[MohamedMabrouk]

Creating DSL/Bindings for Julia or python to underlaying platform like CUDA is not really replacing it, it is yet adding another layer on top of the existing platforms or creating a prototyping envirnoment for research. The question is not can Julia interface with CUDA, it is can Julia replace C++/CUDA/ROCm in an end-to-end scenarios (preferably with portability across GPUs vendors)? if not then there is no comparison between the stated goals of Julia and Mojo. they are different langauges targeting completly different use cases.

[ChrisRackauckas]

Julia does not just have bindings to CUDA. Native Julia code can compile to build .ptx kernels https://cuda.juliagpu.org/stable/development/kernel/. This same code can also generate kernels for AMD GPUs, Intel GPUs, and Metal.

We for example built software that generates kernels on-demand that embed user functions for all 4 of these systems and showed it's much faster than just CUDA bindings for array functions for certain nonlinear systems (https://www.sciencedirect.com/science/article/abs/pii/S00457...)