Spatiotemporal Wavelet Compression for Visualization of Scientific Simulation Data

Li, Samuel; Sane, Sudhanshu; Orf, Leigh; Mininni, Pablo D.; Clyne, John; Childs, Hank

doi:10.1109/cluster.2017.15

Cited by 11 publications

(8 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In situ analysis algorithms may transform data into reduced representations or surrogate models in order to mitigate large data size, high dimensionality, or long computation times. Low-rank approximation (Austin et al, 2016), statistical summarization (Biswas et al, 2018;Dutta et al, 2017;Hazarika et al, 2018;Lawrence et al, 2017;Lohrmann et al, 2017;Thompson et al, 2011), topological segmentation (Gyulassy et al, 2012(Gyulassy et al, , 2019Landge et al, 2014;Weber, 2013, 2014), wavelet transformation (Li et al, 2017;Salloum et al, 2018), lossy compression (Brislawn et al, 2012;Di and Cappello, 2016;Lindstrom, 2014), geometric modeling (Nashed et al, 2019; Peterka et al, 2018), and feature detection (Guo et al, 2017) may be used to generate reduced or surrogate models.…”

Section: Analysis Algorithmsmentioning

confidence: 99%

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

Peterka

Bard

Bennett

et al. 2020

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

show abstract

Section: Analysis Algorithmsmentioning

confidence: 99%

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

Peterka

Bard

Bennett

et al. 2020

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

show abstract

“…Among these two factors, multiple resolutions have a significantly bigger impact for a given amount of data reduction. We illustrate this impact with different renderings and provide a qualitative evaluation; more quantitative analysis can be found in our previous work [37,38].…”

Section: Rendering Quality Impactmentioning

confidence: 99%

VAPOR: A Visualization Package Tailored to Analyze Simulation Data in Earth System Science

et al. 2019

Self Cite

View full text Add to dashboard Cite

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Sciences (ESS) are no exception. However, within ESS, specialized visualization requirements and data models, particularly for those data arising from numerical models, often make general purpose visualization packages difficult, if not impossible, to use effectively. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly-interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR’s utility in the study of a numerically- simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

show abstract

“…Li et al . [LSO*17] also applied 4D wavelet compression to scientific simulation data, and demonstrated an approximately 2 X benefit over 3D wavelet compression with respect two visual analytics tasks. Finally, interested readers can learn more on wavelet transforms from the textbook by Strang and Nguyen [SN96].…”

Section: Lossy Compressionmentioning

confidence: 99%

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Marsaglia

Garth

et al. 2018

Computer Graphics Forum

View full text Add to dashboard Cite

Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.

show abstract

Spatiotemporal Wavelet Compression for Visualization of Scientific Simulation Data

Cited by 11 publications

References 34 publications

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

VAPOR: A Visualization Package Tailored to Analyze Simulation Data in Earth System Science

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Contact Info

Product

Resources

About