Spatial parallel disturbance region update method with OpenMP for steady compressible flows

Abstract

We present the spatial parallelization of the disturbance region update method (DRUM) with OpenMP, named the spatial parallel disturbance region update method (spDRUM). DRUM is an acceleration methodology for compressible flows, employing dynamic computational domains (DCDs) to eliminate the worthless computational effort of the conventional global-update method. However, the DCDs may cause a load-imbalance issue, making DRUM difficult to execute efficiently in parallel. In this work, parallel strategies of steps in spDRUM are introduced, and a dynamic load balancer that is established based on the equivalent number of cells in the DCDs and a combined grid partitioning method is proposed to exploit the full acceleration potential of spDRUM. Besides, a concept of sub-blocks that takes advantage of the memory-shared characteristic of OpenMP is embedded in the data structure to simplify the inner-boundary treatment. Numerical test cases demonstrate that spDRUM accomplishes remarkable convergence speed for solving all steady compressible flow problems; benefiting from the coupled contribution of parallelism and DCDs, the total speed-up ratio of spDRUM could be significantly greater than that of the ideal case ignoring parallel overheads and that of the global-update method with the same number of threads; the proposed grid partitioning method could generate various types of partitions efficiently and flexibly, keeping load balanced for multiblock structured grids; embedding the concept of sub-blocks in the data structure makes the number of inner-boundary cells unchanged and remains optimal, regardless of the variation in partitions.