SuperLU_DIST: A scalable distributed-memory sparse direct solver for unsymmetric linear systems
ACM Transactions on Mathematical Software (TOMS)
A column pre-ordering strategy for the unsymmetric-pattern multifrontal method
ACM Transactions on Mathematical Software (TOMS)
Direct simulation of particle suspensions in sliding bi-periodic frames
Journal of Computational Physics
Constructing memory-minimizing schedules for multifrontal methods
ACM Transactions on Mathematical Software (TOMS)
Mesh locking effects in the finite volume solution of 2-D anisotropic diffusion equations
Journal of Computational Physics
Journal of Computational Physics
Parallel unsymmetric-pattern multifrontal sparse LU with column preordering
ACM Transactions on Mathematical Software (TOMS)
On the I/O Volume in Out-of-Core Multifrontal Methods with a Flexible Allocation Scheme
High Performance Computing for Computational Science - VECPAR 2008
Reducing the I/O volume in an out-of-core sparse multifrontal solver
HiPC'07 Proceedings of the 14th international conference on High performance computing
Acoustic inverse scattering via Helmholtz operator factorization and optimization
Journal of Computational Physics
Reducing the I/O Volume in Sparse Out-of-core Multifrontal Methods
SIAM Journal on Scientific Computing
FPGA accelerated parallel sparse matrix factorization for circuit simulations
ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
The university of Florida sparse matrix collection
ACM Transactions on Mathematical Software (TOMS)
Algorithm 915, SuiteSparseQR: Multifrontal multithreaded rank-revealing sparse QR factorization
ACM Transactions on Mathematical Software (TOMS)
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A well-known approach to computing the LU factorization of a general unsymmetric matrix A is to build the elimination tree associated with the pattern of the symmetric matrix A + AT and use it as a computational graph to drive the numerical factorization. This approach, although very efficient on a large range of unsymmetric matrices, does not capture the unsymmetric structure of the matrices. We introduce a new algorithm which detects and exploits the structural asymmetry of the submatrices involved during the processing of the elimination tree. We show that with the new algorithm, significant gains, both in memory and in time, to perform the factorization can be obtained.