I am the author of DCompute, a compiler/library/runtime framework for abstracting OpenCL/CUDA for D. You can write kernels already, although the API automation is still a work in progress.
I'm hoping that this should level the field a bit, because let's face it, people use CUDA for two reasons: the OpenCL driver API sucks; and the utility libraries (cuDNN et al) for CUDA. Possibly driver quality as well.
By having an API thats not horrible to use, that advantage is gone. The utility libraries will be more of a challenge to undermine, but since it targets CUDA natively there is no disadvantage to users of nvidia's hardware, but there is no advantage to others, yet (see GLAS[1] for what is possible with relative ease). Using D as the kernel language will also bring significant advantages over C/C++, static reflection, sane templates and compile time code generation to name a few.
By having an API thats not horrible to use, that advantage is gone. The utility libraries will be more of a challenge to undermine, but since it targets CUDA natively there is no disadvantage to users of nvidia's hardware, but there is no advantage to others, yet (see GLAS[1] for what is possible with relative ease). Using D as the kernel language will also bring significant advantages over C/C++, static reflection, sane templates and compile time code generation to name a few.
You can find it at https://github.com/libmir/dcompute.
If you have any question, please ask!
[1] https://github.com/libmir/mir-glas