000811971 001__ 811971
000811971 005__ 20221109161713.0
000811971 037__ $$aFZJ-2016-04275
000811971 041__ $$aEnglish
000811971 1001_ $$0P:(DE-Juel1)144723$$aDi Napoli, Edoardo$$b0$$eCorresponding author$$ufzj
000811971 1112_ $$aParallel Matrix Algorithms and Applications$$cBordeaux$$d2016-07-06 - 2016-07-08$$gPMAA 16$$wFrance
000811971 245__ $$aThe ChASE library on distributed and heterogeneous platforms
000811971 260__ $$c2016
000811971 3367_ $$033$$2EndNote$$aConference Paper
000811971 3367_ $$2DataCite$$aOther
000811971 3367_ $$2BibTeX$$aINPROCEEDINGS
000811971 3367_ $$2DRIVER$$aconferenceObject
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000811971 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1470913012_13972$$xAfter Call
000811971 520__ $$aWe  propose  to  step  away  from  the  black-box  approach  and  allow  the eigensolver to accept as much information as it is available from the application.  Such a strategy implies that the resulting library is tailored to the specific application, or class of applications, and loose generality of usage. On the other hand, the resulting eigensolver maximally exploits knowledge from the application and become very efficient.  With this general strategy in  mind,  we  present  here  a  version  of a Chebyshev Accelerated Subspace iteration Eigensolver (ChASE) which targets extremal eigenpairs of dense eigenproblems.  In particular, ChASE focuses of on a class of applications resulting in having to solve sequences of eigenvalue problems where adjacent problems possess a certain degree of correlation.  A typical example of such applications is Density Functional Theory where the solution to a non-linear partial differential equation is worked out by generating and solving dozens of algebraic eigenvalue problems in a self- consistent fashion over dozens of iterations. Similarly, any non-linear eigenvalue problem, which can be solved by the method of successive linearization, gives rise to sequences of correlated algebraic eigenproblems that are the target of ChASE. We re-design the eigensolver so as to minimize its complexity and have better control of its numerical features.  Following the algorithm optimizations, we strive to adopt  a  strategy  leading  to  an  implementation  that  would  lends  itself  to high-performance parallel computing and avoid, at the same time, issues related to portability to heterogeneous architectures.  We achieve such a goal  by  implementing  parallel  kernels  for  the  modular  tasks  of  the  eigensolver using programming strategies out of MPI, OpenMP, and CUDA.
000811971 536__ $$0G:(DE-HGF)POF3-511$$a511 - Computational Science and Mathematical Methods (POF3-511)$$cPOF3-511$$fPOF III$$x0
000811971 536__ $$0G:(DE-Juel1)SDLQM$$aSimulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM)$$cSDLQM$$fSimulation and Data Laboratory Quantum Materials (SDLQM)$$x2
000811971 909CO $$ooai:juser.fz-juelich.de:811971$$pVDB
000811971 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144723$$aForschungszentrum Jülich$$b0$$kFZJ
000811971 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
000811971 9141_ $$y2016
000811971 915__ $$0StatID:(DE-HGF)0550$$2StatID$$aNo Authors Fulltext
000811971 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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