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