For symmetric (hermitian) (dense or banded) matrices the computation of eigenvalues and eigenvectors $Ax=\lambda Bx$ 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.     «

For symmetric (hermitian) (dense or banded) matrices the computation of eigenvalues and eigenvectors $Ax=\lambda Bx$ 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 trans...     »