Distributed Neural Representation for Reactive In Situ Visualization.

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.

Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms.

This work presents recent advances in visualizing multi-physics, fluid-structure interaction (FSI) phenomena in cerebral aneurysms. Realistic FSI simulations produce very large and complex data sets, yielding the need for parallel data processing and visualization. Here we present our efforts to develop an interactive visualization tool which enables the visualization of such FSI simulation data. Specifically, we present a ParaView-NekTar interface that couples the ParaView visualization engine with NekTar's parallel libraries, which are employed for the calculation of derived fields in both the fluid and solid domains with spectral accuracy. This interface allows the flexibility of independently choosing the resolution for visualizing both the volume data and the surface data from each of the solid and fluid domains, which significantly facilitates the visualization of complex structures under large deformations. The animation of the fluid and structure data is synchronized in time, while the ParaView-NekTar interface enables the visualization of different fields to be superimposed, e.g. fluid jet and structural stress, to better understand the interactions in this multi-physics environment. Such visualizations are key towards elucidating important biophysical interactions in health and disease, as well as disseminating the insight gained from our simulations and further engaging the medical community in this effort of bringing computational science to the bedside.

Runtime visualization of the human arterial tree.

Large-scale simulation codes typically execute for extended periods of time and often on distributed computational resources. Because these simulations can run for hours, or even days, scientists like to get feedback about the state of the computation and the validity of its results as it runs. It is also important that these capabilities be made available with little impact on the performance and stability of the simulation. Visualizing and exploring data in the early stages of the simulation can help scientists identify problems early, potentially avoiding a situation where a simulation runs for several days, only to discover that an error with an input parameter caused both time and resources to be wasted. We describe an application that aids in the monitoring and analysis of a simulation of the human arterial tree. The application provides researchers with high-level feedback about the state of the ongoing simulation and enables them to investigate particular areas of interest in greater detail. The application also offers monitoring information about the amount of data produced and data transfer performance among the various components of the application.