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CFD-based analysis and two-level aerodynamic optimization on graphics processing units

机译：基于CFD的图形处理单元分析和二级空气动力学优化

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This paper presents the porting of 2D and 3D Navier-Stokes equations solvers for unstructured grids, from the CPU to the graphics processing unit (GPU; NVIDIA'S Ge-Force GTX 280 and 285), using the CUDA language. The performance of the GPU implementations, with single, double or mixed precision arithmetic operations, is compared to that of the CPU code.rnIssues regarding the optimal handling of the unstructured grid topology on the GPU, particularly for vertex-centered CFD algorithms, are discussed. Restructuring the existing codes was necessary in order to maximize the parallel efficiency of the GPU implementations. The mixed precision implementation, in which the left-hand-side operators are computed with single precision, was shown to bridge the gap between the single and double precision speed-ups. Based on the different speed-ups and prediction accuracy of the aforementioned GPU implementations of the Navier-Stokes equations solver, a hierarchical optimization method which is suitable for GPUs is proposed and demonstrated in inviscid and turbulent 2D flow problems. The search for the optimal solution(s) splits into two levels, both relying upon evolutionary algorithms (EAs) though with different evaluation tools each. The low level EA uses the very fast single precision GPU implementation with relaxed convergence criteria for the inexpensive evaluation of candidate solutions. Promising solutions are regularly broadcast to the high level EA which uses the mixed precision GPU implementation of the same flow solver. Single- and two-objective aerodynamic shape optimization problems are solved using the developed software.
机译：本文介绍了使用CUDA语言将2D和3D Navier-Stokes方程求解器从CPU移植到图形处理单元（GPU； NVIDIA的Ge-Force GTX 280和285）的非结构化网格。比较了具有单精度，双精度或混合精度算术运算的GPU实现的性能与CPU代码的性能。讨论了关于GPU上非结构化网格拓扑的最佳处理的问题，尤其是针对以顶点为中心的CFD算法。为了使GPU实现的并行效率最大化，必须重组现有代码。混合精度实现（其中左侧运算符以单精度计算）被证明可以弥补单精度和双精度加速之间的差距。基于Navier-Stokes方程求解器的上述GPU实现的不同提速和预测精度，提出了适用于GPU的分层优化方法，并在无粘性和湍流的二维流问题中得到了证明。对最佳解决方案的搜索分为两个级别，尽管每个级别都有不同的评估工具，但都依赖于进化算法（EA）。底层EA使用非常快速的单精度GPU实施以及宽松的收敛标准来对候选解决方案进行廉价评估。有希望的解决方案会定期广播到高级EA，该高级EA使用同一流求解器的混合精度GPU实现。使用开发的软件解决了单目标和两个目标的空气动力学形状优化问题。

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