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| Home > Publications database > ChASE: Chebyshev Accelerated Subspace iteration Eigensolver for sequences of Hermitian eigenvalue problems > print |
| 001 | 848080 | ||
| 005 | 20221109161715.0 | ||
| 024 | 7 | _ | |a arXiv:1805.10121 |2 arXiv |
| 024 | 7 | _ | |a 10.1145/3313828 |2 doi |
| 024 | 7 | _ | |a 2128/22149 |2 Handle |
| 024 | 7 | _ | |a WOS:000482133700008 |2 WOS |
| 037 | _ | _ | |a FZJ-2018-03363 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 620 |
| 100 | 1 | _ | |a Winkelmann, Jan |0 P:(DE-Juel1)167415 |b 0 |u fzj |
| 245 | _ | _ | |a ChASE: Chebyshev Accelerated Subspace iteration Eigensolver for sequences of Hermitian eigenvalue problems |
| 260 | _ | _ | |a New York, NY |c 2019 |b ACM |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Solving dense Hermitian eigenproblems arranged in a sequence with direct solvers fails to take advantage of those spectral properties that are pertinent to the entire sequence and not just to the single problem. When such features take the form of correlations between the eigenvectors of consecutive problems, as is the case in many real-world applications, the potential benefit of exploiting them can be substantial. We present the Chebyshev Accelerated Subspace iteration Eigensolver (ChASE), a modern algorithm and library based on subspace iteration with polynomial acceleration. Novel to ChASE is the computation of the spectral estimates that enter in the filter and an optimization of the polynomial degree that further reduces the necessary floating-point operations. ChASE is written in C++ using the modern software engineering concepts that favor a simple integration in application codes and a straightforward portability over heterogeneous platforms. When solving sequences of Hermitian eigenproblems for a portion of their extremal spectrum, ChASE greatly benefits from the sequence’s spectral properties and outperforms direct solvers in many scenarios. The library ships with two distinct parallelization schemes, supports execution over distributed GPUs, and is easily extensible to other parallel computing architectures. |
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| 536 | _ | _ | |0 G:(DE-Juel1)PHD-NO-GRANT-20170405 |x 1 |c PHD-NO-GRANT-20170405 |a PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) |
| 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 |
| 588 | _ | _ | |a Dataset connected to arXivarXiv |
| 700 | 1 | _ | |a Springer, Paul |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Di Napoli, Edoardo |0 P:(DE-Juel1)144723 |b 2 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1145/3313828 |g Vol. 45, no. 2, p. 1 - 34 |0 PERI:(DE-600)2006421-4 |n 2 |p 21 |t ACM transactions on mathematical software |v 45 |y 2019 |x 0098-3500 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/848080/files/1805.10121.pdf |y OpenAccess |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/848080/files/1805.10121.pdf?subformat=pdfa |x pdfa |y OpenAccess |
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