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

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E. Wes Bethel,Burlen Loring,Utkarsh Ayachit,David Camp,Earl P. N. Duque,Nicola Ferrier,Joseph Insley number of transmissions and the communication overhead can be reduced significantly. Particularly for applications which require periodic dissemination of information into a large region, aggregation becomes prerequisite. Let us consider an example of self-organized infrastructure less traffic info

DIS

David H. Rogers,Soumya Dutta,Divya Banesh,Terece L. Turton,Ethan Stam,James Ahrensible by empowering on-board infrastructure as this enhances operational efficiency while reducing cost. If a broadband technology is made available in a dynamic train environment that could address critical signaling and control system requirements as well as empowers the train passengers - with on-

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re exchanged. Smart analysis of this collected data could be achieved for many purposes: traffic prediction, driver profile detection, anomaly detection, etc. Anomaly detection is an important issue for road operators. An anomaly on roads could be caused by various reasons: potholes, obstacles, weat

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obstruct

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Conference proceedings 2022ion 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

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Unlocking Large Scale Uncertainty Quantification with In Transit Iterative Statisticsrk that computes in transit statistical quantities of interest. Melissa currently implements the on-the-fly computation of the statistics necessary for the realization of large scale uncertainty quantification studies: moment-based statistics (mean, standard deviation, higher orders), quantiles, Sobol’ indices, and threshold exceedance.

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The Adaptable IO System (ADIOS)escription for the ADIOS framework and the abstractions provided. We demonstrate the capabilities of the ADIOS framework using a number of examples, including strong coupling of simulation codes, in situ visualization running on a separate computing cluster, and streaming of experimental data between Asia and the United States.

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Ascent: A Flyweight In Situ Library for?Exascale Simulationstractions, interface, and design. It concludes with success stories that highlight its capabilities: in situ visualization of a 97.8 billion element inertial confinement fusion simulation using 16,384 GPUs, delivering radiography capabilities for a Kelvin-Helmholtz simulation, and native rendering of higher-order elements.