Reducing Complexity in Parallel Algebraic Multigrid Preconditioners
Reducing Complexity in Parallel Algebraic Multigrid Preconditioners
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DOI：
10.1137/040615729
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发表时间：
2004-09
期刊：
影响因子：
--
通讯作者：
H. Sterck;U. Yang;J. Heys
中科院分区：
文献类型：
--
作者：
H. Sterck;U. Yang;J. Heys
Algebraic multigrid (AMG) is a very efficient iterative solver and preconditioner for large unstructured linear systems. Traditional coarsening schemes for AMG can, however, lead to computational complexity growth as problem size increases, resulting in increased memory use and execution time, and diminished scalability. Two new parallel AMG coarsening schemes are proposed, that are based on solely enforcing a maximum independent set property, resulting in sparser coarse grids. The new coarsening techniques remedy memory and execution time complexity growth for various large three-dimensional (3D) problems. If used within AMG as a preconditioner for Krylov subspace methods, the resulting iterative methods tend to converge fast. This paper discusses complexity issues that can arise in AMG, describes the new coarsening schemes and examines the performance of the new preconditioners for various large 3D problems.
