Performance optimizations and bounds for sparse matrix-vector multiply

Proceedings of the International Conference on Supercomputing

Volumn 2002-November, Issue , 2002, Pages

  Vuduc, Richard ^a   Demmel, James W ^a   Yelick, Katherine A ^a   Kamil, Shoaib ^a   Nishtala, Rajesh ^a   Lee, Benjamin ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CACHE MEMORY; MATRIX ALGEBRA; OPTIMIZATION; VECTORS;

BLOCK SIZES; COMPUTATIONAL KERNELS; PERFORMANCE; PERFORMANCE BOUNDS; PERFORMANCE OPTIMIZATIONS; PERFORMANCE TUNING; SCIENTIFIC APPLICATIONS; SPARSE MATRICES; SPARSE MATRIX-VECTOR MULTIPLY; STRUCTURE REORGANIZATIONS;

FINITE ELEMENT METHOD;

EID: 84990830919     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SC.2002.10025     Document Type: Conference Paper

References (32)

1. R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo, C. Romine, and H. V. der Vorst. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd Edition. SIAM, Philadelphia, PA, 1994.
2. A. J. C. Bik and H. A. G. Wijshoff. Automatic nonzero structure analysis. SIAM Journal on Computing, 28(5):1576-1587, 1999.
3. J. Bilmes, K. Asanović, C. Chin, and J. Demmel. Optimizing matrix multiply using PHiPAC: a portable, high-performance, ANSI C coding methodology. In Proceedings of the International Conference on Supercomputing, Vienna, Austria, July 1997. ACM SIGARC. see http://www.icsi.berkeley.edu/~bilmes/phipac.
4. S. Blackford, G. Corliss, J. Demmel, J. Dongarra, I. Duff, S. Hammarling, G. Henry, M. Heroux, C. Hu, W. Kahan, L. Kaufman, B. Kearfott, F. Krogh, X. Li, Z. Maany, A. Petitet, R. Pozo, K. Remington, W. Walster, C. Whaley, and J. W. von Gudenberg. Document for the Basic Linear Algebra Subprograms (BLAS) standard: BLAS Technical Forum, 2001. www.netlib.org/blast.
5. R. F. Boisvert, R. Pozo, K. Remington, R. Barrett, and J. J. Dongarra. The Matrix Market: A web resource for test matrix collections. In R. F. Boisvert, editor, Quality of Numerical Software, Assessment and Enhancement, pages 125-137, London, 1997. Chapman and Hall. math.nist.gov/MatrixMarket.
6. S. Browne, J. Dongarra, N. Garner, K. London, and P. Mucci. A scalable cross-platform infrastructure for application performance tuning using hardware counters. In Proceedings of Supercomputing, November 2000.
7. S. Carr and K. Kennedy. Compiler blockability of numerical algorithms. In Proceedings of Supercomputing, pages 114-124, 1992.
8. S. Chatterjee, E. Parker, P. J. Hanlon, and A. R. Lebeck. Exact analysis of the cache behavior of nested loops. In Proceedings of the ACM SIGPLAN 2001 Conference on Programming Language Design and Implementation, pages 286-297, Snowbird, UT, USA, June 2001.
9. T. Davis. UF Sparse Matrix Collection. www.cise.ufl.edu/research/sparse/matrices.
10. B. B. Fraguela, R. Doallo, and E. L. Zapata. Memory hierarchy performance prediction for sparse blocked algorithms. Parallel Processing Letters, 9(3), March 1999.
11. S. Ghosh, M. Martonosi, and S. Malik. Cache miss equations: a compiler framework for analyzing and tuning memory behavior. ACM Transactions on Programming Languages and Systems, 21(4):703-746, 1999.
12. W. D. Gropp, D. K. Kasushik, D. E. Keyes, and B. F. Smith. Towards realistic bounds for implicit CFD codes. In Proceedings of Parallel Computational Fluid Dynamics, pages 241-248, 1999.
13. G. Heber, A. J. Dolgert, M. Alt, K. A. Mazurkiewicz, and L. Stringer. Fracture mechanics on the Intel Itanium architecture: A case study. In Workshop on EPIC Architectures and Compiler Technology (ACM MICRO 34), Austin, TX, December 2001.
14. G. M. Henry. Flexible, high-performance matrix multiply via a self-modifying runtime code. Technical Report TR-2001-46, University of Texas at Austin, December 2001.
15. D. B. Heras, V. B. Perez, J. C. C. Dominguez, and F. F. Rivera. Modeling and improving locality for irregular problems: sparse matrix-vector product on cache memories as a case study. In HPCN Europe, pages 201-210, 1999.
16. E.-J. Im. Optimizing the performance of sparse matrix-vector multiplication. PhD thesis, University of California, Berkeley, May 2000.
17. E.-J. Im and K. A. Yelick. Optimizing sparse matrix computations for register reuse in SPARSITY. In Proceedings of the International Conference on Computational Science, volume 2073 of LNCS, pages 127-136. Springer, May 2001.
18. Intel. Intel Itanium processor reference manual for software optimization, November 2001.
19. I. James B. White and P. Sadayappan. On improving the performance of sparse matrix-vector multiplication. In Proceedings of the International Conference on High-Performance Computing, 1997.
20. J. D. McCalpin. Memory bandwidth and machine balance in current high performance computers. Newsletter of the IEEE Technical Committee on Computer Architecture, December 1995. http://tab.computer.org/tcca/NEWS/DEC95/DEC95.HTM.
21. J. D. McCalpin. STREAM: Measuring sustainable memory bandwidth in high performance computers, 1995. http://www.cs.virginia.edu/stream.
22. K. S. McKinley, S. Carr, and C.-W. Tseng. Improving data locality with loop transformations. ACM Transactions on Programming Languages and Systems, 18(4):424-453, July 1996.
23. J. J. Navarro, E. García, J. L. Larriba-Pey, and T. Juan. Algorithms for sparse matrix computations on high-performance workstations. In Proceedings of the 10th ACM International Conference on Supercomputing, pages 301-308, Philadelpha, PA, USA, May 1996.
24. A. Pinar and M. Heath. Improving performance of sparse matrix-vector multiplication. In Proceedings of Supercomputing, 1999.
25. W. Pugh and T. Shpeisman. Generation of efficient code for sparse matrix computations. In Proceedings of the 11th Workshop on Languages and Compilers for Parallel Computing, LNCS, August 1998.
26. K. Remington and R. Pozo. NIST Sparse BLAS: User's Guide. Technical report, NIST, 1996. gams.nist.gov/spblas.
27. R. H. Saavedra-Barrera. CPU Performance Evaluation and Execution Time Prediction Using Narrow Spectrum Benchmarking. PhD thesis, University of California, Berkeley, February 1992.
28. P. Stodghill. A Relational Approach to the Automatic Generation of Sequential Sparse Matrix Codes. PhD thesis, Cornell University, August 1997.
29. O. Temam and W. Jalby. Characterizing the behavior of sparse algorithms on caches. In Proceedings of Supercomputing'92, 1992.
30. 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.
31. C. Whaley and J. Dongarra. Automatically tuned linear algebra software. In Proc. of Supercomp., 1998.
32. M. E. Wolf and M. S. Lam. A data locality optimizing algorithm. In Proceedings of the ACM SIGPLAN'91 Conference on Programming Language Design and Implementation, June 1991.