This title appears in the Scientific Report :
2014
Please use the identifier:
http://dx.doi.org/10.1007/978-3-642-55195-6_37 in citations.
A Parallel and Scalable Iterative Solver for Sequences of Dense Eigenproblems Arising in FLAPW
A Parallel and Scalable Iterative Solver for Sequences of Dense Eigenproblems Arising in FLAPW
In one of the most important methods in Density Functional Theory – the Full-Potential Linearized Augmented Plane Wave (FLAPW) method – dense generalized eigenproblems are organized in long sequences. Moreover each eigenproblem is strongly correlated to the next one in the sequence. We propose a nov...
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Personal Name(s): | Berljafa, Mario |
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Di Napoli, Edoardo (Corresponding Author) | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Published in: |
Parallel Processing and Applied Mathematics |
Imprint: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2014
|
Physical Description: |
395 - 406 |
ISBN: |
978-3-642-55195-6 (electronic) 978-3-642-55194-9 (print) |
DOI: |
10.1007/978-3-642-55195-6_37 |
Conference: | The 10th International Conference on Parallel Processing and Applied Mathematics, Warsaw (Poland), 2013-09-08 - 2013-09-11 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Simulation and Data Laboratory Quantum Materials (SDLQM) Computational Science and Mathematical Methods |
Series Title: |
Lecture Notes in Computer Science
8385 |
Publikationsportal JuSER |
In one of the most important methods in Density Functional Theory – the Full-Potential Linearized Augmented Plane Wave (FLAPW) method – dense generalized eigenproblems are organized in long sequences. Moreover each eigenproblem is strongly correlated to the next one in the sequence. We propose a novel approach which exploits such correlation through the use of an eigensolver based on subspace iteration and accelerated with Chebyshev polynomials. The resulting solver, parallelized using the Elemental library framework, achieves excellent scalability and is competitive with current dense parallel eigensolvers. |