The assembly of sparse matrices is a key operation in finite element methods. In this study we analyze several factors that may have an influence on the efficiency of the assembly procedure. Different insertion strategies are compared using two metrics: a Cost function (the number of memory movements) and actual computing time. An improved algorithm implemented in MATLAB is proposed. It reduces both memory operations and computing time for all tested cases. The efficiency of the assembly process is found to be highly dependent on node and element numbering. The effect of the classic reverse Cuthill–McKee algorithm is, in most cases, positive and reduces computation costs. Finally, the case where a sparse matrix has to be re-assembled at eac...
Efficient Matlab codes in 2D and 3D have been proposed recently to assemble finite element matrices....
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
This paper proposes a partial refactorization for faster nonlinear analysis based on sparse matrix s...
Abstract. We develop and implement in this paper a fast sparse assembly algorithm, the fundamental o...
In parallel finite element solvers, sparse matrix assembly is often a bottleneck. Implemented using ...
The finite element method (FEM) is one of the most commonly used techniques for the solution of part...
Abstract. We have extended the matrix computation language and environment Matlab to include sparse ...
The finite element method (FEM) is one of the most commonly used techniques for the solution of part...
We are concerned with the memory usage of sparse direct solvers. We particula- rly focus on the infl...
Abstract. In certain applications the non-zero elements of large sparse matrices are formed by addin...
A partial differential equation (PDE) is an equation relating functions of multiple variables, and t...
(eng) We are concerned with the memory usage of sparse direct solvers. We particularly focus on the ...
Abstract. Traditionally, numerical simulations based on finite element methods consider the algorith...
We discuss how to implement the linear finite element method for solving the Poisson equation. We be...
We describe different optimization techniques to perform the assembly of finite element matrices in ...
Efficient Matlab codes in 2D and 3D have been proposed recently to assemble finite element matrices....
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
This paper proposes a partial refactorization for faster nonlinear analysis based on sparse matrix s...
Abstract. We develop and implement in this paper a fast sparse assembly algorithm, the fundamental o...
In parallel finite element solvers, sparse matrix assembly is often a bottleneck. Implemented using ...
The finite element method (FEM) is one of the most commonly used techniques for the solution of part...
Abstract. We have extended the matrix computation language and environment Matlab to include sparse ...
The finite element method (FEM) is one of the most commonly used techniques for the solution of part...
We are concerned with the memory usage of sparse direct solvers. We particula- rly focus on the infl...
Abstract. In certain applications the non-zero elements of large sparse matrices are formed by addin...
A partial differential equation (PDE) is an equation relating functions of multiple variables, and t...
(eng) We are concerned with the memory usage of sparse direct solvers. We particularly focus on the ...
Abstract. Traditionally, numerical simulations based on finite element methods consider the algorith...
We discuss how to implement the linear finite element method for solving the Poisson equation. We be...
We describe different optimization techniques to perform the assembly of finite element matrices in ...
Efficient Matlab codes in 2D and 3D have been proposed recently to assemble finite element matrices....
AbstractThe matrix-vector multiplication operation is the kernel of most numerical algorithms.Typica...
This paper proposes a partial refactorization for faster nonlinear analysis based on sparse matrix s...