Massively parallel direction splitting algorithms for complex flow

高性能计算机（HPC）的当前发展趋势是创建具有非常大数量的核的分布式集群（下一代HPC预计具有大约一百万个核）。 因此，科学计算的仿真算法的最重要的特征之一是它们在大型并行集群上实现时的性能。 偏微分方程离散化的经典算法并不总是很好地适用于这样的任务。 最近，我们已经开发了一个非常快速和并行化的方向分裂算法的不可压缩和所谓的变密度不可压缩流。 它使我们能够执行涉及数十亿未知数的模拟，并使我们处于大规模并行计算流体动力学的最前沿。

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.