[Numpy-discussion] is numerical python only for prototyping ?
Sebastian Haase
haase at msg.ucsf.edu
Tue Jul 26 09:42:28 CDT 2005
Hi,
This is not sopposed to be an evil question; instead I'm hoping for the
answer: "No, generally we get >=95% the speed of a pure C/fortran
implementation" ;-)
But as I am the strongest Python/numarray advocate in our group I get often
the answer that Matlab is (of course) also very convenient but generally
memory handling and overall execution performance is so bad that for final
implementation one would generally have to reimplement in C.
We are a bio-physics group at UCSF developping new algorithms for
deconvolution (often in 3D). Our data sets are regularly bigger than several
100MB. When deciding for numarray I was assuming that the "Hubble Crowd" had
a similar situation and all the operations are therefore very much optimized
for this type of data.
Is 95% a reasonable number to hope for ? I did wrap my own version of FFTW
(with "plan-caching"), which should give 100% of the C-speed. But concerns
arise from expression like "a=b+c*a" (think "convenience"!): If a,b,c are
each 3D-datastacks creation of temporary data-arrays for 'c*a' AND then also
for 'b+...' would have to be very costly. (I think this is at least happening
for Numeric - I don't know about Matlab and numarray)
Hoping for comments,
Thanks
Sebastian Haase
UCSF, Sedat Lab
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