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000811970 037__ $$aFZJ-2016-04274
000811970 041__ $$aEnglish
000811970 1001_ $$0P:(DE-Juel1)144723$$aDi Napoli, Edoardo$$b0$$eCorresponding author
000811970 1112_ $$aJoint Laboratory for Extreme Scale Computing$$cLyon$$d2016-06-27 - 2016-06-29$$gJLESC$$wFrance
000811970 245__ $$aApplication of the ChASE eigensolver to excitonic Hamiltonians$$f2016-06-28 - 
000811970 260__ $$c2016
000811970 3367_ $$033$$2EndNote$$aConference Paper
000811970 3367_ $$2DataCite$$aOther
000811970 3367_ $$2BibTeX$$aINPROCEEDINGS
000811970 3367_ $$2ORCID$$aLECTURE_SPEECH
000811970 3367_ $$0PUB:(DE-HGF)31$$2PUB:(DE-HGF)$$aTalk (non-conference)$$btalk$$mtalk$$s1470912544_13979$$xOther
000811970 3367_ $$2DINI$$aOther
000811970 520__ $$aNumerically solving the Bethe-Salpeter equation for the optical polarization function is a very successful approach for describing excitonic effects in first-principles simulations of materials. Converged results for optical spectra and exciton binding energies are directly comparable to experiment and are of predictive quality, thus allowing for computational materials design. However, these accurate results come at high computational cost: For modern complex materials this approach leads to large, dense matrices with sizes reaching up to n~400k. Since the experimentally most relevant exciton binding energies require only the lowest eigenpairs of these matrices, iterative schemes are a feasible alternative to prohibitively expensive direct diagonalization.The Chebyshev Accelerated Subspace iteration Eigensolver (ChASE), which is developed at JSC, is an ideal solver for solving such large dense eigenvalue problems. ChASE leverages on the preponderant use of BLAS 3 subroutines to achieve close-to-peak performance. Moreover, the code is parallelized for many- and multi-core platforms. In the initial phase of the project we are conducting feasibility tests comparing the shared memory parallelization of ChASE with the state-of-the-art direct eigensolver on problems ranging from n~20k up to n~60k. The long-term objective is to develop a distributed CPU/GPU parallelization of ChASE in order to solve larger eigenproblems by effectively exploiting heterogeneous multi-GPU architectures.
000811970 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000811970 536__ $$0G:(DE-Juel1)SDLQM$$aSimulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM)$$cSDLQM$$fSimulation and Data Laboratory Quantum Materials (SDLQM)$$x2
000811970 7001_ $$0P:(DE-HGF)0$$aSchleife, Andre$$b1
000811970 909CO $$ooai:juser.fz-juelich.de:811970$$pVDB
000811970 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144723$$aForschungszentrum Jülich$$b0$$kFZJ
000811970 9131_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000811970 9141_ $$y2016
000811970 915__ $$0StatID:(DE-HGF)0550$$2StatID$$aNo Authors Fulltext
000811970 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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