Telescopic hybrid fast solver for 3D elliptic problems with point singularities

This paper describes a telescopic solver for two dimensional h adaptive grids with point singularities. The input for the telescopic solver is an h refined two dimensional computational mesh with rectangular finite elements. The candidates for point singularities are first localized over the mesh by using a greedy algorithm. Having the candidates for point singularities, we execute either a direct solver, that performs multiple refinements towards selected point singularities and executes a parallel direct solver algorithm which has logarithmic cost with respect to refinement level. The direct solvers executed over each candidate for point singularity return local Schur complement matrices that can be merged together and submitted to iterative solver. In this paper we utilize a parallel multi-thread GALOIS solver as a direct solver. We use Incomplete LU Preconditioned Conjugated Gradients (ILUPCG) as an iterative solver. We also show that elimination of point singularities from the refined mesh reduces significantly the number of iterations to be performed by the ILUPCG iterative solver

Towards Domain Decomposition with Balanced Halo

International audienceNested Dissection has been introduced by A. George and is a well-known and very popular heuristic for sparse matrix ordering to reduce both the fill-in and the operation count during the numerical factorization. Considering now hybrid methods mixing both direct and iterative solvers, obtaining a domain decomposition leading to a good balancing of both the size of domain interiors and the size of interfaces is a key point for load balancing and efficiency in a parallel context. For this purpose, we revisit the algorithm introduced by Lipton, Rose and Tarjan which per- formed the recursion in a different manner

Nested dissection with balanced halo

International audienceNested Dissection has been introduced by A. George and is a well-known and very popular heuristic for sparse matrix ordering to reduce both the fill-in and the operation count during the numerical factorization. Considering now hybrid methods mixing both direct and iterative solvers, obtaining a domain decomposition leading to a good balancing of both the size of domain interiors and the size of interfaces is a key point for load balancing and efficiency in a parallel context. For this purpose, we revisit the algorithm introduced by Lipton, Rose and Tarjan which per- formed the recursion in a different manner

Hierarchical Schur complement preconditioner for the stochastic Galerkin finite element methods

Use of the stochastic Galerkin finite element methods leads to large systems of linear equations obtained by the discretization of tensor product solution spaces along their spatial and stochastic dimensions. These systems are typically solved iteratively by a Krylov subspace method. We propose a preconditioner which takes an advantage of the recursive hierarchy in the structure of the global matrices. In particular, the matrices posses a recursive hierarchical two-by-two structure, with one of the submatrices block diagonal. Each one of the diagonal blocks in this submatrix is closely related to the deterministic mean-value problem, and the action of its inverse is in the implementation approximated by inner loops of Krylov iterations. Thus our hierarchical Schur complement preconditioner combines, on each level in the approximation of the hierarchical structure of the global matrix, the idea of Schur complement with loops for a number of mutually independent inner Krylov iterations, and several matrix-vector multiplications for the off-diagonal blocks. Neither the global matrix, nor the matrix of the preconditioner need to be formed explicitly. The ingredients include only the number of stiffness matrices from the truncated Karhunen-Lo\`{e}ve expansion and a good preconditioned for the mean-value deterministic problem. We provide a condition number bound for a model elliptic problem and the performance of the method is illustrated by numerical experiments.Comment: 15 pages, 2 figures, 9 tables, (updated numerical experiments

Memory Optimization to Build a Schur Complement in an Hybrid Solver

Solving linear system $Ax=b$ in parallel where $A$ is a large sparse matrix is a very recurrent problem in numerical simulations. One of the state-of-the-art most promising algorithm is the hybrid method based on domain decomposition and Schur complement. In this method, a direct solver is used as a subroutine on each subdomain matrix. This approach is subject to serious memory overhead. In this paper, we investigate new techniques to reduce memory consumption during the build of the Schur complement by a direct solver. Our method allows memory peak reduction from 10% to 30% on each processus for typical test cases

Hierarchical hybrid sparse linear solver for multicore platforms

The solution of large sparse linear systems is a critical operationfor many numerical simulations. To cope with the hierarchical designof modern supercomputers, hybrid solvers based on Domain DecompositionMethods (DDM) have been been proposed. Among them, approachesconsisting of solving the problem on the interior of the domains witha sparse direct method and the problem on their interface with apreconditioned iterative method applied to the related SchurComplement have shown an attractive potential as they can combine therobustness of direct methods and the low memory footprint of iterativemethods. In this report, we consider an additive Schwarz preconditionerfor the Schur Complement, which represents a scalable candidate butwhose numerical robustness may decrease when the number of domainsbecomes too large. We thus propose a two-level MPI/thread parallelapproach to control the number of domains and hence the numericalbehaviour. We illustrate our discussion with large-scale matricesarising from real-life applications and processed on both a moderncluster and a supercomputer. We show that the resulting method canprocess matrices such as tdr455k for which we previously either ranout of memory on few nodes or failed to converge on a larger number ofnodes. Matrices such as Nachos_4M that could not be correctly processedin the past can now be efficiently processed up to a very large numberof CPU cores (24,576 cores). The corresponding code has beenincorporated into the MaPHyS package

A parallel direct/iterative solver based on a Schur complement approach.

8 pages double colonnesInternational audienceIn this paper, we present HIPS (Hierarchical Iterative Parallel Solver) a parallel sparse linear solver that combines effectively direct and iterative methods through a Schur complement approach. The corner stone of our method is to use a special decomposition and ordering of the matrix that allows to construct a reduced system and a robust preconditioner at low memory cost. The parallelization scheme we describe is original for this type of solver and provide a natural way to find a good trade-off between memory and convergence. Eventually, we give some results obtained by our solver on large referenced test cases