Chris Lee wrote:
On Jun 15, 2008, at 10:33 PM, Jeff Senn wrote:
On Jun 15, 2008, at 3:57 PM, Simon Pickles wrote:
Hi NRB,
Neutral Robot Boy wrote:
alright, so i'm still in 'beginner' mode with stackless here. i did
a bit of reading which suggested that stackless should be able to
distribute processing across multiple cores without trouble, and i
decided to write a really simple script and look at how much of a
load it puts on my cpu.
The stackless scheduler which you activate by calling
stackless.run() only runs in one thread. Each tasklet is added to
that scheduler and called in turn. No other core will be used.
I suppose one should point out that this is not merely a limitation
of Stackless.
e.g. running schedulers in more than one thread won't even help.
Python itself, even using multiple native threads, can only make use
of one core
at a time due to the GIL (Global Interpreter Lock). If you are
interested
in the whys-and-wherefores, a search through the archives of this list
(and/or Google) will provide a bunch of discussions.
-Jas
Yes indeed, I run simulation code which can benefit from as many cores
and processors that are available. To achieve this in python I used
parallelpython, which acts as a job server and pickles the parameters,
modules, and functions for use by a new instance of python. Using
this, I can pretty much use all the processing power available on the
computer. It can even run across multiple machines, if I go to the
trouble to set up the permissions on each machine.
Really? eek, I had misunderstood the GIL, I think. So Carlos's example
is multicore but not parallel?
Thats bad for me. My server had several interpreters running
'concurrently' using twisted.PerspectiveBroker to communicate. I guess
this model works for clusters but not for SMPs.....
eek again!
Si
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