The research reported in this paper presents a new idea of the storage structure of sparse matrices. This structure is used in the multi-option solver of linear equation systems with unsymmetrical sparse coefficient matrices. The new solver is compared comprehensively with the analogous (identical numerical method used) solver of the classical type. The tests of new solver have been performed on a quite broad spectrum of hardware platforms
For large scale problems in electric circuit simulation as well as in chemical process simulation, t...
The irregular nature of sparse matrix-vector multiplication, Ax = y, has led to the development of a...
The irregular nature of sparse matrix-vector multiplication, Ax = y, has led to the development of a...
Out-of-core sparse direct solvers reduce the amount of main memory needed to factorize and solve lar...
AbstractThe numerical treatment of many mathematical models (which arise, for example, in physics, c...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
Sparse matrices are often used in numerical algorithms that solve linear equation systems. Many meth...
A sparse matrix is a matrix with very few nonzero elements. Many applications in diverse fields gi...
Vectorized sparse solvers for direct solutions of positive-negative-indefinite symmetric systems of ...
The envelope data structure and the Choleski based (bordering) method for the solution of symmetric ...
Abstract. We investigate several ways to improve the performance of sparse LU factorization with par...
The coefficient matrix of a very large system of equations is generally very sparse, i. e., non-zero...
The multiplication of a sparse matrix by a dense vector is a centerpiece of scientific computing app...
AbstractCoarse grain parallel codes for solving sparse systems of linear algebraic equations can be ...
This paper describes implementations of eight algorithms of Newton and quasi-Newton type for solving...
For large scale problems in electric circuit simulation as well as in chemical process simulation, t...
The irregular nature of sparse matrix-vector multiplication, Ax = y, has led to the development of a...
The irregular nature of sparse matrix-vector multiplication, Ax = y, has led to the development of a...
Out-of-core sparse direct solvers reduce the amount of main memory needed to factorize and solve lar...
AbstractThe numerical treatment of many mathematical models (which arise, for example, in physics, c...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
Sparse matrices are often used in numerical algorithms that solve linear equation systems. Many meth...
A sparse matrix is a matrix with very few nonzero elements. Many applications in diverse fields gi...
Vectorized sparse solvers for direct solutions of positive-negative-indefinite symmetric systems of ...
The envelope data structure and the Choleski based (bordering) method for the solution of symmetric ...
Abstract. We investigate several ways to improve the performance of sparse LU factorization with par...
The coefficient matrix of a very large system of equations is generally very sparse, i. e., non-zero...
The multiplication of a sparse matrix by a dense vector is a centerpiece of scientific computing app...
AbstractCoarse grain parallel codes for solving sparse systems of linear algebraic equations can be ...
This paper describes implementations of eight algorithms of Newton and quasi-Newton type for solving...
For large scale problems in electric circuit simulation as well as in chemical process simulation, t...
The irregular nature of sparse matrix-vector multiplication, Ax = y, has led to the development of a...
The irregular nature of sparse matrix-vector multiplication, Ax = y, has led to the development of a...