Hierarchically semiseparable (HSS) matrix algorithms are emerging techniques in constructing the superfast direct solvers for both dense and sparse linear systems. Here, we develope a set of novel parallel algorithms for the key HSS operations that are used for solving large linear systems. These include the parallel rank-revealing QR factorization, the HSS constructions with hierarchical compression, the ULV HSS factorization, and the HSS solutions. The HSS tree based parallelism is fully exploited at the coarse level. The BLACS and ScaLAPACK libraries are used to facilitate the parallel dense kernel operations at the #12;ne-grained level. We have appplied our new parallel HSS-embedded multifrontal solver to the anisotropic Helmholtz equat...
The parallel linear equations solver capable of effectively using 1000þ processors becomes the bottl...
A novel fast scalable parallel algorithm is proposed for the solution of large 3-D scattering proble...
In this thesis, we study a important class of structured matrices: "Hierarchically Semi-Separable" m...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
We present a distributed-memory library for computations with dense structured matrices. A matrix is...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
The dissertation presents some fast direct solvers and efficient preconditioners mainly for sparse m...
Structured dense matrices result from boundary integral problems in electrostatics and geostatistics...
International audienceHierarchical matrices (H-matrices) have become important in applications where...
AbstractLeast Squares with QR-factorization (LSQR) method is a widely used Krylov subspace algorithm...
In this paper we review the technique of hierarchical matrices and put it into the context of black-...
The parallel linear equations solver capable of effectively using 1000þ processors becomes the bottl...
A novel fast scalable parallel algorithm is proposed for the solution of large 3-D scattering proble...
In this thesis, we study a important class of structured matrices: "Hierarchically Semi-Separable" m...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimina...
Abstract. Randomized sampling has recently been proven a highly efficient technique for computing ap...
We present a distributed-memory library for computations with dense structured matrices. A matrix is...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
The dissertation presents some fast direct solvers and efficient preconditioners mainly for sparse m...
Structured dense matrices result from boundary integral problems in electrostatics and geostatistics...
International audienceHierarchical matrices (H-matrices) have become important in applications where...
AbstractLeast Squares with QR-factorization (LSQR) method is a widely used Krylov subspace algorithm...
In this paper we review the technique of hierarchical matrices and put it into the context of black-...
The parallel linear equations solver capable of effectively using 1000þ processors becomes the bottl...
A novel fast scalable parallel algorithm is proposed for the solution of large 3-D scattering proble...
In this thesis, we study a important class of structured matrices: "Hierarchically Semi-Separable" m...