On 17/04/07, Lou Pecora <[EMAIL PROTECTED]> 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 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
