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

Publication ,  Journal Article
Kaplan, M; Kneifel, C; Orlikowski, V; Dorff, J; Newton, M; Howard, A; Shinn, D; Bishawi, M; Chidyagwai, S; Balogh, P; Randles, A
Published in: Computing in science & engineering
November 2020

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.

Duke Scholars

Published In

Computing in science & engineering

DOI

ISSN

1521-9615

Publication Date

November 2020

Volume

22

Issue

6

Start / End Page

37 / 47

Related Subject Headings

  • Fluids & Plasmas
  • 46 Information and computing sciences
  • 40 Engineering
  • 0805 Distributed Computing
  • 0802 Computation Theory and Mathematics
  • 0103 Numerical and Computational Mathematics
 

Citation

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Kaplan, M., Kneifel, C., Orlikowski, V., Dorff, J., Newton, M., Howard, A., … Randles, A. (2020). Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting. Computing in Science & Engineering, 22(6), 37–47. https://doi.org/10.1109/mcse.2020.3024062
Kaplan, Michael, Charles Kneifel, Victor Orlikowski, James Dorff, Mike Newton, Andy Howard, Don Shinn, et al. “Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting.Computing in Science & Engineering 22, no. 6 (November 2020): 37–47. https://doi.org/10.1109/mcse.2020.3024062.
Kaplan M, Kneifel C, Orlikowski V, Dorff J, Newton M, Howard A, et al. Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting. Computing in science & engineering. 2020 Nov;22(6):37–47.
Kaplan, Michael, et al. “Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting.Computing in Science & Engineering, vol. 22, no. 6, Nov. 2020, pp. 37–47. Epmc, doi:10.1109/mcse.2020.3024062.
Kaplan M, Kneifel C, Orlikowski V, Dorff J, Newton M, Howard A, Shinn D, Bishawi M, Chidyagwai S, Balogh P, Randles A. Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting. Computing in science & engineering. 2020 Nov;22(6):37–47.

Published In

Computing in science & engineering

DOI

ISSN

1521-9615

Publication Date

November 2020

Volume

22

Issue

6

Start / End Page

37 / 47

Related Subject Headings

  • Fluids & Plasmas
  • 46 Information and computing sciences
  • 40 Engineering
  • 0805 Distributed Computing
  • 0802 Computation Theory and Mathematics
  • 0103 Numerical and Computational Mathematics