Parallel Algorithm with Parameters Based on Alternating Direction for Solving Banded Linear Systems | Zendy

Xinrong Ma | Zendy; Sanyang Liu | Zendy; Manyu Xiao | Zendy; Gongnan Xie | Zendy

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Parallel Algorithm with Parameters Based on Alternating Direction for Solving Banded Linear Systems

Author(s) -

Xinrong Ma,

Sanyang Liu,

Manyu Xiao,

Gongnan Xie

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/752651

Subject(s) - coefficient matrix , algorithm , iterative method , convergence (economics) , computer science , hermite polynomials , parallel algorithm , matrix (chemical analysis) , distributed memory , rate of convergence , space (punctuation) , system of linear equations , parallel computing , mathematics , shared memory , geometry , mathematical analysis , key (lock) , eigenvalues and eigenvectors , physics , materials science , computer security , quantum mechanics , economics , composite material , economic growth , operating system

An efficient parallel iterative method with parameters on distributed-memory multicomputer is investigated for solving the banded linear equations in this work. The parallel algorithm at each iterative step is executed using alternating direction by splitting the coefficient matrix and using parameters properly. Only it twice requires the communications of the algorithm between the adjacent processors, so this method has high parallel efficiency. Some convergence theorems for different coefficient matrices are given, such as a Hermite positive definite matrix or an -matrix. Numerical experiments implemented on HP rx2600 cluster verify that our algorithm has the advantages over the multisplitting one of high efficiency and low memory space, which has a considerable advantage in CPU-times costs over the BSOR one. The efficiency for Example 1 is better than BSOR one significantly. As to Example 2, the acceleration rates and efficiency of our algorithm are better than the PEk inner iterative one.

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