Abstract

The present work is devoted to accelerating the NOISEtte code and lowering its memory consumption. This code for scale-resolving supercomputer simulations of compressible turbulent flows is based on higher-accuracy methods for unstructured mixed-element meshes and hierarchical MPI \(+\) OpenMP parallelization for cluster systems with manycore processors. We demonstrate modifications of the underlying numerical method and its parallel implementation, which consist, in particular, in using a simplified approximation method for viscous fluxes and mixed floating-point precision. The modified version has been tested on several representative cases. The performance measurements and validation results are presented.

中文翻译：

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用于湍流尺度解析超级计算机仿真的NOISEtte代码加速

摘要

当前的工作致力于加速NOISEtte代码并降低其内存消耗。该代码用于可压缩湍流的尺度解析超级计算机仿真，是基于非结构化混合元素网格的高精度方法以及具有许多核处理器的集群系统的分层MPI \（+ \） OpenMP并行化的。我们演示了对基础数值方法及其并行实现的修改，特别是通过对粘性通量和混合浮点精度使用简化的近似方法。修改后的版本已在几种代表性案例中进行了测试。给出了性能测量和验证结果。