Parallel Iterative Solution Methods for Linear Systems arising from Discretized PDE's
Parallel Iterative Solution Methods for Linear Systems arising from Discretized PDE's
复制标题
由离散偏微分方程产生的线性系统的并行迭代求解方法
DOI：
--
复制
发表时间：
1995
期刊：
影响因子：
--
通讯作者：
H. A. Vorst
中科院分区：
文献类型：
--
作者：
H. A. Vorst
In these notes we will present an overview of a number of related iterative methods for the solution of linear systems of equations. These methods are so-called Krylov projection type methods and the include popular methods as Conjugate Gradients, Bi-Conjugate Gradients, CGST Bi-CGSTAB, QMR, LSQR and GMRES. We will show how these methods can be derived from simple basic iteration formulas. We will not give convergence proofs, but we will refer for these, as far as available, to litterature.
Iterative methods are often used in combination with so-called preconditioning operators (approximations for the inverses of the operator of the system to be solved). Since these preconditions are not essential in the derivation of the iterative methods, we will not give much attention to them in these notes. However, in most of the actual iteration schemes, we have included them in order to facilitate the use of these schemes in actual computations.
For the application of the iterative schemes one usually thinks of linear sparse systems, e.g., like those arising in the finite element or finite difference approximatious of (systems of) partial differential equations. However, the structure of the operators plays no explicit role in any of these schemes, and these schemes might also successfully be used to solve certain large dense linear systems. Depending on the situation that might be attractive in terms of numbers of floating point operations.
It will turn out that all of the iterative are parallelizable in a straight forward manner. However, especially for computers with a memory hierarchy (i.e. like cache or vector registers), and for distributed memory computers, the performance can often be improved significantly through rescheduling of the operations. We will discuss parallel implementations, and occasionally we will report on experimental findings.
DOI：
10.1137/0913035
发表时间：
1992-03-01
期刊：
SIAM JOURNAL ON SCIENTIFIC AND STATISTICAL COMPUTING
影响因子：
--
作者：
VANDERVORST, HA
通讯作者：
VANDERVORST, HA