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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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
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