  We have developed GPU versions for two major high-performance-computing (HPC)
applications originating from two different scientific domains. GENE is a
plasma microturbulence code which is employed for simulations of nuclear fusion
plasmas. VERTEX is a neutrino-radiation hydrodynamics code for "first
principles"-simulations of core-collapse supernova explosions. The codes are
considered state of the art in their respective scientific domains, both
concerning their scientific scope and functionality as well as the achievable
compute performance, in particular parallel scalability on all relevant HPC
platforms. GENE and VERTEX were ported by us to HPC cluster architectures with
two NVidia Kepler GPUs mounted in each node in addition to two Intel Xeon CPUs
of the Sandy Bridge family. On such platforms we achieve up to twofold gains in
the overall application performance in the sense of a reduction of the time to
solution for a given setup with respect to a pure CPU cluster. The paper
describes our basic porting strategies and benchmarking methodology, and
details the main algorithmic and technical challenges we faced on the new,
heterogeneous architecture.
