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000153314 0247_ $$2arXiv$$aarXiv:1404.4161
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000153314 037__ $$aFZJ-2014-02955
000153314 1001_ $$0P:(DE-HGF)0$$aBerljafa, Mario$$b0
000153314 245__ $$aAn Optimized and Scalable Eigensolver for Sequences of Eigenvalue Problems
000153314 260__ $$c2014
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000153314 500__ $$a20 Pages, 6 figures. Invited submission to special issue of  Concurrency and Computation: Practice and Experience
000153314 520__ $$aIn many scientific applications the solution of non-linear differential equations are obtained through the set-up and solution of a number of successive eigenproblems. These eigenproblems can be regarded as a sequence whenever the solution of one problem fosters the initialization of the next. In addition, some eigenproblem sequences show a connection between the solutions of adjacent eigenproblems. Whenever is possible to unravel the existence of such a connection, the eigenproblem sequence is said to be a correlated. When facing with a sequence of correlated eigenproblems the current strategy amounts to solving each eigenproblem in isolation. We propose a novel approach which exploits such correlation through the use of an eigensolver based on subspace iteration and accelerated with Chebyshev polynomials (ChFSI). The resulting eigensolver, is optimized by minimizing the number of matvec multiplications and parallelized using the Elemental library framework. Numerical results shows that ChFSI achieves excellent scalability and is competitive with current dense linear algebra parallel eigensolvers.
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000153314 7001_ $$0P:(DE-Juel1)131042$$aWortmann, Daniel$$b1$$ufzj
000153314 7001_ $$0P:(DE-Juel1)144723$$aDi Napoli, Edoardo$$b2$$eCorresponding Author$$ufzj
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000153314 9132_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data $$vComputational Science and Mathematical Methods$$x0
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000153314 9141_ $$y2014
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000153314 9201_ $$0I:(DE-Juel1)IAS-1-20090406$$kIAS-1$$lQuanten-Theorie der Materialien$$x1
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