The transition from petascale to exascale computing has brought unprecedented computational power but has also introduced significant challenges in adapting software to efficiently utilize the new architectures. One of the most prominent challenges is the increasing fragmentation of the software stack, particularly with GPU programming. Nvidia had an early advantage for being a pioneer in this area. This created a relatively unified ecosystem where developers could rely on a single set of tools and libraries to harness the power of GPUs. However, as GPU computing gained wider adoption, other vendors such as AMD and Intel entered the market, each with their own programming models (HIP and SYCL, respectively). This proliferation of models has led to a fragmented landscape, making it increasingly difficult for developers to write portable and efficient code that can run seamlessly across different GPU platforms or heterogeneous systems.