A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

Fan, Ya Ju; Kamath, Chandrika

doi:10.19139/soic.v4i3.207

Title: A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

Abstract

The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction. In this paper, we explore the use of compressed sensing (CS) techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression. Also, we provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Finally, our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity.

Authors:

Fan, Ya Ju ^[1]; Kamath, Chandrika ^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Publication Date:: Thu Sep 01 00:00:00 EDT 2016

Research Org.:: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1341945

Report Number(s):: LLNL-JRNL-681293
Journal ID: ISSN 2311-004X

Grant/Contract Number:: AC52-07NA27344

Resource Type:: Accepted Manuscript

Journal Name:: Statistics, Optimization & Information Computing

Additional Journal Information:: Journal Volume: 4; Journal Issue: 3; Journal ID: ISSN 2311-004X

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; Compressed Sensing; Sparse Recovery; Simulation Data; Algorithms

Citation Formats


                    Fan, Ya Ju, and Kamath, Chandrika. A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data.  United States: N. p., 2016. 
Web.  doi:10.19139/soic.v4i3.207.

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                    Fan, Ya Ju, & Kamath, Chandrika. A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data.  United States.  https://doi.org/10.19139/soic.v4i3.207

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                    Fan, Ya Ju, and Kamath, Chandrika. Thu .  
"A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data".  United States.  https://doi.org/10.19139/soic.v4i3.207.  https://www.osti.gov/servlets/purl/1341945.

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@article{osti_1341945,

  title        = {A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data},

  author       = {Fan, Ya Ju and Kamath, Chandrika},

  abstractNote = {The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction. In this paper, we explore the use of compressed sensing (CS) techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression. Also, we provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Finally, our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity.},

  doi          = {10.19139/soic.v4i3.207},

  journal      = {Statistics, Optimization & Information Computing},

  number       = 3,

  volume       = 4,

  place        = {United States},

  year         = {Thu Sep 01 00:00:00 EDT 2016},

  month        = {Thu Sep 01 00:00:00 EDT 2016}

}

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Works referencing / citing this record:

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International Journal of Data Science and Analytics, Vol. 9, Issue 1
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Title: A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

Abstract

Citation Formats

Compressing unstructured mesh data from simulations using machine learning journal, April 2019

Compressing unstructured mesh data from simulations using machine learning
journal, April 2019