Walter: > By fundamental technical issue, I mean things like Python's numeric types > which > require runtime testing for every operation, and are very resistant to known > techniques of optimization.
Life is a bit more complex than that: - The Lua JIT has shown once and for all that dynamic typing is not as bad (performance-wise) as you think. I have floating-point heavy benchmarks that run faster in jitted Lua than in D compiled with DMD. - Python is a language, and there are many ways to run a language. You are able to interpret D code and to compile Python code. Since several years I am helping the develpment of ShedSkin, a Python->C++ compiler, that uses powerful type inferencing to infer all the static types of an implicitly static Python program. The results are sometimes faster than D programs compiled with DMD, because they are essentially pretty clean C++ programs compiled by G++. - Today GPUs are used more and more where the max performance is needed, and example: http://www.i-programmer.info/news/105-artificial-intelligence/2176-kinects-ai-breakthrough-explained.html And you are able to write and run such GPU kernels from Python code too. Bye, bearophile
