[Numpy-discussion] Question about Optimization (Inline and Pyrex)

Lou Pecora lou_boog2000@yahoo....
Tue Apr 17 14:21:49 CDT 2007


Now, I didn't know that.  That's cool because I have a
new dual core Intel Mac Pro.  I see I have some
learning to do with multithreading.  Thanks.

--- Anne Archibald <peridot.faceted@gmail.com> wrote:

> On 17/04/07, Lou Pecora <lou_boog2000@yahoo.com>
> wrote:
> > You should probably look over your code and see if
> you
> > can eliminate loops by using the built in
> > vectorization of NumPy.  I've found this can
> really
> > speed things up.  E.g. given element by element
> > multiplication of two n-dimensional arrays x and y
> > replace,
> >
> > z=zeros(n)
> > for i in xrange(n):
> >   z[i]=x[i]*y[i]
> >
> > with,
> >
> > z=x*y   # NumPy will handle this in a vector
> fashion
> >
> > Maybe you've already done that, but I thought I'd
> > offer it.
> 
> It's also worth mentioning that this sort of
> vectorization may allow
> you to avoid python's global interpreter lock.
> 
> Normally, python's multithreading is effectively
> cooperative, because
> the interpreter's data structures are all stored
> under the same lock,
> so only one thread can be executing python bytecode
> at a time.
> However, many of numpy's vectorized functions
> release the lock while
> running, so on a multiprocessor or multicore machine
> you can have
> several cores at once running vectorized code.
> 
> Anne M. Archibald
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> Numpy-discussion@scipy.org
>
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> 


-- Lou Pecora,   my views are my own.
---------------
"I knew I was going to take the wrong train, so I left early." 
--Yogi Berra

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