Kaplan Michael, Kneifel Charles, Orlikowski Victor, Dorff James, Newton Mike, Howard Andy, Shinn Don, Bishawi Muath, Chidyagwai Simbarashe, Balogh Peter, Randles Amanda
Duke University School of Medicine.
Duke University Office of Information Technology.
Comput Sci Eng. 2020 Sep 21;22(6):37-47. doi: 10.1109/MCSE.2020.3024062. eCollection 2020 Nov.
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.
开发了一种针对特定患者的气流模拟，以帮助应对在新冠疫情期间扩大呼吸机容量的迫切需求。该计算模型为如何在两名或更多呼吸生理不同的患者之间分配一台呼吸机提供指导。为满足快速部署和确定针对特定患者的最佳调整的需求，需要在短时间内模拟数亿种不同的临床相关参数组合。在这种严峻形势驱动下的这项任务带来了独特的计算和研究挑战。我们在此介绍关于如何在24小时内设计并部署大规模强大云实例以及在72小时内使用80万个计算小时的指导原则和经验教训。我们讨论了为实现模型的快速周转、执行模拟以及创建直观且交互式界面所做的设计选择。
