Automatic tuning of the sparse matrix vector product on GPUs based on the ELLR-T approach

Parallel Computing

Volumn 38, Issue 8, 2012, Pages 408-420

  Vázquez, Francisco ^a   Fernández, José Jesús ^a,b   Garzón, Ester M ^a  

^a UNIVERSITY OF ALMERÍA   (Spain)

^b CENTRO NACIONAL DE BIOTECNOLOGÍA   (Spain)

Author keywords

GPU computing; GPU performance modeling; Sparse matrix vector product

Indexed keywords

ACCELERATION FACTORS; AUTOMATIC TUNING; AUTOTUNING; COMPARATIVE ANALYSIS; EVALUATION RESULTS; GPU COMPUTING; GRAPHICS PROCESSING UNITS; MEMORY OPERATIONS; OPTIMUM SELECTION; PERFORMANCE MODELING; SPARSE MATRICES; TEST MATRIX; TWO PARAMETER;

COMPUTER GRAPHICS; MATRIX ALGEBRA; MEMORY ARCHITECTURE;

PROGRAM PROCESSORS;

EID: 84862637273     PISSN: 01678191     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.parco.2011.08.003     Document Type: Article

References (21)

1. F. Vázquez, E.M. Garzón, and J.J. Fernández A matrix approach to tomographic reconstruction and its implementation on GPUs Journal of Structural Biology 170 2010 146 151
2. A.T. Ogielski, and W. Aiello Sparse matrix computations on parallel processor arrays SIAM Journal of Scientific Computing 14 1992 519 530
3. S. Toledo Improving memory-system performance of sparse matrix-vector multiplication IBM Journal of Research and Development 41 6 1997 711 725 (Pubitemid 127557044)
4. S. Williams, L. Oliker, R. Vuduc, J. Shalf, K. Yelick, and J. Demmel Optimization of sparse matrix-vector multiplication on emerging multicore platforms Parallel Computing 35 3 2009 178 194
5. NVIDIA, CUDA Programming guide. Version 2.3, 2009.
6. Kronos, Group, OpenCL - the open standard for parallel programming of heterogeneous systems.
7. N. Bell, and M. Garland Implementing sparse matrix-vector multiplication on throughput-oriented processors SC'09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis 2009 ACM New York, NY, USA 1 11
8. L. Buatois, G. Caumon, and B. Lévy Concurrent number cruncher - A GPU implementation of a general sparse linear solver International Journal of Parallel Emergent and Distributed Systems 24 3 2009 205 223
9. M.M. Baskaran, R. Bordawekar, Optimizing sparse matrix-vector multiplication on GPUs, Tech. Rep. Research Report RC24704, IBM, April 2009.
10. J.W. Choi, A. Singh, and R. Vuduc Model-driven autotuning of sparse matrix-vector multiply on GPUs PPoPP'10: Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming 2010 ACM New York, NY, USA 115 126
11. A. Monakov, A. Lokhmotov, A. Avetisyan, Automatically tuning sparse matrix-vector multiplication for GPU architectures, in: Proceedings of HiPEAC 2010, LNCS 5952, 2010, pp. 111-125.
12. F. Vázquez, J.J. Fernández, E.M. Garzón, A new approach for sparse matrix vector product on NVIDIA GPUs, Concurrency and Computation: Practice And Experience. .
13. F. Vázquez, G. Ortega, J.J. Fernández, E.M. Garzón, Improving the performance of the sparse matrix vector product with GPUs, in: 10th IEEE International Conference on Computer and Information Technology. CIT 2010, IEEE Computer Society, 2010, pp. 1146-1151. .
14. D.R. Kincaid, T.C. Oppe, D.M. Young, ITPACKV 2D User's guide, Tech. Rep. CNA-232, Center for Numerical Analysis. University of Texas at Austin, 1989.
15. S. Hong, H. Kim, An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness, in: SIGARCH Comput. Archit. News 37, vol. 3, ACM, New York, NY, USA, 2009, pp. 152-163. .
16. S. Baghsorkhi, M. Delahaye, S. Patel, W. Gropp, and W. Hwu An adaptive performance modeling tool for gpu architectures PPoPP'10: Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming 2010 ACM New York, NY, USA 105 114
17. NVIDIA, Next generation CUDA architecture. Fermi Architecture. , 2010.
18. NVIDIA, CUDA C programming. Best practices guide. CUDA Toolkit 2.3, July 2009.
19. J. Kurzak, W. Alvaro, and J. Dongarra Optimizing matrix multiplication for a short-vector SIMD architecture - CELL processor Parallel Computing 35 3 2009 138 150 Revolutionary Technologies for Acceleration of Emerging Petascale Applications
20. INTEL, Math kernel library. reference manual, 2009.
21. M.M. Baskaran, R. Bordawekar, Sparse matrix-vector multiplication toolkit for graphics processing units, 2009. .