This article introduces two strategies to reduce the memory cost of the Adaptive Multipreconditioned FETI method (AMPFETI) while preserving its capability to solve ill conditioned systems efficiently. Their common principle is to gather search directions into aggregates which are frequently adapted in order to achieve the best compromise between the decrease of the solver error and the computational resources employed. The methods are assessed on two weak scalability studies on highly heterogeneous problems up to 10,368 cores and half a billion of unknowns, and on two ill-conditioned industrial applications, related to the numerical homogenization of solid propellant and to the simulation of a multiperforated aircraft combustion chamber.

本文介绍了两种策略，它们可以降低自适应多条件FETI方法（AMPFETI）的存储成本，同时保留其有效解决问题系统的能力。它们的共同原理是将搜索方向收集到集合中，这些集合经常进行调整，以便在减少求解器误差和使用计算资源之间取得最佳折衷。这些方法是在两项针对最多10,368个核心和十亿个未知数的高度异构问题的微弱可扩展性研究以及与固体推进剂的数值均质化和多孔飞机燃烧模拟相关的两种病态工业应用中进行评估的室。