Distributed Computing in a Pandemic: A Review of Technologies Available for Tackling COVID-19

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: vaccine development, designing or repurposing existing pharmacological agents for treatment by identifying druggable targets, predicting and diagnosing the disease, and tracking and reducing the spread. 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, which aggregate their compute nodes using high-bandwidth networking and support a high-degree of inter-process communication, are ubiquitous across scientific research - they will be used to carry out much of this work. Several bigdata processing tasks used in reducing the spread of SARS-CoV-2 require highthroughput 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. Likewise, RSC's TORNADO has been employed in aptamer design. 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.