Heh ... one of my employees took it into their head to code up some arithmetic algorithms in C++ a month or so ago. We do not use C++ for anything, we are all Python and JVM based. But he decided that he was going to achieve an amazing win by optimising some numerical code to get order of magnitude benefits, and without asking invested 4 hours into coding it up. I wrote a naive implementation of the same thing in Groovy, of all languages. My implementation was initially 20 times as fast and I coded it in 30 minutes.

So he debugged some more and figured out that he misunderstood some of the inner workings of how vectors copy data and also that he did not understand the threading library he was using properly. He then fixed those two things. After this further exercise he reduced the difference to factor of 4. However he was never able to work out why my code was 4 times as fast as his C++ and abandoned it.

I know for sure that with appropriate expertise the C++ could probably be made to go perhaps twice as fast as my Groovy code. But the point is, none of the supposed benefits come automatically regardless what language you are using. And unless you flip over to GPU or FPGA accelerated methods, the final outcome is well and truly in the same ballpark anyway.

But all this is to say that "rarely translated" might be true at the for applications that are completely in the high performance domain. But for all the applications where the high performance code is in niches at the edge and there simply aren't resources or expertise to fully tune the native implementation ... I think it's translated all the time.