[Numpy-discussion] NumPy re-factoring project
Fri Jun 11 10:12:40 CDT 2010
On Fri, Jun 11, 2010 at 8:31 AM, Sturla Molden <firstname.lastname@example.org> wrote:
> It would also make sence to evaluate expressions like "y = b*x + a"
> without a temporary array for b*x. I know roughly how to do it, but
> don't have time to look at it before next year. (Yes I know about
> numexpr, I am talking about plain Python code.)
If I may chime in here with my own experience with NumPy code...
I typically use older, "weaker" computers for my work. I am not doing
real-time modeling or some other really advanced, difficult computations.
For me, NumPy works "fast enough", even on an EeePC. My main issue is the
one given above by Sturla. I find that NumPy's memory usage can go
out-of-control very easily in long mathematical expressions. With a mix of
constants and large matricies, each step in the order of operations seems to
take up more memory. Often, I would run into a major slow-down from
trashing the swap. This is fairly trivial to get around by operating over
slices of the matrices at a time, but -- to me -- all of this talk about
optimizing the speed of the operations without addressing the temporaries
issue is like trying to tune-up the engine of a car without bothering to
take the lead weights out of the trunk.
Just my 2 cents.
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