Thanks. That's useful feedback. Obviously, this topic is complicated with several factors to muddy the waters.
> 90% of your time is usually spent in 5% of your code, so it's really the ability to optimize easily when you need to without resorting to convoluted tricks and hooks into other languages.
Interestingly, the Python/glue-language philosophy is just the opposite, for exactly the same reasons. Since it is just the 5% of the code that needs optimization, it says: why not write that in C/C++ and the rest in something easier?
These days, you can get GPU power outside C++. For instance, Theano brings GPU math to Python. I don't have experience with comparing the performance differences between say PyCUDA/jcuda vs straight C/C++ CUDA. The net difference may not be 100x, but C/C++ programmers will certainly tend to do deeper optimizations.