Framework: Software: Next-Generation Cyberinfrastructure for Large-Scale Computer-Based Scientific Analysis and Discovery

最近的数据可用性革命从根本上改变了科学，工业和政府许多领域的活动。例如，当代模拟药物特性可能需要整个数据中心的计算能力，而最近在天文学方面的努力将很快产生历史上最大的图像数据集。在这种极端环境下，科学发现的唯一可行途径取决于开发和利用超级计算机和软件的下一代计算网络基础设施。这种新的计算基础设施的开发需要大量的工程资源，因此最大限度地发挥基础设施的潜力，在尽可能多的技术领域中发挥高影响力和广泛采用至关重要。不幸的是，尽管有这种必要性，现有的开发过程往往产生的软件是有限的，以特定的硬件，或需要额外的专业知识来正确使用，或过于专业化的一个特定的问题域。这种“一次性”软件工具的范围有限，导致广大科学界利用不足。相比之下，该项目旨在开发基于计算机的科学分析方法和软件，这些方法和软件足够强大，灵活和易于访问，以（i）使领域专家能够在其领域内取得重大进展，（ii）使先进的计算技术在意想不到的科学，技术和工业应用中的创新使用成为可能。该项目将把这些工具应用于天文学、医学和能源管理领域的各种研究团队所面临的各种具体科学挑战。这些团队计划利用拟议的工作来绘制新的星星系统，开发新的救生药物，并设计新的电力系统，以比现有系统更低的成本为更多的家庭和企业提供更多的能源。最后，该项目将通过接触最先进的计算技术，在更广泛的科学和工程界教育学生和从业人员，从而寻求留下持续的社会效益遗产。通过与统计天文学，药物计量学，电力系统优化和高性能计算研究团队的密切合作，该项目将提供网络基础设施，有效，轻松地实现下一代基于计算机的科学分析和发现。为了确保所开发的网络基础设施的实用性，该项目将侧重于三个目标科学应用：（i）经济上可行的电力网络脱碳，（ii）极端规模天文图像数据的实时分析，以及（iii）药物分析和发现的药物计量建模和模拟。虽然解决这些具体问题将构成对拟议网络基础设施的初步压力测试，但该项目的最终目标是，所开发的工具具有足够的性能、可访问性、可组合性、灵活性和适应性，以适用于尽可能广泛的问题领域。为了实现这一愿景，该项目将构建和改进用于计算优化、机器学习、并行计算和基于模型的仿真的各种软件工具。将特别关注拟议的网络基础设施与新的和现有的科学分析和发现工具的可组合性。追求这些目标将需要设计和实现新的编程语言抽象，以允许高级语言功能与低级编译器优化的紧密集成。此外，最大限度地利用拟议的网络基础设施将需要研究新的方法，结合联合收割机最先进的技术，从优化，机器学习和高性能计算。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

Recent revolutions in data availability have radically altered activities across many fields within science, industry, and government. For instance, contemporary simulations medication properties can require the computational power of entire data centers, and recent efforts in astronomy will soon generate the largest image datasets in history. In such extreme environments, the only viable path forward for scientific discovery hinges on the development and exploitation of next-generation computational cyberinfrastructure of supercomputers and software. The development of this new computational infrastructure demands significant engineering resources, so it is paramount to maximize the infrastructure's potential for high impact and wide adoption across as many technical domains as possible. Unfortunately, despite this necessity, existing development processes often produce software that is limited to specific hardware, or requires additional expertise to use properly, or is overly specialized to a specific problem domain. Such "single-use" software tools are limited in scope, leading to underutilization by the wider scientific community. In contrast, this project seeks to develop methods and software for computer-based scientific analysis that are sufficiently powerful, flexible and accessible to (i) enable domain experts to achieve significant advancements within their domains, and (ii) enable innovative use of advanced computational techniques in unexpected scientific, technological and industrial applications. This project will apply these tools to a wide variety of specific scientific challenges faced by various research teams in astronomy, medicine, and energy management. These teams plan on using the proposed work to map out new star systems, develop new life-saving medications, and design new power systems that will deliver more energy to a greater number of homes and businesses at a lower cost than existing systems. Finally, this project will seek to leave a legacy of sustained societal benefit by educating students and practitioners in the broader scientific and engineering communities via exposure to state-of-the-art computational techniques. Through close collaboration with research teams in statistical astronomy, pharmacometrics, power systems optimization, and high-performance computing, this project will deliver cyberinfrastructure that will effectively and effortlessly enable the next generation of computer-based scientific analysis and discovery. To ensure the practical applicability of the developed cyberinfrastructure, the project will focus on three target scientific applications: (i) economically viable decarbonization of electrical power networks, (ii) real-time analysis of extreme-scale astronomical image data, and (iii) pharmacometric modeling and simulation for drug analysis and discovery. While tackling these specific problems will constitute an initial stress test of the proposed cyberinfrastructure, it is the ultimate goal of the project that the developed tools be sufficiently performant, accessible, composable, flexible and adaptable to be applied to the widest possible range of problem domains. To achieve this vision, the project will build and improve various software tools for computational optimization, machine learning, parallel computing, and model-based simulation. Particular attention will be paid to the proposed cyberinfrastructure's composability with new and existing tools for scientific analysis and discovery. The pursuit of these goals will require the design and implementation of new programming language abstractions to allow close integration of high-level language features with low-level compiler optimizations. Furthermore, maximally exploiting proposed cyberinfrastructure will require research into new methods that combine state-of-the-art techniques from optimization, machine learning, and high-performance computing.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.