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| Hauptseite > Publikationsdatenbank > New Preconditioned Solvers for Large Sparse Eigenvalue Problems on Massively Parallel Computers > print |
| Hauptseite > Publikationsdatenbank > New Preconditioned Solvers for Large Sparse Eigenvalue Problems on Massively Parallel Computers > print |
| 001 | 155238 | ||
| 005 | 20210129214039.0 | ||
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| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Basermann, Achim |0 P:(DE-HGF)0 |b 0 |e Corresponding Author |
| 111 | 2 | _ | |a Eighth SIAM Conference on Parallel Processing for Scientific Computing |g PPSC 1997 |c Minneapolis |d 1997-03-14 - 1997-03-17 |w USA |
| 245 | _ | _ | |a New Preconditioned Solvers for Large Sparse Eigenvalue Problems on Massively Parallel Computers |
| 260 | _ | _ | |a Philadelphia, Pa. |c 1997 |b Society for Industrial and Applied Mathematics |
| 295 | 1 | 0 | |a Proceedings of the Eighth SIAM Conference on Parallel Processing for Scientific Computing, PPSC 1997 |
| 300 | _ | _ | |a 8 p. |
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| 520 | _ | _ | |a We present preconditioned solvers to find a few eigenvalues and eigenvectors of large dense or sparse symmetric matrices based on the Jacobi-Davidson (JD) method by G. L. G. Sleijpen and H. A. van der Vorst. For preconditioning, we apply a new adaptive approach using the QMR iteration. To parallelize the solvers, we divide the interesting part of the spectrum into a few overlapping intervals and asynchronously exchange eigenvector approximations from neighboring intervals to keep the solutions separated. Per interval, matrix-vector and vector-vector operations of the JD iteration are parallelized by determining a data distribution and a communication scheme from an automatic analysis of the sparsity pattern of the matrix. We demonstrate the efficiency of these parallelization strategies by timings on an Intel Paragon and a Cray T3E system with matrices from real applications. |
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| 700 | 1 | _ | |a Steffen, Bernhard |0 P:(DE-Juel1)132269 |b 1 |u fzj |
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