Abstract
The present paper discusses scalable implementations of sparse matrix-vector products, which are crucial for high performance solutions of large-scale linear equations, on a cc-NUMA machine SGI Altix3700. Three storage formats for sparse matrices are evaluated, and scalability is attained by implementations considering the page allocation mechanism of the NUMA machine. Influences of the cache/memory bus architectures on the optimum choice of the storage format are examined, and scalable converters between storage formats shown to facilitate exploitation of storage formats of higher performance.
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References
Barrett, R., et al.: Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods. SIAM, Philadelphia (1994)
Duff, I., Grimes, R., Lewis, J.: Sparse matrix test problems. ACM Trans. Math. Soft. 15, 1–14 (1989)
Saad, Y.: SPARSKIT: a basic took kit for sparse matrix computations, version 2, (June 1994), http://www.cs.umn.edu/~saad/software/SPARSKIT/sparskit.html
Kincaid, D., Oppe, T., Respess, J., Young, D.: ITPACKV2C User’s Guide, Report CNA191. The University of Texas at Austin (1984)
Saad, Y.: Krylov subspace methods on supercomputers. SIAM J. Sci. Stat. Comput. 10, 1200–1232 (1989)
Matrix Market, http://math.nist.gov/MatrixMarket
Dongarra, J., Eijkhout, V., Kalhan, A.: Reverse communication interface for linear algebra templates for iterative methods. Technical Report UT-CS-95-291, University of Tennessee (May 1995)
Balay, S., Buschelman, K., Eijkhout, V., Gropp, W., Kaushik, D., Knepley, M., McInnes, L., Smith, B., Zhang, H.: PETSc users manual. Technical Report ANL-95/11, Argonne National Laboratory (August 2004)
Tuminaro, R.S., Heroux, M., Hutchinson, S.A., Shadid, J.N.: Official Aztec user’s guide, version 2.1. Technical Report SAND99-8801J, Sandia National Laboratories (November 1999)
Toledo, S.: Improving the memory-system performance of sparse-matrix vector multiplication. IBM Journal of Research and Development 41(6), 711–725 (1997)
Pinar, A., Heath, M.T.: Improving Performance of Sparse Matrix-Vector Multiplication. Supercomputing 99 (1999)
Im, E.J.: Optimizing the performance of sparse matrix-vector multiplication. Ph.D. thesis, University of California (May 2000)
Demmel, J., Dongarra, J., Eijkhout, V., Fuentes, E., Petitet, A., Vuduc, R., Whaley, R.C., Yelick, K.: Self adapting linear algebra algorithms and software. Proceedings of the IEEE: Special Issue on Program Generation, Optimization, and Adaptation 93(2), 293–312 (2005)
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Kotakemori, H., Hasegawa, H., Kajiyama, T., Nukada, A., Suda, R., Nishida, A. (2008). Performance Evaluation of Parallel Sparse Matrix–Vector Products on SGI Altix3700. In: Mueller, M.S., Chapman, B.M., de Supinski, B.R., Malony, A.D., Voss, M. (eds) OpenMP Shared Memory Parallel Programming. IWOMP 2005. Lecture Notes in Computer Science, vol 4315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68555-5_13
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DOI: https://doi.org/10.1007/978-3-540-68555-5_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68554-8
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