XPS: EXPL: FP: Collaborative Research: SPANDAN: Scalable Parallel Algorithms for Network Dynamics Analysis

XPS：EXPL：FP：协作研究：SPANDAN：用于网络动态分析的可扩展并行算法

• 批准号: 1533918
• 负责人: Sajal Das
• 金额: $ 15.32万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533918&HistoricalAwards=false
• 关键词: XPS; EXPL; FP; Collaborative; Research

The goal of SPANDAN project is to create a novel architecture-independent framework for designing efficient, portable and scalable parallel algorithms for analyzing large-scale dynamic networks. SPANDAN will not only provide an intuitive methodology for efficiently translating sequential algorithms into scalable parallel algorithms for dynamic networks, but also provide mechanisms for their analytical evaluation and serve as a mediatory layer between applications and system level tuning. To evaluate the effectiveness of SPANDAN framework in real-world applications, the PIs will collaborate with social scientists and biologists. They will also integrate research findings into various courses such as network analysis, parallel algorithms, and bioinformatics. They will further collaborate with high schools to develop summer courses with the goal of encouraging women and minority students to pursue IT-related careers. As the underlying methodology, the SPANDAN framework will exploit graph sparsification techniques to divide the network into sparse subgraphs (certificates) that form the leaves of a sparsification tree. This innovative approach will lead to the design and analysis of efficient parallel algorithms for updating dynamic networks, and reduction of memory latency associated with parallelizing unstructured data. Specifically parallel algorithms will be designed for maintaining network topological characteristics, and updating influential vertices and communities. To demonstrate portability and performance, the developed algorithms will be implemented on the distributed memory clusters, shared memory multicores, and massively multithreaded CRAY-XMT.For further information see the project web site at: http://cs.mst.edu/labs/crewman/projects/SPANDAN/

SPANDAN 项目的目标是创建一个新颖的独立于架构的框架，用于设计高效、可移植和可扩展的并行算法来分析大规模动态网络。 SPANDAN 不仅提供了一种直观的方法，可以有效地将顺序算法转换为动态网络的可扩展并行算法，而且还提供了分析评估的机制，并充当应用程序和系统级调整之间的中介层。为了评估 SPANDAN 框架在实际应用中的有效性，PI 将与社会科学家和生物学家合作。他们还将研究成果整合到网络分析、并行算法和生物信息学等各种课程中。他们将进一步与高中合作开发暑期课程，旨在鼓励女性和少数族裔学生从事与信息技术相关的职业。作为底层方法，SPANDAN 框架将利用图稀疏化技术将网络划分为稀疏子图（证书），形成稀疏树的叶子。这种创新方法将导致设计和分析用于更新动态网络的高效并行算法，并减少与并行化非结构化数据相关的内存延迟。具体来说，将设计并行算法来维护网络拓扑特征，并更新有影响力的顶点和社区。为了演示可移植性和性能，所开发的算法将在分布式内存集群、共享内存多核和大规模多线程 CRAY-XMT 上实现。有关更多信息，请参阅项目网站：http://cs.mst.edu/labs /船员/项目/SPANDAN/