Elements: RADICAL-Cybertools: Middleware Building Blocks for NSF's Cyberinfrastructure Ecosystem.

该项目建立在先前由罗格斯大学高级分布式网络基础设施和应用实验室（RADICAL）研究资助的中间件开发工作的基础上;先前开发的中间件构建块被称为RADICAL-Cybertools（RCT）。 在当前的项目中，该团队追求一组有针对性的开发，这是由扩展软件组件、用户和支持平台的数量的需求驱动的;并提高性能、工程流程和可持续性。 由此产生的能力将服务于多个领域的科学应用，包括软件工程，化学物理，材料科学，健康科学，气候科学，药物发现和粒子物理。该项目建立在之前的原型投资基础上，该投资为领先的HPC机器开发了一个试点系统，并实现了SAGA的Python实现，SAGA是一种分布式计算标准。 目前的工作围绕三个活动组织：-扩展RCT功能，以可靠地支持一系列新的大规模应用（例如将传统HPC模拟与机器学习方法紧密耦合）; -增强RCT，以支持新的NSF系统，例如Frontera超级计算系统和其他新系统; -原型化一个新组件：用于计算资源管理的活动管理器。数据驱动的方法将用于改进软件开发、工程和生命周期管理，并增强RCT和受支持社区的长期可持续性。 该项目包括RCT参与和支持的不断增长的社区的典型案例，例如ATLAS高能物理项目和QCArary项目，这些项目实现了大规模力场构建和物理属性预测。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。

This project builds upon a previously funded middleware development effort by the Research in Advanced Distributed Cyberinfrastructure and Applications Laboratory (RADICAL) at Rutgers University; the previously developed middleware building blocks were known as RADICAL-Cybertools (RCT). In the current project, the team pursues a targeted set of developments, driven by the need to scale the number of software components, user, and supported platforms; and improve performance, engineering processes, and sustainability. The resulting capabilities will serve scientific applications in multiple domains, including software engineering, chemical physics, materials science, health science, climate science, drug discovery and particle physics.This project builds upon a prior prototype investment, which developed a pilot system for leadership-class HPC machines, and a Python implementation of SAGA, a distributed computing standard. The current effort is organized around three activities: - Extending RCT functionality to reliably support a range of novel applications at scale (examples include tightly coupling traditional HPC simulations with machine-learning methods); - Enhancing RCT to be ready to support new NSF systems, such as the Frontera supercomputing system and other new systems; - Prototyping a new component: a campaign manager for computational resource management. Data-driven approaches will be used to improve software development, engineering, and life-cycle management, and to enhance the long-term sustainability of RCT and the supported communities. The project includes use cases that are representative examples of the growing community that RCT engages and supports, such as the ATLAS high-energy physics project and the QCArchive project enabling large-scale force-field construction and physical property prediction.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.