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Aztec: A parallel iterative package for the solving linear systems arising in Newton-Krylov Methods
Last modified 15:07:58, 4 Sep 1996 - 3.2K - text
Announcement of iterative library that greatly simplifies the parallelization process when solving a sparse linear system of equations Ax = b where A is a user supplied nxn sparse matrix, b is a user supplied vector of length n and x is a vector of length n to be computed.
Available publicaly through a research license from the authors.
See also <URL:http://www.cs.sandia.gov/HPCCIT/aztec.html> for more details including Postscript papers.
Authors: Ray S. Tuminaro (tuminaro@cs.sandia.gov); Tel: +1 505 845-7298; John N. Shadid (jnshadi@cs.sandia.gov); Tel: +1 505 845-7876 and Scott A. Hutchinson (sahutch@cs.sandia.gov); Tel: +1 505 845-7996; FAX: +1 505 845-7442. Sandia National Laboratories, Department 9221, Parallel Computational Sciences, MS 1111 P.O. Box 5800, Albuquerque, NM 87185-1111, USA.
peigs/
PeIGS parallel eigensolver from Pacific Northwest Laboratories (PNL), USA.
Anatomy of a Parallel Out-of-Core Dense Linear Solver
Last modified 16:05:19, 1 Jun 1995 - 2.1K - text
Paper at <URL:http://www.cs.utexas.edu/users/rvdg/conference.html>
Abstract: In this paper, we describe the design and implementation of the Platform Independent Parallel Solver (PIPSolver) package for the out-of-core (OOC) solution of complex dense linear systems. Our approach is unique in that it allows essentially all of RAM to be filled with the current portion of the matrix (slab) to be updated and factored, thereby greatly improving the computation to I/O ratio over previous approaches. This work could be viewed in part as an exercise in maximal code reuse: By formulating the OOC LU factorization just right, we managed to reuse essentially all of a very robust and efficient incore solver, leading directly to a very robust and efficient OOC solver. Experiences and performance are reported for the Cray T3D system.
Authors: Robert van de Geijn (rvdg@cs.utexas.edu) and Kenneth Klimkowski (ken@ticam.utexas.edu). Texas Institute for Computational and Applied Mathematics and Department of Computer Sciences, University of Texas, Austin, TX 78712, USA.