Mahmoudi, Sidi ; Université de Mons > Faculté Polytechnique > Information, Signal et Intelligence artificielle ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle
Zback, Mostapha
Manneback, Pierre ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle
Language :
English
Title :
Performance evaluation of sparse matrix-vector product (SpMV) computation on GPU architecture
Publication date :
15 October 2014
Event name :
WCCS14
Event place :
Agadir, Morocco
Event date :
2014
Research unit :
F114 - Informatique, Logiciel et Intelligence artificielle
Research institute :
R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
F. Vázquez, A new approach for sparse matrix vector product on NVIDIA GPUs, Concurrency and Computation :Practice and Experience, vol. 23, issue. 8, pp. 815826, 2011.
F. Vázquez, G. Ortega, J. J. Fernández, and E. M. Garzón, Improving the Performance of the Sparse Matrix Vector Product with GPUs, 2010 10th IEEE Int. Conf. Comput. Inf. Technol., no. Cit, pp. 1146-1151, Jun. 2010.
X. Feng, H. Jin, and R. Zheng, A segmentbased sparse matrixvector multiplication on CUDA, Concurrency and Computation :Practice and Experience, vol. 26, issue. 1, pp. 271-286, 2014.
F. Vázquez, E. M. Garzón, and J. J. Fernández, A matrix approach to tomographic reconstruction and its implementation on GPUs., J. Struct. Biol., vol. 170, N. 1, pp. 14651, Apr. 2010.
N. Bell and M. Garland, Implementing sparse matrix-vector multiplication on throughput-oriented processors, Proc. Conf. High Perform. Comput. Networking, Storage Anal. - SC 09, no. 1, p. 1, 2009.
NVIDIA Documentation, Cuda C programming guide, Version 5.5. February, 2014.
T. A. Davis and Y. Hu, The university of Florida sparse matrix collection, ACM Trans. Math. Softw., vol. 38, no. 1, pp. 125, Nov. 2011.
J. W. Choi, A. Singh, and R. W. Vuduc, Model-driven autotuning of sparse matrix-vector multiply on GPUs, Proc. 15th ACM SIGPLAN Symp. Princ. Pract. parallel Program. - PPoPP 10, p. 115, 2010.
F. Vázquez, J. J. Fernández, and E. M. Garzón, Automatic tuning of the sparse matrix vector product on GPUs based on the ELLR-T approach, Parallel Comput., vol. 38, no. 8, pp. 408420, Aug. 2012.
S. Williams, L. Oliker, R. Vuduc, J. Shalf, K. Yelick, and J. Demmel, Optimization of sparse matrix-vector multiplication on emerging multicore platforms, Proc. 2007 ACM/IEEE Conf. Supercomput. - SC 07, no. c, p. 1, 2007.
B, Ucar and C. Aykanat, A library for Parallel Sparse Matrix Vector Multiplies, technical Report BU-CE-0506, Dept of Computer Eng, Bilkent Univ, 2005.
H.-V. Dang and B. Schmidt, The Sliced COO Format for Sparse MatrixVector Multiplication on CUDA-enabled GPUs, Procedia Comput. Sci., vol. 9, pp. 5766, Jan. 2012.
M. S. Celebi, A. Duran, M. Tuncel, and B. Akaydn, "Partnership for Advanced Computing in Europe Performance Analysis of BLAS Libraries in SuperLU DIST for SuperLU MCDT (Multi Core Distributed) Development,"PRACE (Partnership for Advanced Computing in Europe), PRACE-2IP white paper, 2013.
S. Sengupta and M. Harris, Y. Zhang and J. D. Owens "Scan primitives for GPU computing," Graphics Hardware, 2007.
R. Koenker and P. Ng, SparseM: A sparse matrix package for R, CRAN Packag. Arch., N. 2002, pp. 19, 2011.
