@inproceedings{a4129d066dda448d9ef4d4a8bc243d01,

title = "ELPA: A parallel solver for the generalized eigenvalue problem",

abstract = "For symmetric (hermitian) (dense or banded) matrices the computation of eigenvalues and eigenvectors Ax = λBx is an important task, e.g. in electronic structure calculations. If a larger number of eigenvectors are needed, often direct solvers are applied. On parallel architectures the ELPA implementation has proven to be very efficient, also compared to other parallel solvers like EigenExa or MAGMA. The main improvement that allows better parallel efficiency in ELPA is the two-step transformation of dense to band to tridiagonal form. This was the achievement of the ELPA project. The continuation of this project has been targeting at additional improvements like allowing monitoring and autotuning of the ELPA code, optimizing the code for different architectures, developing curtailed algorithms for banded A and B, and applying the improved code to solve typical examples in electronic structure calculations. In this paper we will present the outcome of this project.",

keywords = "ELPA-AEO, Eigensolver, Electronic structure calculations, Parallel",

author = "Bungartz, {Hans Joachim} and Christian Carbogno and Martin Galgon and Thomas Huckle and Simone K{\"o}cher and Kowalski, {Hagen Henrik} and Pavel Kus and Bruno Lang and Hermann Lederer and Valeriy Manin and Andreas Marek and Karsten Reuter and Michael Rippl and Matthias Scheffler and Christoph Scheurer",

note = "Publisher Copyright: {\textcopyright} 2020 The authors and IOS Press.",

year = "2020",

doi = "10.3233/APC200095",

language = "English",

series = "Advances in Parallel Computing",

publisher = "IOS Press BV",

pages = "647--668",

editor = "Ian Foster and Joubert, {Gerhard R.} and Ludek Kucera and Nagel, {Wolfgang E.} and Frans Peters",

booktitle = "Parallel Computing",

}