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arXiv 2604.04644math.NAcs.NA

面向架构的h到p优化:混合元素网格的谱/hp元算子

Architecture-aware $h$-to-$p$ optimisation: spectral/$hp$ element operators for mixed-element meshes

Jacques Y. Xing, Boyang Xia, Diego Renner, Chris D. Cantwell, David Moxey, Robert M. Kirby, Spencer J. Sherwin

AI总结:

本文提出基于张量展开的混合元素网格谱/hp元算子,通过优化不同元素形状和多项式阶数的实现策略,提升计算性能。

摘要:

我们扩展了早期国际努力,将基于六面体的谱元方法在GPU和向量化的CPU上的优化扩展到包含棱柱、金字塔和四面体形状的混合网格。我们证明了有限元算子(如质量矩阵和霍普沃茨矩阵)的实现策略取决于元素形状、多项式阶数和系统架构,以实现最佳性能。此外,我们引入了一种新方法,高效评估涉及展开导数内积的复杂操作,如刚度矩阵。该方法利用节点张量展开的插值性质,与经典求积规则相结合,以最大化运算效率。我们的GPU性能测试显示,四面体元素的霍普沃茨算子吞吐量比六面体元素慢至多2.5倍,尽管四面体的浮点运算次数多六倍。

英文摘要:

We extend earlier international efforts to optimise hexahedral-based spectral element methods on GPUs and vectorised CPUs to mixed element meshes additionally involving prismatic, pyramidic, and tetrahedral shapes using tensorial expansions. We demonstrate that common finite element operators (such as the mass and Helmholtz matrices) benefit from alternative implementation strategies depending on the element shape, choice of polynomial order, and system architecture in order to achieve optimal performance. In addition, we introduce a new approach/interpretation to efficiently evaluate more complex operations involving inner products with the derivative of the expansions as part of the integrand such as the stiffness matrix. This approach seeks to maximise operations using the collocation properties of the nodal tensorial expansion associated with classical quadrature rules. Our GPU performance tests demonstrate that the throughput of the Helmholtz operator on tetrahedral elements is at most 2.5 times slower than on hexahedral elements, despite tetrahedra having a factor of six greater floating-point operations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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