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@INPROCEEDINGS{Basermann:190268,
      author       = {Basermann, Achim},
      title        = {{P}arallelizing {I}terative {S}olvers for {S}parse
                      {S}ystems of {E}quations and {E}igenproblems on
                      {D}istributed {M}emory {M}achines},
      reportid     = {FZJ-2015-03179},
      pages        = {22 p.},
      year         = {1994},
      comment      = {Proceedings of the Colorado Conference on Iterative
                      Methods},
      booktitle     = {Proceedings of the Colorado Conference
                       on Iterative Methods},
      abstract     = {For the analysis and solution of discretized ordinary or
                      partial differential equations it is necessary to solve
                      systems of equations or eigenproblems with coefficient
                      matrices of different sparsity patterns, depending on the
                      discretization method. In many cases, the use of the finite
                      element method (FE) results in largely unstructured systems
                      of equations. The main computational cost in iterative
                      methods for solving these problems consists of matrix-vector
                      products. When iterative solvers are parallelized on a
                      multiprocessor system with distributed memory, the data
                      distribution and the communication scheme - depending on the
                      data structures used for sparse matrices - are of the
                      greatest importance for an efficient execution. Here, data
                      distribution and communication schemes are presented that
                      are based on the analysis of the column indices of the
                      non-zero matrix elements. Performance tests, using the
                      conjugate gradient method (CG) and the Lanczos algorithm for
                      the symmetric eigenproblem, were carried out on the
                      distributed memory systems Intel iPSC/860 and Paragon XP/S
                      10 of the Research Centre Jülich with sparse matrices from
                      FE models. The parallel variants of the algorithms showed
                      good scaling behavior for matrices with very different
                      sparsity patterns.},
      month         = {Apr},
      date          = {1994-04-04},
      organization  = {Colorado Conference on Iterative
                       Methods, Breckenridge, CO. (USA), 4 Apr
                       1994 - 8 Apr 1994},
      cin          = {ZAM / JSC},
      cid          = {I:(DE-Juel1)VDB62 / I:(DE-Juel1)JSC-20090406},
      pnm          = {899 - ohne Topic (POF2-899)},
      pid          = {G:(DE-HGF)POF2-899},
      typ          = {PUB:(DE-HGF)8},
      url          = {https://juser.fz-juelich.de/record/190268},
}