Nested grids ILU-decomposition (NGILU)
Proceedings of the 6th international congress on Computational and applied mathematics
ILUM: a multi-elimination ILU preconditioner for general sparse matrices
SIAM Journal on Scientific Computing
Experimental study of ILU preconditioners for indefinite matrices
Journal of Computational and Applied Mathematics
Approximate Inverse Preconditioners via Sparse-Sparse Iterations
SIAM Journal on Scientific Computing
BILUTM: A Domain-Based Multilevel Block ILUT Preconditioner for General Sparse Matrices
SIAM Journal on Matrix Analysis and Applications
Distributed Schur Complement Techniques for General Sparse Linear Systems
SIAM Journal on Scientific Computing
Computational Partial Differential Equations: Numerical Methods and Diffpack Programming
Computational Partial Differential Equations: Numerical Methods and Diffpack Programming
A Scalable Parallel Algorithm for Incomplete Factor Preconditioning
SIAM Journal on Scientific Computing
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Non-stationary iterative solvers on a PC cluster
Advances in Engineering Software
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Solving large sparse linear systems is a computationallyintensive component of many important large-scale applications. We present a few experiments stemming from a number of realistic applications including magneto-hydrodynamics structural mechanics, and ultrasound modeling, which have become possible due to the advances in parallel iterative solution techniques. Among such techniques is a recently developed Parallel Algebraic Recursive Multilevel Solver (pARMS). This is a distributed-memory iterative method that adopts the general framework of distributed sparse matrices and relies on solving the resulting distributed Schur complement systems. We discuss some issues related to parallel performance for various linear systems which arise in realistic applications. In particular, we consider the effect of different parameters and algorithms on the overall performance.