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With large-scale simulation models on massively parallel supercomputers generating increasingly large data sets, in-situ visualization is a promising way to avoid bottlenecks. Enabling in-situ visualization in a simulation model asks for special attention to the interface between a parallel simulation model and the data analysis part of the visualization, and to presentation and interaction scenarios. Modifications to scientific workflows would potentially result in a paradigm shift, which affects compute and data intensive applications generally. We present our approach for enabling in-situ visualization within the highly parallelized climate model ICON using the DSVR visualization framework. We focus on the requirements for generalized grid and data structures, and for universal, scalable algorithms for volume and flow visualization of time series. In-situ pathline extraction as a technique for the visualization of unsteady flows has been integrated in the climate simulation model ICON and verified in first studies.

Development and Integration of an In-Situ Framework for Flow Visualization of Large-Scale, Unsteady Phenomena