In situ visualization and steering of computational modeling can be
effectively achieved using reactive programming, which leverages temporal
abstraction and data caching mechanisms to create dynamic workflows. However,
implementing a temporal cache for large-scale simulations can be challenging.
Implicit neural networks have proven effective in compressing large volume
data. However, their application to distributed data has yet to be fully
explored. In this work, we develop an implicit neural representation for
distributed volume data and incorporate it into the DIVA reactive programming
system. This implementation enables us to build an in situ temporal caching
system with a capacity 100 times larger than previously achieved. We integrate
our implementation into the Ascent infrastructure and evaluate its performance
using real-world simulations.

本文介绍了使用反应式编程实现计算建模的 in situ 可视化和控制，并提出利用隐式神经网络实现分布式数据的时间缓存系统。该系统容量为先前实现的 100 倍，并成功应用于 Ascent 基础设施的真实世界模拟中。