Collaborative Research: PPoSS: Planning: Extreme-scale Sparse Data Analytics

协作研究：PPoSS：规划：超大规模稀疏数据分析

• 批准号: 2118385
• 负责人: David Bader
• 金额: $ 5万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2118385&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Extreme

The graph data structure is used for storing and manipulating relational data. Tensors are a higher-order generalization of the two-dimensional matrix representation. Both graphs and tensors are used in exploratory and automated data analysis. Applications areas include cybersecurity, complex system analysis, and personalized healthcare. There exist a myriad of known algorithms for typical data analysis tasks in these areas. For instance, the problem of community identification in graphs, referring to automatically identifying well-connected groups of vertices in graphs, has dozens of algorithms. Analogous to the singular value decomposition in matrices, several tensor factorizations exist with diverse use-cases. Both graph algorithms and tensor factorizations use computer storage formats inspired by matrix computations. This project focuses on data analysis use-cases that result in large-scale graphs and tensors, necessitating parallel and distributed processing. The project's novelties are in identifying and developing unifying parallel algorithm design principles that span multiple graph computations and tensor factorizations. In the planning stage, several focused research tasks will explore eight unifying themes.The project aims to develop the foundations for an end-to-end streaming data analytics system with performance comparable to highly tuned static graph analysis benchmarks on current high-end workstations and supercomputers. The investigators' multi-disciplinary expertise span high-performance computing, theory and algorithms, computer architecture, and programming languages and compilers. The cross-cutting research aims include generalizable principles to orchestrate intra- and inter-node communication, multiple approaches for exploiting hierarchical parallelism, locality-enhancing strategies, and automatic performance tuning. The software artifacts from the planning stage could form the basis for new data analytic benchmarks. The investigators will incorporate research findings into the courses they teach. Engaging experts from the national laboratories and the industry in the planning stage will help solidify future large-scale efforts. The investigators will leverage and contribute to existing institutional programs that broaden participation in computing research.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.

图数据结构用于存储和操纵关系数据。张量是二维基质表示的高阶概括。图和张量都用于探索性和自动化数据分析。应用领域包括网络安全，复杂的系统分析和个性化医疗保健。在这些领域中，存在无数的已知算法，用于典型的数据分析任务。例如，图中社区识别的问题是指在图中自动识别良好连接的顶点组，具有数十种算法。类似于矩阵中的奇异值分解，有几种张量因子因用例而存在。图算法和张量因子化都使用了受矩阵计算启发的计算机存储格式。该项目着重于数据分析用例，这些用例可导致大规模图和张量，需要并行和分布式处理。该项目的新颖性在于识别和开发跨越多个图计算和张量因子化的统一并行算法设计原理。在计划阶段，一些重点的研究任务将探索八个统一主题。该项目旨在为端到端流数据分析系统开发基础，其性能与当前高端工作站和超级计算机的高度调谐静态图分析基准相当。研究人员的多学科专业知识涵盖了高性能计算，理论和算法，计算机架构以及编程语言和编译器。横切研究的目的包括可策划的原则，用于协调节点内和节点间交流，多种利用层次并行性的方法，局部增强策略以及自动绩效调整。计划阶段的软件工件可能构成新数据分析基准测试的基础。调查人员将将研究结果纳入他们教授的课程中。参与国家实验室和行业的专家参与计划阶段，将有助于巩固未来的大规模努力。调查人员将利用并为扩大计算研究参与的现有机构计划做出贡献。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。