OAC Core: Small: Sust-CI: A Machine Learning based Approach to Make Advanced Cyberinfrastructure Applications More Efficient and Sustainable

许多高端工程和科学应用通常采用先进的网络基础设施（CI）。CI由高性能计算（HPC）系统、软件、应用程序开发人员和应用程序用户的组合组成。在这些高端应用程序中，越来越多的应用程序需要在HPC系统上重复运行，而这些HPC系统的设计并不适合它们的执行。不断变化的硬件环境进一步加剧了这些挑战。面对这些挑战，CI应用程序开发人员如何以高效和可持续的方式开发和部署应用程序？这是本项目试图解决的核心研究问题。总体目标是开发一种系统和结构化的方法来探索使用机器学习技术的CI配置的设计空间，并通过实际应用展示应用价值和发现潜力。其他项目活动整合和利用该项目的研究成果，同时准备下一代科学劳动力。该项目还将引导并行算法/应用程序和机器学习课程模块的开发，以及更广泛的会议教程。该项目将培养两名从事跨学科研究的博士生。该项目为一种称为Sust-CI的新型计算框架奠定了基础，该框架使开发人员能够设计和优化网络基础设施以提高效率。该框架协同结合了算法抽象、编程工具和机器学习技术，以实现自适应网络基础设施。这种方法将自动学习策略，以数据驱动的方式做出设计决策，以优化开发人员指定的目标（例如，性能）。该项目正在开发用于CI设计空间探索和优化的样本高效机器学习算法。关键思想是为高级CI应用程序提供一种新的能力，通过探索给定训练问题实例上的不同执行轨迹（计算行为）来获取知识。该研究将形成首个同类设计空间探索框架，使CI资源能够可持续地用于科学和工程领域的领导力应用。该奖项反映了美国国家科学基金会的法定使命，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。

Many high-end engineering and scientific applications routinely employ advanced cyber infrastructure (CI). CI is formed of a combination of high performance computing (HPC) systems, software, application developers, and application users. Among these high-end applications, a growing number require repeated runs on HPC systems that are not designed optimally for their executions. These challenges are further exacerbated by the continuously changing hardware landscape. Faced with these challenges how is a CI application developer expected to develop and deploy applications in an efficient and sustainable manner? This is the central research question that this project seeks to address. The overarching goal is to develop a systematic and structured way to explore design spaces of CI configurations using machine learning techniques, and to demonstrate value in application and discovery potential through real-world applications. Other project activities integrate and leverage upon the research outcomes of this project, while preparing the next generation scientific workforce. The project is also leading to the development of curricular modules in parallel algorithms/applications and machine learning, and conference tutorials for broader outreach. The project will lead to the training of two PhD students in performing interdisciplinary research.This project lays the foundations for a novel computational framework referred as Sust-CI that enables the developers to design and optimize cyber infrastructures for efficiency. This framework synergistically combines algorithmic abstractions, programming tools, and machine learning techniques to enable adaptive cyber infrastructures. This approach will automatically learn policies to make design decisions to optimize an objective specified by the developer (e.g., performance) in a data-driven manner. The project is leading to the development of sample-efficient machine learning algorithms for CI design space exploration and optimization. The key idea is to provide advanced CI applications a new capability to derive knowledge by exploring different execution traces (computational behavior) on the given training problem instances. The research will lead to a first-of-its-kind design space exploration framework to enable a sustainable use of CI resources toward leadership applications in science and engineering.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.