Parallel diagonalization performance on high-performance computers

Abstract

Eigenvalue and eigenvector computations arise in a wide range of scientific and engineering applications. For example, in quantum chemistry and atomic physics, the computation of eigenvalues is often required to obtain electronic energy states. For large-scale complex systems in such areas, the eigensolver calculation usually represents a huge computational challenge. It is therefore imperative that suitable, highly efficient eigensolver methods are used in order to facilitate the solution of the most demanding scientific problems. This presentation will analyze the performance of parallel eigensolvers from numerical libraries such as ScaLAPACK on the latest parallel architectures using data sets derived from large-scale scientific applications.