MAC网格上不可压缩Navier-Stokes方程的优化GPU仿真

L. Itu, F. Moldoveanu, A. Postelnicu, C. Suciu
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引用次数: 4

摘要

本文介绍了一种基于GPU的不可压缩Navier-Stokes方程的优化实现,该方程采用人工可压缩性方法求解。数值格式基于有限差分法。所模拟的域是一个面向后向的阶跃问题,离散化是在MAC网格上进行的。最耗时的部分,即速度和压力值的计算,已经转移到GPU上。由于GPU网格块之间没有通信,所以定义了两个独立的内核。一些优化策略逐渐提高了这两个内核的性能。最重要的是:合并的全局内存,减少读取和复制操作以及共享内存的最佳使用。CPU和GPU性能之间的比较结果表明,从最粗糙的网格的不到一个数量级到最精细的网格的两个数量级的加速变化。
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Optimized GPU based simulation of the incompressible Navier-Stokes equations on a MAC grid
The paper introduces an optimized GPU based implementation of the incompressible Navier-Stokes equations which are solved using the artificial compressibility method. The numerical scheme is based on a finite difference method. The domain on which the simulations have been performed is a backward facing step problem and the discretizations have been carried out on a MAC grid. The most time consuming parts, i.e. the computations of the velocities and of the pressure values, have been moved onto the GPU. Two separate kernels have been defined because there is no communication between the blocks of the GPU grid. Several optimization strategies have incrementally increased the performance of the two kernels. The most important ones are: coalesced global memory, reduced read and copy operations and optimum usage of shared memory. The results of the comparison between the CPU and GPU performance indicate a speed-up which varies from just under one order of magnitude for the coarsest grid up to two orders of magnitude for the finest grid.
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