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

  • 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.

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Metadaten
Document Type:Conference Object
Language:English
Author:Stefan Zellmann, Ingo Wald, Alper Sahistan, Matthias Hellmann, Will Usher
Parent Title (English):Bujack, Tierny et al. (Eds.): EGPGV 2022, 22nd Eurographics Symposium on Parallel Graphics and Visualization, Rome, Italy, June 13, 2022
Pagenumber:11
First Page:61
Last Page:71
ISBN:978-3-03868-175-5
URN:urn:nbn:de:hbz:1044-opus-64135
DOI:https://doi.org/10.2312/pgv.20221066
Publisher:The Eurographics Association
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2022/06/02
Copyright:© 2022 The Author(s). Attribution 4.0 International License
Funding Information:We acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—grant no. 456842964. We also thank the Ministry of Culture and Science of the State of North Rhine-Westphalia for supporting the work through the PROFILBILDUNG grant PROFILNRW-2020-038C.
Departments, institutes and facilities:Fachbereich Informatik
Institute of Visual Computing (IVC)
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
Entry in this database:2022/09/06
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International