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 02-24306DeFanti、托马斯 A.格罗斯曼、罗伯特 L.；利，杰森；尼尔森，彼得·C.； Yu, Oliver 伊利诺伊大学芝加哥分校 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 该项目开发高级网格计算技术，重点关注可视化和数据挖掘应用程序，旨在建立一个高性能、高带宽网格与可视化和数据挖掘研究相匹配。网格由 Lambda（PC 集群）组成，这些 Lambda 通过高带宽连接与其他集群相连。将构建支持高性能应用的软件和工具包，目标是实现用于数据挖掘和可视化应用的高速光纤城域网。该项目将网格技术扩展到基础设施、协议、网络存储器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迅速从超昂贵的长途运营商实施迁移到经济实惠的区域和城域社区网络，因此该项目探索这些新技术的固有灵活性，以支持大规模数据、可视化和协作密集型应用具有非常先进的实时要求。因此，将创建应用感知软件和中间件，以帮助通过千兆位/秒到太位/秒网络将未来的万亿级机器与分布式拍字节数据存储、远程传感器、仪器和可视化互连。 LambdaNodes 被定义为具有存储和可视化功能的 PC 集群，通过多个波长（称为 lambda）与类似集群耦合，将通过 lambda 网络连接来创建原型城市规模 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，以电子科学和国土安全模型应用作为预期的驱动因素（实现比今天高 10 倍或更高的端到端改进），设备和支持使以下：1。将集群上的数据挖掘和可视化功能与新兴的波长丰富的网络相匹配，2。分布式并行计算和渲染以实现高分辨率体积可视化，3。提供电子和光学开关 lambda 的应用信号和控制，4。测量和监控多波长的多千兆位电路，5。为用户提供已知且可知的带宽和延迟的网络，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。解决安全领域的实时应用程序问题，并最终解决数据的安全问题，8。将大都市规模的 LambdaGrid 与新兴的全球电网和国家 TeraGrid 相集成，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裔美国人为中心的本科生课程和研究中。