Optimal Compressed Sensing and Reconstruction of Unstructured Mesh Datasets

Data Science and Engineering - Tập 3 Số 1 - Trang 1-23 - 2018
Maher Salloum1, Nathan Fabian2, David M. Hensinger2, Jina Lee1, Elizabeth M. Allendorf3, Ankit Bhagatwala4, Myra Blaylock1, Jacqueline H. Chen1, Jeremy Alan Templeton1, Irina Tezaur1
1Sandia National Laboratories, Livermore, CA, USA.
2Sandia National Laboratories, Albuquerque, NM, USA
3University of California, Los Angeles, CA USA
4Pilot AI Labs, Redwood City, CA, USA

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Tài liệu tham khảo

Klasky S, Abbasi H, Logan J, Parashar M, Schwan K, Shoshani A et al (2011) In situ data processing for extreme scale computing. In: Proceedings of SciDAC 2011

Evans LC (2010) Partial differential equations, graduate studies in mathematics. American Mathematical Society, Providence

Gersho A, Gray RM (2012) Vector quantization and signal compression. Springer, New York

Lindstrom P (2014) Fixed-rate compressed floating-point arrays. IEEE Trans Vis Comput Graph 20(12):2674–2683

Donoho D (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

Lehmann H, Jung B (2014) In-situ multi-resolution and temporal data compression for visual exploration of large-scale scientific simulations. In: IEEE 4th symposium on large data analysis and visualization (LDAV), Paris, France

Lakshminarasimhan S, Zou X, Boyuka DA, Pendse SV, Jenkins J, Vishwanath V, Papka ME, Klasky S, Samatova NF (2014) DIRAQ: scalable in situ data-and resource-aware indexing for optimized query performance. Clust Comput 17(4):1101–1119

Bernardon FF, Callahan SP, Comba JLD, Silva CT (2005) Rendering of time-varying scalar fields on unstructured meshes. Technical report, Lawrence Radiation Laboratory

Zhao K, Sakamoto N, Koyamada K (2015) Time-varying volume compression in spatio-temporal domain. J Adv Simul Sci Eng 1(1):171–187

Austin W, Ballard G, Kolda TG (2016) Parallel tensor compression for large-scale scientific data. Technical report. arXiv:1510.06689v2

Sen P, Darabi S (2011) Compressive rendering: a rendering application of compressed sensing. IEEE Trans Vis Comput Graph 17(4):487–499

Xu X, Sakhaee E, Entezari A (2014) Volumetric data reduction in a compressed sensing. Comput Graph Forum 33(3):111–120

Liu X, Alim UR (2015) Compressive volume rendering. Comput Graph Forum 34(3):101–110

Yu H, Wang C, Grout RW, Chen JH, Ma K (2010) In situ visualization for large-scale combustion simulations. IEEE Comput Graph Appl 30(3):45–57

Sauer F, Yu H, Ma K (2013) An analytical framework for particle and volume data of large-scale combustion simulations. In: Proceedings of the 8th international workshop on ultrascale visualization. ACM, New York, USA

Fabian N, Moreland K, Thompson D, Bauer AC, Marion P, Geveci B, Rasquin M, Jansen KE (2011) The paraview coprocessing library: a scalable, general purpose in situ visualization library. In: 2011 IEEE symposium on large data analysis and visualization (LDAV)

Woodring J, Ahrens J, Figg J, Wendelberger J, Habib S, Heitmann K (2011) In situ sampling of a large scale particle simulation for interactive visualization and analysis. SIAM J Math Anal 30(3):1151–1160

Bennett JC, Comandur S, Pinar A, Thompson D (2013) Sublinear algorithms for in-situ and in-transit data analysis at the extreme-scale. In: DOE workshop on applied mathematics research for exascale computing, Washington, DC, USA

Alpert B, Beylkin G, Coifman R, Rokhlin V (1993) Wavelet-like bases for the fast solution of second-kind integral equations. SIAM J Sci Comput 14(1):159–184

Pogossova E, Egiazarian K, Gotchev A, Astola J (2005) Tree-structured legendre multi-wavelets. In: Computer aided systems theory EUROCAST 2005. Volume 3643 of lecture notes in computer science. Springer, pp 291–300

Donoho DL, Tsaig Y, Drori I, Starck J-L (2012) Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Trans Inf Theory 58(2):1094–1121

Tsaig Y, Donoho D (2006) Extensions of compressed sensing. Signal Process 86(3):533–548

Candes E, Wakin M (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25(2):21–30

Radunovic DM (2009) Wavelets: from math to practice. Springer, New York

Jansen M, Oonincx P (2005) Second generation wavelets and applications. Springer, New York

Sweldens W (1998) The lifting scheme: a construction of second generation wavelets. SIAM J Math Anal 29(2):511–546

Maggioni M, Bremer JC, Coifman RR, Szlam AD (2005) Biorthogonal diffusion wavelets for multiscale representations on manifolds and graphs. In: Proceedings of SPIE 5914, Wavelets XI, 59141M, San Diego, USA

Alpert BK (1993) A class of bases in L$$^2$$ for the sparse representation of integral operators. SIAM J Math Anal 24(1):246–262

Press WH, Teukolsky SA, Vetterling WT, Flannery BP (2007) Numerical recipes 3rd edition: the art of scientific computing. Cambridge University Press, Cambridge

Lodhi MA, Voronin S, Bajwa WU (2016) YAMPA: yet another matching pursuit algorithm for compressive sensing. In: Proceedings of SPIE 9857, compressive sensing V: from diverse modalities to big data analytics, 98570E, Baltimore, USA

Yin P, Esser E, Xin J (2014) Ratio and difference of $${L}_1$$ and $${L}_2$$ norms and sparse representation with coherent dictionaries. Commun Inf Syst 14(2):87–109

Heroux MA, Bartlett RA, Howle VE, Hoekstra RJ, Hu JJ, Kolda TG, Lehoucq RB, Long KR, Pawlowski RP, Phipps ET, Salinger AG, Thornquist HK, Tuminaro RS, Willenbring JM, Williams A, Stanley KS (2005) An overview of the Trilinos project. ACM Trans Math Softw 31(3):397–423

Ayachit U (2015) The paraview guide: a parallel visualization application. Kitware, Clifton Park

Swinzip v1.0 (2016) A Matlab and C++ library for scientific lossy data compression and reconstruction using compressed sensing and tree-wavelets transforms. Sandia National Laboratories. http://www.sandia.gov/~mnsallo/swinzip/swinzip-v1.0.tgz

Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122

Yin W, Osher S, Goldfarb D, Darbon J (2008) Bregman iterative algorithms for $$\ell _1$$-minimization with applications to compressed sensing. SIAM J Imaging Sci 1(1):143–168

Needell D, Tropp JA (2010) CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Commun ACM 57(12):93–100

Wright SJ, Nowak RD, Figueiredo MAT (2009) Sparse reconstruction by separable approximation. IEEE Trans Signal Process 57(7):2479–2493

Lakshminarasimhan S, Zou X, Boyuka DA II, Pendse SV, Jenkins J, Vishwanath V, Papka ME, Klasky S, Samatova NF (2014) DIRAQ: scalable in situ data-and resource-aware indexing for optimized query performance. Clust Comput 14(4):1101–1119

Kokjohn SL, Hanson RM, Splitter DA, Reitz RD (2011) Fuel reactivity controlled compression ignition (RCCI): a pathway to controlled high-efficiency clean combustion. Int J Engine Res 12:209–226

Bhagatwala A, Sankaran R, Kokjohn S, Chen JH. Numerical investigation of spontaneous flame propagation under RCCI conditions. Combust Flame (under review)

Safta C, Blaylock M, Templeton J, Domino S, Sargsyan K, Najm H (2016) Uncertainty quantification in LES of channel flow. Int J Numer Methods Fluids 83:376–401

Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge

Salloum M, Templeton J (2014) Inference and uncertainty propagation of atomistically-informed continuum constitutive laws, part 1: Bayesian inference of fixed model forms. Int J Uncertain Quantif 4(2):151–170

Wang C, Ma K-L (2008) A statistical approach to volume data quality assessment. IEEE Trans Vis Comput Graph 14(3):590–602

Salloum M, Bennett JC, Pinar A, Bhagatwala A, Chen JH (2015) Enabling adaptive scientific workflows via trigger detection. In: Proceedings of the first workshop on in situ infrastructures for enabling extreme-scale analysis and visualization, pp 41–45

Chen JH, Choudhary A, De Supinski B, DeVries M, Hawkes ER, Klasky S, Liao WK, Ma KL, Mellor-Crummey J, Podhorszki N et al (2009) Terascale direct numerical simulations of turbulent combustion using S3D. Comput Sci Discov 2(1):015001

zfp & fpzip (2015) Floating point compression. Lawrence Livermore National Laboratories. http://computation.llnl.gov/projects/floating-point-compression/download/zfp-0.4.1.tar.gz

Tezaur I, Perego M, Salinger A, Tuminaro R, Price S (2015) Albany/felix: a parallel, scalable and robust finite element higher-order stokes ice sheet solver built for advanced analysis. Geosci Model Dev 8:1–24