CISE Research Resources: Matching Advanced Visualization and Intelligent Data Mining to High-Performance Experimental Networks

CISE 研究资源：将高级可视化和智能数据挖掘与高性能实验网络相匹配

• 批准号: 0224306
• 负责人: Thomas DeFanti
• 金额: $ 85万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-11-01 至 2006-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0224306&HistoricalAwards=false
• 关键词: CISE; Research; Resources; Matching; Advanced

EIA 02-24306DeFanti, Thomas A.Grossman, Robert L.; Leigh, Jason; Nelson, Peter C.; Yu, OliverUniversity of Illinois Chicago CISE RR: Matching Advanced Visualization and Intelligent Data Mining to High-Performance Experimental Networks This project, developing techniques for advanced grid computing with focus on visualization and data-mining applications, aims at setting up a high-performance, high-bandwidth Grid matched with Visualization and Data Mining Research. The Grid is made up of Lambdas (clusters of PCs) that are connected by high-bandwidths connections to other clusters. Software and toolkits to enable high-performance applications will be built with the goal of attaining a high-speed optical metropolitan-area network to be used in the data mining and visualization applications. The project expands grid technology to infrastructure, protocols, network memory and distributed control, and applications in constraint logic programming. Since optical networking technology is rapidly migrating from ultra-expensive long-haul carrier implementations to affordable regional- and metro-area community networks, this project explores inherent flexibilities in these new technologies to support large-scale data, visualization, and collaboration-intensive applications with very advanced real-time demands. Thus, application-aware software and middleware will be created to help interconnect tomorrow's terascale-class machines with distributed petabyte data stores, remote sensors, instrumentation and visualization over gigabit/sec to terabit/sec networks. LambdaNodes, defined as PC clusters with storage and visualization coupled to like clusters by numerous wavelengths (called lambdas), will be used connected by lambda networks to create a prototype metropolitan-scale LambdaGrid. With the goal of optimally matching data mining and visualization to high-performance optical networks with e-Science and homeland security model applications as expected drivers (achieving a 10x or greater end-to-end improvement over today), the equipment and support enables the following:1. Matching data-mining and visualization capabilities on clusters to emerging wavelength-rich networks,2. Distributing parallel computation and rendering for high-resolution volume visualization,3. Providing applications signaling and control of both electronically and optically switched lambdas,4. Measuring and monitoring multi-gigabit circuits over multiple wavelengths,5. Providing users with networks that have known and knowable bandwidth and latency,6. Investigating high-availability/uninterrupted cluster computing to support time-critical collaborations,7. Addressing real-time applications in security domain, and, eventually, security of the data as well,8. Integrating metropolitan-scale LambdaGrids with the emerging Global Grid and the National TeraGrid, and9. Incorporating distributed data mining and visualization into undergraduate African-American-centered coursework and research through the Virtual Harlem Project.

EIA 02- 24306 DeFanti，托马斯A.格罗斯曼，罗伯特L.;作者声明：纳尔逊，彼得C.芝加哥大学CISE RR：将高级可视化和智能数据挖掘与高性能实验网络相匹配本项目旨在开发高级网格计算技术，重点关注可视化和数据挖掘应用，旨在建立与可视化和数据挖掘研究相匹配的高性能、高带宽网格。网格由Lambdas（PC集群）组成，这些集群通过高带宽连接到其他集群。将建立能够实现高性能应用的软件和工具包，目标是建立一个高速光学城域网，用于数据挖掘和可视化应用。该项目将网格技术扩展到基础设施、协议、网络内存和分布式控制，以及约束逻辑编程中的应用。由于光网络技术正在从超昂贵的长途运营商实施快速迁移到负担得起的区域和城域社区网络，该项目探索了这些新技术的固有灵活性，以支持具有非常先进的实时需求的大规模数据，可视化和协作密集型应用。因此，应用感知软件和中间件将被创建，以帮助将未来的万亿级机器与分布式PB数据存储、远程传感器、仪器和可视化通过千兆位/秒到兆兆位/秒的网络互连。LambdaNode被定义为PC集群，其存储和可视化通过许多波长（称为Lambda）耦合到类似的集群，将通过Lambda网络连接起来，以创建一个原型大都市规模的LambdaGrid。为了将数据挖掘和可视化与高性能光网络进行最佳匹配，并将e-Science和国土安全模型应用作为预期驱动程序（与目前相比实现10倍或更高的端到端改进），设备和支持实现了以下目标：1.将集群上的数据挖掘和可视化功能与新兴的富波长网络相匹配，2.分布式并行计算和绘制实现高分辨率体可视化; 3.提供电子和光学开关式光纤收发器的信号和控制应用。测量和监控多个波长上的多千兆位电路，5.为用户提供具有已知和可知带宽和延迟的网络，6.研究高可用性/不间断集群计算，以支持时间关键型协作，7.解决安全领域中的实时应用程序，并最终解决数据的安全问题。将大都市规模的Lambda网格与新兴的全球网格和国家TeraGrid相结合，以及9。通过虚拟哈莱姆项目，将分布式数据挖掘和可视化纳入以非洲裔美国人为中心的本科课程和研究。