I agree, but in HPC, the primary consumer of GPGPU so far, there is a lot of knowledge locked into C/C++/Fortran codebases and porting to a new language is a hu

I agree, but in HPC, the primary consumer of GPGPU so far, there is a lot of knowledge locked into C/C++/Fortran codebases and porting to a new language is a huge commitment with an uncertain reward. I think this is a big problem with language design for heterogeneous computing. I've been following the development of OpenACC, which is has been gaining momentum and lets you write GPU code directly with C with pragmas (like OpenMP). It's a bit more performance portable between platforms than CUDA or OpenCL with a low initial investment. I'm a little concerned about the lack of diversity of compilers supporting it, but it seems like the most feasible route to move those big codebases to heterogeneous platforms. [1] http://www.openacc.org/ http://www.openacc.org/