CAREER: Scalable Software Infrastructure for Analyzing Complex Networks

职业：用于分析复杂网络的可扩展软件基础设施

• 批准号: 2339607
• 负责人: Md Ariful Azad
• 金额: $ 56.34万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2028-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339607&HistoricalAwards=false
• 关键词: CAREER; Scalable; Software; Infrastructure; Analyzing

Interactions among entities are fundamental to physical, social, and cyber-physical systems worldwide. In these complex networks, vertices symbolize entities, and edges depict their interactions. Large-scale networks are prevalent in scientific and business applications, such as protein similarity networks with billions of vertices and trillions of edges. As networks continue to grow, there is an increasing demand for algorithms and software capable of utilizing large-scale cyberinfrastructure for analyzing massive networks across scientific domains. This project addresses this need by developing a software infrastructure consisting of foundational algorithms for scalable, portable, and user-friendly graph analysis, ensuring scalability to trillions of edges, optimal performance on heterogeneous infrastructures, and accessibility for domain scientists. This software infrastructure directly enhances vital applications in extreme weather prediction, the discovery of novel proteins, and forecasting energy usage in industrial settings. The project extends the accessibility of these advanced technologies to students at various academic levels. Integration with university courses and initiatives for high school students and teachers in rural Indiana ensures widespread educational impact.A complex network, modeled as a graph in mathematics, reveals intricate topological features encompassing dynamic edges, vertices, and a mixture of static and dynamic ones. Due to such networks' unpredictable and dynamic nature, the independent development of scalable algorithms and software for each application has become prohibitively costly in terms of time, effort, and research funding. This project addresses these challenges by introducing a general-purpose software infrastructure tailored to analyze and learn from complex networks. Users can leverage this infrastructure to expedite a multitude of graph-based applications. Confronting the diversity of graphs and computing platforms, the project employs a flexible two-layer framework. This framework seamlessly maps dynamic graph and machine learning computations to a concise set of sparse matrix operations, followed by the development of parallel algorithms. This linear-algebraic mapping offers a transparent pathway from mathematical algorithm descriptions to sparse-matrix functions, ensuring multiple levels of parallelism, communication reduction, and extreme scalability. Usability, the second challenge in this undertaking, is addressed through a comprehensive set of novel unsupervised and supervised graph algorithms tailored for complex and dynamic networks. Integrating these innovative graph algorithms with massively parallel sparse matrix operations results in a versatile software framework that analyzes complex spatiotemporal systems such as streamflow, traffic flow, and energy systems.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.

实体之间的相互作用是全球物理，社会和网络物理系统的基础。在这些复杂的网络中，顶点象征实体，边缘描绘了它们的相互作用。大规模网络在科学和业务应用中很普遍，例如具有数十亿个顶点和数万亿个边缘的蛋白质相似性网络。随着网络的不断增长，对算法和软件的需求不断增长，能够利用大规模的网络基础设施来分析跨科学领域的大规模网络。该项目通过开发由用于可扩展，便携式和用户友好的图形分析的基础算法组成的软件基础架构来满足这一需求，可确保对数万亿个边缘的可扩展性，在异构基础架构上的最佳性能以及领域科学家的可访问性。该软件基础架构在极端天气预测，新蛋白质的发现以及在工业环境中预测能源使用情况下直接增强了重要应用。该项目将这些高级技术的可访问性扩展到各个学术层面的学生。与印第安纳州农村地区的高中生和教师的大学课程和倡议的融合确保了广泛的教育影响。一个复杂的网络以数学的图表为模型，揭示了包含动态边缘，顶点的复杂拓扑特征，以及静态和动态的拓扑特征。由于这样的网络的不可预测和动态性质，在时间，精力和研究资金方面，针对每个应用程序的可扩展算法和软件的独立开发变得昂贵。该项目通过引入用于分析和从复杂网络学习的通用软件基础架构来解决这些挑战。用户可以利用此基础架构来加快基于图的大量应用程序。该项目面对图形和计算平台的多样性，采用了灵活的两层框架。该框架无缝将动态图和机器学习计算映射到一组简洁的稀疏基质操作，然后开发并行算法。该线性偏格映射提供了从数学算法描述到稀疏矩阵函数的透明途径，可确保多个并行性，沟通降低和极端可扩展性。可用性是这项事业的第二个挑战，是通过针对复杂和动态网络量身定制的一系列全面的无监督和监督的图形算法来解决的。将这些创新的图形算法与大量平行的稀疏矩阵操作整合在一起，导致了一个多功能的软件框架，该框架分析了复杂的时空系统，例如水流，交通流量和能源系统。这项奖项反映了NSF的法定任务，并通过使用该基金会的知识优点和广泛的影响来评估NSF的法定任务。