流行病中的分布式计算：可用于解决COVID-19的技术综述
Distributed Computing in a Pandemic: A Review of Technologies Available for Tackling COVID-19
摘要：
目前由SARS-CoV-2 β冠状病毒引起的COVID-19全球大流行已导致超过100万人死亡，并产生严重的社会经济影响，因此迫切需要找到解决关键研究挑战的方法。COVID-19的大部分研究都依赖于分布式计算。在本文中，我将回顾分布式架构--各种类型的集群、网格和云--这些架构可用于大规模、高吞吐量、高度并行地执行这些任务，也可用于协作工作。高性能计算（HPC）集群将用于执行大部分工作。用于减少SARS-CoV-2传播的几个大数据处理任务需要高吞吐量方法和Hadoop和Spark提供的各种工具，甚至使用商品硬件。大规模的COVID-19研究还利用了一些世界上最快的超级计算机，例如IBM的SUMMIT -用于针对SARS-CoV-2靶标的集成对接高通量筛选，用于药物再利用和高通量基因分析-以及Sentinel，这是一种基于XPE-Cray的系统，用于探索天然产物。网格计算促进了世界上第一台Exascale网格计算机的形成。这加速了COVID-19研究，通过并行计算对SARS-CoV-2刺突蛋白相互作用进行分子动力学模拟，并使用Folding@home平台使用超过100万台志愿者计算设备进行。网格和云也可以用于国际合作，通过访问重要的数据集和提供服务，使研究人员能够专注于研究，而不是耗时的数据管理任务。
英文摘要：
The current COVID-19 global pandemic caused by the SARS-CoV-2 betacoronavirus has resulted in over a million deaths and is having a grave socio-economic impact, hence there is an urgency to find solutions to key research challenges. Much of this COVID-19 research depends on distributed computing. In this article, I review distributed architectures -- various types of clusters, grids and clouds -- that can be leveraged to perform these tasks at scale, at high-throughput, with a high degree of parallelism, and which can also be used to work collaboratively. High-performance computing (HPC) clusters will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require high-throughput approaches, and a variety of tools, which Hadoop and Spark offer, even using commodity hardware. Extremely large-scale COVID-19 research has also utilised some of the world's fastest supercomputers, such as IBM's SUMMIT -- for ensemble docking high-throughput screening against SARS-CoV-2 targets for drug-repurposing, and high-throughput gene analysis -- and Sentinel, an XPE-Cray based system used to explore natural products. Grid computing has facilitated the formation of the world's first Exascale grid computer. This has accelerated COVID-19 research in molecular dynamics simulations of SARS-CoV-2 spike protein interactions through massively-parallel computation and was performed with over 1 million volunteer computing devices using the Folding@home platform. Grids and clouds both can also be used for international collaboration by enabling access to important datasets and providing services that allow researchers to focus on research rather than on time-consuming data-management tasks.
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