  Over the last decade, most of the increase in computing power has been gained
by advances in accelerated many-core architectures, mainly in the form of
GPGPUs. While accelerators achieve phenomenal performances in various computing
tasks, their utilization requires code adaptations and transformations. Thus,
OpenMP, the most common standard for multi-threading in scientific computing
applications, introduced offloading capabilities between host (CPUs) and
accelerators since v4.0, with increasing support in the successive v4.5, v5.0,
v5.1, and the latest v5.2 versions. Recently, two state-of-the-art GPUs -- the
Intel Ponte Vecchio Max 1100 and the NVIDIA A100 GPUs -- were released to the
market, with the oneAPI and NVHPC compilers for offloading, correspondingly. In
this work, we present early performance results of OpenMP offloading
capabilities to these devices while specifically analyzing the portability of
advanced directives (using SOLLVE's OMPVV test suite) and the scalability of
the hardware in representative scientific mini-app (the LULESH benchmark). Our
results show that the coverage for version 4.5 is nearly complete in both
latest NVHPC and oneAPI tools. However, we observed a lack of support in
versions 5.0, 5.1, and 5.2, which is particularly noticeable when using NVHPC.
From the performance perspective, we found that the PVC1100 and A100 are
relatively comparable on the LULESH benchmark. While the A100 is slightly
better due to faster memory bandwidth, the PVC1100 reaches the next problem
size (400^3) scalably due to the larger memory size.
