Publications
T. Auckenthaler, V. Blum, H.-J. Bungartz, T. Huckle, R. Johanni, L. Kraemer, B. Lang, H. Lederer, P. R. Willems:
Parallel solution of partial symmetric eigenvalue problems from electronic structure calculations,
Parallel Computing, Vol. 27, Issue 12, p. 783-794 (2011), doi:10.1016/j.parco.2011.05.002
Online version: http://www.sciencedirect.com/science/article/pii/S0167819111000494
T. Auckenthaler, H.-J. Bungartz, T. Huckle, L. Krämer, B. Lang and P. Willems
Developing algorithms and software for the parallel solution of the symmetric eigenvalue problem,
Journal of Computational Science, 2011, doi:10.1016/j.jocs.2011.05.002
Online version: http://www.sciencedirect.com/science/article/pii/S1877750311000330
R. Johanni, A. Marek, H. Lederer and V. Blum:
Scaling of Eigenvalue Solver Dominated Simulations in: Juelich Blue Gene/P Extreme Scaling Workshop 2011,
Eds: B. Mohr, W. Frings, Technical Report FZJ-JSC-IB-2011-02, p. 27-30, April 2011
Online version: http://juser.fz-juelich.de/record/15866/files/ib-2011-02.pdf
T. Auckenthaler ⇑, T. Huckle, R. Wittmann: A blocked QR-decomposition for the parallel symmetric
eigenvalue problem, Parallel Computing 40 (2014) 186–194
Online version: https://www.sciencedirect.com/science/article/pii/S0167819114000404
Andreas Marek, Volker Blum, Rainer Johanni, Ville Havu, Bruno Lang, Thomas Auckenthaler, Alexander Heinecke, Hans-Joachim Bungartz, and Hermann Lederer: The ELPA Library - Scalable Parallel Eigenvalue Solutions for Electronic Structure Theory and Computational Science The Journal of Physics: Condensed Matter 26, 213201 (2014).
Online version: http://stacks.iop.org/0953-8984/26/213201
Pavel Kus, Andreas Marek, and Hermann Lederer: GPU Optimization of Large Scale Eigensolver In: Radu F., Kumar K., Berre I., Nordbotten J., Pop I. (eds) Numerical Mathematics and Advanced Applications ENUMATH 2017. ENUMATH 2017. Lecture Notes in Computational Science and Engineering, vol 126. Springer, ChamOnline version:
Online version: https://www.springerprofessional.de/gpu-optimization-of-large-scale-eigenvalue-solver/16377520
P. Kus, A. Marek, S. S. Koecher, H.-H. Kowalski , Ch. Carbogno, Ch. Scheurer, K. Reuter, M. Scheffler and H. Lederer: Optimizations of the Eigensolvers in the ELPA Library Parallel Computing 85, 167-177 (2019)
Online version: https://www.sciencedirect.com/science/article/pii/S016781911830334X
A Alvermann et al. Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects Japan Journal of Industrial and Applied Mathematics, pp 1- 19 (2019)
Online version: https://link.springer.com/article/10.1007/s13160-019-00360-8
B. Lang: Efficient reduction of banded Hermitian positive definite generalized eigenvalue problems to banded standard eigenvalue problems. SIAM J. Sci. Comput. 41(1), C52–C72 (2019)
Online version: http://www.siam.org/journals/siscs/41-1/M116732.html
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