Massively parallel direction splitting algorithms for complex flow

目前高性能计算机(HPC)的发展趋势是创建具有大量核心的分布式集群(下一代HPC预计将具有大约一百万个核心)。因此，科学计算模拟算法最重要的特点之一就是在大型并行集群上实现时的性能。传统的偏微分方程组离散化算法并不总是适合于这样的任务。最近，我们针对不可压缩流动和所谓的变密度不可压缩流动发展了一种非常快速且可并行化的方向分裂算法。它使我们能够进行涉及数十亿个未知数的模拟，并使我们处于大规模并行计算流体力学的前沿。

The current trend in the development of High Performance Computers (HPC) is to create distributed clusters of very large number of cores (the next generation of HPC are expected to have of the order of one million cores). Therefore, one of the most important features of simulation algorithms for scientific computing is their performance when implemented on large parallel clusters. The classical algorithms for discretization of Partial Differential Equations (PDEs) are not always well suited for such a task. Recently we have developed a very fast and parallelizable direction-splitting algorithm for incompressible and the so-called variable density incompressible flows. It enabled us to perform simulations involving billions of unknowns and placed us at the forefront of large scale parallel computational fluid dynamics.