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Algorithm 818: A reference model implementation of the sparse BLAS in fortran 95

Authors:

Christof VömelAuthors Info & Claims

ACM Transactions on Mathematical Software (TOMS), Volume 28, Issue 2

Pages 268 - 283

https://doi.org/10.1145/567806.567811

Published: 01 June 2002 Publication History

Abstract

The Basic Linear Algebra Subprograms for sparse matrices (Sparse BLAS) as defined by the BLAS Technical Forum are a set of routines providing basic operations for sparse matrices and vectors. A principal goal of the Sparse BLAS standard is to aid in the development of iterative solvers for large sparse linear systems by specifying on the one hand interfaces for a high-level description of vector and matrix operations for the algorithm developer and on the other hand leaving enough freedom for vendors to provide the most efficient implementation of the underlying algorithms for their specific architectures.The Sparse BLAS standard defines interfaces and bindings for the three target languages: C, Fortran 77 and Fortran 95. We describe here our Fortran 95 implementation intended as a reference model for the Sparse BLAS. We identify the underlying complex issues of the representation and the handling of sparse matrices and give suggestions to other implementors of how to address them.

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References

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Blackford, S., Corliss, G., Demmel, J., Dongarra, J., Duff, I., Hammarling, S., Henry, G., Heroux, M., Hu, C., Kahan, W., Kaufmann, L., Kearfott, B., Krogh, F., Li X., Maany, Z., Petitet, A., Pozo, R., Remington, K., Walster, W., Whaley, C., Wolff V. Gudenberg, J., and Lumsdaine, A. 2001. Basic Linear Algebra Subprograms Technical (BLAST) Forum Standard. Int. J. High Perform. Comput. 15, 3--4 (also available at www.netlib.org/blas/blast-forum).

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Duff I. S. and Vömel, C. 2000. Level 2 and Level 3 Basic Linear Algebra Subprograms for Sparse Matrices: A Fortran 95 instantiation. Tech. Rep. TR/PA/00/18. CERFACS, Toulouse, France.

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Duff, I. S., Vömel, C., and Youan, M. 2000. Implementing the Sparse BLAS in Fortran 95. Tech. Rep. TR/PA/00/82. CERFACS, Toulouse, France.

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Im, E. 2000. Optimizing the performance of sparse matrix-vector multiplication. Ph.D. dissertation, University of California, Berkeley, Berkeley, Calif.

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Im, E. and Yelick, K. 1999. Optimizing sparse matrix-vector multiplication on SMPs. In Proceedings of the 9th SIAM Conference on Parallel Processing for Scientific Computing. SIAM, Philadelphia, Pa.

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https://doi.org/10.1007/978-3-031-42144-0_12
Gentle JGentle J(2017)Software for Numerical Linear AlgebraMatrix Algebra10.1007/978-3-319-64867-5_12(539-585)Online publication date: 12-Oct-2017
https://doi.org/10.1007/978-3-319-64867-5_12
Gentle JGentle J(2017)Numerical Linear AlgebraMatrix Algebra10.1007/978-3-319-64867-5_11(523-538)Online publication date: 12-Oct-2017
https://doi.org/10.1007/978-3-319-64867-5_11
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Index Terms

Algorithm 818: A reference model implementation of the sparse BLAS in fortran 95
1. Computing methodologies
  1. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Linear algebra algorithms
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Computations on matrices

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cover image ACM Transactions on Mathematical Software

ACM Transactions on Mathematical Software Volume 28, Issue 2

June 2002

151 pages

ISSN:0098-3500

EISSN:1557-7295

DOI:10.1145/567806

Issue’s Table of Contents

Copyright © 2002 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2002

Published in TOMS Volume 28, Issue 2

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Cited By

Gentle JGentle J(2024)Software for Numerical Linear AlgebraMatrix Algebra10.1007/978-3-031-42144-0_12(607-650)Online publication date: 2024
https://doi.org/10.1007/978-3-031-42144-0_12
Gentle JGentle J(2017)Software for Numerical Linear AlgebraMatrix Algebra10.1007/978-3-319-64867-5_12(539-585)Online publication date: 12-Oct-2017
https://doi.org/10.1007/978-3-319-64867-5_12
Gentle JGentle J(2017)Numerical Linear AlgebraMatrix Algebra10.1007/978-3-319-64867-5_11(523-538)Online publication date: 12-Oct-2017
https://doi.org/10.1007/978-3-319-64867-5_11
Martone MFilippone SPaprzycki MTucci S(2010)On BLAS Operations with Recursively Stored Sparse MatricesProceedings of the 2010 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing10.1109/SYNASC.2010.72(49-56)Online publication date: 23-Sep-2010
https://dl.acm.org/doi/10.1109/SYNASC.2010.72
Gao BJiang JLiu KWu ZLu WLuo Y(2007)An efficient first‐principle approach for electronic structures calculations of nanomaterialsJournal of Computational Chemistry10.1002/jcc.2079929:3(434-444)Online publication date: 13-Jul-2007
https://doi.org/10.1002/jcc.20799
Usman ALuján MFreeman LGurd J(2006)Performance evaluation of storage formats for sparse matrices in fortranProceedings of the Second international conference on High Performance Computing and Communications10.1007/11847366_17(160-169)Online publication date: 13-Sep-2006
https://dl.acm.org/doi/10.1007/11847366_17
Vuduc RDemmel JYelick K(2005)OSKI: A library of automatically tuned sparse matrix kernelsJournal of Physics: Conference Series10.1088/1742-6596/16/1/07116(521-530)Online publication date: 31-Aug-2005
https://doi.org/10.1088/1742-6596/16/1/071
Duff IHeroux MPozo R(2002)An overview of the sparse basic linear algebra subprogramsACM Transactions on Mathematical Software10.1145/567806.56781028:2(239-267)Online publication date: 1-Jun-2002
https://dl.acm.org/doi/10.1145/567806.567810

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