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Integrating Trilinos Solvers to SEAM code

Integrating Trilinos Solvers to SEAM code. Dagoberto A.R. Justo – UNM Tim Warburton – UNM Bill Spotz – Sandia. SEAM (NCAR). Trilinos (Sandia Lab). AztecOO Epetra Nox Ifpack PETSc Komplex. Spectral Element Atmospheric Method. AztecOO. Solvers CG, CGS, BICGStab, GMRES, Tfqmr

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Integrating Trilinos Solvers to SEAM code

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  1. Integrating Trilinos Solvers to SEAM code Dagoberto A.R. Justo – UNM Tim Warburton – UNM Bill Spotz – Sandia

  2. SEAM (NCAR) Trilinos (Sandia Lab) • AztecOO • Epetra • Nox • Ifpack • PETSc • Komplex Spectral Element Atmospheric Method

  3. AztecOO • Solvers • CG, CGS, BICGStab, GMRES, Tfqmr • Preconditioners • Diagonal Jacobi, Least Square, Neumann, Domain Decomposition, Symmetric Gauss-Seidel • Matrix Free implementation • C++ (Fortran interface) • MPI

  4. Implementation A Z T E C SEAM CODE . . . Pcg_solver . . (F90) Pcg_solver . . Aztec_solvers( ) . (F90) Sub Aztec_solvers . AZ_Iterate( ) (C) Matrix_vector_C (C) Prec_Jacobi_C (C) Matrix_vector . (F90) Prec_Jacobi . (F90)

  5. Machines used • Pentium III Notebook (serial) • Linux, LAM-MPI, Intel Compilers • Los Lobos at HPC@UNM • Linux Cluster • 256 nodes • IBM Pentium III 750 MHz, 256 KB L2 Cache, 1 Gb RAM • Portland Group compiler • MPICH for Myrinet interconnections

  6. Graphical Results from SEAM Mass Energy

  7. Memory(in Mbytes per processor)

  8. Speed Up • From 1 to 160 processors. • Time of Simulation 144 time iterations x 300 s = 12 h simulation • Verify results using mass, energy,… • (Different result for 1 proc)

  9. Speed Up – SEAMselecting # of elements ne=24x24x6

  10. Speed Up – SEAMselecting order np=6

  11. Speed Up – SEAM+Aztecbest: cgs solver

  12. Speed Up – SEAM+Aztecbest: cgs solver + Least Square preconditioner

  13. Speed Up – SEAM+Aztecincreasing np -> increases speedup

  14. Upshot – SEAM(One CG iteration)

  15. Upshot – SEAM(matrix times vector communication)

  16. Upshot – SEAM+Aztec(One CG iteration)

  17. Upshot – SEAM+Aztec(Matrix times vector communication)

  18. Upshot – SEAM+Aztec(Vector Reduction)

  19. Time (24x24x6 elements, 2 proc.)

  20. Conclusions &Suggested Future Efforts • SEAM+Aztec works! • SEAM+Aztec is 2x slower difference in CG algorithms SEAM+Aztec time-iteration is 50% slower  0.1% of time lost in calls, preparation for Aztec. • More time  better tune-up. • Domain decomposition Preconditioners

  21. Conclusions &Suggested Future Efforts • SEAM + Aztec works! • More time  better tune-up.

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