Scientific collaboration is critical in high performance computational (HPC) environments. Computer security incidents, however, are a constant threat in today's interconnected computational environments. Many sites struggle with finding a balance between the needs of users and the threat from unauthorized usage or access. Understanding the constantly changing battlefield is critical for effective and optimal deployment of computer security resources.This tutorial will provide an underlying knowledge of the current state of computer security, its effects on HPC environments and mitigation strategies. Topics we will address include:1.HPC Security Fundamentals2.HPC Security Protection Fundamentals3.Intrusion Detection Using Bro4.The Changing Computer Protection Environment5.Cross-site Implications of Collaborative Environments6.Keeping One Step Ahead7.Risk Assessment, Mitigation and Compliance8.Components of a Good Protection ProgramWe will also show how described techniques are in use at SC06 to protect SCinet.

Our analytics task is to identify, characterize, and visualize anomalous subsets of as large of a collection of network connection data as possible. We use a combination of HPC resources, advanced algorithms, and visualization techniques. To effectively and efficiently identify the salient portions of the data, we rely on a multistage workflow that includes data acquisition, summarization (feature extraction), novelty detection, and classification. Once these subsets of interest have been identified and automatically characterized, we use a stateof- the-art high-dimensional query system to extract this data for interactive visualization. Our approach is equally useful for other large-data analysis problems where it is more practical to identify interesting subsets of the data for visualization than it is to render all data elements. By reducing the size of the rendering workload, we enable highly interactive and useful visualizations.

Visapult is a prototype application and framework for remote visualization of large scientific datasets. We approach the technical challenges of tera-scale visualization with a unique architecture that employs high speed WANs and network data caches for data staging and transmission. This architecture allows for the use of available cache and compute resources at arbitrary locations on the network. High data throughput rates and network utilization are achieved by parallelizing I/O at each stage in the application, and by pipelining the visualization process. On the desktop, the graphics interactivity is effectively decoupled from the latency inherent in network applications. We present a detailed performance analysis of the application, and improvements resulting from field-test analysis conducted as part of the DOE Combustion Corridor project.

Radiance is a software package developed at Lawrence Berkeley National Laboratory for lighting visualization. Lighting visualization predicts how lighting would appear if a modelled scene were to be physically realized. Unlike most lighting systems, Radiance physically models the effects of lighting, providing an image that is closer to physical reality. This is of obvious benefit to architects and lighting designers. Such visualizations are computationally expensive: rendering a single image can take hours on a standard workstation environment. Ideally, an architect would like to be able to interactively navigate through a scene to get a full impression of the true appearance of a particular model. With this goal in mind, we have (1) developed a geometric-based method (point cloud) to reuse pixels from a previous frame and (2) developed a parallel, distributed memory implementation of Radiance and the point cloud using MPI for inter-processor communication.