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Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering

Authors:

Tobias HöllererAuthors Info & Claims

Computer Graphics Forum, Volume 36, Issue 3

Pages 179 - 188

https://doi.org/10.1111/cgf.13178

Published: 01 June 2017 Publication History

Abstract

Web-based visualization libraries are in wide use, but performance bottlenecks occur when rendering, and especially animating, a large number of graphical marks. While GPU-based rendering can drastically improve performance, that paradigm has a steep learning curve, usually requiring expertise in the computer graphics pipeline and shader programming. In addition, the recent growth of virtual and augmented reality poses a challenge for supporting multiple display environments beyond regular canvases, such as a Head Mounted Display HMD and Cave Automatic Virtual Environment CAVE. In this paper, we introduce a new web-based visualization library called Stardust, which provides a familiar API while leveraging GPU's processing power. Stardust also enables developers to create both 2D and 3D visualizations for diverse display environments using a uniform API. To demonstrate Stardust's expressiveness and portability, we present five example visualizations and a coding playground for four display environments. We also evaluate its performance by comparing it against the standard HTML5 Canvas, D3, and Vega.

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Cited By

Yang JJoo HYerramreddy SMoritz DBattle L(2024)Optimizing Dataflow Systems for Scalable Interactive VisualizationProceedings of the ACM on Management of Data10.1145/36392762:1(1-25)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639276
Du RLi NJin JCarney MMiles SKleiner MYuan XZhang YKulkarni ALiu XSabie AOrts-Escolano SKar AYu PIyengar RKowdle AOlwal A(2023)Rapsai: Accelerating Machine Learning Prototyping of Multimedia Applications through Visual ProgrammingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581338(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581338
Li QWang XFu LCao XWang XZhang JZhou C(2023)VSAN: A new visualization method for super-large-scale academic networksFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-2078-518:1Online publication date: 12-Aug-2023
https://dl.acm.org/doi/10.1007/s11704-022-2078-5
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Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering
1. Computing methodologies
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cover image Computer Graphics Forum

Computer Graphics Forum Volume 36, Issue 3

June 2017

639 pages

ISSN:0167-7055

EISSN:1467-8659

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The Eurographs Association & John Wiley & Sons, Ltd.

Chichester, United Kingdom

Publication History

Published: 01 June 2017

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Cited By

Yang JJoo HYerramreddy SMoritz DBattle L(2024)Optimizing Dataflow Systems for Scalable Interactive VisualizationProceedings of the ACM on Management of Data10.1145/36392762:1(1-25)Online publication date: 26-Mar-2024
https://dl.acm.org/doi/10.1145/3639276
Du RLi NJin JCarney MMiles SKleiner MYuan XZhang YKulkarni ALiu XSabie AOrts-Escolano SKar AYu PIyengar RKowdle AOlwal A(2023)Rapsai: Accelerating Machine Learning Prototyping of Multimedia Applications through Visual ProgrammingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581338(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581338
Li QWang XFu LCao XWang XZhang JZhou C(2023)VSAN: A new visualization method for super-large-scale academic networksFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-022-2078-518:1Online publication date: 12-Aug-2023
https://dl.acm.org/doi/10.1007/s11704-022-2078-5
Korkut ESurer E(2023)Visualization in virtual reality: a systematic reviewVirtual Reality10.1007/s10055-023-00753-827:2(1447-1480)Online publication date: 17-Jan-2023
https://dl.acm.org/doi/10.1007/s10055-023-00753-8
Madera-Ramirez FLopez-Martinez JMoo-Mena FGomez-Montalvo J(2022)CPU–GPU buffer communication using compute shader to fill volumes with spheresThe Journal of Supercomputing10.1007/s11227-021-04136-178:5(6448-6460)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11227-021-04136-1
Thompson JLiu ZStasko JKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Data Animator: Authoring Expressive Animated Data GraphicsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445747(1-18)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445747
Ge TLee BWang YKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)CAST: Authoring Data-Driven Chart AnimationsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445452(1-15)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445452
Lobo MHurter CIrani P(2020)Flex-ERProceedings of the ACM on Human-Computer Interaction10.1145/34273234:ISS(1-20)Online publication date: 4-Nov-2020
https://dl.acm.org/doi/10.1145/3427323
Butcher PJohn NRitsos PBrewster SFitzpatrick GCox AKostakos V(2019)VRIA - A Framework for Immersive Analytics on the WebExtended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290607.3312798(1-6)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290607.3312798
Ren DLee BHöllerer TMorishima SItoh YShiratori TYue YLindeman R(2018)XRCreatorProceedings of the 24th ACM Symposium on Virtual Reality Software and Technology10.1145/3281505.3283400(1-2)Online publication date: 28-Nov-2018
https://dl.acm.org/doi/10.1145/3281505.3283400

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