Fast implementation of DGEMM on Fermi GPU

Proceedings of 2011 SC - International Conference for High Performance Computing, Networking, Storage and Analysis

Volumn , Issue , 2011, Pages

  Tan, Guangming ^a   Li, Linchuan ^a   Triechle, Sean ^b   Phillips, Everett ^b   Bao, Yungang ^a   Sun, Ninghui ^a  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b NVIDIA   (United States)

Author keywords

CUDA; GPU; High performance computing; Matrix matrix multiplication

Indexed keywords

CUDA; FAST IMPLEMENTATION; GPU; HIGH PERFORMANCE COMPUTING; INSTRUCTION SCHEDULING; MACHINE LANGUAGES; MATRIX-MATRIX MULTIPLICATION; MEMORY HIERARCHY; MEMORY OPERATIONS; MICRO ARCHITECTURES; OPTIMAL ALGORITHM; OPTIMIZATION STRATEGY; PEAK PERFORMANCE; PERFORMANCE MODELING; SHARED MEMORIES; SOFTWARE PIPELINING;

ALGORITHMS; COMPUTER SOFTWARE SELECTION AND EVALUATION; MATRIX ALGEBRA; MEMORY ARCHITECTURE; MULTITASKING; OPTIMIZATION;

BENCHMARKING;

EID: 83155160943     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2063384.2063431     Document Type: Conference Paper

References (17)

1. AMD. ATI Stream Computing OpenCL Programming Guide, rev1.05, August 2010.
2. E. Anderson, Z. Bai, C. Bischof, J. W. Demmel, J. J. Dongarra, J. D. Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. C. Sorensen. LAPACK: A portable linear algebra library for high-performance computers. Technical Report 20, LAPACK Working Note, May 1990.
3. J. Demmel, J. Dongarra, V. Eijkhout, E. Fuentes, A. Petitet, R. Vuduc, R. Whaley, and K. Yelick. Self-adapting linear algebra algorithms and software. In Proceedings of the IEEE, volume 93, pages 293-312, 2005.
4. J. J. Dongarra, J. Du Croz, S. Hammarling, and I. S. Duff. A set of level 3 basic linear algebra subprograms. ACM Trans. Math. Softw., 16:1-17, March 1990. (Pubitemid 20684794)
5. K. Goto and R. A. v. d. Geijn. Anatomy of high-performance matrix multiplication. ACM Trans. Math. Softw., 34:12:1-12:25, May 2008.
6. C. Jang. Gatlas gpu automatically tuned linear algebra software. http://golem5.org/gatlas/.
7. Y. Li, J. Dongarra, and S. Tomov. A note on auto-tuning gemm for gpus. In Proceedings of the 9th International Conference on Computational Science: Part I, ICCS'09, pages 884-892, Berlin, Heidelberg, 2009. Springer-Verlag.
8. N. Nakasato. A fast gemm implementation on the cypress gpu. SIGMETRICS Perform. Eval. Rev., 38:50-55, March 2011.
9. R. Nath, S. Tomov, and J. Dongarra. An improved magma gemm for fermi gpus. Technical Report 227, LAPACK Working Note, July 2010.
10. NVIDIA. Cuda Community Showcase. http://www.nvidia.com/object/ cudaappsflashnew.html.
11. NVIDIA. Nvidia's next generation cuda compute architecture: Fermi. http://www.nvidia.com/object/fermi architecture.html, 2009.
12. NVIDIA. CUDA C Programming Guide, Version 3.2, 2010.
13. S. Ryoo, C. I. Rodrigues, S. S. Baghsorkhi, S. S. Stone, D. B. Kirk, and W.-m. W. Hwu. Optimization principles and application performance evaluation of a multithreaded gpu using cuda. In Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, PPoPP'08, pages 73-82, New York, NY, USA, 2008. ACM.
14. S. Ryoo, C. I. Rodrigues, S. S. Stone, S. S. Baghsorkhi, S.-Z. Ueng, J. A. Stratton, and W.-m. W. Hwu. Program optimization space pruning for a multithreaded gpu. In Proceedings of the 6th annual IEEE/ACM international symposium on Code generation and optimization, CGO'08, pages 195-204, New York, NY, USA, 2008. ACM. (Pubitemid 351667266)
15. V. Volkov and J. W. Demmel. Benchmarking gpus to tune dense linear algebra. In Proceedings of the 2008 ACM/IEEE conference on Supercomputing, SC'08, pages 31:1-31:11, Piscataway, NJ, USA, 2008. IEEE Press.
16. H. Wong, M. Papadopoulou, M. Sadooghi-Alvandi, and A. Moshovos. Demystifying gpu microarchitecture through microbenchmarking. In 2010 IEEE International Symposium on Performance Analysis of Systems & Software, ISPASS'10, pages 235-246, 2010.
17. K. Yotov, X. Li, G. Ren, M. Garzaran, D. Padua, K. Pingali, and P. Stodghill. Is search really necessary to generate high-performance blas? In Proceedings of the IEEE, volume 93, pages 358-386, 2005.