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PARDISO

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Encyclopedia of Parallel Computing

Definition

PARDISO, short for “PARallel DIrect SOlver,” is a thread-safe software library for the solution of large sparse linear systems of equations on shared-memory multicore architectures. It is written in Fortran and C and it is available at www.pardiso-project.org. The solver implements an efficient supernodal method, which is a version of Gaussian elimination for large sparse systems of equations, especially those arising, for example, from the finite element method or in nonlinear optimization. It is the only sparse solver package that supports all kinds of matrices such as complex, real, symmetric, nonsymmetric, or indefinite. PARDISO can be called from various environments including MATLAB (via MEX), Python (via pypardiso), C/C++, and Fortran. PARDISO version 4.0.0 was released in October 2009.

Discussion

Introduction

The solution of large sparse linear systems lies at the heart of many calculations in computational science and engineering and is also of increasing importance...

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Further Reading

  1. Davis T (2006) Direct methods for sparse linear systems. Society for industrial mathematics, ISBN:0898716136

    Google Scholar 

Bibliography

  1. Amestoy R, Davis TA, Duff IS (1996) An approximate minimum degree ordering algorithm. SIAM J Matrix Anal Appl 17:886–905

    MATH  MathSciNet  Google Scholar 

  2. Demmel JW, Eisenstat SC, Gilbert JR, Li XS, Liu JWH (1999) A supernodal approach to sparse partial pivoting. SIAM J Matrix Anal Appl 20:720–755

    MATH  MathSciNet  Google Scholar 

  3. Duff IS, Koster J (1999) The design and use of algorithms for permuting large entries to the diagonal of sparse matrices. SIAM J Matrix Anal Appl 20(4):889–901

    MATH  MathSciNet  Google Scholar 

  4. Gould NIM, Hu Y, Scott JA (2007) A numerical evaluation of sparse direct solvers for the solution of large sparse, symmetric linear systems of equations. ACM Trans Math Software (TOMS) 33(2):1–32

    MathSciNet  Google Scholar 

  5. Grasedyck L, Hackbusch W, Kriemann R (2008) Performance of H-LU preconditioning for sparse matrices. Comput Methods Appl Math 8(4):336–349

    MATH  MathSciNet  Google Scholar 

  6. Hagemann M, Schenk O (2006) Weighted matchings for preconditioning of symmetric indefinite linear systems. SIAM J Sci Comput 28:403–420

    MATH  MathSciNet  Google Scholar 

  7. Karypis G, Kumar V (1998) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20:359–392

    MathSciNet  Google Scholar 

  8. Liu J (1985) Modification of the minimum-degree algorithm by multiple elimination. ACM Trans Math Software 11(2):141–153

    MATH  MathSciNet  Google Scholar 

  9. Ng E, Peyton B (1993) Block sparse Cholesky algorithms on advanced uniprocessor computers. SIAM J Sci Comput 14:1034–1056

    MATH  MathSciNet  Google Scholar 

  10. Manguoglu M, Sameh A, Schenk O (2009) PSPIKE Parallel sparse linear system solver. In: Proceedings of the 15th international Euro-Par conference on parallel processing. Lecture Notes in Computer Science, vol 5704, pp 797–808, DOI 10.1007/978-3-642-03869-3 (vol 14, pp 1034–1056)

    Google Scholar 

  11. Polizzi E, Sameh AH (2006) A parallel hybrid banded system solver: the SPIKE algorithm. Parallel Comput 32(2):177–194

    MathSciNet  Google Scholar 

  12. Schenk O (2000) Scalable parallel sparse LU factorization methods on shared memory multiprocessors. PhD thesis, ETH ZĂĽrich

    Google Scholar 

  13. Schenk O, Bollhöfer M, Römer RA (2008) On large-scale diagonalization techniques for the Anderson model of localization. SIAM Rev 50:91–112

    MATH  MathSciNet  Google Scholar 

  14. Schenk O, Christen M, Burkhart H (2008) Algorithmic performance studies on graphics processing unit. J Parallel Distrib Comput 28:1360–1369

    Google Scholar 

  15. Schenk O, Gärtner K (2004) Solving unsymmetric sparse systems of linear equations with PARDISO. J Future Gener Comput Syst 20(3):475–487

    Google Scholar 

  16. Schenk O, Gärtner K (2006) On fast factorization pivoting methods for symmetric indefinite systems. Electron Trans Numer Anal 23:158–179

    MATH  MathSciNet  Google Scholar 

  17. Schenk O, Wächter A, Hagemann M (2007) Matching-based preprocessing algorithms to the solution of saddle-point problems in large-scale nonconvex interior-point optimization. Comput Optim Appl 36(2–3):321–341

    MATH  MathSciNet  Google Scholar 

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Schenk, O., Gärtner, K. (2011). PARDISO. In: Padua, D. (eds) Encyclopedia of Parallel Computing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09766-4_90

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