Sparse matrix storage revisited

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Sparse matrix storage revisited

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Conference On Computing Frontiers archive
Proceedings of the 2nd conference on Computing frontiers table of contents

Ischia, Italy

SESSION: Track 10: workload characterization of emerging applications table of contents

Pages: 230 - 235

Year of Publication: 2005

ISBN:1-59593-019-1

Author

Malik Silva

University of Colombo School of Computing, Sri Lanka

Sponsor

ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 31, Downloads (12 Months): 220, Citation Count: 3

Additional Information:

abstract references cited by index terms review

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ABSTRACT

In this paper, we consider alternate ways of storing a sparse matrix and their effect on computational speed. They involve keeping both the indices and the non-zero elements in the sparse matrix in a single data structure. These schemes thus help reduce memory system misses that occur when the usual indexing based storage schemes are used to store sparse matrices and give promising performance improvements.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	E.-J. Im and K. Yelick. Model-Based Memory Hierarchy Optimizations for Sparse Matrices, October 1998. Workshop on Profile and Feedback-Directed Compilation, Paris, France.
	2	John L. Hennessy , David A. Patterson, Computer architecture (2nd ed.): a quantitative approach, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1996
	3	M. Silva and R. Wait. Go for both types of data locality! In L. Jenkins and Ivengar, editors, Proceedings of HPC Asia 2002, Bangalore, volume 2, 2002.
	4	Enterprise Text Processing: A Sparse Matrix Approach, Proceedings of the International Conference on Information Technology: Coding and Computing, p.71, April 02-04, 2001
	5	R. Barrett, M. Berry, T. F. Chan, J. Demmel, J. Donato, J. Dongarra, V. Eijkhout, R. Pozo, C. Romine, and H. van der Vorst. Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods. SIAM, Philadelphia, 1994.
	6	U.Ruede. Technological trends and their impact on the future of supercomputers. In F. H.J. Bungartz and C.Zenger, editors, High Performance Scientific and Engineering Computing, Proceedings of the International FORTWIHR Conference on HPSEC, Vol. 8 of Lecture Notes in Computational Science and Engineering, pages 459--471. Springer, Mar. 1998.
	7	Richard Vuduc , James W. Demmel , Katherine A. Yelick , Shoaib Kamil , Rajesh Nishtala , Benjamin Lee, Performance optimizations and bounds for sparse matrix-vector multiply, Proceedings of the 2002 ACM/IEEE conference on Supercomputing, p.1-35, November 16, 2002, Baltimore, Maryland

CITED BY 3

	Kornilios Kourtis , Georgios Goumas , Nectarios Koziris, Optimizing sparse matrix-vector multiplication using index and value compression, Proceedings of the 2008 conference on Computing frontiers, May 05-07, 2008, Ischia, Italy
	Guilherme Flach , Marcelo Johann , Renato Hentschke , Ricardo Reis, Cell placement on graphics processing units, Proceedings of the 20th annual conference on Integrated circuits and systems design, September 03-06, 2007, Copacabana, Rio de Janeiro
	Piyush Harsh , Richard Newman, Using geo-spatial session tagging for smart multicast session discovery, Proceedings of the 2009 ACM symposium on Applied Computing, March 08-12, 2009, Honolulu, Hawaii

INDEX TERMS

Primary Classification:
B. Hardware
B.6 LOGIC DESIGN
B.6.0 General

Keywords:
caches, memory wall, sparse matrix computations, sparse matrix storage, spatial and temporal locality

REVIEW

"Timothy R. Hopkins : Reviewer"

Increasing effort is being invested in attempting to squeeze every ounce of performance from the currently available processors; much of this investment is going into minimizing the effect of very slow memory access times on overall computational more...

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