Abstract
Many simulations in science and engineering give rise to sparse linear systems of equations. It is a well known fact that the cost of the simulation process is almost always governed by the solution of the linear systems especially for large-scale problems. The emergence of extreme-scale parallel platforms, along with the increasing number of processing cores available on a single chip pose significant challenges for algorithm development. Machines with tens of thousands of multicore processors place tremendous constraints on the communication as well as memory access requirements of algorithms. The increase in number of cores in a processing unit without an increase in memory bandwidth aggravates an already significant memory bottleneck. Sparse linear algebra kernels are well-known for their poor processor utilization. This is a result of limited memory reuse, which renders data caching less effective. In view of emerging hardware trends, it is necessary to develop algorithms that strike a more meaningful balance between memory accesses, communication, and computation. Specifically, an algorithm that performs more floating point operations at the expense of reduced memory accesses and communication is likely to yield better performance. We present two alternative variations of DS factorization based methods for solution of sparse linear systems on parallel computing platforms. Performance comparisons to traditional LU factorization based parallel solvers are also discussed. We show that combining iterative methods with direct solvers and using DS factorization, one can achieve better scalability and shorter time to solution.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amestoy, P.R., Duff, I.S.: Multifrontal parallel distributed symmetric and unsymmetric solvers. Comput. Methods Appl. Mech. Eng. 184, 501–520 (2000)
Amestoy, P.R., Duff, I.S., L’Excellent, J.Y., Koster, J.: A fully asynchronous multifrontal solver using distributed dynamic scheduling. SIAM J. Matrix Anal. Appl. 23(1), 15–41 (2001)
Amestoy, P.R., Guermouche, A., L’Excellent, J.Y., Pralet, S.: Hybrid scheduling for the parallel solution of linear systems. Parallel Comput. 32(2), 136–156 (2006)
Anderson, E., Bai, Z., Bischof, C., Blackford, S., Demmel, J., Dongarra, J., Croz, J.D., Greenbaum, A., Hammerling, S., McKenney, A., Sorensen, D.: LAPACK Users’ Guide, 3rd edn. SIAM, Philadelphia (1999)
Barnard, S.T., Pothen, A., Simon, H.: A spectral algorithm for envelope reduction of sparse matrices. Numer. Linear Algebra Appl. 2(4), 317–334 (1995)
Berry, M.W., Sameh, A.: Multiprocessor schemes for solving block tridiagonal linear systems. Int. J. Supercomput. Appl. 1(3), 37–57 (1988)
Blackford, L., Choi, J., Cleary, A., D’Azevedo, E., Demmel, J., Dhillon, I., Dongarra, J., Hammarling, S., Henry, G., Petitet, A., Stanley, K., Walker, D., Whaley, R.: ScaLAPACK User’s Guide. SIAM, Philadelphia (1997). See also www.netlib.org/scalapack
Davis, T.A.: University of Florida sparse matrix collection. NA Digest (1997)
Dongarra, J.J., Sameh, A.H.: On some parallel banded system solvers. Parallel Comput. 1(3), 223–235 (1984)
Duff, I., Koster, J.: On algorithms for permuting large entries to the diagonal of a sparse matrix (1999). citeseer.comp.nus.edu.sg/duff99algorithms.html
Duff, I.S., Koster, J.: The design and use of algorithms for permuting large entries to the diagonal of sparse matrices. SIAM J. Matrix Anal. Appl. 20(4), 889–901 (1999). citeseer.ist.psu.edu/duff97design.html
Fiedler, M.: Algebraic connectivity of graphs. Czechoslov. Math. J. 23(2), 298–305 (1973)
Gupta, A.: Recent advances in direct methods for solving unsymmetric sparse systems of linear equations. ACM Trans. Math. Softw. 28(3), 301–324 (2002). http://doi.acm.org/10.1145/569147.569149
Gupta, A., Koric, S., George, T.: Sparse matrix factorization on massively parallel computers. In: SC’09 USB Key. ACM/IEEE, Portland, p. A1 (2009)
He, X., Zha, H., Ding, C.H., Simon, H.D.: Web document clustering using hyperlink structures. Comput. Stat. Data Anal. 41(1), 19–45 (2002)
Higham, D.J., Kalna, G., Kibble, M.: Spectral clustering and its use in bioinformatics. J. Comput. Appl. Math. 204(1), 25–37 (2007). http://www.sciencedirect.com/science/article/B6TYH-4K3D33V-3/2/204acbb72a44113bdd062272c3513921. Special issue dedicated to Professor Shinnosuke Oharu on the occasion of his 65th birthday
HSL: A collection of Fortran codes for large-scale scientific computation (2004). See http://www.cse.scitech.ac.uk/nag/hsl/
Hu, Y., Scott, J.: HSL_MC73: a fast multilevel Fiedler and profile reduction code. Technical Report RAL-TR-2003-036 (2003)
Karypis, G., Kumar, V.: Multilevel k-way partitioning scheme for irregular graphs. J. Parallel Distrib. Comput. 48, 96–129 (1998)
Kundu, S., Sorensen, D., Philiphsi, G.N.J.: Automatic domain decomposition of proteins by a Gaussian network model. Proteins 57(4), 725–733 (2004)
Lawrie, D.H., Sameh, A.H.: The computation and communication complexity of a parallel banded system solver. ACM Trans. Math. Softw. 10(2), 185–195 (1984)
Li, X., Demmel, J.W.: SuperLU DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems. ACM Trans. Math. Softw. 29, 110–140 (2003)
Manguoglu, M.: A parallel hybrid sparse linear system solver. In: Computational Electromagnetics International Workshop, 2009. CEM 2009, pp. 38–43 (2009)
Manguoglu, M.: A domain-decomposing parallel sparse linear system solver. J. Comput. Appl. Math. 236(3), 319–325 (2011)
Manguoglu, M., Cox, E., Saied, F., Sameh, A.: TRACEMIN-fiedler: A parallel algorithm for computing the Fiedler vector. In: High Perf. Comput. Comput. Sci.–VECPAR 2010, pp. 449–455 (2011)
Manguoglu, M., Koyuturk, M., Sameh, A., Grama, A.: Weighted matrix ordering and parallel banded preconditioners for iterative linear system solvers. SIAM J. Sci. Comput. 32(3), 1201–1216 (2010)
Manguoglu, M., Sameh, A., Schenk, O.: Pspike: A parallel hybrid sparse linear system solver. In: Euro-Par 2009 Parallel Processing, pp. 797–808 (2009)
Manguoglu, M., Takizawa, K., Sameh, A., Tezduyar, T.: Nested and parallel sparse algorithms for arterial fluid mechanics computations with boundary layer mesh refinement. Int. J. Numer. Methods Fluids 65, 135–149 (2011). doi:10.1002/fld.2415
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems, vol. 14, pp. 849–856. MIT Press, Cambridge (2001)
Polizzi, E., Sameh, A.H.: A parallel hybrid banded system solver: the SPIKE algorithm. Parallel Comput. 32(2), 177–194 (2006)
Polizzi, E., Sameh, A.H.: SPIKE: A parallel environment for solving banded linear systems. Comput. Fluids 36(1), 113–120 (2007)
Pothen, A., Simon, H.D., Liou, K.P.: Partitioning sparse matrices with eigenvectors of graphs. SIAM J. Matrix Anal. Appl. 11(3), 430–452 (1990)
Qiu, H., Hancock, E.R.: Graph matching and clustering using spectral partitions. Pattern Recognit. 39(1), 22–34 (2006)
Chen, S.C., Kuck, D.J., Sameh, A.H.: Practical parallel band triangular system solvers. ACM Trans. Math. Softw. 4(3), 270–277 (1978)
Sameh, A., Tong, Z.: The trace minimization method for the symmetric generalized eigenvalue problem. J. Comput. Appl. Math. 123(1-2), 155–175 (2000)
Sameh, A.H., Kuck, D.J.: On stable parallel linear system solvers. J. ACM 25(1), 81–91 (1978)
Sameh, A.H., Sarin, V.: Hybrid parallel linear system solvers. Int. J. Comput. Fluid Dyn. 12, 213–223 (1999)
Sameh, A.H., Wisniewski, J.A.: A trace minimization algorithm for the generalized eigenvalue problem. SIAM J. Numer. Anal. 19(6), 1243–1259 (1982)
Schenk, O., Gärtner, K.: Solving unsymmetric sparse systems of linear equations with PARDISO. Future Gener. Comput. Syst. 20(3), 475–487 (2004)
Schenk, O., Gärtner, K.: On fast factorization pivoting methods for sparse symmetric indefinite systems. Electron. Trans. Numer. Anal. 23, 158–179 (2006)
Schenk, O., Manguoglu, M., Sameh, A., Christian, M., Sathe, M.: Parallel scalable PDE-constrained optimization: antenna identification in hyperthermia cancer treatment planning. Comput. Sci. Res. Dev. 23(3–4) (2009)
Shepherd, S.J., Beggs, C.B., Jones, S.: Amino acid partitioning using a Fiedler vector model. Eur. Biophys. J. 37(1), 105–109 (2007)
van der Vorst, H.A.: BI-CGSTAB: a fast and smoothly converging variant of BI-CG for the solution of nonsymmetric linear systems. SIAM J. Sci. Stat. Comput. 13(2), 631–644 (1992)
Acknowledgements
I would like to thank Ahmed Sameh, Ananth Grama, Faisal Saied, Eric Cox, Kenji Takizawa, Madan Sathe, Mehmet Koyuturk, Olaf Schenk, and Tayfun Tezduyar for their help and many useful discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this chapter
Cite this chapter
Manguoglu, M. (2012). Parallel Solution of Sparse Linear Systems. In: Berry, M., et al. High-Performance Scientific Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2437-5_8
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2437-5_8
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2436-8
Online ISBN: 978-1-4471-2437-5
eBook Packages: Computer ScienceComputer Science (R0)