Case study of SARS-CoV-2 transmission risk assessment in indoor environments using cloud computing resources

File

2021-GanapathysubramanianBaskar-CaseStudy.pdf (3.29 MB)

Date

2021

Authors

Saurabh, Kumar
Adavani, Santi
Tan, Kendrick
Ishii, Masado
Gao, Boshun
Krishnamurthy, Adarsh
Sundar, Hari

Publisher

arXiv

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Organizational Units

Mechanical Engineering

Organizational Unit

Electrical and Computer Engineering

Organizational Unit

Department

Mechanical EngineeringElectrical and Computer Engineering

Abstract

Complex flow simulations are conventionally performed on HPC clusters. However, the limited availability of HPC resources and steep learning curve of executing on traditional supercomputer infrastructure has drawn attention towards deploying flow simulation software on the cloud. We showcase how a complex computational framework -- that can evaluate COVID-19 transmission risk in various indoor classroom scenarios -- can be abstracted and deployed on cloud services. The availability of such cloud-based personalized planning tools can enable educational institutions, medical institutions, public sector workers (courthouses, police stations, airports, etc.), and other entities to comprehensively evaluate various in-person interaction scenarios for transmission risk. We deploy the simulation framework on the Azure cloud framework, utilizing the Dendro-kT mesh generation tool and PETSc solvers. The cloud abstraction is provided by RocketML cloud infrastructure. We compare the performance of the cloud machines with state-of-the-art HPC machine TACC Frontera. Our results suggest that cloud-based HPC resources are a viable strategy for a diverse array of end-users to rapidly and efficiently deploy simulation software.

Comments

This is a pre-print of the article Saurabh, Kumar, Santi Adavani, Kendrick Tan, Masado Ishii, Boshun Gao, Adarsh Krishnamurthy, Hari Sundar, and Baskar Ganapathysubramanian. "Case study of SARS-CoV-2 transmission risk assessment in indoor environments using cloud computing resources." arXiv preprint arXiv:2111.09353 (2021). Attribution 4.0 International (CC BY 4.0). Posted with permission.