Distance visualization: data exploration on the grid

Foster, Ian; Insley, Joseph A.; Laszewski, Gregor von; Kesselman, Carl; Thiebaux, Marcus

doi:10.1109/2.809249

Cited by 43 publications

(22 citation statements)

References 6 publications

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“…Foster et al [14] studied the distance visualization in widely distributed environments; major technical challenges are identified and an online collaborative system is described to reconstruct and analyze tomographic data from remote X-ray sources and electron microscopes. Grid…”

Section: Ieee Transactions On Computersmentioning

confidence: 99%

Self-Adaptive Configuration of Visualization Pipeline Over Wide-Area Networks

Gao

Zhu

et al. 2008

IEEE Trans. Comput.

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Next-generation scientific applications require the capabilities to visualize large archival datasets or on-going computer simulations of physical and other phenomena over wide-area network connections. To minimize the latency in interactive visualizations across wide-area networks, we propose an approach that adaptively decomposes and maps the visualization pipeline onto a set of strategically selected network nodes. This scheme is realized by grouping the modules that implement visualization and networking subtasks, and mapping them onto computing nodes with possibly disparate computing capabilities and network connections. Using estimates for communication and processing times of subtasks, we present a polynomial-time algorithm to compute a decomposition and mapping to achieve minimum end-to-end delay of the visualization pipeline. We present experimental results using geographically distributed deployments to demonstrate the effectiveness of this method in visualizing datasets from three application domains.

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Section: Ieee Transactions On Computersmentioning

confidence: 99%

Self-Adaptive Configuration of Visualization Pipeline Over Wide-Area Networks

Gao

Zhu

et al. 2008

IEEE Trans. Comput.

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“…Visualization of performance data from scientific applications is also related to this aspect [62]. The importance of data mining in providing decision support in large-scale simulations [63] has also been recognized in other projects [64][65][66][67][68][69]. Our framework is novel in that it captures the entire problem-solving process prior to the scheduling of a simulation and the semistructured format for model instances allows the embedding of mining functions as primitives into standard query languages (akin to [70]).…”

Section: Reasoning and Problem Solvingmentioning

confidence: 99%

Programming environments for multidisciplinary Grid communities

Ramakrishnan

Watson

Kafura

et al. 2002

Concurrency and Computation

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SUMMARYAs the power of computational Grids increases, there is a corresponding need for better usability for large and diverse communities. The focus in this paper is on supporting multidisciplinary communities of scientists and engineers. We discuss requirements for Grid computing environments (GCEs) in this context, and describe several core support technologies developed to meet these requirements. Our work extends the notion of a programming environment beyond the compile-schedule-execute paradigm, to include functionality such as collaborative application composition, information services, and data and simulation management. Systems designed for five different applications communities are described. These systems illustrate common needs and characteristics arising in multidisciplinary communities and motivate a highlevel design framework for building GCEs that meet those needs.

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“…In the scientific domain, large-scale data-intensive applications exist in computational biology, tomography [33], remote visualization [13], remote instrument control [31], and distributed data analysis [28], [8]. Beyond the scientific domain are rich media collaboration and pervasive computing.…”

Section: Introductionmentioning

confidence: 99%

Dynamic querying of streaming data with the dQUOB system

Plale

Schwan

2003

IEEE Trans. Parallel Distrib. Syst.

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Abstract-Data streaming has established itself as a viable communication abstraction in data-intensive parallel and distributed computations, occurring in applications such as scientific visualization, performance monitoring, and large-scale data transfer. A known problem in large-scale event communication is tailoring the data received at the consumer. It is the general problem of extracting data of interest from a data source, a problem that the database community has successfully addressed with SQL queries, a time tested, user-friendly way for noncomputer scientists to access data. By leveraging the efficiency of query processing provided by relational queries, the dQUOB system provides a conceptual relational data model and SQL query access over streaming data. Queries can be used to extract data, combine streams, and create new streams. The language augments queries with an action to enable more complex data transformations such as Fourier transforms. The dQUOB system has been applied to two large-scale distributed applications: a safety critical autonomous robotics simulation and scientific software visualization for global atmospheric transport modeling. In this paper, we present the dQUOB system and the results of performance evaluation undertaken to assess its applicability in data-intensive wide-area computations, where the benefit of portable data transformation must be evaluated against the cost of continuous query evaluation.

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Distance visualization: data exploration on the grid

Cited by 43 publications

References 6 publications

Self-Adaptive Configuration of Visualization Pipeline Over Wide-Area Networks

Self-Adaptive Configuration of Visualization Pipeline Over Wide-Area Networks

Programming environments for multidisciplinary Grid communities

Dynamic querying of streaming data with the dQUOB system

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