CISE Research Resources: A Shared Data Cluster For Real Time Interaction With Massive Datasets

EIA-0130869 Benjamin WatsonNorthwestern UniversityCISE Research Resources：A Shared Data Cluster for真实的Time Interaction With Massive DatasetsThe研究在西北越来越关注的问题，在提供交互式和负担得起的访问大规模（TB级）数据集。 特别是，他们专注于使用基于感知的计算机图形技术对这些数据集进行可视化，并使用PC CAVE和Active Mural组合显示可视化。 他们还研究了从移动的设备无处不在地访问高分辨率空间数据集所涉及的问题，并利用强大的无线网络。 他们专注于基于预测的分布式交互应用程序的适应和调度，并正在建立一个计算集群来支持这项工作，在现有的共享研究基础设施中缺少的是一个设施，以低延迟和高带宽的交互式存储和服务大量数据集。 研究人员将建立这样一个共享数据集群。 数据集群将是非常高性能和容量的PC RAID，与多GB/秒的SAN互连。 SAN将连接到其计算群集、CAVE/Mural、移动的设备和有线客户端机器。 组合的数据和计算集群将足以以200 MB/s的速度从磁盘泵送到CAVE/Mural屏幕。 数据集群、计算集群、CAVE/Mural、本地网络基础设施以及桌面和移动的客户端的组合将使他们能够研究大规模的交互性。

EIA-0130869 Benjamin WatsonNorthwestern UniversityCISE Research Resources: A Shared Data Cluster for Real Time Interaction With Massive DatasetsThe research at Northwestern is increasingly concerned with the issues involved in providing interactive and affordable access to massive (terabyte scale) datasets. In particular, they focus on visualization of these datasets using perceptually based computer graphics techniques and display the visualizations using a combination PC CAVE and Active Mural. They also study the issues involved with ubiquitous access to high-resolution spatial datasets from mobile devices, and leverage a powerful wireless network. They focus on prediction-based adaptation and scheduling for distributed interactive applications and are building a compute cluster to support this work.What is missing in the existing shared research infrastructure is a facility to store and serve massive datasets interactively, with low latency and high bandwidth. The researchers will build such a shared data cluster. The data cluster will be very high performance and capacity PC RAIDs interconnected with a multi-GB/sec SAN. The SAN will connect to their compute cluster, CAVE/Mural, mobile devices and wired client machines. The combined data and compute clusters will be sufficient to pump 200 MB/s from disk to the CAVE/Mural screens. The combination of the data cluster, compute cluster, CAVE/Mural, local network infrastructure, and desktop and mobile clients will let them study interactivity in the large.