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

Michael Kaplan, Charles Kneifel, Victor Orlikowski, James Dorff, Mike Newton, Andy Howard, Don Shinn, Muath Bishawi, Simbarashe Chidyagwai, Peter Balogh, Amanda Randles

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number9201333
Pages (from-to)37-47
Number of pages11
JournalComputing in Science and Engineering
Volume22
Issue number6
DOIs
StatePublished - Nov 1 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Engineering

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