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

Anne Archibald peridot.faceted@gmail....
Tue Apr 17 13:55:45 CDT 2007

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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