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. Some important areas of focus are: developing a vaccine, designing or re-purposing existing pharmacological agents for treatment by identifying druggable targets, predicting and diagnosing the disease e.g. clinical decision support, and tracking and reducing the spread. Much of this COVID-19 research is dependent on computation, particularly distributed computing -- a model in which software and computational resources are distributed amongst networked computers and used collectively to solve complex, computationally demanding problems, and to process bigdata. In this article, I review distributed computing technologies -- 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. For each architecture, I provide a technical introduction and, where they exist, discuss COVID-19 focused projects that have utilised the technology, as well as projects that can be employed for this important research.
翻译:由SARS-COV-2 betacorona病毒引起的当前COVID-19全球流行病已造成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,一些重要的重点领域是:开发疫苗、设计或重新定位现有的治疗药剂,方法是确定可药目标、预测和诊断疾病,例如临床决策支助、跟踪和减少传播。这种COVID-19研究大部分取决于计算,特别是分布式计算 -- -- 一种在网络计算机之间分配软件和计算资源的模型,并集体用于解决复杂、计算要求高的问题和处理大数据。在文章中,我审查了分布式计算技术 -- -- 各类分类技术 -- -- 各种分类、电网和云 -- -- 能够用于规模化完成这些任务,在高通气量、高度平行性、跟踪和减少传播范围也能用于协作。对于每一种结构,我都提供了技术介绍,并在存在的情况下,讨论利用COVID-19重点项目来利用这一技术进行这种研究。