The lack of an efficient standardized programming model for GPUs has resulted in a number of issues for developers. Firstly, it creates vendor lock-in, where codes written for one platform cannot easily be migrated to another without substantial code refactoring. For example, adopting NVIDIA programming models involves significant trade-offs between performance and portability across different hardware environments . Another issue stemming from this fragmentation is the lack of compiler support for all programming models on all platforms. For instance, (Fig. ), while CUDA is well supported on Nvidia GPUs through the CUDA Toolkit, its support on AMD and Intel hardware is limited and relies on indirect means like translation layers or third-party libraries and utilities . This forces developers to rely on vendorspecific compilers, which often lack the universality and feature-richness of their CPU counterparts.