Preconditioning techniques for large linear systems: a survey
Journal of Computational Physics
hypre: A Library of High Performance Preconditioners
ICCS '02 Proceedings of the International Conference on Computational Science-Part III
Parallel Iterative Methods in Modern Physical Applications
ICCS '02 Proceedings of the International Conference on Computational Science-Part II
pARMS: A Package for Solving General Sparse Linear Systems on Parallel Computers
PPAM '01 Proceedings of the th International Conference on Parallel Processing and Applied Mathematics-Revised Papers
Journal of Computational Physics
Using the parallel algebraic recursive multilevel solver in modern physical applications
Future Generation Computer Systems - Special issue: Selected numerical algorithms
Conceptual interfaces in hypre
Future Generation Computer Systems
Conceptual interfaces in hypre
Future Generation Computer Systems
A Supernodal Approach to Incomplete LU Factorization with Partial Pivoting
ACM Transactions on Mathematical Software (TOMS)
Adaptive Techniques for Improving the Performance of Incomplete Factorization Preconditioning
SIAM Journal on Scientific Computing
SIAM Journal on Scientific Computing
A bit-compatible parallelization for ILU(k) preconditioning
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part II
Sparse triangular solves for ILU revisited: data layout crucial to better performance
International Journal of High Performance Computing Applications
Parallel acceleration of krylov solvers by factorized approximate inverse preconditioners
VECPAR'04 Proceedings of the 6th international conference on High Performance Computing for Computational Science
An Empirical Analysis of the Performance of Preconditioners for SPD Systems
ACM Transactions on Mathematical Software (TOMS)
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We describe a parallel algorithm for computing incomplete factor (ILU) preconditioners. The algorithm attains a high degree of parallelism through graph partitioning and a two-level ordering strategy. Both the subdomains and the nodes within each subdomain are ordered to preserve concurrency. We show through an algorithmic analysis and through computational results that this algorithm is scalable. Experimental results include timings on three parallel platforms for problems with up to 20 million unknowns running on up to 216 processors. The resulting preconditioned Krylov solvers have the desirable property that the number of iterations required for convergence is insensitive to the number of processors.