[Numpy-discussion] Re: my Numpy statements are slower than indexed formulations in somecases

[Rob]

Problem solved.  I had one last for loop in there.  Its odd that as I
gradually got rid of the for loops, the speed went down, but when I got
rid of the last on, bingo!   That routine now runs at a 1/10 of its
original time.  Rob.


Rob wrote:
> 
> Rob wrote:
> >
> > I have a number of thse routines in some EM code.  I've tried to
> > Numpyize them, but end up with code that runs even slower.
> >
> > Here is the old indexed routing:
> >
> > -----------------
> > for JJ in range(0,TotExtMetalEdgeNum):
> >
> >     McVector+=Ccc[0:TotExtMetalEdgeNum,JJ] * VcVector[JJ]
> > ----------------
> >
> > Here is the Numpy version:
> >
> > ---------------------
> > McVector= add.reduce(transpose(Ccc[...] * VcVector[...]))
> > ---------------------
> >
> > I wonder if there is another faster way to do this?  Thanks,  Rob.
> >
> > --
> 
> I did speed things up just a tiny bit by using:
> 
> add.reduce(Ccc*VcVector,1) instead of
> add.reduce(transpose(Ccc*VcVector).
> 
> But I'm still running way slower than an indexed array scheme.   Rob.
> > The Numeric Python EM Project
> >
> > www.pythonemproject.com
> 
> --
> The Numeric Python EM Project
> 
> www.pythonemproject.com
> 
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