On Fri, 22 Apr 2011 12:19:56 -0700 (PDT), sturlamolden <sturlamol...@yahoo.no> wrote: : To optimise computational code, notice that Python itself : gives you a 200x performance penalty. That is much more : important than not using all 4 cores on a quadcore processor. : In this case, start by identifying bottlenecks using the : profiler. Then apply C libraries or these or rewrite to Cython. : If that is not sufficient, you can start to think about using : more hardware (e.g. multithreading in C or Cython). This advice : only applies to computational code though.
And not necessarily even there. The extra programmers to recode in C come with more than a 200x cost factor. It is almost trivial to make a multithread map implementation which could have exploited umpteen core box were it not for GIL. That would be a cheap gain. It matters little that you could gain 100x more at 200x cost ... Besides, the bottleneck is likely to be deeply embedded in some library like numpy or scipy already. -- :-- Hans Georg -- http://mail.python.org/mailman/listinfo/python-list
