  The adoption of hybrid GPU-CPU nodes in traditional supercomputing platforms
opens acceleration opportunities for electronic structure calculations in
materials science and chemistry applications, where medium sized Hermitian
generalized eigenvalue problems must be solved many times. The small size of
the problems limits the scalability on a distributed memory system, hence they
can benefit from the massive computational performance concentrated on a single
node, hybrid GPU-CPU system. However, new algorithms that efficiently exploit
heterogeneity and massive parallelism of not just GPUs, but of multi/many-core
CPUs as well are required. Addressing these demands, we implemented a novel
Hermitian general eigensolver algorithm. This algorithm is based on a standard
eigenvalue solver, and existing algorithms can be used. The resulting
eigensolvers are state-of-the-art in HPC, significantly outperforming existing
libraries. We analyze their performance impact on applications of interest,
when different fractions of eigenvectors are needed by the host electronic
structure code.
