Skip to main content
Log in

Mixed Precision Bisection

  • Published:
Mathematics in Computer Science Aims and scope Submit manuscript

Abstract

We discuss the implementation of the bisection algorithm for the computation of the eigenvalues of symmetric tridiagonal matrices in a context of mixed precision arithmetic. This approach is motivated by the emergence of processors which carry out floating-point operations much faster in single precision than they do in double precision. Perturbation theory results are used to decide when to switch from single to double precision. Numerical examples are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Anderson, E., et al.: LAPACK Users’ Guide. SIAM, Philadelphia (1999)

    Book  MATH  Google Scholar 

  2. Blackford, L., et al.: ScaLAPACK Users’ Guide. SIAM, Philadelphia (1997)

    Book  MATH  Google Scholar 

  3. Buttari, A., et al.: Mixed precision iterative refinement techniques for the solution of dense linear systems. Int. J. High Perform. Comput. Appl. 21, 457–466 (2007)

    Article  Google Scholar 

  4. Buttari, A., et al.: Using mixed precision for sparse matrix computations to enhance the performance while achieving 64-bit accuracy. ACM Trans. Math. Softw. TOMS 34(4), 17:1–17:22 (2008)

    MathSciNet  MATH  Google Scholar 

  5. Demmel, J.W.: The inherent inaccuracy of implicit tridiagonal QR. LAPACK working note 15 (1992)

  6. Demmel, J.W., Li, X.: Faster numerical algorithms via exception handling. IEEE Trans. Comput. 43, 983–992 (1992)

    Article  MATH  Google Scholar 

  7. Demmel, J.W., Dhillon, I., Ren, H.: On the correctness of some bisection-like parallel eigenvalue algorithms in floating point arithmetic. Electr. Trans. Numer. Anal. 3, 116–149 (1995)

    MathSciNet  MATH  Google Scholar 

  8. Demmel, J.W.: Applied Numerical Linear Algebra. SIAM, Philadelphia (1997)

    Book  MATH  Google Scholar 

  9. Giraud, l, Haidar, A., Watson, L.: Mixed-precision preconditioners in parallel domain decomposition solvers. Lect. Notes Comput. Sci. Eng 60, 357–364 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  10. Golub, G., Van Loan, C.: Matrix Computations, 2nd edn. The Johns Hopkins University Press, Baltimore (1989)

    MATH  Google Scholar 

  11. Higham, N.: The rise of mixed precision arithmetic. https://nickhigham.wordpress.com/2015/10/20/the-rise-of-mixed-precision-arithmetic/. Accessed Nov 2015

  12. Kurzak, J., Dongarra, J.: Implementation of mixed precision in solving systems of linear equations on the CELL processor. Concurr. Comput. Pract. Exp. 19, 1371–1385 (2007)

    Article  Google Scholar 

  13. Langou, J., et al.: Exploiting the performance of 32 bit floating point arithmetic in obtaining 64 bit accuracy (revisiting iterative refinement for linear systems. In: Proceedings of the ACM/IEEE 2006 Conference. IEEE Computer Society Press (2006)

  14. Parlett, B.N.: The Symmetric Eigenvalue Problem. Prentice-Hall, Englewood Cliffs (1998)

    Book  MATH  Google Scholar 

  15. Petschow, M., Quintana-Orti, E.S., Bientinesi, P.: Improved accuracy and parallelism for MRRR-based eigensolvers—a mixed precision approach. SIAM J. Sci. Comput. 36(2), C240–C263 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  16. Ralha, R.: Perturbation splitting for more accurate eigenvalues. SIAM J. Matrix Anal. Appl. 31, 75–91 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  17. Volkov, V., Demmel, J.W.: Using GPUs to accelerate the bisection algorithm for finding eigenvalues of symmetric tridiagonal matrices. LAPACK working note 197 (2008)

  18. Wilkinson, J.H.: The Algebraic Eigenvalue Problem. Oxford University Press, London (1965)

    MATH  Google Scholar 

  19. Yamazaki, I., Tomov, S., Dongarra, J.: Mixed-precision Choleski QR factorization and its case studies on multicore CPU with multiple GPUs. SIAM J. Sci. Comput. 37(3), C307–C330 (2015)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rui Ralha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ralha, R. Mixed Precision Bisection. Math.Comput.Sci. 12, 173–181 (2018). https://doi.org/10.1007/s11786-018-0336-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11786-018-0336-6

Keywords

Mathematics Subject Classification

Navigation