高性能云计算实现蛋白质间的完全对接
High-performance cloud computing for exhaustive protein-protein docking
摘要：
公共云计算环境，例如Amazon AWS、Microsoft Azure和Google Cloud Platform，近年来在计算性能方面取得了显著的改进，并且还有望能够执行大规模并行计算。由于云使用户能够随意使用成千上万的CPU核心和GPU加速器，并且各种软件类型可以通过云图像非常容易地使用，因此云开始被用于生物信息学领域。在这项研究中，我们将原始的蛋白质-蛋白质相互作用预测（蛋白质-蛋白质对接）软件MEGADOCK移植到Microsoft Azure中，作为HPC云环境的一个例子。使用4种CPU实例类型和2种GPU实例类型构建了最多1,600个CPU核心和960个GPU的云并行计算环境，并对并行计算性能进行了评估。我们在Azure系统上的MEGADOCK显示，当使用100个实例的H16实例与50个实例相比时，CPU实例的缩放值为0.93，当使用20个实例的NC 24实例与5个实例相比时，GPU实例的缩放值为0.89。此外，使用费和总计算时间的结果支持使用GPU实例减少了MEGADOCK的计算时间和计算所需的云使用费。部署在云上的开发环境具有高度的可移植性，使其适用于需要按需和大规模HPC环境的应用程序。
英文摘要：
Public cloud computing environments, such as Amazon AWS, Microsoft Azure, and the Google Cloud Platform, have achieved remarkable improvements in computational performance in recent years, and are also expected to be able to perform massively parallel computing. As the cloud enables users to use thousands of CPU cores and GPU accelerators casually, and various software types can be used very easily by cloud images, the cloud is beginning to be used in the field of bioinformatics. In this study, we ported the original protein-protein interaction prediction (protein-protein docking) software, MEGADOCK, into Microsoft Azure as an example of an HPC cloud environment. A cloud parallel computing environment with up to 1,600 CPU cores and 960 GPUs was constructed using four CPU instance types and two GPU instance types, and the parallel computing performance was evaluated. Our MEGADOCK on Azure system showed a strong scaling value of 0.93 for the CPU instance when H16 instance with 100 instances were used compared to 50, and a strong scaling value of 0.89 for the GPU instance when NC24 instance with 20 were used compared to 5. Moreover, the results of the usage fee and total computation time supported that using a GPU instance reduced the computation time of MEGADOCK and the cloud usage fee required for the computation. The developed environment deployed on the cloud is highly portable, making it suitable for applications in which an on-demand and large-scale HPC environment is desirable.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
