Thanks, i must admit the concept of the GIL is cloudy to me - for example, if the python interpreter on a single machine is handling one process and locks until it is done, then on to the next one, and so on - isn't that what causes speed issues? I was wondering why python can't implicitly handle multiple processes at once by using all machine cores (have many threads, each invoking the interpreter and handling a process).
Maybe i should get up to speed on threads first to get the bigger picture? On Fri, Nov 27, 2009 at 3:38 PM, Kent Johnson <ken...@tds.net> wrote: > On Fri, Nov 27, 2009 at 4:57 AM, OkaMthembo <zebr...@gmail.com> wrote: > > Hi All, > > > > Is there a python implementation that takes advantage of all cores on > modern > > multicore machines? > > Presumably you mean something like, "Is there a python implementation > that can run multiple compute-bound processes on multiple cores > concurrently." > > Some options: > - the multiprocessing module in the std lib - here is an example of > using it with numpy: > http://folk.uio.no/sturlamo/python/multiprocessing-tutorial.pdf > > - Jython and IronPython both have threading models that allow multiple > threads to run concurrently on multiple processors. > > Kent > -- Regards, Lloyd
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