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ABSTRACT
This paper describes an extension to the set of Basic Linear Algebra Subprograms. The extension is targeted at sparse vector operations, with the goal of providing efficient, but portable, implementations of algorithms for high-performance computers.
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.
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ASHCRAFT, C. C , GRIMES, R. G., LEWIS, J. G, PEYTON, B. W, AND SIMON, H.D. Progress in sparse matrix methods for large hnear systems on vector supercomputers Int. J. Supercornput. Appl. 1, 4 (Dec., 1987), 10-30.
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GRIMES, R. G., LEwis, J. G. AND SIMON, H. D. Eigenvalue problems and algorithms in structural engineering. In Large Scale Eigenvalue Problems, J. Cullum and R. Willoughby, Eds., Elsevier North-Holland, 1986, pp. 81-93.
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CITED BY 9
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R. C. Agarwal , F. G. Gustavson , M. Zubair, A high performance algorithm using pre-processing for the sparse matrix-vector multiplication, Proceedings of the 1992 ACM/IEEE conference on Supercomputing, p.32-41, November 16-20, 1992, Minneapolis, Minnesota, United States
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REVIEW
"Mohamed E. El-Hawary : Reviewer"
Adopting a standardized set of basic routines for problems in
linear algebra is acknowledged to improve program clarity, portability,
modularity, and maintainability. The original set is known as the Basic
Linear Algebra Subprograms (BLAS) and
more...
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