Nathaniel Smith wrote:
> On Thu, Mar 12, 2009 at 9:51 AM, Mark Larsen <larsen...@gmail.com> wrote:
>> I took your advice and implemented using "Parallel Python".  Can you comment
>> on your choice of pyprocessing?  It seems like a simple re-write to switch
>> over.
> 
> I use pyprocessing partly because it gave me the impression that it
> was more tuned in to the general python community... but mostly
> because it's just what I happened to bump into first.  I'm sure they
> both work fine; whatever works for you... (I've also used IPython's
> distributed computing stuff, and it was nice too.)
> 
> The fact that pyprocessing just forks has sometimes come in handy --
> in one case I had a giant multi-gigabyte structure that I needed to
> pull data out of, so I loaded it once to a global variable in the
> parent process before spawning the workers, and they all shared the
> data structure via COW memory without any extra work on my part. (This
> is an ugly Unix-specific hack, of course, but an effective one!) And
> I've sometimes had trouble when calling into rpy2 with memory usage
> growing unboundedly (still need to track this down),

I have experienced a growing process when running rpy2 for a long time 
(although this was not with multiprocessing).
An explicit request for garbage collection on the Python side
(import gc; gc.collect()) seem to have solved the issue so far (I have 
not found the time to look closely at it).

> and in that case
> it's really handy that it's cheap and easy to just restart the slaves.
> 
> -- Nathaniel
> 
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