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

  • Ya Ju Fan Lawrence Livermore National Laboratory
  • Chandrika Kamath Lawrence Livermore National Laboratory
Keywords: Compressed sensing, exascale computing, sparse recovery


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.  We provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. 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.


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How to Cite
Fan, Y. J., & Kamath, C. (2016). A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data. Statistics, Optimization & Information Computing, 4(3), 194-213. https://doi.org/10.19139/soic.v4i3.207
Research Articles