Communication-avoiding QR decomposition for GPUs

Proceedings - 25th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2011

Volumn , Issue , 2011, Pages 48-58

  Anderson, Michael ^a   Ballard, Grey ^a   Demmel, James ^a   Keutzer, Kurt ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

GPGPU; Linear Algebra; QR Decomposition; Robust PCA

Indexed keywords

BACKGROUND SUBTRACTION; GENERAL PURPOSE PROCESSORS; GPGPU; GPU IMPLEMENTATION; GRAPHICS PROCESSOR; KERNEL LIBRARIES; LARGE CLASS; MATRIX; MATRIX SIZE; MULTI CORE; Q R DECOMPOSITION; QR FACTORIZATIONS; ROBUST PCA; STATISTICAL APPROACH;

BANDWIDTH; COMMUNICATION; DATA TRANSFER; DISTRIBUTED PARAMETER NETWORKS; FACTORIZATION; GENERAL PURPOSE COMPUTERS; LINEAR ALGEBRA; PARALLEL PROCESSING SYSTEMS; PROGRAM PROCESSORS;

SINGULAR VALUE DECOMPOSITION;

EID: 80053251338     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2011.15     Document Type: Conference Paper

References (21)

1. E. Candes, X. Li, Y. Ma, and J. Wright, "Robust principal component analysis," Preprint, 2009.
2. M. Mohiyuddin, M. Hoemmen, J. Demmel, and K. Yelick, "Minimizing communication in sparse matrix solvers," in Proceedings of the 2009 ACM/IEEE conference on supercomputing, ser. SC '09. New York, NY, USA: ACM, 2009, pp. 36:1-36:12.
3. V. Volkov and J. Demmel, "LU, QR and Cholesky factorizations using vector capabilities of GPUs," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2008-49, May, pp. 2008-49, 2008.
4. A. Kerr, D. Campbell, and M. Richards, "QR decomposition on GPUs," in Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units. ACM, 2009, pp. 71-78.
5. J. Humphrey Jr, D. Price, K. Spagnoli, A. Paolini, and E. Kelmelis, "CULA: hybrid GPU accelerated linear algebra routines," in Proceedings of SPIE, vol. 7705, 2010, p. 770502.
6. J. Demmel, L. Grigori, M. Hoemmen, and J. Langou, "Communication- optimal parallel and sequential QR and LU factorizations," LAPACK Working Note #204.
7. E. Anderson, Z. Bai, and C. Bischof, LAPACK Users' guide. Society for Industrial Mathematics, 1999.
8. C. NVIDIA, "CUBLAS Library," NVIDIA Corporation, Santa Clara, California, 2008.
9. J. Demmel, L. Grigori, and M. Hoemmen, LAPACK Working Note #204.
10. B. Hadri, H. Ltaief, E. Agullo, and J. Dongarra, "Enhancing parallelism of tile qr factorization for multicore architectures,"Matrix.
11. M. A. Heroux, R. A. Bartlett, V. E. Howle, R. J. Hoekstra, J. J. Hu, T. G. Kolda, R. B. Lehoucq, K. R. Long, R. P. Pawlowski, E. T. Phipps, A. G. Salinger, H. K. Thornquist, R. S. Tuminaro, J. M. Willenbring, A. Williams, and K. S. Stanley, "An overview of the trilinos project," ACM Trans. Math. Softw., vol. 31, no. 3, pp. 397-423, 2005.
12. E. Agullo, C. Coti, J. Dongarra, T. Herault, and J. Langem, "QR factorization of tall and skinny matrices in a grid computing environment," in Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium on. IEEE, 2010, pp. 1-11.
13. J. Demmel, Applied numerical linear algebra. Society for Industrial and Applied Mathematics, Philadelphia, 1997.
14. E. Agullo, J. Demmel, J. Dongarra, B. Hadri, J. Kurzak, J. Langou, H. Ltaief, P. Luszczek, and S. Tomov, "Numerical linear algebra on emerging architectures: The PLASMA and MAGMA projects," in Journal of Physics: Conference Series, vol. 180. IOP Publishing, 2009, p. 012037.
15. G. Quintana-Ortí, F. Igual, E. Quintana-Ortí, and R. van de Geijn, "Solving dense linear systems on platforms with multiple hardware accelerators," ACM SIGPLAN Notices, vol. 44, no. 4, pp. 121-130, 2009.
16. V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra," in Proceedings of the 2008 ACM/IEEE conference on Supercomputing. IEEE Press, 2008, pp. 1-11.
17. K. Datta, M. Murphy, V. Volkov, S. Williams, J. Carter, L. Oliker, D. Patterson, J. Shalf, and K. Yelick, "Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures," in High Performance Computing, Networking, Storage and Analysis, 2008. SC 2008. International Conference for. IEEE, 2009, pp. 1-12.
18. NERSC, "Experimental GPU cluster: Dirac,"http://www.nersc.gov/ nusers/systems/dirac/.
19. X. Yuan and J. Yang, "Sparse and low-rank matrix decomposition via alternating direction methods," Preprint, 2009.
20. R. Vezzani and R. Cucchiara, "ViSOR: Video surveillance on-line repository for annotation retrieval," in Multimedia and Expo, 2008 IEEE International Conference on. IEEE, 2008, pp. 1281-1284.
21. Y. Dong and G. DeSouza, "Adaptive Learning of Multi-Subspace for Foreground Detection under Illumination Changes," Computer Vision and Image Understanding, 2010.