A model-driven partitioning and auto-tuning integrated framework for sparse matrix-vector multiplication on GPUs

Proceedings of the TeraGrid 2011 Conference: Extreme Digital Discovery, TG'11

Volumn , Issue , 2011, Pages

  Guo, Ping ^a   Huang, He ^a   Chen, Qichang ^a   Wang, Liqiang ^a   Lee, En Jui ^a   Chen, Po ^a  

^a UNIVERSITY OF WYOMING   (United States)

Author keywords

auto tuning; GPU; performance modeling; sparse matrix vector multiplication

Indexed keywords

AUTOTUNING; EXECUTION TIME; GPU; GRAPHICS PROCESSING UNIT; HIGH-PERFORMANCE COMPUTING; INTEGRATED FRAMEWORKS; MODEL-DRIVEN; PERFORMANCE IMPROVEMENTS; PERFORMANCE MODEL; PERFORMANCE MODELING; PROCESSING CAPABILITY; SPARSE MATRICES; SPARSE MATRIX-VECTOR MULTIPLICATION; STORAGE FORMATS;

COMPUTER GRAPHICS EQUIPMENT; COMPUTER SOFTWARE SELECTION AND EVALUATION; PROGRAM PROCESSORS; VECTORS;

MATRIX ALGEBRA;

EID: 80052311496     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2016741.2016744     Document Type: Conference Paper

References (12)

1. J. Kurzak, W. Alvaro, and J. Dongarra. Optimizing matrix multiplication for a short-vector simd architecture-cell processor. Parallel Comput., 35(3):138-150, 2009.
2. S. Williams, L. Oliker, R. Vuduc, J. Shalf, K. Yelick, and J. Demmel. Optimization of sparse matrix-vector multiplication on emerging multicore platforms. In In Proc. 2007 ACM/IEEE Conference on Supercomputing, 2007.
3. N. Bell and M. Garland. Implementing sparse matrix-vector multiplication on throughput-oriented processors. In SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, pages 1-11, New York, NY, USA, 2009.
4. M. M. Baskaran and R. Bordawekar. Optimizing sparse matrix-vector multiplication on gpus using compile-time and run-time strategies. Technical report, Research Report RC24704, IBM TJ Watson Research Center, december 2008.
5. NVIDIA CUDA (Compute Unified Device Architecture): Programming Guide,Version 2.1, December 2008.
6. J. Demmel, J. Dongarra, V. Eijkhout, E. Fuentes, A. Petitet, R. C. Whaley R. Vuduc, and K. Yelick. Self-adapting linear algebra algorithms and software. Proceeding of IEEE, 93(2):293-312, 2005.
7. Eun-Jin Im, K.a.t.h.e.r.i.n.e. Yelick, and R.i.c.h.a.r.d. Vuduc. Sparsity: Optimization framework for sparse matrix kernels. Int. J. High Perform. Comput. Appl., 18(1):135-158, 2004.
8. J. Bolz, I. Farmer, E. Grinspun, and P. Schroder. Sparse matrix solvers on the gpu: Conjugate gradients and multigrid. ACM Trans. Graph., 22(3):917-924, 2003.
9. J. W. Choi, A. Singh, and R. W. Vuduc. Model-driven autotuning of sparse matrix-vector multiply on gpus. In PPoPP '10: Proceedings of the 15th ACM SIGPLAN symposium on Principles and practice of parallel programming, pages 115-126, New York, NY, USA, 2010.
10. N. Bell and M. Garland. Efficient sparse matrix-vector multiplication on cuda. Technical report, Nvidia Technical Report NVR-2008-004, 2008.
11. A. Nukada and S. Matsuoka. Auto-tuning 3-d fft library for cuda gpus. In SC '09: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, pages 1-10, New York, NY, USA, 2009.
12. Ping Guo and Liqiang Wang. Auto-tuning cuda parameters for sparse matrix-vector multiplication on gpus. Computational and Information Sciences, International Conference on, 0:1154-1157, 2010.