Developing algorithms and software for the parallel solution of the symmetric eigenvalue problem

Nowadays, the development, maintenance, and ongoing adaptation of simulation software due to new algorithmic or hardware developments are highly complex tasks involving larger teams, often from different groups and disciplines, and located at different places. This requires an increased use of methods and tools from software engineering. At the same time, the high computational demands from the fields of application make it necessary to optimize the modules for code performance and scalability in order to fully exploit the potential of modern parallel architectures.This paper presents a case study on the ongoing endeavor of improving and developing library software for the parallel computation of eigenvalues for dense symmetric matrices, driven by fields of application such as quantum chemistry. A widespread approach is to, first, transform the matrix to tridiagonal form and, second, to solve the tridiagonal eigenvalue problem, before a back transformation provides the eigenvectors of the original matrix. For overall performance, each of these steps must be optimized in a specific way with respect to numerical and parallel efficiency, which shows the importance of involving different experts and of designing the parallel eigensolver in a modular way. Optimizations for the reduction and the back transformation are discussed in this paper, including numerical results demonstrating their effectiveness.