Distributed Computing in a Pandemic

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
J. Alnasir
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引用次数: 2

Abstract

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.
大流行中的分布式计算
当前由SARS-CoV-2乙型冠状病毒引起的COVID-19全球大流行已导致100多万人死亡,并正在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办法。COVID-19的大部分研究都依赖于分布式计算。在本文中,我将回顾分布式体系结构——各种类型的集群、网格和云——它们可以用于大规模、高吞吐量、高度并行地执行这些任务,还可以用于协同工作。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各种工具,Hadoop和Spark提供了这些方法,甚至使用商用硬件。极其大规模的COVID-19研究也利用了一些世界上最快的超级计算机,如IBM的SUMMIT——用于对SARS-CoV-2靶点进行集合对接高通量筛选,以进行药物重新利用,以及高通量基因分析——以及Sentinel,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百亿亿次网格计算机的形成。通过大规模并行计算,这加快了COVID-19研究在SARS-CoV-2刺突蛋白相互作用的分子动力学模拟中的速度,并使用Folding@home平台使用超过100万台志愿者计算设备进行了研究。网格和云都可以用于国际合作,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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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