A significant unsolved problem in scientific visualization is how to efficiently visualize extremely large time-varying datasets. Using parallelism provides a promising solution. One drawback of this approach is the high overhead and specialized knowledge often required to create parallel visualization programs. In this paper, we present a parallel visualization system that is scalable, portable and encapsulates parallel programming details for its users. Our approach was to augment an existing visualization system, the visualization toolkit(VTK). Process and communication abstractions were added in order to support task, pipeline and data parallelism. The resulting system allows users to quickly write parallel visualization programs and avoid rewriting these programs when porting to new platforms. The performance of a collection of parallel visualization programs written using this system and run on both a cluster of SGI Origin 2000s and a Linux-based PC cluster is presented. In addit...

he visualization window (right). Researchers can select UI (user interaction) buttons on many of the modules that allow control and feedback of parameters within a particular module (left). interactively via a component-based visual programming model. SCIRun enables scientists to modify geometric models and interactively change numerical parameters and boundary conditions, as well as to modify the level of mesh adaptation needed for an accurate numerical solution. As opposed to the typical "off-line" simulation mode - in which the scientist manually sets input parameters, computes results, visualizes the results via a separate visualization package, then starts again at the beginning - SCIRun "closes the loop" and allows interactive steering of the design, computation, and visualization phases of a simulation. The BioPSE project seeks to release state-of-the-art software, datasets, and documentation for researchers investigating bioelectric field problems. The immediate applicat

The benefits of component technologies are well known: they enable encapsulation, modular construction of applications and software reuse. The DOE sponsored Common Component Architecture (CCA) [3] project adopts a component-based approach for building large scale scientific applications. On the other hand, the Web servicesbased Open Grid Service Architecture (OGSA) and Infrastructure (OGSI) [14] come close to defining a component architecture for the Grid. Using an approach where a CCA component is modeled as a set of Grid services, the XCAT3 framework allows for CCA components to be compatible with the OGSI specification. This enables CCA components to be accessible via standard Grid clients, especially the ones that are portal-based. For CCA compatibility, XCAT3 uses interfaces generated by the Babel [5] toolkit, and for OGSI compatibility, it uses the Extreme GSX [12] toolkit. In this paper, we describe our experience in implementing the XCAT3 system, and how it can be used to compose complex distributed applications on the Grid in a modular fashion.

1 Introduction Consider an extreme example of a disaster scenario in which a major waste spilloccurs in a river flowing through the center of a major city. In short time, the waste will be on kilometers of the city's shoreline. Sensors can now be dropped into an open water body to measure where thecontamination is, where the contaminant is going to go, and to monitor the

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This thesis investigates several issues in data and computation modeling for scientific problem solving environments (PSEs). A PSE is viewed as a software system that provides (i) a library of simulation components, (ii) experiment management, (ii) reasoning about simulations and data, and (iv) problem solving abstractions. Three specific ideas, in functionalities (ii)-(iv), form the contributions of this thesis. These include the EMDAG system for experiment management, the BSML markup language for data interchange, and the use of data mining for conducting non-trivial parameter studies. This work emphasizes data modeling and management, two important aspects that have been largely neglected in modern PSE research. All studies are performed in the context of S 4 W, a sophisticated PSE for wireless system design.

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committee and by majority vote has been found to be satisfactory. Chair: Steven G. Parker

SCIRun is a problem solving environment (PSE) that allows the interactive construction, debugging, and steering of large-scale scientific computations. Over the past few years, we have developed two additional problem solving environments that extend SCIRun’s capabilities: BioPSE and Uintah. The mission of the BioPSE project is to release state-of-the-art software, datasets, and documentation for researchers investigating bioelectric field problems. Uintah is designed to specifically address the problems of interdisciplinary, massively-parallel scientific computation on terascale computing platforms. These three systems, SCIRun, BioPSE, and Uintah, together target a broad range of vastly different problem domains and application users. SCIRun provides a component model, based on generalized dataflow programming, that allows different computational components and visualization components to be connected together in a tightly integrated fashion. SCIRun facilitates the interactive construction, debugging and steering of large-scale, typically parallel, scientific computations. SCIRun can be envisioned as a “computational workbench, ” in which a scientist can design and modify simulations interactively via a component-based visual programming Figure 1. The SCIRun PSE showing the module network (middle), and the visualization window (right). Researchers can select UI (user interaction) buttons on many of the modules that allow control and

Over the last several years, measurement technology has undergone a transformation from systems with many transducers attached to a central computer to distributed measurement systems where each transducer has an attached CPU, downloadable code, and a network connection. Even though measurement technology has changed dramatically, measurement systems are still built using old-fashioned and difficult to debug program logic control (PLC) technology, which lacks important features such as fault tolerance, flexibility, and visualization capabilities. Research in software agent technology has been underway for several years, resulting in many high-performance agent systems. With a few exceptions, most existing agent systems focus on low-level technical details, such as performance, mobility and communication and do not address deployment, scaling, and especially the management issues of hundreds or thousands of agents. Many of them also ignore higher-level issues, such as intelligence and autonomous behavior. Based on experiments and case studies in two different domains, we demonstrate that software agents combined with a visual programming language address the shortcomings