C/C++ is no longer best for speed, not even close.
Now, all that matters is which language has the libraries that make it easiest to get custom code onto the GPU. Python and Lua seem to be winning there, by far.
This is interesting. How is it possible that python and lua have more efficient wrappers around GPU libraries? Also there are many GPU libraries for C/C++ too. Armadillo can use NVBLAS as a backend too. I'm not sure if I get your point of C/C++ being slow.
Google / Facebook and many other huge companies are using Theano and Torch7 in production, at scale. The ML industry has been continuously moving in this direction for years now.
On these optimized ML systems, only a tiny fraction of CPU time is spent outside of the GPU. The goal in many of these companies is to migrate all tasks that can be done on GPUs to GPUs, as soon as possible. It's far faster and more cost efficient.
I would have thought that if you were going to run prod systems in the gpu you would actually write CUDA (C++) or similar to avoid the inefficiency of the abstraction layer.
(also, this comment is bordering on the uncivil).
I can only bring the horse to the water (or Google as its sometimes called these days) :)
>Google/Facebook and many other huge companies ... You are totally wrong.
That totally settles it then thank you, who am I to argue and surely there are no ML jobs that spend time outside of GPU.
(i) how nonsensical such a comparison is
(ii) there are many algorithms for which GPU offers no speedup at all in fact the the data transfer can actually hurt. There are instances where using CPU's SIMD instructions makes more sense than GPU.