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arXiv 1808.03595math.NAcs.NAphysics.comp-ph
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迈向星辰——一种线性缩放的椭圆求解器用于p-多网格

Scaling to the stars -- a linearly scaling elliptic solver for $p$-multigrid

Institute of Fluid Mechanics, TU Dresden(德累斯顿工业大学流体力学研究所);Center for Advancing Electronics Dresden (cfaed)(德累斯顿先进电子中心)

Immo Huismann, Jörg Stiller, Jochen Fröhlich

AI总结:

本文提出了一种线性缩放的椭圆求解器,用于p-多网格方法,通过推导出一种矩阵自由的显式逆运算,实现了在立方子域内的静态凝结操作的高效求解,从而提高了高阶方法的适用性。

摘要:

高阶方法在计算流体力学中受到越来越多的关注。然而,由于不可压缩情况下的半隐式时间步进导致的时间步长限制,这些方法的潜在优势严重依赖于高效的椭圆求解器。由于操作次数与多项式次数p和自由度数目n_DOF成正比,目前最好的多网格求解器的运行时间与O(p·n_DOF)成正比。这种与p的缩放显著降低了高阶方法在高阶应用中的适用性。虽然残差评估的算子可以使用静态凝结进行线性化,但Schwarz型平滑器需要在其固定子域上求其逆。在凝结情况下,没有已知的显式逆,矩阵-矩阵乘法与p·n_DOF成正比。本文推导出一个矩阵自由的显式逆,用于静态凝结操作在立方子域中的逆运算。该逆运算每个元素的缩放为p³,即全局n_DOF,从而允许线性缩放的加性Schwarz平滑器,产生一个运算次数为O(n_DOF)的p-多网格循环。所得到的求解器在所有多项式次数下使用少于四次迭代以将残差减少十个数量级,并且对于多项式次数至少达到48时,其运行时间与n_DOF线性缩放。此外,当使用CPU的一个核心时,在广泛参数范围内,运行时间小于每个未知数一微秒,从而使得不可压缩纳维-斯托克斯方程的时间步进中,显式处理的对流项所用时间与椭圆求解器所用时间相同。

英文摘要:

High-order methods gain increased attention in computational fluid dynamics. However, due to the time step restrictions arising from the semi-implicit time stepping for the incompressible case, the potential advantage of these methods depends critically on efficient elliptic solvers. Due to the operation counts of operators scaling with with the polynomial degree $p$ times the number of degrees of freedom $n_{\mathrm{DOF}}$, the runtime of the best available multigrid solvers scales with $\mathcal{O}( p \cdot n_{\mathrm{DOF}})$. This scaling with $p$ significantly lowers the applicability of high-order methods to high orders. While the operators for residual evaluation can be linearized when using static condensation, Schwarz-type smoothers require their inverses on fixed subdomains. No explicit inverse is known in the condensed case and matrix-matrix multiplications scale with ${p \cdot n_{\mathrm{DOF}}}$. This paper derives a matrix-free explicit inverse for the static condensed operator in a cuboidal subdomain. It scales with $p^3$ per element, i.e. ${n_{\mathrm{DOF}}}$ globally, and allows for a linearly scaling additive Schwarz smoother, yielding a $p$-multigrid cycle with an operation count of $\mathcal{O}(n_{\mathrm{DOF}})$. The resulting solver uses fewer than four iterations for all polynomial degrees to reduce the residual by ten orders and has a runtime scaling linearly with ${n_{\mathrm{DOF}}}$ for polynomial degrees at least up to $48$. Furthermore the runtime is less than one microsecond per unknown over wide parameter ranges when using one core of a CPU, leading to time-stepping for the incompressible Navier-Stokes equations using as much time for explicitly treated convection terms as for the elliptic solvers.

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