论文标题
使用有限元部分组件加速高阶网格优化
Accelerating High-Order Mesh Optimization Using Finite Element Partial Assembly on GPUs
论文作者
论文摘要
在本文中,我们提出了一种基于高阶有限元素的新的面向GPU的网格优化方法。我们的方法依赖于固定拓扑的节点运动,通过目标 - 矩阵优化范式(TMOP),并在整个计算网格上使用全局非线性求解,即所有网格节点都一起移动。该方法的一个关键属性是,在有限元操作方面,网格优化过程是重铸造的,这使我们能够利用GPU加速高阶高阶有限元算法领域的最新进展。例如,我们通过使用张量分解和无基质方法来减少数据运动,与传统的完整有限元矩阵组件相比,它们具有较高的性能特征,并为基于GPU的HPC硬件提供了优势。我们描述了该方法的主要数学组成部分及其有效的面向GPU的实现。此外,我们提出了一项易于重现的网格优化测试,可以作为网格优化社区的性能基准。
In this paper we present a new GPU-oriented mesh optimization method based on high-order finite elements. Our approach relies on node movement with fixed topology, through the Target-Matrix Optimization Paradigm (TMOP) and uses a global nonlinear solve over the whole computational mesh, i.e., all mesh nodes are moved together. A key property of the method is that the mesh optimization process is recast in terms of finite element operations, which allows us to utilize recent advances in the field of GPU-accelerated high-order finite element algorithms. For example, we reduce data motion by using tensor factorization and matrix-free methods, which have superior performance characteristics compared to traditional full finite element matrix assembly and offer advantages for GPU-based HPC hardware. We describe the major mathematical components of the method along with their efficient GPU-oriented implementation. In addition, we propose an easily reproducible mesh optimization test that can serve as a performance benchmark for the mesh optimization community.