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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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發(fā)表于 2025-3-28 15:34:59 | 只看該作者
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發(fā)表于 2025-3-29 01:53:19 | 只看該作者
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發(fā)表于 2025-3-29 07:01:07 | 只看該作者
Resource-Aware Optimal Scheduling of In Situ Analysistimization problem to maximize the number of online analyses and minimize overall runtime, subject to resource constraints such as I/O bandwidth, network bandwidth, rate of computation and available memory. We also demonstrate the effectiveness of our approach through real application case studies on supercomputers.
45#
發(fā)表于 2025-3-29 08:12:41 | 只看該作者
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發(fā)表于 2025-3-29 13:22:23 | 只看該作者
Sampling for Scientific Data Analysis and Reductionar value, local smoothness etc.) and multivariate association among scalar values to determine the importance of a location. For temporal sampling, changes in the local and global properties across time steps are taken into account as importance criteria. In this chapter, spatial sampling approaches
47#
發(fā)表于 2025-3-29 16:47:57 | 只看該作者
48#
發(fā)表于 2025-3-29 23:29:03 | 只看該作者
Exploratory Time-Dependent Flow Visualization via In Situ Extracted Lagrangian Representationsdiscussed. The chapter then considers post hoc exploration using the Lagrangian representation, as well as the corresponding challenges involved. Finally, the chapter concludes with a qualitative evaluation to demonstrate the efficacy of the technique and a discussion of the current state of the art
49#
發(fā)表于 2025-3-30 02:04:53 | 只看該作者
Decaf: Decoupled Dataflows for In Situ Workflowsic integrity during data redistribution, (ii) flow control designed to prevent overflows in the communication channels between tasks, and (iii) a data contract mechanism that allows users to specify the required data in the parallel communication of the workflow tasks. Decaf has been used in a varie
50#
發(fā)表于 2025-3-30 05:02:49 | 只看該作者
Parameter Adaptation In Situ: Design Impacts and Trade-Offsor remote visualization allows a user to monitor the progress of a simulation and to steer visualization parameters. Compression settings are adapted dynamically based on predictions via convolutional neural networks across different parts of images to achieve high frame rates for high-resolution di
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