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Using multiple levels of parallelism to enhance the performance of domain decomposition solvers

机译:使用多个并行级别来增强域分解求解器的性能

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摘要

Large-scale scientific simulations are nowadays fully integrated in many scientific and industrial applications. Many of these simulations rely on modelisations based on PDEs that lead to the solution of huge linear or nonlinear systems of equations involving millions of unknowns. In that context, the use of large high performance computers in conjunction with advanced fully parallel and scalable numerical techniques is mandatory to efficiently tackle these problems.rnIn this paper, we consider a parallel linear solver based on a domain decomposition approach. Its implementation naturally exploits two levels of parallelism, that offers the flexibility to combine the numerical and the parallel implementation scalabilities. The combination of the two levels of parallelism enables an optimal usage of the computing resource while preserving attractive numerical performance. Consequently, such a numerical technique appears as a promising candidate for intensive simulations on massively parallel platforms.rnThe robustness and parallel numerical performance of the solver is investigated on large challenging linear systems arising from the finite element discretization in structural mechanics applications.
机译:如今,大型科学模拟已完全集成到许多科学和工业应用中。这些仿真中的许多仿真都基于基于PDE的建模,这些建模导致求解包含数百万个未知数的庞大的线性或非线性方程组。在这种情况下,必须结合使用大型高性能计算机和先进的完全并行和可扩展的数值技术来有效解决这些问题。在本文中,我们考虑一种基于域分解方法的并行线性求解器。它的实现自然会利用两个级别的并行性,这为组合数字和并行实现可扩展性提供了灵活性。并行度的两个级别的组合使计算资源得以最佳利用,同时又保留了引人注目的数值性能。因此,这种数值技术似乎是在大规模并行平台上进行密集模拟的有前途的候选者。在结构力学应用中由于有限元离散化而产生的具有挑战性的大型线性系统上,研究了求解器的鲁棒性和并行数值性能。

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