Parallel hybrid sparse solvers through flexible incomplete cholesky preconditioning

Authors:
Keita Teranishi;Padma Raghavan
Affiliations:
Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA;Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA
Venue:
PARA'04 Proceedings of the 7th international conference on Applied Parallel Computing: state of the Art in Scientific Computing
Year:
2004

Citing 11
Cited 0

The role of elimination trees in sparse factorization

SIAM Journal on Matrix Analysis and Applications
Factorized sparse approximate inverse preconditionings I: theory

SIAM Journal on Matrix Analysis and Applications
On finding supernodes for sparse matrix computations

SIAM Journal on Matrix Analysis and Applications
Block sparse Cholesky algorithms on advanced uniprocessor computers

SIAM Journal on Scientific Computing
Parallel Preconditioning with Sparse Approximate Inverses

SIAM Journal on Scientific Computing
A parallel algorithm for multilevel graph partitioning and sparse matrix ordering

Journal of Parallel and Distributed Computing
Computer Solution of Large Sparse Positive Definite

Computer Solution of Large Sparse Positive Definite
A new data-mapping scheme for latency-tolerant distributed sparse triangular solution

Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Iterative Methods for Sparse Linear Systems

Iterative Methods for Sparse Linear Systems
Parallel Implementation and Practical Use of Sparse Approximate Inverse Preconditioners with a Priori Sparsity Patterns

International Journal of High Performance Computing Applications
Scalable hybrid sparse linear solvers

Scalable hybrid sparse linear solvers

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider parallel preconditioning schemes to accelerate the convergence of Conjugate Gradients (CG) for sparse linear system solution. We develop methods for constructing and applying preconditioners on multiprocessors using incomplete factorizations with selective inversion for improved latency-tolerance. We provide empirical results on the efficiency, scalability and quality of our preconditioners for sparse matrices from model grids and some problems from practical applications. Our results indicate that our preconditioners enable more robust sparse linear system solution.