  With the appearance of the heterogeneous platform OpenPower,many-core
accelerator devices have been coupled with Power host processors for the first
time. Towards utilizing their full potential, it is worth investigating
performance portable algorithms that allow to choose the best-fitting hardware
for each domain-specific compute task. Suiting even the high level of
parallelism on modern GPGPUs, our presented approach relies heavily on abstract
meta-programming techniques, which are essential to focus on fine-grained
tuning rather than code porting. With this in mind, the CUDA-based open-source
plasma simulation code PIConGPU is currently being abstracted to support the
heterogeneous OpenPower platform using our fast porting interface cupla, which
wraps the abstract parallel C++11 kernel acceleration library Alpaka. We
demonstrate how PIConGPU can benefit from the tunable kernel execution
strategies of the Alpaka library, achieving portability and performance with
single-source kernels on conventional CPUs, Power8 CPUs and NVIDIA GPUs.
