Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
LAPACK's user's guide
Stability of block algorithms with fast level-3 BLAS
ACM Transactions on Mathematical Software (TOMS)
Analysis of multilevel graph partitioning
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
A parallel algorithm for multilevel graph partitioning and sparse matrix ordering
Journal of Parallel and Distributed Computing
A Supernodal Approach to Sparse Partial Pivoting
SIAM Journal on Matrix Analysis and Applications
Future Generation Computer Systems - I. High Performance Numerical Methods and Applications. II. Performance Data Mining: Automated Diagnosis, Adaption, and Optimization
Solving Linear Systems on Vector and Shared Memory Computers
Solving Linear Systems on Vector and Shared Memory Computers
A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling
SIAM Journal on Matrix Analysis and Applications
Solving Unsymmetric Sparse Systems of Linear Equations with PARDISO
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
Design and Evaluation of Parallel Block Algorithms: LU Factorization on an IBM 3090 VF/600J
Proceedings of the Fifth SIAM Conference on Parallel Processing for Scientific Computing
Computing and Visualization in Science
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In this work we present a new parallel direct linear solver for matrices resulting from finite element problems. The algorithm follows the nested dissection approach, where the resulting Schur complements are also distributed in parallel. The sparsity structure of the finite element matrices is used to pre-compute an efficient block structure for the LU factors. We demonstrate the performance and the parallel scaling behavior by several test examples.