We have developed GPU versions for two major high-performance-computing (HPC) applications originating from two different scientific domains. GENE [1,2] is a plasma microturbulence code which is employed for simulations of nuclear fusion plasmas. VERTEX [3,4,5] is a neutrino-radiation hydrodynamics code for “first principles”-simulations of core-collapse supernova explosions [6,7,8]. 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.