This paper describes a methodology for solving efficiently the sparse network equations on multiprocessor computers. The methodology is based on the matrix inverse factors (W-matrix) approach to the direct solution phase of A(x) = b systems. A partitioning scheme of W-matrix , based on the leaf-nodes of the factorization path tree, is proposed. The methodology allows the performance of all the updating operations on vector b in parallel, within each partition, using a row-oriented processing. The approach takes advantage of the processing power of the individual processors. Performance results are presented and discussed
Abstract. A parallel algorithm is presented for the LU decomposition of a general sparse matrix on a...
Parallel computing on networks of workstations are intensively used in some application areas such a...
The Sherman--Morrison formula is one scheme for computing the approximate inverse preconditioner of ...
A coarse-grain parallel implementation is presented of LU factorisation, forward and backward substi...
A set program modules for solving linear equation systems in real time simulation of processes struc...
Sparse-matrix solution is a dominant part of execution time in simulating VLSI circuits by a detaile...
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
Solving a system of linear simultaneous equations representing an electrical circuit is one of the m...
Recent advances in linear programming solution methodology have focused on interior point algorithms...
. The efficiency of solving sparse linear systems on parallel processors and more complex multiclust...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
This thesis presents research into parallel linear solvers for block-diagonal-bordered sparse matric...
This article presents two fast, sparsity-based power system matrices computation procedures. The fir...
Abstract. A parallel algorithm is presented for the LU decomposition of a general sparse matrix on a...
Parallel computing on networks of workstations are intensively used in some application areas such a...
The Sherman--Morrison formula is one scheme for computing the approximate inverse preconditioner of ...
A coarse-grain parallel implementation is presented of LU factorisation, forward and backward substi...
A set program modules for solving linear equation systems in real time simulation of processes struc...
Sparse-matrix solution is a dominant part of execution time in simulating VLSI circuits by a detaile...
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
With the increase of size and complexity of interconnected power system, the dynamic stability simul...
Solving a system of linear simultaneous equations representing an electrical circuit is one of the m...
Recent advances in linear programming solution methodology have focused on interior point algorithms...
. The efficiency of solving sparse linear systems on parallel processors and more complex multiclust...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
This thesis presents research into parallel linear solvers for block-diagonal-bordered sparse matric...
This article presents two fast, sparsity-based power system matrices computation procedures. The fir...
Abstract. A parallel algorithm is presented for the LU decomposition of a general sparse matrix on a...
Parallel computing on networks of workstations are intensively used in some application areas such a...
The Sherman--Morrison formula is one scheme for computing the approximate inverse preconditioner of ...