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PARDISO, improved parallel efficiency for direct sparse LU-factorization

Collaborator: K. Gärtner

Cooperation with: O. Schenk (Universität Basel, IFI, Switzerland)

Description: PARDISO is a SMP-parallel, direct solver for sparse linear systems with coefficient matrices close to structural symmetry. The development started within in the framework of the ETH-CRAY-SuperCluster collaboration and was continued over the years by both authors. PARDISO is well established in some application areas and distributed via computer vendor libraries (NEC, Compaq), too. The activities during the year 2002 focused on:

The supported matrix types (complex and real, spd, Hermitian, complex symmetric), the low operation count, the BLAS3 performance reached during the factorization, and the fill-in close to ln(n) n for 2D problems (n number of unknowns) make PARDISO a workhorse for solving many (especially 2D) partial differential equation problems.

An increased penetration speed of the code into WIAS application problems was observed during 2002. To name a few: diffractive optics (see page [*]), eigenvalue problems for Maxwell's equations in microwave guides and lasers (see page [*]), crystal growth modeling (see page [*]).



Within an evaluation of the new ev7-based HP-AlphaServer generation, PARDISO was used, too. Typical (wall-clock) times are: a standard test problem (Laplace equation on a triangular unstructured grid with 151389 nodes) is factorized now in
0.9 sec on the 1.0 GHz single ev7 CPU, the 3D laser diode structural symmetric test problem (311819 unknowns) can be solved on the 8 CPU configuration in 6:13 min with parallel efficiency above 70 % without the two-level scheduling. On the other hand that last number is in the same range that could be reached on a single CPU SX-5 two years ago.





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References:

  1. I.S. DUFF, J. KOSTER, The design and use of algorithms for permuting large entries to the diagonal of sparse matrices, SIAM J. Matrix Anal. Appl., 20 (1999), pp. 889-901.
  2. O. SCHENK, K. GÄRTNER, Two-level dynamic scheduling in PARDISO: Improved scalability on shared memory multiprocessing systems, Parallel Comput., 28 (2002), pp. 187-197.



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