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000155238 037__ $$aFZJ-2014-04412
000155238 041__ $$aEnglish
000155238 1001_ $$0P:(DE-HGF)0$$aBasermann, Achim$$b0$$eCorresponding Author
000155238 1112_ $$aEighth SIAM Conference on Parallel Processing for Scientific Computing$$cMinneapolis$$d1997-03-14 - 1997-03-17$$gPPSC 1997$$wUSA
000155238 245__ $$aNew Preconditioned Solvers for Large Sparse Eigenvalue Problems on Massively Parallel Computers
000155238 260__ $$aPhiladelphia, Pa.$$bSociety for Industrial and Applied Mathematics$$c1997
000155238 29510 $$aProceedings of the Eighth SIAM Conference on Parallel Processing for Scientific Computing, PPSC 1997
000155238 300__ $$a8 p.
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000155238 520__ $$aWe present preconditioned solvers to find a few eigenvalues and eigenvectors of large dense or sparse symmetric matrices based on the Jacobi-Davidson (JD) method by G. L. G. Sleijpen and H. A. van der Vorst. For preconditioning, we apply a new adaptive approach using the QMR iteration. To parallelize the solvers, we divide the interesting part of the spectrum into a few overlapping intervals and asynchronously exchange eigenvector approximations from neighboring intervals to keep the solutions separated. Per interval, matrix-vector and vector-vector operations of the JD iteration are parallelized by determining a data distribution and a communication scheme from an automatic analysis of the sparsity pattern of the matrix. We demonstrate the efficiency of these parallelization strategies by timings on an Intel Paragon and a Cray T3E system with matrices from real applications.
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000155238 7001_ $$0P:(DE-Juel1)132269$$aSteffen, Bernhard$$b1$$ufzj
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000155238 9201_ $$0I:(DE-Juel1)VDB62$$kZAM$$lZentralinstitut für Angewandte Mathematik$$x0
000155238 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x1
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