Optimization of sparse matrix-vector multiplication using reordering techniques on GPUs

Microprocessors and Microsystems

Volumn 36, Issue 2, 2012, Pages 65-77

  Pichel, Juan C ^a   Rivera, Francisco F ^a   Fernández, Marcos ^b   Rodríguez, Aurelio ^b  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

^b Centro de Supercomputacion de Galicia   (Spain)

Author keywords

GPUs; Optimization; Performance; Reordering; Sparse matrix

Indexed keywords

OPTIMIZATION; PROGRAM PROCESSORS;

COMMON STRATEGY; DOUBLE PRECISION; GPUS; PERFORMANCE; REORDERING; SPARSE MATRICES; SPARSE MATRIX-VECTOR MULTIPLICATION; STORAGE FORMATS;

MATRIX ALGEBRA;

EID: 84857332778     PISSN: 01419331     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.micpro.2011.05.005     Document Type: Article

References (25)

1. P.R. Amestoy, T.A. Davis, and I.S. Duff An approximate minimum degree ordering algorithm SIAM Journal on Matrix Analysis and Applications 17 4 1996 886 905
2. M.M. Baskaran, R. Bordawekar, Optimizing sparse matrix-vector multiplication on GPUs, Technical report, IBM Research Report RC24704 (W0812-047), 2008.
3. M. Belgin, G. Back, C.J. Ribbens, Pattern-based sparse matrix representation for memory-efficient SMVM kernels, in: Proceedings of the International Conference on Supercomputing, 2009, pp. 100-109.
4. N. Bell, M. Garland, Efficient sparse matrix-vector multiplication on CUDA, Technical report, NVIDIA, 2008.
5. N. Bell, M. Garland, Implementing sparse matrix-vector multiplication on throughput-oriented processors, in: Proceedings of ACM/IEEE Conference on Supercomputing (SC), 2009.
6. J. Bolz, I. Farmer, E. Grinspun, P. Schröder, Sparse matrix solvers on the GPU: conjugate gradients and multigrid, in: Proceedings of ACM SIGGRAPH, 2005, pp. 171-178.
7. A. Buttari, V. Eijkhout, J. Langou, and S. Filippone Performance optimization and modeling of blocked sparse kernels International Journal of High Performance Computing Applications 21 4 2007 467 484 (Pubitemid 350011340)
8. J.W. Choi, A. Singh, R. Vuduc, Model-driven autotuning of sparse matrix-vector multiply on gpus, in: Proceedings of the 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2010, pp. 115-126.
9. A.L.G.A. Coutinho, M.A.D. Martins, R.M. Sydenstricker, and R.N. Elias Performance comparison of data-reordering algorithms for sparse matrix-vector multiplication in edge-based unstructured grid computations International Journal for Numerical Methods in Engineering 66 3 2006 431 460
10. E. Cuthill, and J. McKee Several strategies for reducing the bandwidth of matrices 1972 Rose and Willoughby
11. T. Davis University of Florida sparse matrix collection NA Digest 97 23 1997 < http://www.cise.ufl.edu/research/sparse/matrices >
12. B.B. Fraguela, R. Doallo, and E.L. Zapata Memory hierarchy performance prediction for blocked sparse algorithms Parallel Processing Letters 9 3 1999 347 360 (Pubitemid 30568721)
13. E.J. Im, K.A. Yelick, and R. Vuduc SPARSITY: framework for optimizing sparse matrix-vector multiply International Journal of High Performance Computing Applications 18 1 2004 135 158
14. V. Karakasis, G. Goumas, N. Koziris, A comparative study of blocking storage methods for sparse matrices on multicore architectures, in: Proceedings of IEEE International Conference on Computational Science and Engineering, 2009, pp. 247-256.
15. G. Karypis, V. Kumar, METIS: a software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices, 1997.
16. J.J. Navarro, E. García, J.L. Larriba-Pey, T. Juan, Block algorithms for sparse matrix computations on high performance workstations, in: Proceedings IEEE International Conference on Supercomputing, 1996, pp. 301-309.
17. L. Oliker, X. Li, P. Husbands, and R. Biswas Effects of ordering strategies and programming paradigms on sparse matrix computations SIAM Review 44 3 2002 373 393
18. J.C. Pichel, D.B. Heras, J.C. Cabaleiro, and F.F. Rivera Performance optimization of irregular codes based on the combination of reordering and blocking techniques Parallel Computing 31 8-9 2005 858 876 (Pubitemid 41383385)
19. J.C. Pichel, D.E. Singh, J. Carretero, Reordering algorithms for increasing locality on multicore processors, in: Proceedings of the IEEE International Conference on High Performance Computing and Communications, 2008, pp. 123-130.
20. A. Pinar, M. Heath, Improving performance of sparse matrix-vector multiplication, in: Proceedings of Supercomputing, 1999.
21. S. Sengupta, M. Harris, Y. Zhang, J.D. Owens, Scan primitives for GPU computing, in: Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, 2007, pp. 97-106.
22. M.M. Strout, L. Carter, J. Ferrante, and B. Kreaseck Sparse tiling for stationary iterative methods International Journal of High Performance Computing Applications 18 1 2004 95 113
23. S. Toledo, Improving memory-system performance of sparse matrix-vector multiplication, in: Proceedings of the 8th SIAM Conference on Parallel Processing for Scientific Computing, March 1997.
24. R. Vuduc, H. Moon, Fast sparse matrix-vector multiplication by exploiting variable block structure, in: High Performance Computing and Communications, Lecture Notes in Computer Science, vol. 3726, 2005, pp. 807-816.
25. S. Williams, L. Oliker, R. Vuduc, J. Shalf, K. Yelick, J. Demmel, Optimization of sparse matrix-vector multiply on emerging multicore platforms, in: Proceedings of the ACM/IEEE Conference on Supercomputing, 2007.