Collaborative Research: PPoSS: Planning: Performance Scalability, Trust, and Reproducibility: A Community Roadmap to Robust Science in High-throughput Applications

协作研究：PPoSS：规划：性能可扩展性、信任和可重复性：高通量应用中稳健科学的社区路线图

• 批准号: 2028955
• 负责人: Mary Hall
• 金额: $ 3万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028955&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Performance

This project is focused on a critical issue in computational science. As scientists in all fields increasingly rely on high-throughput applications (which combine multiple components into increasingly complex multi-modal workflows on heterogeneous systems), the increasing complexities of those applications hinder the scientists’ ability to generate robust results. The project recruits a cross-disciplinary community working together to define, design, implement, and use a set of solutions for robust science. In so doing, the community defines a roadmap that enables high-throughput applications to withstand and overcome adverse conditions such as heterogeneous, unreliable architectures at all scales including extreme scale, rigorous testing under uncertainties, unexplainable algorithms (e.g., in machine learning), and black-box methods. The project’s novelties are its comprehensive, cross-disciplinary study of high-throughput applications for robust scientific discovery from hardware and systems all the way to policies and practices.Through three virtual mini-workshops called virtual world cafes, this project engages a community of scientists at campuses (through the Computing Alliance of Hispanic-Serving Institutions [CAHSI], the Coalition for Academic Scientific Computing [CASC], and the Southern California Earthquake Center [SCEC]), at national laboratories, and in industry. The scientists participate in defining scalability, trust, and reproductivity in an initial set of high-throughput applications; identifying a set of experimental practices that support the in-concert successful progress of these applications’ workflows; advancing towards a vision of general hardware and software solutions for robust science by evaluating the generality and transferability of experimental practices and by identifying any missing parts; and defining a research agenda for the next-generation workflows.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的重点是计算科学中的一个关键问题。随着各个领域的科学家越来越依赖于高通量应用程序（将联合收割机组合成异构系统上日益复杂的多模式工作流程），这些应用程序的日益复杂性阻碍了科学家产生稳健结果的能力。 该项目招募了一个跨学科的社区，共同定义，设计，实施和使用一套强大的科学解决方案。 在这样做的过程中，社区定义了一个路线图，使高吞吐量应用程序能够承受和克服不利条件，例如所有规模的异构，不可靠的架构，包括极端规模，不确定性下的严格测试，无法解释的算法（例如，在机器学习中）和黑盒方法。该项目的创新之处在于它对高通量应用进行了全面的跨学科研究，以促进从硬件和系统到政策和实践的强大科学发现。通过三个称为虚拟世界咖啡馆的虚拟小型研讨会，该项目吸引了校园内的科学家社区（通过西班牙裔服务机构计算联盟[CAHSI]，学术科学计算联盟[CASC]，和南加州地震中心[SCEC]）、国家实验室和工业界。科学家们参与定义一组初始高通量应用程序的可扩展性，信任和再现性;确定一组支持这些应用程序工作流程一致成功进展的实验实践;通过评估实验实践的一般性和可转移性并通过确定任何缺失部分，朝着稳健科学的通用硬件和软件解决方案的愿景前进;该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。