Direct methods for sparse matrices
Direct methods for sparse matrices
An Approximate Minimum Degree Ordering Algorithm
SIAM Journal on Matrix Analysis and Applications
Highly Scalable Parallel Algorithms for Sparse Matrix Factorization
IEEE Transactions on Parallel and Distributed Systems
The Design and Use of Algorithms for Permuting Large Entries to the Diagonal of Sparse Matrices
SIAM Journal on Matrix Analysis and Applications
The Multifrontal Solution of Indefinite Sparse Symmetric Linear
ACM Transactions on Mathematical Software (TOMS)
A Fully Asynchronous Multifrontal Solver Using Distributed Dynamic Scheduling
SIAM Journal on Matrix Analysis and Applications
Proceedings of the 11 IPPS/SPDP'99 Workshops Held in Conjunction with the 13th International Parallel Processing Symposium and 10th Symposium on Parallel and Distributed Processing
Domain Decomposition Solvers for Large Scale Industrial Finite Element Problems
PARA '00 Proceedings of the 5th International Workshop on Applied Parallel Computing, New Paradigms for HPC in Industry and Academia
Domain Decomposition Solvers for Large Scale Industrial Finite Element Problems
PARA '00 Proceedings of the 5th International Workshop on Applied Parallel Computing, New Paradigms for HPC in Industry and Academia
Parallel solution of large-scale and sparse generalized algebraic riccati equations
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Parallel algorithms for balanced truncation model reduction of sparse systems
PARA'04 Proceedings of the 7th international conference on Applied Parallel Computing: state of the Art in Scientific Computing
Fine granularity sparse QR factorization for multicore based systems
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume 2
Computers & Mathematics with Applications
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MUMPS is a public domain software package for the multifrontal solution of large sparse linear systems on distributed memory computers. The matrices can be symmetric positive definite, general symmetric, or unsymmetric, and possibly rank deficient. MUMPS exploits parallelism coming from the sparsity in the matrix and parallelism available for dense matrices. Additionally, large computational tasks are divided into smaller subtasks to enhance parallelism. MUMPS uses a distributed dynamic scheduling technique that allows numerical pivoting and the migration of computational tasks to lightly loaded processors. Asynchronous communication is used to overlap communication with computation. In this paper, we report on recently integrated features and illustrate the present performance of the solver on an SGI Origin 2000 and a CRAY T3E.