Towards extreme scale dissipative particle dynamics simulations using multiple GPGPUs
Towards extreme scale dissipative particle dynamics simulations using multiple GPGPUs
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DOI：
10.1016/j.cpc.2020.107159
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发表时间：
2020-06-01
影响因子：
6.3
通讯作者：
O'Cais, Alan
中科院分区：
文献类型：
--
作者：
Castagna, Jony;Guo, Xiaohu;O'Cais, Alan
介绍了一种基于耗散粒子动力学方法的多GPGPU中尺度模拟系统的开发。这种分布式GPU加速开发是DL_MESO软件包到MPI+CUDA的扩展，目的是在混合CPU-GPU架构上利用最新NVIDIA显卡的计算能力。详细介绍了广泛适用的算法实现和内存合并数据结构。并对短程力作用下的粒子对最近邻表搜索、数据交换以及计算与通信的重叠等关键算法进行了优化。我们已经对多达4096个GPU进行了强和弱扩展性能分析。在瑞士国家超级计算机中心的Piz Daint超级计算机上运行了一个包含18亿个粒子的两相混合物分离测试用例。通过CUDA感知MPI、适当的GPU亲和性、多GPU版本的通信和计算重叠优化，最终优化结果显示弱伸缩效率超过94%，强伸缩效率超过80%。据我们所知，这是DPD模拟在大量GPU上运行的文献中的第一篇报道。最后还讨论了剩余的挑战和未来的工作。皇冠版权所有（C）2020由Elsevier B. V.出版
A multi-GPGPU development for Mesoscale Simulations using the Dissipative Particle Dynamics method is presented. This distributed GPU acceleration development is an extension of the DL_MESO package to MPI+CUDA in order to exploit the computational power of the latest NVIDIA cards on hybrid CPU-GPU architectures. Details about the extensively applicable algorithm implementation and memory coalescing data structures are presented. The key algorithms' optimizations for the nearest-neighbour list searching of particle pairs for short range forces, exchange of data and overlapping between computation and communications are also given. We have carried out strong and weak scaling performance analyses with up to 4096 GPUs. A two phase mixture separation test case with 1.8 billion particles has been run on the Piz Daint supercomputer from the Swiss National Supercomputer Center. With CUDA aware MPI, proper GPU affinity, communication and computation overlap optimizations for multi-GPU version, the final optimization results demonstrated more than 94% efficiency for weak scaling and more than 80% efficiency for strong scaling. As far as we know, this is the first report in the literature of DPD simulations being run on this large number of GPUs. The remaining challenges and future work are also discussed at the end of the paper. Crown Copyright (C) 2020 Published by Elsevier B.V.