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

EIA 02-24306 DeFanti，Thomas A.Grossman，Robert L.；Leigh，Jason；Nelson，Peter C.；Yu，OliverUniversity of Illinois Chicago 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旨在开发高级网格计算技术，重点是可视化和数据挖掘应用，旨在建立一个与可视化和数据挖掘研究相匹配的高性能、高带宽网格。网格由通过高带宽连接到其他集群的lambdas(PC集群)组成。将建立能够实现高性能应用的软件和工具包，以实现用于数据挖掘和可视化应用的高速光城域网络。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，本项目探讨了这些新技术的内在灵活性，以支持具有非常高级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造出来，以帮助将未来的千兆级机器与千兆位/秒到千兆位/秒网络上的分布式PB级数据存储、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的PC集群，通过多个波长耦合到类似的集群(称为Lambdas)，将通过Lambda网络连接起来，创建一个大都市规模的LambdaGrid原型。以将数据挖掘和可视化与以e-Science和国土安全模型应用为预期驱动力的高性能光网络最佳匹配为目标(实现比目前10倍或更大的端到端改进)，该设备和支持实现以下功能：1.将集群上的数据挖掘和可视化能力与新兴的波长丰富的网络相匹配；2.为高分辨率体可视化分布并行计算和绘制；3.向应用提供电子和光交换波长的信令和控制；4.测量和监控多个波长上的数千兆电路；5.向用户提供具有已知和已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.解决安全域中的实时应用，并最终解决数据的安全问题；8.将大都市规模的LambdaGrid与新兴的全球网格和国家TeraGrid进行集成，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裔美国人为中心的本科生课程和研究。

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.