An architecture for a scientific visualization system

… , 2008. SCC'08. IEEE …, 2008

ACM SIGGRAPH Computer Graphics, 1995

Cameron introdu, es modular visualization environments and /he motivation behind employing the data-flow paradigm and visual programming. In a tr~. ~. data-flow implementation, all modules are pure functions. Hence, processes are stateh, ss with no side-effects (Arvind and Brobst [l I). Consider Figure , a visual program that imports data, compl tes both an isosurface and a planar mapping and renders the results in a single image. Iff agine a set of available processes waiting fo' their inputs from the processes upstream i l the network or asynchronously from ass(<iated input devices or interactors. The Cc Ilect module waits for inputs from the Iso,. urface and MapToPlane modules. When their inputs are received, they run, and when finished they distribute their results to the modul~.s waiting downstream. Import would send i,s results to the waiting Isosurface and MapTo~'lane modules. In effect, this execution mode is entirely data-driven and top-down: the ~.xecution of modules is dependent solely c n the passage of data through the system.

1989

Abstract A software system for developing interactive scientific visualization applications quickly, with a minimum of programming effort, is described. This application visualization system (AVS) is an application framework targeted at scientists and engineers. The goal of the system is to make applications that combine interactive graphics and high computational requirements easier to develop for both programmers and nonprogrammers.

Ibm Systems Journal, 1994

The Hy+ system is a generic visualization tool that supports a novel visual query language called GraphLog. In Hy+, visualizations are based on a graphical formalism that allows comprehensible representations of databases, queries, and query answers to be interactively manipulated. This paper describes the design, architecture, and features of Hy+ with a number of applications in software engineering and network management. V isual presentations are widely considered an effective tool to help manage large and complex collections of data. Researchers in scientific visualization (see, for example, McCormick et a1.l) were the first to exploit computer graphics technology to achieve dramatic improvements in the ability of people to understand the data with which they work. The motivation for this exploitation is summarized in the following words from the "Panel Report on Visualization in Scientific Computing,'' which appeared in the November 1987 issue of Computer Graphics: The gigabit bandwidth of the eyekisual cortex system permits much faster perception of geometric and spatial relationships than any other mode, making the power of supercomputers more accessible. Users from industry, universities, medicine and government are largely unable to comprehend or influence the "fire 458 CONSENS ET AL by M. P. Consens F. Ch. Eigler

2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009

Scientific visualization is the process of transforming raw numeric data into a visual form, and is a key element of computational science. While many tools exist, they are unnecessarily difficult to use. This complexity increases time to insight and inhibits casual inquiry. The complexity derives from the need to support arbitrarily formatted data and many visualization algorithms. EnVision addresses both sources of complexity. Its design is predicated on two key insights. First, though the number of data file formats is unbounded, the structure of any one can be described using a small number of parameters. Second, the set of visualization algorithms applicable to a given type of data is small, and the subset used within a specific scientific discipline is smaller. EnVision utilizes domain-specific knowledge and user-directed semi-automation to dramatically simplify data importation and visualization algorithm selection. Its web-based interface facilitates access to remote hardware resources and provides a collaborative visualization environment.

Concurrency and Computation: Practice and Experience, 2004

In this paper we propose a middleware infrastructure adapted for supporting scientific visualization applications over a Grid environment. We instantiate a middleware system from CoDIMS, which is an environment for the generation of configurable data integration middleware systems. CoDIMS adaptive architecture is based on the integration of special components managed by a control module that executes users workflows. We exemplify our proposal with a middleware system generated for computing particles' trajectories within the constraints imposed by a Grid environment. Copyright © 2004 John Wiley & Sons, Ltd.

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2005

The grid has the potential to transform collaborative scientific investigations through the use of closely coupled computational and visualization resources, which may be geographically distributed, in order to harness greater power than is available at a single site. Scientific applications to benefit from the grid include visualization, computational science, environmental modelling and medical imaging. Unfortunately, the diversity, scale and location of the required resources can present a dilemma for the scientific worker because of the complexity of the underlying technology. As the scale of the scientific problem under investigation increases so does the nature of the scientist&#39;s interaction with the supporting infrastructure. The increased distribution of people and resources within a grid-based environment can make resource sharing and collaborative interaction a critical factor to their success. Unless the technological barriers affecting user accessibility are reduced,...

Online Journal of Space Communication, 2003

2000

In previous work, the first author argued for simple lightweight visualisations. These are surprisingly complex to produce due to the need for infrastructure to read files, etc. onCue, a desktop 'agent', aids the rapid production of such visualisations and their integration with desktop and Internet applications. Two examples are used dancing histograms for 2D tables and pieTrees for hierarchical numeric data. A major focus is the importance of architecture, both that of onCue itself and the underlying component infrastructure on which it is built -separation of concerns, mixed initiative computation and plug-and-play components lead to easily produced and easily used systems.

This paper addresses the use of component-based development to build interactive scientific visualization applications. Our overall approach is to make this programming technique more accessible to non-computer-scientists. Therefore, we present a method to, out of constraints given by the user, automatically build and coordinate the dataflow of a real-time interactive scientific visualization application. This type of applications must run as fast as possible while preserving the accuracy of their results. These two aspects are often conflicting, for example when it comes to allowing message dropping or not. Our approach aims at automatically finding the best balance between these two requirements when building the application. An overview of a prototype implementation based on the FlowVR middleware is also given.