State of In Situ Visualization in Simulations: We are fast. But are we inspiring?

TL;DR

This paper reviews the progress and challenges of in situ visualization in high-performance scientific computing, questioning whether current methods produce truly insightful and inspiring visualizations at exascale levels.

Contribution

It provides a comprehensive overview of the state of in situ visualization, highlighting recent advancements and emphasizing the need to assess the quality and impact of visualizations.

Findings

In situ visualization has scaled to exascale systems.

Current visualizations may lack insight and inspiration.

Scaling challenges remain for producing meaningful visualizations.

Abstract

Visualization of dynamic processes in scientific high-performance computing is an immensely data intensive endeavor. Application codes have recently demonstrated scaling to full-size Exascale machines, and generating high-quality data for visualization is consequently on the machine-scale, easily spanning 100s of TBytes of input to generate a single video frame. In situ visualization, the technique to consume the many-node decomposed data in-memory, as exposed by applications, is the dominant workflow. Although in situ visualization has achieved tremendous progress in the last decade, scaling to system-size together with the application codes that produce its data, there is one important question that we cannot skip: is what we produce insightful and inspiring?