首页> 外文期刊>Journal of Computational Physics >PARALLEL BLOCK PRECONDITIONING TECHNIQUES FOR THE NUMERICAL SIMULATION OF THE SHALLOW WATER FLOW USING FINITE ELEMENT METHODS
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PARALLEL BLOCK PRECONDITIONING TECHNIQUES FOR THE NUMERICAL SIMULATION OF THE SHALLOW WATER FLOW USING FINITE ELEMENT METHODS

机译:有限元法数值模拟浅水流动的并行块预处理技术

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In this paper, we report our work on applying Krylov iterative methods, accelerated by parallelizable domain-decomposed (DD) preconditioners, to the solution of nonsymmetric linear algebraic equations arising from implicit time discretization of a finite element model of the shallow water equations on a limited-area domain. Two types of previously proposed DD preconditioners are employed and a novel one is advocated to accelerate, with post-preconditioning, the convergence of three popular and competitive Krylov iterative linear solvers. Performance sensitivities of these preconditioners to inexact subdomain solvers are also reported. Autotasking, the parallel processing capability representing the third phase of multitasking libraries on GRAY Y-MP, has been exploited and successfully applied to both loop and subroutine level parallelization. Satisfactory speedup results were obtained. On the other hand, automatic loop-level parallelization, made possible by the autotasking preprocessor, attained only a speedup smaller than a factor of two. (C) 1995 Academic Press, Inc. [References: 39]
机译:在本文中,我们报告了将Krylov迭代方法(由可并行域分解(DD)预条件子加速)应用于非对称线性代数方程组的解决方案的工作,该方程由隐式时间离散化了一个浅水方程组的有限元模型引起。有限区域域。使用了两种先前提出的DD前置条件器,并且提倡一种新颖的DD前置条件器,通过后置前置条件,可以加速三个流行且有竞争力的Krylov迭代线性求解器的收敛。还报道了这些预处理器对不精确的子域求解器的性能敏感性。自动任务处理是并行处理能力,代表了GREY Y-MP上多任务库的第三阶段,已被开发并成功应用于循环和子例程级并行化。获得了令人满意的加速结果。另一方面,自动任务预处理器使自动循环级并行化仅实现了小于二分之一的加速。 (C)1995 Academic Press,Inc. [参考:39]

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