COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among domain experts, mathematical modelers, and scientific computing specialists. Computationally, however, it also revealed critical gaps in the ability of researchers to exploit advanced computing systems. These challenging areas include gaining access to scalable computing systems, porting models and workflows to new systems, sharing data of varying sizes, and producing results that can be reproduced and validated by others. Informed by our team's work in supporting public health decision makers during the COVID-19 pandemic and by the identified capability gaps in applying high-performance computing (HPC) to the modeling of complex social systems, we present the goals, requirements, and initial implementation of OSPREY, an open science platform for robust epidemic analysis. The prototype implementation demonstrates an integrated, algorithm-driven HPC workflow architecture, coordinating tasks across federated HPC resources, with robust, secure and automated access to each of the resources. We demonstrate scalable and fault-tolerant task execution, an asynchronous API to support fast time-to-solution algorithms, an inclusive, multi-language approach, and efficient wide-area data management. The example OSPREY code is made available on a public repository.

翻译：COVID-19对科学合作产生了前所未有的影响。这场疫情及科学界的广泛应对，在领域专家、数学建模者和科学计算专家之间建立了新型合作关系。然而在计算层面，疫情也暴露了研究人员在利用先进计算系统方面的关键短板。这些挑战领域包括：获取可扩展计算系统、将模型和工作流移植至新系统、共享不同规模的数据，以及产出可被他人复现验证的结果。基于本团队在COVID-19疫情期间支持公共卫生决策的工作经验，以及在高性能计算（HPC）应用于复杂社会系统建模过程中识别出的能力缺口，我们提出OSPREY（用于稳健流行病分析的开源科学平台）的目标、需求及初步实现方案。原型系统展示了集成化、算法驱动的HPC工作流架构，能够协调联邦式HPC资源间的任务调度，并支持对各类资源的稳健、安全及自动化访问。我们实现了可扩展的容错任务执行、支持快速求解算法的异步API、包容性多语言方案及高效的广域数据管理。OSPREY代码示例已发布于公共代码仓库。