A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization

Hoang, Duong; Klacansky, Pavol; Bhatia, Harsh; Bremer, Peer-Timo; Lindstrom, Peter; Pascucci, Valerio

doi:10.1109/TVCG.2018.2864853

Title: A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization

Abstract

There presently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g., through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Additionally, we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results proposed in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.

Authors:

Hoang, Duong ^[1]; Klacansky, Pavol ^[1]; Bhatia, Harsh ^[2]; Bremer, Peer-Timo ^[2]; Lindstrom, Peter ^[2]; Pascucci, Valerio ^[1]

Univ. of Utah, Salt Lake City, UT (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Publication Date:: Mon Aug 20 00:00:00 EDT 2018

Research Org.:: Univ. of Utah, Salt Lake City, UT (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC); National Science Foundation (NSF)

OSTI Identifier:: 1591612

Grant/Contract Number:: NA0002375; DMREF-1629660; AC52-07NA27344; SC0007446; SC0010498

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Visualization and Computer Graphics

Additional Journal Information:: Journal Volume: 25; Journal Issue: 1; Journal ID: ISSN 1077-2626

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Large Data; Data Management; data compression; bit ordering; multi-resolution; data analysis

Citation Formats


                    Hoang, Duong, Klacansky, Pavol, Bhatia, Harsh, Bremer, Peer-Timo, Lindstrom, Peter, and Pascucci, Valerio. A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization.  United States: N. p., 2018. 
Web.  doi:10.1109/TVCG.2018.2864853.

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                    Hoang, Duong, Klacansky, Pavol, Bhatia, Harsh, Bremer, Peer-Timo, Lindstrom, Peter, & Pascucci, Valerio. A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization.  United States.  https://doi.org/10.1109/TVCG.2018.2864853

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                    Hoang, Duong, Klacansky, Pavol, Bhatia, Harsh, Bremer, Peer-Timo, Lindstrom, Peter, and Pascucci, Valerio. Mon .  
"A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization".  United States.  https://doi.org/10.1109/TVCG.2018.2864853.  https://www.osti.gov/servlets/purl/1591612.

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@article{osti_1591612,

  title        = {A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization},

  author       = {Hoang, Duong and Klacansky, Pavol and Bhatia, Harsh and Bremer, Peer-Timo and Lindstrom, Peter and Pascucci, Valerio},

  abstractNote = {There presently exist two dominant strategies to reduce data sizes in analysis and visualization: reducing the precision of the data, e.g., through quantization, or reducing its resolution, e.g., by subsampling. Both have advantages and disadvantages and both face fundamental limits at which the reduced information ceases to be useful. The paper explores the additional gains that could be achieved by combining both strategies. In particular, we present a common framework that allows us to study the trade-off in reducing precision and/or resolution in a principled manner. We represent data reduction schemes as progressive streams of bits and study how various bit orderings such as by resolution, by precision, etc., impact the resulting approximation error across a variety of data sets as well as analysis tasks. Additionally, we compute streams that are optimized for different tasks to serve as lower bounds on the achievable error. Scientific data management systems can use the results proposed in this paper as guidance on how to store and stream data to make efficient use of the limited storage and bandwidth in practice.},

  doi          = {10.1109/TVCG.2018.2864853},

  journal      = {IEEE Transactions on Visualization and Computer Graphics},

  number       = 1,

  volume       = 25,

  place        = {United States},

  year         = {Mon Aug 20 00:00:00 EDT 2018},

  month        = {Mon Aug 20 00:00:00 EDT 2018}

}

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https://doi.org/10.1109/TVCG.2018.2864853

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Figure 1: Visualization of the diffusivity field at 0.2 bits per sample (bps) and its Laplacian field at 1.5 bps, using two of the bit streams studied in the paper. Compared to the by bit plane stream, the by wavelet norm stream produces a better reconstruction of the original functionmore »

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Title: A Study of the Trade-off Between Reducing Precision and Reducing Resolution for Data Analysis and Visualization

Abstract

Citation Formats

Figures / Tables:

Compression Challenges in Large Scale Partial Differential Equation Solvers journal, September 2019

Compression Challenges in Large Scale Partial Differential Equation Solvers
journal, September 2019