[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




More information about the Numpy-discussion mailing list