SIAM Journal on Scientific Computing, Volume 43, Issue 1, Page C1-C30, January 2021.
We present a study of the effectiveness of asynchronous incomplete LU factorization preconditioners for the time-implicit solution of compressible flow problems while exploiting thread-parallelism within a compute node. A block variant of the asynchronous fine-grained parallel preconditioner adapted to a finite volume discretization of the compressible Navier--Stokes equations on unstructured grids is presented, and convergence theory is extended to the new variant. Experimental (numerical) results on the performance of these preconditioners on inviscid and viscous laminar two-dimensional steady-state test cases are reported. It is found, for these compressible flow problems, that the block variant performs much better in terms of convergence, parallel scalability, and reliability than the original scalar asynchronous ILU preconditioner. For viscous flow, it is found that the ordering of unknowns may determine the success or failure of asynchronous block-ILU preconditioning, and an ordering of grid cells suitable for solving viscous problems is presented.

中文翻译：

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异步不完整块LU预条件器，用于非结构化网格上的计算流体动力学

SIAM科学计算杂志，第43卷，第1期，C1-C30页，2021年1月。
我们提出了对异步不完全LU分解前置条件对可压缩流问题的时间隐式解决方案的有效性的研究，同时利用了计算节点内的线程并行性。提出了异步细粒度并行预处理器的块变体，该变体适用于非结构化网格上可压缩Navier-Stokes方程的有限体积离散化，并且收敛理论扩展到了新变体。报告了这些预处理器在粘性和粘性层流二维稳态测试用例上的性能的实验（数字）结果。对于这些可压缩流问题，发现该块变量在收敛性，并行可伸缩性和可靠性方面比原始的标量异步ILU预处理器表现更好。