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DTSTAMP:20210402T160154Z
LOCATION:Track 7
DTSTART;TZID=America/New_York:20201117T133000
DTEND;TZID=America/New_York:20201117T140000
UID:submissions.supercomputing.org_SC20_sess286_sotp118@linklings.com
SUMMARY:Lessons Learned from Massively Parallel Model of Ventilator Splitt
 ing
DESCRIPTION:State of the Practice Talk\n\nLessons Learned from Massively P
 arallel Model of Ventilator Splitting\n\nKaplan, Kneifel, Orlikowski, Dorf
 f, Newton...\n\nThere has been a pressing need for an expansion of the ven
 tilator capacity in response to the recent COVID19 pandemic. To help addre
 ss this need, a patient-specific airflow simulation was developed to suppo
 rt clinical decision-making for efficacious and safe splitting of a ventil
 ator among two or more patients with varying lung compliances and tidal vo
 lume requirements. The computational model provides guidance regarding how
  to split a ventilator among two or more patients with differing respirato
 ry physiologies. There was a need to simulate hundreds of millions of diff
 erent clinically relevant parameter combinations in a short time. This tas
 k, driven by the dire circumstances, presented unique computational and re
 search challenges. In order to support FDA submission, a large-scale and r
 obust cloud instance was designed and deployed within 24 hours, and 800,00
 0 compute hours were utilized in a 72-hour period.\n\nTag: Best Practices,
  Cloud and Distributed Computing\n\nRegistration Category: Tech Program Re
 g Pass
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