曲线坐标系下高阶有限差分法加速CFD对现代GPU团簇的模拟
Accelerating CFD simulation with high order finite difference method on curvilinear coordinates for modern GPU clusters
摘要：
高雷诺数复杂几何体的高精度流动模拟（$Re$）仍然是一个具有挑战性的课题，这对高性能计算系统的计算能力提出了更高的要求。然而，传统CPU架构的高性能计算由于其高功耗和技术难度而受到瓶颈。异构体系结构计算被认为是解决高性能计算开发困难的一个有效途径。GPU加速技术已应用于结构化网格上的低阶格式CFD求解器和非结构化网格上的高阶格式CFD求解器。结构网格上的高阶有限差分方法具有高效、鲁棒、低存储等优点，但高阶有限差分格式点间依赖性强的特点限制了其在GPU平台上的应用。在目前的工作中，我们提出了一套硬件感知技术，以优化CPU和GPU之间的数据传输效率，以及GPU之间的通信效率。将一个基于曲线坐标系的高阶有限差分法的多块结构化CFD求解器移植到GPU平台上，在单个CPU核上获得了令人满意的性能，最大加速比约2000倍。本文的工作为在现有GPU异构计算机上采用某些高阶有限差分方法将GPU计算应用于CFD模拟提供了有效解决方案。测试结果表明，对于不同的GPU，该算法都能取得显著的加速效果。
英文摘要：
A high fidelity flow simulation for complex geometries for high Reynolds number ($Re$) flow is still very challenging, which requires more powerful computational capability of HPC system. However, the development of HPC with traditional CPU architecture suffers bottlenecks due to its high power consumption and technical difficulties. Heterogeneous architecture computation is raised to be a promising solution of difficulties of HPC development. GPU accelerating technology has been utilized in low order scheme CFD solvers on structured grid and high order scheme solvers on unstructured meshes. The high order finite difference methods on structured grid possess many advantages, e.g. high efficiency, robustness and low storage, however, the strong dependence among points for a high order finite difference scheme still limits its application on GPU platform. In present work, we propose a set of hardware-aware technology to optimize the efficiency of data transfer between CPU and GPU, and efficiency of communication between GPUs. An in-house multi-block structured CFD solver with high order finite difference methods on curvilinear coordinates is ported onto GPU platform, and obtain satisfying performance with speedup maximum around 2000x over a single CPU core. This work provides efficient solution to apply GPU computing in CFD simulation with certain high order finite difference methods on current GPU heterogeneous computers. The test shows that significant accelerating effects can been achieved for different GPUs.
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