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

OAC 核心：小型：Sust-CI：基于机器学习的方法，使先进的网络基础设施应用程序更加高效和可持续

• 批准号: 1910213
• 负责人: Janardhan Rao Doppa
• 金额: $ 50万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910213&HistoricalAwards=false
• 关键词: OAC; Core; Small; Sust; CI

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.

许多高端工程和科学应用程序通常使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组成。在这些高端应用程序中，越来越多的应用程序需要在没有针对其执行进行优化设计的HPC系统上重复运行。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，如何期望CI应用程序开发人员以高效和可持续的方式开发和部署应用程序？这是该项目试图解决的中心研究问题。总体目标是开发一种系统和结构化的方法，使用机器学习技术探索CI配置的设计空间，并通过现实世界的应用程序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，同时培养下一代科学劳动力。该项目还导致开发并行算法/应用程序和机器学习的课程模块，并为更广泛的外联活动编写会议教程。该项目将培训两名博士生进行跨学科研究。该项目为称为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，以实现自适应的网络基础设施。这种方法将自动学习策略，以做出设计决策，以数据驱动的方式优化开发人员指定的目标(例如，性能)。该项目正在开发样本高效的机器学习算法，用于CI设计、空间探索和优化。其核心思想是为高级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。这项研究将产生首个此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先应用。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Design and Optimization of Energy-Accuracy Tradeoff Networks for Mobile Platforms via Pretrained Deep Models

• DOI: 10.1145/3366636
• 发表时间: 2020-02
• 期刊: ACM Transactions on Embedded Computing Systems (TECS)
• 作者: Nitthilan Kanappan Jayakodi;Syrine Belakaria;Aryan Deshwal;J. Doppa

Bayesian optimization of nanoporous materials

• DOI: 10.1039/d1me00093d
• 发表时间: 2021-10-06
• 期刊: MOLECULAR SYSTEMS DESIGN & ENGINEERING
• 影响因子: 3.6
• 作者: Deshwal, Aryan;Simon, Cory M.;Doppa, Janardhan Rao
• 通讯作者: Doppa, Janardhan Rao

Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach

多保真多目标贝叶斯优化：一种输出空间熵搜索方法

• DOI: 10.1609/aaai.v34i06.6560
• 发表时间: 2020
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Syrine Belakaria, Aryan Deshwal

Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach

• DOI: 10.48550/arxiv.2206.12708
• 发表时间: 2022-06
• 作者: Syrine Belakaria;Rishit Sheth;J. Doppa;Nicoló Fusi

IMpart: A Partitioning-based Parallel Approach to Accelerate Influence Maximization

• DOI: 10.1109/hipc56025.2022.00028
• 发表时间: 2022-12
• 期刊: 2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC)
• 作者: Reet Barik;Marco Minutoli;M. Halappanavar;A. Kalyanaraman