Name
Title | ||
|---|---|---|
Cloud Computing for COVID-19: Lessons Learned From Massively Parallel Models of Ventilator Splitting |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/MCSE.2020.3024062 | Computing in Science & Engineering |
Keywords | DocType | Volume |
Computational modeling,Numerical models,Mathematical model,Resistors,Electron tubes,Atmospheric modeling,Task analysis | Journal | 22 |
Issue | ISSN | Citations |
6 | 1521-9615 | 0 |
PageRank | References | Authors |
0.34 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Kaplan | 1 | 0 | 0.34 |
| Charles Kneifel | 2 | 0 | 0.34 |
| Victor Orlikowski | 3 | 0 | 0.34 |
| James Dorff | 4 | 0 | 0.34 |
| Mike Newton | 5 | 0 | 0.34 |
| Andy Howard | 6 | 0 | 0.34 |
| Don Shinn | 7 | 0 | 0.34 |
| Muath Bishawi | 8 | 0 | 0.68 |
| Simbarashe Chidyagwai | 9 | 0 | 0.34 |
| Peter Balogh | 10 | 0 | 0.34 |
| Amanda Randles | 11 | 0 | 1.01 |