Design and Evaluation of a GPU Streaming Framework for Visualizing Time-Varying AMR Data

Stefan Zellmann, Ingo Wald, Alper Sahistan, Matthias Hellmann, Will Usher

In Eurographics Symposium on Parallel Graphics and Visualization, 2022.

Fig. 1: The NASA Exajet serves as the motivating use case for our study. The large computational fluid dynamics data set was computed using an adaptive mesh refinement (AMR) code and consists of 656 million cells and 423 time steps. Each time step stores 2.5 GB of data per scalar field. At four scalar fields for density and X/Y/Z velocity components, the full time series occupies over 4 TB. Data sets such as these pose significant challenges for interactive visualization on current GPU workstations. We present and evaluate a prototypical framework targeting GPU workstations that asynchronously streams and renders such data sets at interactive rates and with high quality.


We describe a systematic approach for rendering time-varying simulation data produced by exa-scale simulations, using GPU workstations. The data sets we focus on use adaptive mesh refinement (AMR) to overcome memory bandwidth limitations by representing interesting regions in space with high detail. Particularly, our focus is on data sets where the AMR hierarchy is fixed and does not change over time. Our study is motivated by the NASA Exajet, a large computational fluid dynamics simulation of a civilian cargo aircraft that consists of 423 simulation time steps, each storing 2.5 GB of data per scalar field, amounting to a total of 4 TB. We present strategies for rendering this time series data set with smooth animation and at interactive rates using current generation GPUs. We start with an unoptimized baseline and step by step extend that to support fast streaming updates. Our approach demonstrates how to push current visualization workstations and modern visualization APIs to their limits to achieve interactive visualization of exa-scale time series data sets.


  booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
  editor = {Bujack, R. and Tierny, J. and Sadlo, F.},
  title = {{Design} and {Evaluation} of a {GPU} {Streaming} {Framework} for {Visualizing} {Time}-{Varying} {AMR} {Data}},
  author = {Zellmann, Stefan and Wald, Ingo and Sahistan, Alper and Hellmann, Matthias and Usher, Will},
  DOI = {10.2312/pgv.20221066},
  year = {2022},