Recent efforts to adapt to the heterogeneous architectures of HPC systems highlight the critical importance of balancing performance with portability. Some codes, such as ABINIT and QE, have adopted hardware-agnostic offload programming models (OpenMP or OpenACC), while others, like CP2K, have opted for non-portable, kernel-based models (CUDA and HIP). BerkeleyGW provides both CUDA and OpenACC alternatives, with the latter achieving performance within ten percent of the former . Additionally, different communities are actively developing specialized performance libraries to execute computationally intensive tasks on accelerators. Examples include the Distributed Block Compressed Sparse Row Matrix (DBCSR) library for NVIDIA and AMD GPUs and the LibintX library, which provides efficient implementations of Gaussian integral evaluation, central to integral-direct implementations. The QMCkl library, developed within the TREX CoE, offers high-performance computation kernels for QMC simulations using OpenMP, OpenACC, and SYCL . The wave-function community has been extensively developing tensor libraries that automatically parallelize and accelerate operations on multidimensional arrays, taking advantage of modern HPC architectures .