Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
article
Free access

Level 3 basic linear algebra subprograms for sparse matrices: a user-level interface

Published: 01 September 1997 Publication History

Abstract

This article proposes a set of Level 3 Basic Linear Algebra Subprograms and associated kernels for sparse matrices. A major goal is to design and develop a common framework to enable efficient, and portable, implementations of iterative algorithms for sparse matrices on high-performance computers. We have designed the routines to shield the developer of mathematical software from most of the complexities of the various data structures used for sparse matrices. We have kept the interface and suite of codes as simple as possible while at the same time including sufficient functionality to cover most of the requirements of iterative solvers and sufficient flexibility to cover most sparse matrix data structures. An important aspect of our framework is that it can be easily extended to incorporate new kernels if the need arises. We discuss the design, implementation, and use of subprograms for the multiplication of a fully matrix by a sparse one and for the solution of sparse triangular systems with one or more (full) right-hand sides. We include a routine for checking the input data, generating a new sparse data structure from the input, and scaling a sparse matrix. The new data structure for the transformation can be specified by the user or can be chosen automatically by vendors to be efficient on their machines. We also include a routine for permuting the columns of a sparse matrix and one for permuting the rows of a full matrix.

References

[1]
AEA TECHNOLOGY. 1996. Harwell Subroutine Library: A catalogue of subroutines (release 12). AEA Technology, Didcot, Oxon, United Kingdom.
[2]
AGARWAL, R. C., GUSTAVSON, F. G., AND ZUBAIR, M. 1992. A high performance algorithm using pre-processing for the sparse matrix-vector multiplication. In Supercomputing '92 (Minneapolis, MN, Nov. 16-20, 1992), R. Werner, Ed. IEEE Computer Society Press, Los Alamitos, CA, 32-41.
[3]
AMESTOY, P. R., DAYDI~, M., AND DUFF, I.S. 1989. Use of level 3 BLAS in the solution of full and sparse linear equations. In High Performance Computing (Montpellier, France, March 22-24, 1989), J.-L. Delhaye and E. Gelenbe, Eds. North-Holland Publishing Co., Amsterdam, The Netherlands, 19-31.
[4]
ANDERSON, E., BAI, Z., BISCHOF, C., DEMMEL, J., DONGARRA, J., Du CROZ, J., GREENBAUM, A., HAMMARLING, S., MCKENNEY, A., OSTROUCHOV, S., AND SORENSEN, D. 1992. LAPACK User's Guide. Society for Industrial and Applied Mathematics, Philadelphia, PA.
[5]
ASHBY, S. F. AND SEAGER, M.K. 1990. A proposed standard for iterative solvers. Tech. Rep. 102860, Lawrence Livermore National Laboratory, Livermore, CA.
[6]
CARNEY, S., HEROUX, M. A., AND LI, G. 1993. A proposal for a sparse BLAS toolkit. Tech. Rep. TR/PA/92/90, Revised. CERFACS, Toulouse, France.
[7]
CERIONI, F., COLAJANNI, M., FILIPPONE, S., AND MAIOLATESI, S. 1996. A proposal for parallel sparse BLAS. Tech. Rep. RI.96.05, University of Rome--Tor Vergata, Rome, Italy.
[8]
DODSON, D. S., GRIMES, R. G., AND LEWIS, J.G. 1991. Sparse extensions to the FORTRAN Basic Linear Algebra Subprograms. ACM Trans. Math. Softw. 17, 2 (June), 253-263.
[9]
DONGARRA, J. J., Du CROZ, J., HAMMARLING, S., AND DUFF, I. 1990. A set of level 3 Basic Linear Algebra Subprograms. ACM Trans. Math. Softw. 16, 1 (Mar.), 1-17.
[10]
DONGARRA, J. J., Du CROZ, J., HAMMARLING, S., AND HANSON, R.J. 1988. An extended set of FORTRAN Basic Linear Algebra Subprograms. ACM Trans. Math. Softw. 14, 1 (Mar.), 1-17.
[11]
DUFF, I. S. 1981. Full matrix techniques in sparse Gaussian elimination. In Numerical Analysis Proceedings (Dundee, Scotland), G. A. Watson, Ed. Lecture Notes in Mathematics, vol. 912. Springer-Verlag New York, Inc., New York, NY, 71-84.
[12]
DUFF, I. S. AND REID, J.K. 1996. Exploiting zeros on the diagonal in the direct solution of indefinite sparse symmetric linear systems. ACM Trans. Math. Softw. 22, 2 (June), 227-257.
[13]
DUFF, I. S., GRIMES, R. G., AND LEWIS, J. G. 1989. Sparse matrix test problems. ACM Trans. Math. Softw. 15, 1 (Mar.), 1-14.
[14]
DUFF, I. S., GRIMES, R. G., AND LEWIS, J. G. 1997. The Rutherford-Boeing Sparse Matrix Collection. Tech. Rep. RAL TR-97-031, Rutherford Appleton Laboratory, Didcot, Oxon, United Kingdom.
[15]
ERHEL, J. 1990. Sparse matrix multiplication on vector computers. Int. J. High Speed Comput. 2, 101-116.
[16]
IBM. 1990. IBM Engineering and Scientific Subroutine Library: Guide and Reference. Tech. Rep. IBM Corp., Riverton, NJ.
[17]
LAWSON, C. L., HANSON, R. J., KINCAID, D. R., AND KROGH, F.T. 1979. Basic linear algebra subprograms for Fortran usage. ACM Trans. Math. Softw. 5, 3, 308-323.
[18]
OPPE, T. C. AND KINCAID, D.R. 1990. Are there iterative BLAS?. Tech. Rep. CNA-240, The University of Texas at Austin, Austin, TX.
[19]
OPPE, T. C., JOUBERT, W., AND KINCAID, D. R. 1988. NSPCG user's guide: A package for solving large linear systems by various iterative methods. Tech. Rep. CNA-216, The University of Texas at Austin, Austin, TX.
[20]
PAOLINI, G. V. AND RADICATI DI BROZOLO, G. 1989. Data structures to vectorize CG algorithms for general sparsity patterns. BIT 29, 4, 703-718.
[21]
Pozo, R. AND REMINGTON, K. A. 1996. The NIST sparse BLAS library implementation: Design and performance. Tech. Rep. National Institute of Standards and Technology, Gaithersburg, MD.
[22]
SAAD, Y. 1994. ILUT: A dual threshold incomplete factorization. Num. Lin. Alg. Appl. 1, 4, 387-402.
[23]
THINKING MACHINES. 1992. CMSSL for CM Fortran. Version 3.0. Tech. Rep. Thinking Machines Corporation, Bedford, MA.

Cited By

View all
  • (2024)Software for Numerical Linear AlgebraMatrix Algebra10.1007/978-3-031-42144-0_12(607-650)Online publication date: 2024
  • (2023)2DMAC: A Sustainable and Efficient Medium Access Control Mechanism for Future Wireless NoCsACM Journal on Emerging Technologies in Computing Systems10.1145/357072719:3(1-25)Online publication date: 21-Jun-2023
  • (2023)Automated Generation of Security Assertions for RTL ModelsACM Journal on Emerging Technologies in Computing Systems10.1145/356580119:1(1-27)Online publication date: 19-Jan-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Mathematical Software
ACM Transactions on Mathematical Software  Volume 23, Issue 3
Sept. 1997
152 pages
ISSN:0098-3500
EISSN:1557-7295
DOI:10.1145/275323
  • Editor:
  • Ronald F. Boisvert
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 1997
Published in TOMS Volume 23, Issue 3

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. high-performance computing
  2. iterative solution
  3. programming standards
  4. sparse BLAS
  5. sparse data structures
  6. sparse matrices

Qualifiers

  • Article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)79
  • Downloads (Last 6 weeks)10
Reflects downloads up to 09 Sep 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Software for Numerical Linear AlgebraMatrix Algebra10.1007/978-3-031-42144-0_12(607-650)Online publication date: 2024
  • (2023)2DMAC: A Sustainable and Efficient Medium Access Control Mechanism for Future Wireless NoCsACM Journal on Emerging Technologies in Computing Systems10.1145/357072719:3(1-25)Online publication date: 21-Jun-2023
  • (2023)Automated Generation of Security Assertions for RTL ModelsACM Journal on Emerging Technologies in Computing Systems10.1145/356580119:1(1-27)Online publication date: 19-Jan-2023
  • (2022)Dynamic Data-driven Microscopic Traffic Simulation using Jointly Trained Physics-guided Long Short-Term MemoryACM Transactions on Modeling and Computer Simulation10.1145/355855532:4(1-27)Online publication date: 5-Nov-2022
  • (2022)QuadStreamACM Transactions on Graphics10.1145/3550454.355552441:6(1-13)Online publication date: 30-Nov-2022
  • (2022)LuisaRenderACM Transactions on Graphics10.1145/3550454.355546341:6(1-19)Online publication date: 30-Nov-2022
  • (2022)Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real AdaptationACM Transactions on Graphics10.1145/3550454.355544241:6(1-21)Online publication date: 30-Nov-2022
  • (2022)A Detailed Look at MIMO Performance in 60 GHz WLANsACM SIGMETRICS Performance Evaluation Review10.1145/3547353.353097150:1(25-26)Online publication date: 7-Jul-2022
  • (2022)Toxicity in the Decentralized Web and the Potential for Model SharingACM SIGMETRICS Performance Evaluation Review10.1145/3547353.353096850:1(15-16)Online publication date: 7-Jul-2022
  • (2022)NG-Scope: Fine-Grained Telemetry for NextG Cellular NetworksACM SIGMETRICS Performance Evaluation Review10.1145/3547353.352265250:1(27-28)Online publication date: 7-Jul-2022
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Full Access

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media