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Titlebook: In Situ Visualization for Computational Science; Hank Childs,Janine C. Bennett,Christoph Garth Conference proceedings 2022 The Editor(s) (

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书目名称In Situ Visualization for Computational Science
编辑Hank Childs,Janine C. Bennett,Christoph Garth
视频video
丛书名称Mathematics and Visualization
图书封面Titlebook: In Situ Visualization for Computational Science;  Hank Childs,Janine C. Bennett,Christoph Garth Conference proceedings 2022 The Editor(s) (
描述This book provides an overview of the emerging field of in situ visualization, i.e. visualizing simulation data as it is generated. In situ visualization is a processing paradigm in response to recent trends in the development of high-performance computers. It has great promise in its ability to access increased temporal resolution and leverage extensive computational power. However, the paradigm also is widely viewed as limiting when it comes to exploration-oriented use cases. Furthermore, it will require visualization systems to become increasingly complex and constrained in usage. As research efforts on in situ visualization are growing, the state of the art and best practices are rapidly maturing..Specifically, this book contains chapters that reflect state-of-the-art research results and best practices in the area of in situ visualization. Our target audience are researchers and practitioners from the areas of mathematics computational science, high-performance computing, and computer science that work on or with in situ techniques, or desire to do so in future. .
出版日期Conference proceedings 2022
关键词in situ visualization; scientific visualization; high performance computing; computational science; data
版次1
doihttps://doi.org/10.1007/978-3-030-81627-8
isbn_softcover978-3-030-81629-2
isbn_ebook978-3-030-81627-8Series ISSN 1612-3786 Series E-ISSN 2197-666X
issn_series 1612-3786
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Unlocking Large Scale Uncertainty Quantification with In Transit Iterative Statisticsties. Most of the time, the input parameters of a simulation are modeled as random variables, then simulations are run a (possibly large) number of times with input parameters varied according to a specific design of experiments. Uncertainty quantification for numerical simulations is a hard computa
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Parameter Adaptation In Situ: Design Impacts and Trade-Offs different use cases are analyzed with respect to configuration changes. First, the performance impact of load balancing and resource allocation variants on both simulation and visualization is investigated using the MegaMol framework. Its loose coupling scheme and architecture enable minimally inva
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Resource-Aware Optimal Scheduling of In Situ Analysisis needs to be done in a way such that the simulation throughput is minimally impacted and the analysis output is available immediately within desired intervals. We describe the formulation of optimal resource allocation for simulation and in situ analysis computations as constrained integer linear
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Leveraging Production Visualization Tools In Situtu is a paradigm shift for large-scale visualization, much of the same algorithms and operations apply regardless of whether the visualization is run post hoc or in situ. Thus, there is a great benefit to taking the large-scale code originally designed for post hoc use and leveraging it for use in s
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