Memory Management Issues In Sparse Multifrontal Methods On Multiprocessors

Below is result for Memory Management Issues In Sparse Multifrontal Methods On Multiprocessors in PDF format. You can download or read online all document for free, but please respect copyrighted ebooks. This site does not host PDF files, all document are the property of their respective owners.

Multiprocessing a sparse matrix code on the Alliant FX/8

Keywords: Sparse matrices, multifrontal methods, multiprocessing, parallelism, shared memory machines. 1. Introduction We consider the direct solution of large sparse sets of linear equations Ax=b 0.1) on shared memory multiprocessors. We assume that the sparsity of A is symmetric, but the

NUMERICAL ANALYSIS GROUP PROGRESS REPORT

Sparse matrices and development of the Fortran programming language. Jennifer Scott. Sparse matrix software. Volterra integral equations. Seminar organization. Visitors and Attached Staff Christian Damhaug (Visitor) Solution of finite-elementequations using multifrontal methods. Mike Hopper (Consultant) Support for Harwell Subroutine Library. TSSD.

Direct Solvers for Sparse Matrices X. Li June 2020

Memory management issues in sparse multifrontal methods on multiprocessors. Int. J. Supercomputer Applics, 7:64{82, 1993. [5] C. Ashcraft and R. Grimes. SPOOLES: An

Direct Solvers for Sparse Matrices X. Li September 2006

The multifrontal solutionofindefinite sparse symmetriclinearequations. ACM Transactions on Mathematical Software, 9(3):302 325, September 1983. [18] I.S Duff and J. A. Scott.

Parallel multifrontal method with out-of-coretechniques

2 Memory management in a parallel multifrontal method In multifrontal methods, the task dependencies are represented by a so-called assembly tree [4,6], that is processed from bottom to top during the factorization. At each node of the tree is associated a so-calledfrontal matrix, or front, and a task consisting in the partial

U MI - web.stanford.edu

memory bandwidth. The computationally-intensive step of this class of applications is the so­ lution of linear equations, for which the system matrix is large, sparse, symmetric, and positive definite. These matrices are too large to store all the entries. For effi­ cient use of memory it is necessary to devise compact formats which store

References for direct methods for sparse linear systems

References for direct methods for sparse linear systems Timothy A. Davis June 9, 2016 All of the following references appear in our Acta Numerica paper, A survey of direct methods for sparse linear systems, by Davis, Rajamanickam, and Sid-Lakhdar, Acta Numerica, vol 25, May 2016, pp. 383-566, with one additional reference: the survey paper