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On application of GPUs for modelling of hydrodynamic characteristics of screw marine propellers in OpenFOAM package

机译:GPU在OpenFoam包装中螺杆船舶螺旋桨流体动力学特性建模的应用

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摘要

OpenFOAM is a proven engineering tool for applied hydrodynamics numerical modeling which is typically characterized by complex geometries and large grids of 107-108 cells. Since such calculations are often very long and resource-intesive, any way of speeding them up is of high practical interest. Based on one practical problem of a screw propeller characteristics modeling, optimizations to OpenFOAM via the originally developed SLAE solution plugin is proposed. The plugin is based on SparseLinSol (SLS) library, developed by the authors. The library uses Krylov subspace iterative methods with the Classic AMG preconditioner to effectively solve large SLAEs on supercomputers and features original hybrid communications model which implements MPI and Posix Shared Memory combination. The library also is able to utilize NVIDIA GPU accelerators for a significant part of the implemented algorithms. Test results on 128-node computational system equipped with NVIDIA X2070 accelerators show that: (i) OpenFOAM numerical modeling results are close to those achieved with Star-CCM package and experimental results; (ii) developed SLAE solution methods are more robust than those implemented in original OpenFOAM GAMG-based SLAE solver; (iii) hybrid communication model improves solver scalability a lot and the solver scales linearly up to the maximum number of nodes used in current tests; (iv) GPU usage makes calculations 1.4-3 times faster; (v) SLS solver is faster than hypre solver on the same set of implemented methods and test matrices
机译:OpenFoam是一种经过验证的工艺数值模拟的经过验证的工程工具,其通常是复杂的几何形状和107-108个细胞的大网格。由于这种计算通常很长,资源不良,因此以高速加速它们的方式具有很高的实际兴趣。基于螺钉螺旋桨特性建模的一个实际问题,提出了通过最初开发的SLAE解决方案插件的优化开放式福诺姆。插件基于作者开发的Sparselinsol(SLS)库。库使用Krylov子空间与经典AMG预处理器的迭代方法,以有效地解决在超级计算机上大SLAEs和设有原混合通信模型,该模型工具MPI和POSIX共享内存组合。该库还能够利用NVIDIA GPU加速器,以实现实施算法的重要部分。测试结果128节点计算系统配备了NVIDIA X2070加速器,显示:(i)OpenFoam数值建模结果接近Star-CCM包和实验结果所达到的数字建模结果。 (ii)SLAE溶液方法比原始OpenFoam GAMG的SLAE求解器中实施的方法更加坚固; (iii)混合通信模型提高了求解器可伸缩性,并且求解器直线缩放到当前测试中使用的最大节点数量; (iv)GPU使用率使计算速度1.4-3倍; (v)SLS求解器比同一组实施方法和测试矩阵上的Hypre求解器更快

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