  Accelerated computing is widely used in high-performance computing.
Therefore, it is crucial to experiment and discover how to better utilize
GPUGPUs latest generations on relevant applications. In this paper, we present
results and share insights about highly tuned stencil-based kernels for NVIDIA
Ampere (A100) and Hopper (GH200) architectures. Performance results yield
useful insights into the behavior of this type of algorithms for these new
accelerators. This knowledge can be leveraged by many scientific applications
which involve stencils computations. Further, evaluation of three different
programming models: CUDA, OpenACC, and OpenMP target offloading is conducted on
aforementioned accelerators. We extensively study the performance and
portability of various kernels under each programming model and provide
corresponding optimization recommendations. Furthermore, we compare the
performance of different programming models on the mentioned architectures. Up
to 58% performance improvement was achieved against the previous GPGPU's
architecture generation for an highly optimized kernel of the same class, and
up to 42% for all classes. In terms of programming models, and keeping
portability in mind, optimized OpenACC implementation outperforms OpenMP
implementation by 33%. If portability is not a factor, our best tuned CUDA
implementation outperforms the optimized OpenACC one by 2.1x.
