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Keywords: MUMPS, software, multifrontal, parallel, sparse, ... MUMPS (“MUltifrontal Massively Parallel Solver”) is a package for solving ...

Abstract. We present a fast and approximate multifrontal solver for large-scale sparse linear systems arising from nite-di erence, nite-volume or nite-element discretization of high-frequency wave equations. The proposed solver leverages the butter y algorithm and its hierarchical matrix

The solver developed, here, opens up the way to speed up significantly adjoint state computations in seismic applications for the purpose of full waveform inversion with a large number of events [18]. A strategy for time-harmonic full waveform inversion making use of a multifrontal solver for scalar waves was developed by [14].

PARALLEL MULTI-FRONTAL SOLVER ALGORITHM. All frontal matrices are generated at the same time. Procesor 1 Procesor 2 Procesor 3 Procesor 4 Procesor 5 ...

MUMPS (MUltifrontal Massively Parallel Sparse direct Solver) can solve very large linear systems through in/out-of-core LDLt or LU factorisation.

(2017) Large-scale 3-D EM modelling with a Block Low-Rank multifrontal direct solver. Geophysical Journal International 209 :3, 1558-1571. (2017) Evaluation of MATPOWER and OpenDSS load flow calculations in power systems using parallel computing.

The multifrontal method casts the factorization of a sparse matrix into a series of partial factorizations of many smaller dense matrices and Schur complement updates. It consists in a bottom-up traversal of the assembly tree following a topological order. Processing a node consists of four steps: 1.Assembling the frontal matrix F

14.04.2000 · Multifrontal methods The multifrontal method for the solution of sparse linear equations is a direct method based on the LU factorization of the matrix. We refer the reader to our earlier papers [2], [15], [16] for full details of this technique.

08.04.2015 · #Sparse MultiFrontal Package: A Fast Sparse Matrix Factorization Package This software is a sparse multifrontal solver with fast factorization capabilty. It uses Hierarchically off-diagonal low-rank (HODLR) structures to approximate the frontal matrices and arrives at a fast solver that performs extremely well as a GMRES preconditioner.

General usage information. MUMPS (the MUltifrontal Massively Parallel sparse direct Solver) is a solver for large linear systems. The following environmental variables have been defined:

A frontal solver, conceived by Bruce Irons, is an approach to solving sparse linear systems which is used extensively in finite element analysis. It is a variant of Gauss elimination that automatically avoids a large number of operations involving zero terms. A frontal solver builds a LU or Cholesky decompositionof a sparse matrix given as the assembly of element matrices by assembling the matrix and eliminating equations only on a subset of eleme…

Accelerating the multifrontal direct solve algorithm has been the subject ... ory efficient multifrontal solver for general sparse matrices, ...

MUMPS (MUltifrontal Massively Parallel Sparse direct Solver) can solve very large linear systems through in/out-of-core LDLt or LU factorisation.

Aug 29, 2018 · Intel® MKL Sparse QR [1] is a multifrontal sparse QR factorization method that relies on the processing of blocks of rows. The solver uses nested dissection ordering technique to reduce the fill-in of the factor R. The efficiency of its parallel implementation allows for this choice of ordering algorithm. In addition, the initial matrices can ...

MU ltifrontal M assively P arallel sparse direct S olver MUMPS Main Features Solution of large linear systems with symmetric positive definite matrices general symmetric matrices general unsymmetric matrices Real or complex arithmetic (single or double precision) Parallel factorization and solve phases (uniprocessor version also available)

The main features and the tuning of the algorithms for the direct solution of sparse linear systems on distributed memory computers developed in the context ...

The algorithms use a multifrontal approach and are especially designed to cover a large class of problems. The problems can be symmetric positive definite, general symmetric, or unsymmetric matrices, both possibly rank deficient, and they can be provided by the user in several formats.

A multifrontal solver of Duff and Reid is an improvement of the frontal solver that uses several independent fronts at the same time.

For this specific model problem, our solver is both faster and more memory ecient than a geometry-based multifrontal solver (which is further faster than general-purpose algebraic solvers such as MUMPS and SuperLU DIST). For the 6003mesh size, the structured factors from our solver need about 5.9timeslessmemory.

Linear equations solvers The multifrontal method is a direct method for solving systems of linear equations Ax = b, when A is a sparse matrix and x and bare ...

We present a structured parallel geometry-based multifrontal sparse solver using hierarchically semiseparable (HSS) representations and exploiting the ...

The multifrontal method for the solution of sparse linear equations is a direct method based on the LU factorization of the matrix. We refer the reader to our earlier papers [2], [15], [16] for full details of this technique.

Multifrontal methods transform or reorganize the task of factorizing a large sparse matrix into a sequence of partial factorization of smaller dense frontal ...

structured multifrontal algorithm in detail. Analysis of the rank properties and the com-plexities are presented in Section 3. In Section 5, we demonstrate the parallel performance of our geometric, sparse multifrontal solver. Section 6 is devoted to the conclusions. 2. REVIEW OF THE MULTIFRONTAL AND HSS-STRUCTURED MULTIFRONTAL METHODS

This paper proposes the application of unsymmetric multifrontal method to solve the differential algebraic equations (DAE) encountered in ...

29.08.2018 · Intel® Math Kernel Library (Intel® MKL) version 2019 introduces Sparse QR Solver. Intel® MKL Sparse QR [1] is a multifrontal sparse QR factorization method that relies on the processing of blocks of rows. The solver uses nested dissection ordering technique to reduce the fill-in of the factor R.