Interactive real-time exploration and correlation of multi-terabyte and petabyte datasets from multiple sources has been identified as an critical enabler for scientists to glean new insights in a variety of disciplines vital for national security including climate modeling and prediction, biomedical imaging, geosciences and high energy physics. Practically, these large-scale datasets must flow among a Grid of instruments, physical storage devices, visualization displays, and computational clusters. These applications are now realized by interconnecting Grid resources with dedicated networks dynamically created by concatenating optical lightpaths (lambdas). Critical criterions for these data-intensive applications to achieve high-performance, include, low-latency access to local and remote data, and, time-critical data sharing between the applications running on geographically distributed clusters.

In this talk, I will present LambdaRAM, a high-performance, multi-dimensional, distributed cache that harnesses the memory of multiple clusters interconnected by ultra-high-speed networks and employs efficient latency mitigation heuristics to provide data-intensive applications with low latency, seamless access to local and remote data. I will discuss the integration LambdaRAM with Bioscience applications to interactively visualize remote data and with NASA Goddard's Climate analysis application for computation of wind shear.

Venkat Vishwanath is a PhD candidate at the Electronic Visualization Laboratory(EVL) in Department of Computer Science at the University of Illinois at Chicago(UIC). He works on High Performance Networking at 10Gbps and beyond, Data Intensive Computing and High-Speed Transport Protocols.