# Diva: A Declarative and Reactive Language for In-Situ Visualization

arXiv: https://arxiv.org/abs/2001.11604 · HTML: https://mlsanity.com/paper/2001.11604/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Distributed Neural Representation for Reactive in situ Visualization](https://mlsanity.com/paper/2304.10516/) (2023)
- [Fixed-Budget Gaussian Volume Encoding with Structure-Aware Allocation](https://mlsanity.com/paper/2608.14112/) (2026)

## Abstract (arXiv)

The use of adaptive workflow management for in situ visualization and analysis has been a growing trend in large-scale scientific simulations. However, coordinating adaptive workflows with traditional procedural programming languages can be difficult because system flow is determined by unpredictable scientific phenomena, which often appear in an unknown order and can evade event handling. This makes the implementation of adaptive workflows tedious and error-prone. Recently, reactive and declarative programming paradigms have been recognized as well-suited solutions to similar problems in other domains. However, there is a dearth of research on adapting these approaches to in situ visualization and analysis. With this paper, we present a language design and runtime system for developing adaptive systems through a declarative and reactive programming paradigm. We illustrate how an adaptive workflow programming system is implemented using our approach and demonstrate it with a use case from a combustion simulation.