We present an efficient algorithmic framework for constructing multi-level hp-bases that uses a data-oriented approach that easily extends to any number of dimensions and provides a natural framework for performance-optimized implementations. We only operate on the bounding faces of finite elements without considering their lower-dimensional topological features and demonstrate the potential of the presented methods using a newly written open-source library. First, we analyze a Fichera corner and show that the framework does not increase runtime and memory consumption when compared against the classical p-version of the finite element method. Then, we compute a transient example with dynamic refinement and derefinement, where we also obtain the expected convergence rates and excellent performance in computing time and memory usage.
翻译:我们提出了一个高效的算法框架,用于构建多层次的 hp 基准,这一算法框架使用一种易于扩展至任何层面的数据导向方法,并为绩效优化实施提供了一个自然框架。我们只是在不考虑其较低维度的地形特征的情况下在有限要素的交错面上运作,并使用新书开源图书馆展示所呈现的方法的潜力。首先,我们分析一个Fichera角落,并表明与传统的有限要素转换方法相比,该框架不会增加运行时间和记忆消耗。然后,我们用动态的完善和精细化来计算一个短暂的例子,我们在那里也获得了预期的趋同率和计算时间和记忆使用方面的出色业绩。