Cloud Computing for COVID-19: Lessons Learned from Massively Parallel Models of Ventilator Splitting

Published

Journal Article

© 2020 IEEE. A patient-specific airflow simulation was developed to help address the pressing need for an expansion of the ventilator capacity in response to the COVID-19 pandemic. The computational model provides guidance regarding how to split a ventilator between two or more patients with differing respiratory physiologies. To address the need for fast deployment and identification of optimal patient-specific tuning, there was a need to simulate hundreds of millions of different clinically relevant parameter combinations in a short time. This task, driven by the dire circumstances, presented unique computational and research challenges. We present here the guiding principles and lessons learned as to how a large-scale and robust cloud instance was designed and deployed within 24 hours and 800 000 compute hours were utilized in a 72-hour period. We discuss the design choices to enable a quick turnaround of the model, execute the simulation, and create an intuitive and interactive interface.

Full Text

Duke Authors

Cited Authors

  • Kaplan, M; Kneifel, C; Orlikowski, V; Dorff, J; Newton, M; Howard, A; Shinn, D; Bishawi, M; Chidyagwai, S; Balogh, P; Randles, A

Published Date

  • November 1, 2020

Published In

Volume / Issue

  • 22 / 6

Start / End Page

  • 37 - 47

Electronic International Standard Serial Number (EISSN)

  • 1558-366X

International Standard Serial Number (ISSN)

  • 1521-9615

Digital Object Identifier (DOI)

  • 10.1109/MCSE.2020.3024062

Citation Source

  • Scopus