Parallel Tensor Compression for Large-Scale Scientific Data

Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016

Volumn , Issue , 2016, Pages 912-922

  Austin, Woody ^a   Ballard, Grey ^b   Kolda, Tamara G ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b SANDIA NATIONAL LABORATORIES   (United States)

Author keywords

Compression; Tucker tensor decomposition

Indexed keywords

COMPACTION; DATA COMPRESSION; LINEAR ALGEBRA; VIRTUAL REALITY;

DATA REDISTRIBUTION; HIGH-FIDELITY SIMULATIONS; PARALLEL IMPLEMENTATIONS; PARALLEL LINEAR ALGEBRAS; PARALLEL PERFORMANCE; SCALING EXPERIMENTS; TENSOR DECOMPOSITION; TUCKER DECOMPOSITIONS;

TENSORS;

EID: 84983261802     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2016.67     Document Type: Conference Paper

References (24)

1. R. BALLESTER-RIPOLL and R. PAJAROLA, Lossy volume compression using Tucker truncation and thresholding, Vis Comput, (2015), doi:10. 1007/s00371-015-1130-y.
2. A. BHAGATWALA, J. H. CHEN, and T. LU, Direct numerical simulations of HCCI/SACI with ethanol, Combustion and Flame, 161 (2014), pp. 1826-1841, doi:10. 1016/j. combustflame. 2013. 12. 027.
3. A. BHAGATWALA, Z. LUO, H. SHEN, J. A. SUTTON, T. LU, and J. H. CHEN, Numerical and experimental investigation of turbulent DME jet flames, Proc. Combustion Institute, 35 (2015), pp. 1157-1166, doi:10. 1016/j. proci. 2014. 05. 147.
4. E. CHAN, M. HEIMLICH, A. PURKAYASTHA, and R. VAN DE GEIJN, Collective communication: theory, practice, and experience, Concurrency and Computation: Practice and Experience, 19 (2007), pp. 1749-1783, doi:10. 1002/cpe. 1206.
5. J. H. CHEN ET AL., Terascale direct numerical simulations of turbulent combustion using S3D, Computational Science & Discovery, 2 (2009), 015001, doi:10. 1088/1749-4699/2/1/015001.
6. L. DE LATHAUWER, B. DE MOOR, and J. VANDEWALLE, A multilinear singular value decomposition, SIAM J. Matrix Analysis and Applications, 21 (2000), pp. 1253-1278, doi:10. 1137/S0895479896305696.
7. L. DE LATHAUWER, B. DE MOOR, and J. VANDEWALLE, On the best rank-1 and rank-(R1, R2, RN) approximation of higherorder tensors, SIAM J. Matrix Analysis and Applications, 21 (2000), pp. 1324-1342, doi:10. 1137/S0895479898346995.
8. S. ETTER, Parallel ALS algorithm for the hierarchical Tucker representation, Report 2015-25, Seminar for Applied Mathematics, ETH Zürich, Switzerland, 2015.
9. U. KANG, E. PAPALEXAKIS, A. HARPALE, and C. FALOUTSOS, GigaTensor: Scaling tensor analysis up by 100 times-algorithms and discoveries, in KDD'12, 2012, pp. 316-324, doi:10. 1145/2339530. 2339583.
10. A. KARAMI, M. YAZDI, and G. MERCIER, Compression of hyperspectral images using discerete wavelet transform and Tucker decomposition, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 5 (2012), pp. 444-450, doi:10. 1109/JSTARS. 2012. 2189200.
11. L. KARLSSON, D. KRESSNER, and A. USCHMAJEW, Parallel algorithms for tensor completion in the CP format, Parallel Computing, (2015), doi:10. 1016/j. parco. 2015. 10. 002.
12. O. KAYA and B. UçAR, Scalable sparse tensor decompositions in distributed memory systems, in SC'15, 2015, doi:10. 1145/2807591. 2807624.
13. T. G. KOLDA and B. W. BADER, Tensor decompositions and applications, SIAM Review, 51 (2009), pp. 455-500, doi:10. 1137/07070111X.
14. H. KOLLA, X.-Y. ZHAO, J. H. CHEN, and N. SWAMINATHAN, Velocity and reactive scalar dissipation spectra in turbulent premixed flames, Combustion Science and Technology, (2016). Under review.
15. P. M. KROONENBERG and J. DE LEEUW, Principal component analysis of three-mode data by means of alternating least squares algorithms, Psychometrika, 45 (1980), pp. 69-97, doi:10. 1007/BF02293599.
16. J. LI, C. BATTAGLINO, I. PERROS, J. SUN, and R. VUDUC, An input-adaptive and in-place approach to dense tensor-times-matrix multiply, in SC'15, 2015, doi:10. 1145/2807591. 2807671.
17. A. PHAN and A. CICHOCKI, PARAFAC algorithms for largescale problems, Neurocomputing, 74 (2011), pp. 1970-1984, doi:10. 1016/j. neucom. 2010. 06. 030.
18. M. SCHATZ, Distributed Tensor Computations: Formalizing Distributions, Redistributions, and Algorithm Derivations, PhD thesis, University of Texas, Austin, 2015.
19. S. SMITH, N. RAVINDRAN, N. D. SIDIROPOULOS, and G. KARYPIS, SPLATT: Efficient and parallel sparse tensormatrix multiplication, in IPDPS'15, 2015, pp. 61-70, doi:10. 1109/ipdps. 2015. 27.
20. R. THAKUR, R. RABENSEIFNER, and W. GROPP, Optimization of collective communication operations in MPICH, Intl. J. High Performance Computing Applications, 19 (2005), pp. 49-66, doi:10. 1177/1094342005051521.
21. L. R. TUCKER, Some mathematical notes on three-mode factor analysis, Psychometrika, 31 (1966), pp. 279-311, doi:10. 1007/BF02289464.
22. N. VANNIEUWENHOVEN, R. VANDEBRIL, and K. MEERBERGEN, A new truncation strategy for the higher-order singular value decomposition, SIAM J. Scientific Computing, 34 (2012), pp. A1027-A1052, doi:10. 1137/110836067.
23. Y. YANG, S. B. POPE, and J. H. CHEN, Empirical low-dimensional manifolds in composition space, Combustion and Flame, 160 (2013), pp. 1967-1980, doi:10. 1016/j. combustflame. 2013. 04. 006.
24. G. ZHOU, A. CICHOCKI, and S. XIE, Decomposition of big tensors with low multilinear rank, 2014, arXiv:1412. 1885.