Model-driven autotuning of sparse matrix-vector multiply on GPUs

ACM SIGPLAN Notices

Volumn 45, Issue 5, 2010, Pages 115-125

  Choi, Jee W ^a   Singh, Amik ^b   Vuduc, Richard W ^c  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

^c GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

GPU; Performance modeling; Sparse matrix vector multiplication

Indexed keywords

AUTOTUNING; COMPRESSED SPARSE ROW; DIRECTLY MODEL; EXHAUSTIVE SEARCH; GPU; GRAPHICS PROCESSING UNIT; INPUT MATRICES; MODEL-DRIVEN; MULTITHREADED; OFFLINE; PARAMETER-TUNING; PERFORMANCE LIMITATIONS; PERFORMANCE MODEL; PERFORMANCE MODELING; PERFORMANCE TUNING; RUNTIMES; SPARSE MATRICES; SPARSE MATRIX-VECTOR MULTIPLICATION; STORAGE FORMATS; VECTOR PROCESSORS;

PROGRAM PROCESSORS; TUNING;

VECTORS;

EID: 77957679421     PISSN: 15232867     EISSN: None     Source Type: Journal    
DOI: 10.1145/1837853.1693471     Document Type: Conference Paper

References (22)

1. NVIDIA CUDA (Compute Unified Device Architecture): Programming Guide, Version 2.1, December 2008.
2. Muthu Manikandan Baskaran and Rajesh Bordawekar. Optimizing sparse matrix-vector multiplication on GPUs using compile-time and run-time strategies. Technical Report RC24704 (W0812-047), IBM T.J.Watson Research Center, Yorktown Heights, NY, USA, December 2008.
3. Nathan Bell and Michael Garland. Efficient sparse matrix-vector multiplication on CUDA. In Proc. ACM/IEEE Conf. Supercomputing (SC), Portland, OR, USA, November 2009. (to appear).
4. Jeff Bolz, Ian Farmer, Eitan Grinspun, and Peter Schröder. Sparse matrix solvers on the GPU: Conjugate gradients and multigrid. In Proc. Special Interest Group on Graphics Conf. (SIGGRAPH), San Diego, CA, USA, July 2003. doi: http://dx.doi.org/10.1145/882262.882364.
5. Matthias Christen and Olaf Schenk. Genera-purpose sparse matrix building blocks using the NVIDIA CUDA technology platform. In Proc. Workshop on General-Purpose Processing on Graphics Processing Units (GPGPU), Boston, MA, USA, October 2007.
6. Eduardo F. D'Azevedo, Mark R. Fahey, and Richard T. Mills. Vectorized sparse matrix multiply for compressed row storage. In Proc. Int'l. Conf. Computational Science (ICCS), volume 3514/2005 of LNCS, pages 99-106. Springer Berlin / Heidelberg, 2005. doi: http://dx.doi.org/10.1007/11428831 13.
7. James Demmel, Jack Dongarra, Viktor Eijkhout, Erika Fuentes, Antoine Petitet, Richard Vuduc, R. Clint Whaley, and Katherine Yelick. Self-adapting linear algebra algorithms and software. Proc. IEEE, 93(2):293-312, February 2005. doi: http://dx.doi.org/10.1109/JPROC.2004.840848.
8. Michael Garland. Sparse matrix computations on manycore GPUs. In Proc. ACM/IEEE Design Automation Conf. (DAC), pages 2-6, Anaheim, CA, USA, 2008. doi: http://dx.doi.org/10.1145/1391469.1391473.
9. Roman Geus and Stefan Röllin. Towards a fast sparse symmetric matrix-vector multiplication. Parallel Computing, 27(7):883-896, June 2001. doi: http://dx.doi.org/10.1016/S0167-8191(01)00073-74
10. Sunpyo Hong and Hyesoon Kim. An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness. In Proc. ACM Int'l. Symp. Comp. Arch. (ISCA), pages 152-163, Austin, TX, USA, June 2009. doi: http://dx.doi.org/10.1145/1555815.1555775.
11. Eun-Jin Im, Katherine Yelick, and Richard Vuduc. SPARSITY: Optimization framework for sparse matrix kernels. Int'l J. of High Performance Computing Applications (IJHPCA), 18(1):135-158, February 2004. doi: http://dx.doi.org/10. 1177/1094342004041296.
12. Hiroshi Okuda, Kengo Nakajima, Mikio Iizuka, Li Chen, and Hisashi Nakamura. Parallel finite element analysis platform for the Earth Simulator: GeoFEM. In Proc. Int'l. Conf. Computational Science (ICCS), volume 2659 of LNCS, pages 773-780. Springer, 2003. doi: http://dx.doi.org/10.1007/3-540-44863-2 75.
13. Ali Pinar and Michael T. Heath. Improving performance of sparse matrix-vector multiplication. In Proc. ACM/IEEE Conf. Supercomputing (SC), Portland, OR, USA, 1999. doi: http://dx.doi.org/10.1145/331532.331562.
14. John R. Rice and Ronald F. Boisvert. Solving elliptic problems using ELLPACK. Springer Verlag, 1984.
15. Yousef Saad. SPARSKIT: A basic tool kit for sparse matrix computations, version 2. http://www-users.cs.umn.edu/ saad/software/SPARSKIT /sparskit.html, March 2005.
16. Shubhabrata Sengupta, Mark Harris, Yao Zhang, and John D. Owens. Scan primitives for GPU computing. In Proc. ACM SIGGRAPH/EUROGRAPHICS Symp. Graphics Hardware, San Diego, CA, USA, 2007.
17. Vasily Volkov and James W. Demmel. Benchmarking GPUs to tune dense linear algebra. In Proc. ACM/IEEE Conf. on Supercomputing (SC), Austin, TX, USA, November 2008.
18. Richard Vuduc, JamesW. Demmel, and Katherine A. Yelick. OSKI: A library of automatically tuned sparse matrix kernels. In Proc. SciDAC, J. Phys.: Conf. Series, volume 16, pages 521-530, 2005. doi: http://dx.doi.org/10.1088/1742- 6596/16/1/071.
19. Richard W. Vuduc. Automatic performance tuning of sparse matrix kernels. PhD thesis, University of California, Berkeley, CA, USA, January 2004.
20. Richard W. Vuduc and Hyun-Jin Moon. Fast sparse matrix-vector multiplication by exploiting variable block structure. In Proc. High- Performance Computing and Communications Conf., volume LNCS 3726/2005, pages 807-816, Sorrento, Italy, September 2005. Springer. doi: http://dx.doi.org/10. 1007/11557654 91.
21. Sam Williams, Richard Vuduc, Leonid Oliker, John Shalf, Katherine Yelick, and James Demmel. Optimizing sparse matrix-vector multiply on emerging multicore platforms. Journal of Parallel Computing, 35(3):178-194, March 2009. doi: http://dx.doi.org/10.1016/j.parco.2008.12.006.
22. Kamen Yotov, Xiaoming Li, Gang Ren, María Jesús Garzarán, David Padua, Keshav Pingali, and Paul Stodghill. Is search really necessary to generate high-performance BLAS? Proc. IEEE, 93(2):358-386, February 2005. doi: .