A parallel version of the Algebraic Recursive Multilevel Solver (ARMS) is developed for distributed computing environments. The method adopts the general framework of distributed sparse matrices and relies on solving the resulting distributed Schur complement system. Numerical experiments are presented which compare these approaches on regularly and irregularly structured problems.

Domain-decomposition and multi-level techniques are often formulated for linear systems that arise from the solution of elliptic-type Partial Differential Equations. In this paper, generalizations of these techniques for irregularly structured sparse linear systems are considered. An interesting common approach used to derive successful preconditioners is to resort to Schur complements. In particular, we discuss a multi-level domain decompositiontype algorithm for iterative solution of large sparse linear systems based on independent subsets of nodes. We also discuss a Schur complement technique that utilizes incomplete LU factorizations of local matrices. Key words: Schur complement techniques; Incomplete LU factorization; Schwarz iterations; Multielimination; Multi-level ILU preconditioners; Krylov subspace methods. 1 Introduction A recent trend in parallel preconditioning techniques for general sparse linear systems is to exploit ideas from domain decomposition concepts an...

A sparse approximate inverse technique is introduced to solve general sparse linear systems. The sparse approximate inverse is computed as a factored form and used as a preconditioner to work with some Krylov subspace methods. The new technique is derived from a matrix decomposition algorithm for inverting dense nonsymmetric matrices. Several strategies and special data structures are proposed to implement the algorithm efficiently. Sparsity patterns of the the factored inverse are exploited to reduce computational cost. The computation of the factored sparse approximate inverse is relatively cheaper than the techniques based on norm minimization techniques. The new preconditioner possesses much greater inherent parallelism than traditional preconditioners based on incomplete LU factorizations. Numerical experiments are used to show the effectiveness and efficiency of the new sparse approximate inverse preconditioner.

We discuss issues related to domain decomposition and multilevel preconditioning techniques which are often employed for solving large sparse linear systems in parallel computations. We introduce a class of parallel preconditioning techniques for general sparse linear systems based on a two level block ILU factorization strategy. We give some new data structures and strategies to construct local coefficient matrix and local Schur complement matrix in each processor. The preconditioner constructed is fast and robust for solving certain large sparse matrices. Numerical experiments show that our domain based two level block ILU preconditioners are more robust and more efficient than some published ILU preconditioners based on Schur complement techniques for parallel sparse matrix solutions.

A number of techniques are described for solving sparse linear systems on parallel platforms. The general approach used is a domain decomposition type method in which a processor is assigned a certain number of rows of the linear system to be solved. Strategies that are discussed include non-standard graph partitioners, and a forced load-balance technique for the local iterations. A common practice when partitioning a graph is to seek to minimize the number of cut-edges and to have an equal number of equations per processor. It is shown that partitioners that takeinto account the values of the matrix entries may be more effective.

Abstract. We develop a class of parallel multistep successive preconditioning strategies to enhance efficiency and robustness of standard sparse approximate inverse preconditioning techniques. The key idea is to compute a series of simple sparse matrices to approximate the inverse of the original matrix. Studies are conducted to show the advantages of such an approach in terms of both improving preconditioning accuracy and reducing computational cost, compared to the standard sparse approximate inverse preconditioners. Numerical experiments using one prototype implementation to solve a few sparse matrices on a distributed memory parallel computer are reported.

A multi-level preconditioned iterative method based on a multi-level block ILU factorization preconditioning technique is introduced and is applied to the solution of unstructured sparse linear systems arising from the numerical simulation of coating problems. The coefficient matrices usually have several rows with zero diagonal values that may cause stability difficulty in standard ILU factorization techniques. The new preconditioning strategy employs a diagonal threshold tolerance and a local reordering of individual blocks to increase robustness of the multi-level block ILU factorization process. Keywords: sparse matrices, multi-level preconditioning, ILU factorization 1 Introduction In this paper, a multi-level block incomplete LU (ILU) preconditioning technique is designed for solving unstructured sparse linear systems from the numerical simulation of coating problems. Coating is a delicate process of putting a layer of one liquid material (film) over another solid material unifo...

The recently developed Parallel Algebraic Recursive Multilevel Solver (pARMS) is the subject of this paper. We investigate its behavior in solving large-scale sparse linear systems. In particular, we study the eect of a few parameters and dierent algorithms on the overall performance by conducting numerical experiments that stem from a number of realistic applications including magneto-hydrodynamics, nonlinear acoustic eld simulation, and tire design.

When solving a linear system in parallel, a large overhead in using an incomplete LU factorization as a preconditioner may annihilate any gains made from the improved convergence. This overhead is due to the inherently sequential nature of such a preconditioning. Multicoloring of the subdomains assigned to processors is a common remedy for increasing the parallelism of a global ordering. However, the achieved degree of parallelism is still limited since different colors must be processed sequentially. Further reductions of the parallel overhead are possible. Here we suggest several strategies to decrease the idle time in the multicolor block Gauss-Seidel preconditioning. 1 Introduction Parallel preconditioners may be developed in two distinct ways: extracting parallelism from efficient sequential techniques or designing a preconditioner from the start specifically for parallel platforms. Preconditioners developed from the first approach yield the same good convergence properti...

Abstract. We investigate the use of the multistep successive preconditioning strategies (MSP) to construct a class of parallel multilevel sparse approximate inverse (SAI) preconditioners. We do not use independent set ordering, but a diagonal dominance based matrix permutation to build a multilevel structure. The purpose of introducing multilevel structure into SAI is to enhance the robustness of SAI for solving difficult problems. Forward and backward preconditioning iteration and two Schur complement preconditioning strategies are proposed to improve the performance and to reduce the storage cost of the multilevel preconditioners. One version of the parallel multilevel SAI preconditioner based on the MSP strategy is implemented. Numerical experiments for solving a few sparse matrices on a distributed memory parallel computer are reported. Key words. Sparse matrices, parallel preconditioning, sparse approximate inverse, multilevel preconditioning, multistep successive preconditioning. 1. Introduction. Large