[SciPy-user] Python on Intel Xeon Dual Core Machine

Young, Karl karl.young@ucsf....
Wed Feb 6 12:01:02 CST 2008


> Parallel programming is a massive, complicated field, and many
> high-powered software tools exist to take advantage of it.
> Unfortunately, python has a limitation in this area:
 the Global
> Interpreter Lock. Basically it means no two CPUs can be running python
> code at the same time. This means that you get no speedup at all by
> parallelizing your python code - with a few important exceptions:
> while one thread is doing an array operation, other threads can run
> python code, and while one thread is waiting for I/O (reading from
> disk, for example), other threads can run python code. Parallel python
> is a toolkit that can avoid this problem by running multiple python
> interpreters (though I have little experience with it).

Well yes, but on a cluster (distributed memory) you can still take advantage of parallelization using python tools particularly 
if the parallelization is close to trivial (admittedly it doesn't sound like that is Lorenzo's situation). But I agree with everything 
Anne says in that parallel programming is a massively complicated area and it's really important to do things like profiling your code first. 
But I was (and am) fairly ignorant of many of the important details, other than realizing that unfavorable communication to computation 
ratios could kill you, and was still able to get close to linear speedup (though my problem, while not completely, was close to
trivially parallelizable, i.e I just needed to pass a few things around between steps requiring independent chunks requiring long
calculations). So I still think it's useful to do a quick and dirty estimate of communication/computation and if that looks favorable
explore some "simple" parallel programming tools like pypar. 



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