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A parallel generalized global conjugate gradient squared algorithm for linear systems with multiple right-hand sides

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Abstract

In this paper, based on the generalized global conjugate gradient squared (GGl-CGS) algorithm in Zhang et al. (Appl Math Comput 216:3694–3706, 2010) and the ideas in Gu et al. (Appl Math Comput 186:1243–1253, 2007), we present a parallel generalized Gl-CGS (PGGl-CGS) algorithm for linear systems with multiple right-hand sides. The new algorithm reduces two global synchronization points to one by changing the computation sequence in the generalized Gl-CGS algorithm, and all inner products per iteration are independent and communication time required for inner product can be overlapped with useful computation. Theoretical analysis and numerical experiments show that the PGGl-CGS method has better parallelism and scalability than the generalized Gl-CGS method, and the parallel performance can be improved by a factor of about 3/2.

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References

  • Bücker HM, Sauren M (1996) A parallel version of the quasi-minimal residual method based on coupled two-term recurrences. In: Proceedings of workshop on applied parallel computing in industrial problems and optimization (Para96). Technical University of Denmark, Springer, Lyngby

  • Chi LH, Liu J, Liu XP, Hu QF, Li XM (2005) An improved conjugate residual algorithm for large symmetric linear systems. In: Proceedings of the joint conference of ICCP6 and CCP2003 on computational physics, Rinton Press, New Jersey, pp 325–328

  • Freund RW, Nachtigal NM (1991) QMR: a quasi-minimal residual method for non-Hermitian linear systems. Numerische Mathematik 60:315–339

    Article  MATH  MathSciNet  Google Scholar 

  • Freund RW (1997) A block-QMR algorithm for non-Hermitian linear systems with right-hand. Linear Algebra Appl 254:119–157

    Article  MATH  MathSciNet  Google Scholar 

  • Grama A, Gupta A, Kumar V (1993) Isoefficiency function: a scalability metric for parallel algorithms and architectures. IEEE Parallel Distrib Technol 1(3):12–21

    Article  Google Scholar 

  • Gu TX, Liu XP, Mo ZY (2004) Multiple search direction conjugate gradient method I: methods and their propositions. Int J Comput Math 81(9):1133–1143

    Article  MATH  MathSciNet  Google Scholar 

  • Gu TX, Zuo XY, Zhang LT, Zhang WQ, Sheng ZQ (2007) An improved bi-conjugate residual algorithm suitable for distributed parallel computing. Appl Math Comput 186:1243–1253

    Article  MATH  MathSciNet  Google Scholar 

  • Jbilou K, Messaoudi A, Sadok H (1999) Global FOM and GMRES algorithms for matrix equations. Appl Numer Math 31:49–63

    Article  MATH  MathSciNet  Google Scholar 

  • Jbilou K, Sadok H, Tinzefte A (2005) Oblique projection methods for linear systems with multiple right-hand sides. Electr Trans Numer Anal 20:119–138

    MATH  MathSciNet  Google Scholar 

  • Liu XP, Gu TX, Hang XD, Sheng ZQ (2006) A parallel version of QMRCGSTAB method for large linear systems in distributed parallel environments. Appl Math Comput 172(2):744–752

    Article  MATH  MathSciNet  Google Scholar 

  • O’Leary D (1980) The block conjugate gradient algorithm and related methods. Linear Algebra Appl 29:3–322

    MathSciNet  Google Scholar 

  • Saad Y (1996) Iterative methods for sparse linear systems. PWS, Boston

    MATH  Google Scholar 

  • Simoncini V, Gallopoulos E (1996) Convergence properties of block GMRES and matrix polynomials. Linear Algebra Appl 247:97–119

    Article  MATH  MathSciNet  Google Scholar 

  • Sogabe T, Zhang SL (2003) Extended conjugate residual methods for solving nonsymmetric linear systems. In: Yuan Y-X (ed) Numerical linear algebra and optimization. Science Press, Beijing/New York, pp 88–99

    Google Scholar 

  • Sonneveld P (1989) CGS: a fast lanczos-type solver for nonsymmetric linear systems. SIAM J Sci Stat Comput 10(1):36–52

    Article  MATH  MathSciNet  Google Scholar 

  • de Sturler E, van der Vorst HA (1995) Reducing the effect of the global communication in GMRES(m) and CG on parallel distributed memory computers. Appl Numer Math 18:441–459

    Article  MATH  Google Scholar 

  • de Sturler E (1996) A performance model for Krylov subspace methods on mesh-based parallel computers. Parallel Comput 22:57–74

    Article  MATH  MathSciNet  Google Scholar 

  • Vital B (1990) Etude de quelques de method de resolution de problemes liearies de grade taide sur multiprocesseur, Ph D Thesis, de Rennes, Rennes

  • van der Vorst HA (1992) Bi-CGSTAB: a fast and smoothly converging variant of bi-CG for the solution of nonsymmetric linear systems. SIAM J Sci Stat Comput 13:631–644

    Article  MATH  Google Scholar 

  • Yang TR, Brent RP (2002) The improved BiCGSTAB method for large and sparse nonsymmetric linear systems on parallel distributed memory architectures. In: 5th international conference on algorithms and architectures for parallel processing, IEEE Computer Society, pp 324–328

  • Yang TR, Lin HX (1997) The improved quasi-minimal residual method on massively distributed memory computers. In: Proceedings of the international conference on high performance computing and networking (HPCN-97)

  • Yang TR (2002) The improved CGS method for large and sparse linear systems on bulk synchronous parallel architectures. In:5th international conference on algorithms and architectures for parallel processing, IEEE Computer Society, pp 232–237

  • Yang TR, Brent RP (2003) The improved BiCG method for large and sparse linear systems on parallel distributed memory architectures. Inf J 6:349–360

    MATH  MathSciNet  Google Scholar 

  • Zhang JH, Dai H (2008) Global CGS algorithm for linear systems with multiple right-hand sides. Numerical Mathematics: A Journal of Chinese Universities 30:390–399 (in chinese)

  • Zhang JH (2006) New smoothing iterative block methods for linear systems with multiple right-hand sides. J Inf Comput Sci 1(5):311–317

    Google Scholar 

  • Zhang LT, Huang TZ, Gu TX, Zuo XY (2008) An improved conjugate residual squared algorithm suitable for distributed parallel computing. Microelectr Comput 25(10):12–14

    Google Scholar 

  • Zhang JH, Dai H, Zhao J (2010) Generalized global conjugate gradient squared algorithm. Appl Math Comput 216:326–329

    MathSciNet  Google Scholar 

  • Zhang LT, Zuo XY, Gu TX, Huang TZ (2010) Conjugate residual squared method and its improvement for non-symmetric linear systems. Int J Comput Math 87(7):1578–1590

    Article  MATH  MathSciNet  Google Scholar 

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Acknowledgments

The authors would like to thank the referees and Editor for their helpful and detailed suggestions for revising this manuscript.

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Correspondence to Li-Tao Zhang.

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Communicated by Ernesto G. Birgin.

This research of this author is supported by NSFC Tianyuan Mathematics Youth Fund (11226337), NSFC (61203179, 61202098, 61170309, 91130024 and 11171039), Aeronautical Science Foundation of China (2013ZD55006), Project of Youth Backbone Teachers of Colleges and Universities in Henan Province (2013GGJS-142), ZZIA Innovation team fund (2014TD02), Major project of development foundation of science and technology of CAEP (2012A0202008), Basic and Advanced Technological Research Project of of Henan Province (132300410373).

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Zhang, LT., Zuo, XY., Gu, TX. et al. A parallel generalized global conjugate gradient squared algorithm for linear systems with multiple right-hand sides. Comp. Appl. Math. 34, 901–916 (2015). https://doi.org/10.1007/s40314-014-0158-3

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  • DOI: https://doi.org/10.1007/s40314-014-0158-3

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