrix Anal. Appl. 9, 26–39 (1988)

    Article  MATH  MathSciNet  Google Scholar 

  9. Liu, J.W.H.: The minimum degree ordering with constraints. SIAM J. Sci. Comput. 10, 1136–1145 (1989)

    Article  MATH  Google Scholar 

  10. Liu, J.W.H.: The role of elimination trees in sparse factorization. SIAM J. Matrix Anal. Appl. 11, 134–172 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  11. Liu, J.W.H., Ng, E.G., Peyton, B.W.: On finding supernodes for sparse matrix computations. SIAM J. Matrix Anal. Appl. 14, 242–252 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  12. Pinar, A., Hendrickson, B.: Combinatorial Parallel and Scientific Computing. In: Heroux, M., Raghavan, P., Simon, H. (eds.) Parallel Processing for Scientific Computing, pp. 127–141. SIAM, Philadelphia (2006)

    Google Scholar 

  13. Pinar, A., Hendrickson, B.: Partitioning for complex objectives. In: Parallel and Distributed Processing Symposium, vol. 3, pp. 1232–1237 (2001)

    Google Scholar 

  14. Schloegel, K., Karypis, G., Kumar, V.: A unified algorithm for load-balancing adaptive scientific simulations. In: Proceedings of the ACM/IEEE Symposium on Supercomputing, vol. 59. ACM, New York (2000)

    Google Scholar 

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Jurková, K., Tůma, M. (2010). Factorization-Based Graph Repartitionings. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2009. Lecture Notes in Computer Science, vol 5910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12535-5_92

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  • DOI: https://doi.org/10.1007/978-3-642-12535-5_92

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12534-8

  • Online ISBN: 978-3-642-12535-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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