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| Hauptseite > Publikationsdatenbank > Parallel block Chebyshev subspace iteration algorithm optimized for sequences of correlated dense eigenproblems > print |
| Hauptseite > Publikationsdatenbank > Parallel block Chebyshev subspace iteration algorithm optimized for sequences of correlated dense eigenproblems > print |
| 001 | 127961 | ||
| 005 | 20221109161706.0 | ||
| 037 | _ | _ | |a FZJ-2012-00907 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |0 P:(DE-Juel1)144723 |a Di Napoli, Edoardo |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 5th International Conference of the ERCIM Working Group |c Oviedo |d 2012-12-02 - 2012-12-02 |w Spain |
| 245 | _ | _ | |a Parallel block Chebyshev subspace iteration algorithm optimized for sequences of correlated dense eigenproblems |
| 260 | _ | _ | |c 2012 |
| 336 | 7 | _ | |a Conference Presentation |b conf |m conf |0 PUB:(DE-HGF)6 |s 1357217188_19181 |2 PUB:(DE-HGF) |x Other |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a Other |2 DataCite |
| 336 | 7 | _ | |a LECTURE_SPEECH |2 ORCID |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 520 | _ | _ | |a In many material science applications simulations are made of dozens of sequences, where each sequence groups together eigenproblems with increasing self-consistent cycle outer-iteration index. Successive eigenproblems in a sequence possess a high degree of correlation. In particular it has been demonstrated that eigenvectors of adjacent eigenproblems become progressively more collinear to each other as the outer-iteration index increases. This result suggests one could use eigenvectors, computed at a certain outer-iteration, as approximate solutions to improve the performance of the eigensolver at the next one. In order to opti- mally exploit the approximate solution, we developed a block iterative eigensolver augmented with a Chebyshev polynomial accelerator (BChFSI). Numerical tests show that, when the sequential version of the solver is fed approximate solutions instead of random vectors, it achieves up to a 5X speedup. Moreover the parallel shared memory implementation of the algorithm obtains a high level of efficiency up to 80 \% of the theoretical peak performance. Despite the eigenproblems in the sequence being relatively large and dense, the parallel BChFSI fed with ap- proximate solutions performs substantially better than the corresponding direct eigensolver, even for a significant portion of the sought-after spectrum |
| 536 | _ | _ | |0 G:(DE-HGF)POF2-411 |a 411 - Computational Science and Mathematical Methods (POF2-411) |c POF2-411 |f POF II |x 0 |
| 536 | _ | _ | |a Simulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM) |0 G:(DE-Juel1)SDLQM |c SDLQM |f Simulation and Data Laboratory Quantum Materials (SDLQM) |x 2 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Berljafa, Mario |b 1 |
| 909 | C | O | |o oai:juser.fz-juelich.de:127961 |p VDB |
| 910 | 1 | _ | |0 I:(DE-588b)5008462-8 |6 P:(DE-Juel1)144723 |a Forschungszentrum Jülich GmbH |b 0 |k FZJ |
| 913 | 1 | _ | |0 G:(DE-HGF)POF2-411 |1 G:(DE-HGF)POF2-410 |2 G:(DE-HGF)POF2-400 |a DE-HGF |b Schlüsseltechnologien |l Supercomputing |v Computational Science and Mathematical Methods |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF2 |
| 914 | 1 | _ | |y 2012 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)JSC-20090406 |k JSC |l Jülich Supercomputing Center |x 0 |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a conf |
| 980 | _ | _ | |a I:(DE-Juel1)JSC-20090406 |
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