Numpy, Scipy and a lot of other numerical libraries use Fortran for the underlying operations along with C. Because Fortran is just faster for such operations and will continue to be. Numba is still very much alpha. Julia is still immature for production (Good language from what I hear but still very much in development).
Don't know if C++ can ever be as fast as Fortran because all fortran compilers are optimized for the architecture on which they run. (Best fortran compiler for intel cpus is by intel). As for ViennaCL I don't know much about GPU programming and its performance. Never done it. (My only exp is with Cython and Scipy stuff)
Intel also has C/C++ compilers which probably share optimizers and backend code generators with their Fortran compiler and shouldn't be worse. Ditto for 3rd party commercial vendors like PGI.
And, BTW, I have seen a case where PGI vastly outperformed GCC on the same code. So compilers matter.
Don't know if C++ can ever be as fast as Fortran because all fortran compilers are optimized for the architecture on which they run. (Best fortran compiler for intel cpus is by intel). As for ViennaCL I don't know much about GPU programming and its performance. Never done it. (My only exp is with Cython and Scipy stuff)