Optimizing Sparse Matrix-Vector Multiplication using index and value compression

Conference on Computing Frontiers - Proceedings of the 2008 Conference on Computing Frontiers, CF'08

Volumn , Issue , 2008, Pages 87-96

  Kourtis, Kornilios ^a   Goumas, Georgios ^a   Koziris, Nectarios ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Data compression; Memory bandwidth; Sparse matrix

Indexed keywords

BANDWIDTH; BANDWIDTH COMPRESSION; NUMERICAL METHODS; ONLINE SEARCHING; TELECOMMUNICATION SYSTEMS; VECTORS;

COMPRESSION METHODS; DATA VOLUMES; INDEX COMPRESSIONS; MEMORY BANDWIDTH; NUMERICAL VALUES; RESEARCH WORKS; SPARSE MATRIX; SPEED-UP;

DATA COMPRESSION;

EID: 55849146932     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1366230.1366244     Document Type: Conference Paper

References (24)

1. W. K. Anderson, W. D. Gropp, D. K. Kaushik, D. E. Keyes, and B. F. Smith. Achieving high sustained performance in an unstructured mesh cfd application. In SC '99: Proceedings of the 1999 ACM/IEEE conference on Supercomputing, page 69, New York. NY, USA, 1999. ACM.
2. R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. M. 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. SIAM, Philadelphia, 1994.
3. M. Burtscher and P. Ratanaworabhan. High throughput compression of double-precision floating-point data. In DCC '07: Proceedings of the 2007 Data Compression Conference, pages 293-302, Washington, DC, USA, 2007. IEEE Computer Society.
4. T. Davis. University of Florida sparse matrix collection. NA Digest, 97(23):7, 1997.
5. G. Goumas, K. Kourtis, N. Anastopoulos, V. Karakasis, and N. Koziris. Understanding the performance of sparse matrix-vector multiplication. In PDP '08: Proceedings of the 16th Euromicro International Conference on Parallel, Distributed and Network-based Processing, 2008.
6. E. Im. Optimizing the performance of sparse matrix-vector multiplication. PhD thesis, University of California, Berkeley, May 2000.
7. E. Im and K. Yelick. Optimizing sparse matrix-vector multiplication on SMPs. In 9th SIAM Conference on Parallel Processing for Scientific Computing. SIAM, Mar. 1999.
8. E. Im and K. Yelick. Optimizing sparse matrix computations for register reuse in SPARSITY. Lecture Notes in Computer Science, 2073:127-136, 2001.
9. D. Keyes. Four Horizons for Enhancing the Performance of Parallel Simulations Based on Partial Differential Equations. Springer, 2000.
10. J. Langou, J. Langou, P. Luszczek, J. Kurzak, A. Buttari, and J. Dongarra. Exploiting the performance of 32 bit floating point arithmetic in obtaining 64 bit accuracy (revisiting iterative refinement for linear systems). In SC '06: Proceedings of the 2006 ACM/IEEE conference on Supercomputing, page 113, New York, NY, USA, 2006. ACM.
11. B. Lee, R. Vuduc, J. Demmel, and K. Yelick. Performance models for evaluation and automatic tuning of symmetric sparse matrix-vector multiply. In ICPP '04: Proceedings of the International Conference on Parallel Processing, pages 169-176 vol. 1, 15-18 Aug. 2004.
12. J. Mellor-Crummey and J. Garvin. Optimizing sparse matrix-vector product computations using unroll and jam. International Journal of High Performance Computing Applications, 18(2):225, 2004.
13. J. C. Pichel, D. B. Heras, J. C. Cabaleiro, and F. F. Rivera. Improving the locality of the sparse matrix-vector product on shared memory multiprocessors. In PDP, pages 66-71. IEEE Computer Society, 2004.
14. 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):858-876, 2005.
15. A. Pinar and M. T. Heath. Improving performance of sparse matrix-vector multiplication. In Supercomputing'99, Portland, OR, Nov. 1999. ACM SIGARCH and IEEE.
16. Y. Saad. SPARSKIT: A basic tool kit for sparse matrix computations. Technical report, Computer Science Department, University of Minnesota, Minneapolis, MN 55455, June 1994. Version 2.
17. Y. Saad. Iterative Methods for Sparse Linear Systems. SIAM, Philadelphia, PA, USA, 2003.
18. M. Silva. Sparse matrix storage revisited. In CF '05: Proceedings of the 2nd conference on Computing frontiers, pages 230-235, New York, NY, USA, 2005. ACM.
19. S. Toledo. Improving the memory-system performance of sparse-matrix vector multiplication. IBM Journal of Research and Development, 41(6):711-725, 1997.
20. R. Vuduc, J. Demmel, K. Yelick, S. Kamil, R. Nishtala, and B. Lee. Performance optimizations and bounds for sparse matrix-vector multiply. In Supercomputing, Baltimore, MD, Nov. 2002.
21. R. W. Vuduc and H. Moon. Fast sparse matrix-vector multiplication by exploiting variable block structure. In High Performance Computing and Communications, volume 3726 of Lecture Notes in Computer Science, pages 807-816. Springer, 2005.
22. J. White and P. Sadayappan. On improving the performance of sparse matrix-vector multiplication. In HiPC '97: 4th International Conference on High Performance Computing, 1997.
23. J. Willcock and A. Lumsdaine. Accelerating sparse matrix computations via data compression. In ICS '06: Proceedings of the 20th annual international conference on Supercomputing, pages 307-316, New York, NY, USA, 2006. ACM Press.
24. S. Williams, L. Oilker, R. Vuduc, J. Shalf, K. Yelick, and J. Demmel. Optimization of sparse matrix-vector multiplication on emerging multicore platforms. In SC '07: Proceedings of the 2007 ACM/IEEE conference on Supercomputing, Reno, NV, Nov. 2007.