000127961 001__ 127961
000127961 005__ 20221109161706.0
000127961 037__ $$aFZJ-2012-00907
000127961 041__ $$aEnglish
000127961 1001_ $$0P:(DE-Juel1)144723$$aDi Napoli, Edoardo$$b0$$eCorresponding author
000127961 1112_ $$a5th International Conference of the ERCIM Working Group$$cOviedo$$d2012-12-02 - 2012-12-02$$wSpain
000127961 245__ $$aParallel block Chebyshev subspace iteration algorithm optimized for sequences of correlated dense eigenproblems
000127961 260__ $$c2012
000127961 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1357217188_19181$$xOther
000127961 3367_ $$033$$2EndNote$$aConference Paper
000127961 3367_ $$2DataCite$$aOther
000127961 3367_ $$2ORCID$$aLECTURE_SPEECH
000127961 3367_ $$2DRIVER$$aconferenceObject
000127961 3367_ $$2BibTeX$$aINPROCEEDINGS
000127961 520__ $$aIn many material science applications simulations are made of dozens of sequences, where each sequence groups together eigenproblems with increasing self-consistent cycle outer-iteration index. Successive eigenproblems in a sequence possess a high degree of correlation. In particular it has been demonstrated that eigenvectors of adjacent eigenproblems become progressively more collinear to each other as the outer-iteration index increases. This result suggests one could use eigenvectors, computed at a certain outer-iteration, as approximate solutions to improve the performance of the eigensolver at the next one. In order to opti- mally exploit the approximate solution, we developed a block iterative eigensolver augmented with a Chebyshev polynomial accelerator (BChFSI). Numerical tests show that, when the sequential version of the solver is fed approximate solutions instead of random vectors, it achieves up to a 5X speedup. Moreover the parallel shared memory implementation of the algorithm obtains a high level of efficiency up to 80 \% of the theoretical peak performance. Despite the eigenproblems in the sequence being relatively large and dense, the parallel BChFSI fed with ap- proximate solutions performs substantially better than the corresponding direct eigensolver, even for a significant portion of the sought-after spectrum
000127961 536__ $$0G:(DE-HGF)POF2-411$$a411 - Computational Science and Mathematical Methods (POF2-411)$$cPOF2-411$$fPOF II$$x0
000127961 536__ $$0G:(DE-Juel1)SDLQM$$aSimulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM)$$cSDLQM$$fSimulation and Data Laboratory Quantum Materials (SDLQM)$$x2
000127961 7001_ $$0P:(DE-HGF)0$$aBerljafa, Mario$$b1
000127961 909CO $$ooai:juser.fz-juelich.de:127961$$pVDB
000127961 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144723$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000127961 9131_ $$0G:(DE-HGF)POF2-411$$1G:(DE-HGF)POF2-410$$2G:(DE-HGF)POF2-400$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bSchlüsseltechnologien$$lSupercomputing$$vComputational Science and Mathematical Methods$$x0
000127961 9141_ $$y2012
000127961 920__ $$lyes
000127961 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
000127961 980__ $$aVDB
000127961 980__ $$aUNRESTRICTED
000127961 980__ $$aconf
000127961 980__ $$aI:(DE-Juel1)JSC-20090406