multi threading in multi processor (computer)

[Alan Kennedy]

[ajikoe at gmail.com]
> That's a pity, since when we have to run parallel, with single
> processor is really  not efficient. To use more computers I think is
> cheaper than to buy super computer in developt country.

Although cpython has a GIL that prevents multiple python threads *in the 
same python process* from running *inside the python interpreter* at the 
same time (I/O is not affected, for example), this can be gotten around 
by using multiple processes, each bound to a different CPU, and using 
some form of IPC (pyro, CORBA, bespoke, etc) to communicate between 
those processes.

This solution is not ideal, because it will probably involve 
restructuring your app. Also, all of the de/serialization involved in 
the IPC will slow things down, unless you're using POSH, a shared memory 
based system that requires System V IPC.

http://poshmodule.sf.net

Alternatively, you could simply use either jython or ironpython, both of 
which have no central interpreter lock (because they rely on JVM/CLR 
garbage collection), and thus will support transparent migration of 
threads to multiple processors in a multi-cpu system, if the underlying 
VM supports that.

http://www.jython.org
http://www.ironpython.com

And you shouldn't have to restructure your code, assuming that it is 
already thread-safe?

For interest, I thought I'd mention PyLinda, a distributed object system 
that takes a completely different, higher level, approach to object 
distribution: it creates "tuple space", where objects live. The objects 
can be located and sent messages. But (Py)Linda hides most of gory 
details of how objects actually get distributed, and the mechanics of 
actually connecting with those remote objects.

http://www-users.cs.york.ac.uk/~aw/pylinda/

HTH,

--
alan kennedy
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