在天河1a超级计算机上平衡CPU-GPU协同高阶CFD仿真

Chuanfu Xu, Lilun Zhang, Xiaogang Deng, Jianbin Fang, Guang-Xiong Wang, Wei Cao, Yonggang Che, Yongxian Wang, Wei Liu
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引用次数: 9

摘要

HOSTA是一个内部高阶CFD软件,可以模拟复杂几何形状的复杂流动。使用HOSTA进行大规模高阶CFD模拟需要大量HPC资源,因此促使我们将其移植到像天河1a这样的现代GPU加速超级计算机上。为了实现更高的加速并充分挖掘天河1a的潜力,我们将CPU和GPU协作用于HOSTA,而不是使用单纯的GPU方法。我们提出了多种新颖的技术来平衡存储能力差的GPU和存储能力强的CPU之间的负载,并尽可能地实现协同计算和通信的重叠。考虑到CPU和GPU的负载平衡,我们将HOSTA的每个天河1a节点的最大模拟问题大小提高了2.3倍,同时与仅使用GPU的方法相比,协作方法可以提高约45%的性能。可扩展性测试表明,HOSTA可以在1024个天河1a节点上实现60%以上的并行效率。利用该方法,我们成功地模拟了包含150M网格单元的中国大型民用飞机C919。据我们所知,这是第一篇报道如此复杂网格几何的CPUGPU协同高阶精确气动仿真结果的论文。
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Balancing CPU-GPU Collaborative High-Order CFD Simulations on the Tianhe-1A Supercomputer
HOSTA is an in-house high-order CFD software that can simulate complex flows with complex geometries. Large scale high-order CFD simulations using HOSTA require massive HPC resources, thus motivating us to port it onto modern GPU accelerated supercomputers like Tianhe-1A. To achieve a greater speedup and fully tap the potential of Tianhe-1A, we collaborate CPU and GPU for HOSTA instead of using a naive GPU-only approach. We present multiple novel techniques to balance the loads between the store-poor GPU and the store-rich CPU, and overlap the collaborative computation and communication as far as possible. Taking CPU and GPU load balance into account, we improve the maximum simulation problem size per Tianhe-1A node for HOSTA by 2.3X, meanwhile the collaborative approach can improve the performance by around 45% compared to the GPU-only approach. Scalability tests show that HOSTA can achieve a parallel efficiency of above 60% on 1024 Tianhe-1A nodes. With our method, we have successfully simulated China's large civil airplane configuration C919 containing 150M grid cells. To our best knowledge, this is the first paper that reports a CPUGPU collaborative high-order accurate aerodynamic simulation result with such a complex grid geometry.
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