Downloads provided by UsageCounts
{"references": ["T. A. Davis and I.S. Duff, \"A combined unifrontal/multifrontal method\nfor unsymmetric sparse matrices,\" ACM Trans. Math. Soft., vol. 25, no.\n1, 1997, pp. 1-19.", "O. Schenk, K. Gartner, and W. Fichtner, \"Efficient Sparse LU\nFactorization with Left-right Looking Strategy on Shared Memory\nMultiprocessors,\" BIT, vol. 40, no. 1, 2000, pp. 158-176.", "B. M. Irons, \"A frontal solution scheme for finite element analysis,\"\nNumer. Meth. Engg., vol. 2, 1970, pp. 5-32.", "M. P. Raju, and J. S. T-ien, \"Development of Direct Multifrontal Solvers\nfor Combustion Problems,\" Numerical Heat Transfer-Part B, vol. 53,\n2008, pp. 1-17.", "M. P. Raju, and J. S. T-ien, \"Modelling of Candle Wick Burning with a\nSelf-trimmed Wick,\" Comb. Theory Modell., vol. 12, no. 2, 2008, pp.\n367-388.", "M. P. Raju, and J. S. T-ien, \"Two-phase flow inside an externally heated\naxisymmetric porous wick,\" vol. 11, no. 8, 2008, pp. 701-718.", "P. K. Gupta, and K. V. Pagalthivarthi, \"Application of Multifrontal and\nGMRES Solvers for Multisize Particulate Flow in Rotating Channels,\"\nProg. Comput Fluid Dynam., vol. 7, 2007, pp. 323-336.", "S. Khaitan, J. McCalley, Q. Chen, \"Multifrontal solver for online power\nsystem time-domain simulation,\" IEEE Transactions on Power Systems,\nvol. 23, no. 4, 2008, pp. 1727-1737.", "S. Khaitan, C. Fu, J. D. McCalley, \"Fast parallelized algorithms for\nonline extended-term dynamic cascading analysis,\" PSCE, 2009, pp. 1-\n7.\n[10] J. McCalley, S. Khaitan, \"Risk of Cascading outages\", Final Report,\nPSrec Report, S-26, August 2007. Available at\nhttp://www.pserc.org/docsa/Executive_Summary_Dobson_McCalley_C\nascading_Outage_ S-2626_PSERC_ Final_Report.pdf\n[11] P. R. Amestoy, and I. S. Duff, \"Vectorization of a multiprocessor\nmultifrontal code,\" International Journal of Supercomputer\nApplications, vol. 3, 1989, pp. 41-59.\n[12] P. R. Amestoy, I. S. Duff, J. Koster and J. Y. L-Excellent, \"A fully\nasynchronous multifrontal solver using distributed dynamic scheduling,\"\nSIAM Journal on Matrix Analysis and Applications, vol. 23, no. 1, 2001,\npp. 15-41.\n[13] P. R. Amestoy, I. S. Duff, and J. Y. L-Excellent, \"Multifrontal parallel\ndistributed symmetric and unsymmetric solvers,\" Comput. Methods\nAppl. Mech. Eng., vol. 184, 2000, pp. 501-520.\n[14] O. Schenk, \"Scalable Parallel Sparse LU Factorization Methods on\nShared Memory Multiprocessors,\" Ph.D. dissertation, ETH Zurich,\n2000.\n[15] O. Schenk, and K. Gartner, \"Sparse Factorization with Two-Level\nScheduling in PARDISO,\" in Proc. 10th SIAM conf. Parallel Processing\nfor Scientific Computing, Portsmouth, Virginia, March 12-14, 2001.\n[16] O. Schenk, and K. Gartner, \"Two-level scheduling in PARDISO:\nImproved Scalability on Shared Memory Multiprocessing Systems,\"\nParallel Computing, vol. 28, 2002, pp. 187-197.\n[17] O. Schenk, and K. Gartner, \"Solving Unsymmetric Sparse Systems of\nLinear Equations with PARDISO,\" Journal Future Generation\nComputer Systems, vol. 20, no. 3, 2004, pp. 475-487.\n[18] Intel MKL Reference Manual, Intel\u00ae Math Kernel Library (MKL), 2007.\nAvailable: http://www.intel.com/software/products/mkl/\n[19] J. A. Scott, Numerical Analysis Group Progress Report, RAL-TR-2008-\n001, Rutherford Appleton Laboratory, 2008.\n[20] P. R. Amestoy, T. A. Davis, and I. S. Duff, \"An approximate minimum\ndegree ordering algorithm,\" SIAM Journal on Matrix Analysis and\nApplications, vol. 17, 1996, pp. 886-905.\n[21] P. R. Amestoy, \"Recent progress in parallel multifrontal solvers for\nunsymmetric sparse matrices,\" in Proc. 15th World Congress on\nScientific Computation, Modelling and Applied Mathematics, IMACS,\nBerlin, 1997.\n[22] J. Schulze, \"Towards a tighter coupling of bottom-up and top-down\nsparse matrix ordering methods,\" BIT, vol. 41, no. 4, 2001, pp. 800-841.\n[23] G. Karypis, and V. Kumar, \"METIS - A Software Package for\nPartitioning Unstructured Graphs, Partitioning Meshes, and Computing\nFill-Reducing Orderings of Sparse Matrices - Version 4.0,\" University\nof Minnesota, September 1998."]}
In-core memory requirement is a bottleneck in solving large three dimensional Navier-Stokes finite element problem formulations using sparse direct solvers. Out-of-core solution strategy is a viable alternative to reduce the in-core memory requirements while solving large scale problems. This study evaluates the performance of various out-of-core sequential solvers based on multifrontal or supernodal techniques in the context of finite element formulations for three dimensional problems on a Windows platform. Here three different solvers, HSL_MA78, MUMPS and PARDISO are compared. The performance of these solvers is evaluated on a 64-bit machine with 16GB RAM for finite element formulation of flow through a rectangular channel. It is observed that using out-of-core PARDISO solver, relatively large problems can be solved. The implementation of Newton and modified Newton's iteration is also discussed.
Out-of-core, Newton., PARDISO, MUMPS
Out-of-core, Newton., PARDISO, MUMPS
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 3 | |
| downloads | 17 |

Views provided by UsageCounts
Downloads provided by UsageCounts