how to speedup this code?
exarkun at intarweb.us
Fri Jan 9 17:10:52 CET 2004
On Fri, Jan 09, 2004 at 03:21:29PM +0000, Ognen Duzlevski wrote:
> Hi all,
> I have rewritten a C program to solve a bioinformatics problem. Portion
> where most of the time is spent is:
> def DynAlign(scoremat,insertmat,delmat,tseq,qseq,tlen,qlen):
> global ONEINDELPENALTY,OPENGAPPENALTY
> for ndx1 in range(1,tlen+1):
> for ndx2 in range(1,qlen+1):
> delmat[ndx1][ndx2] = Min(delmat[ndx1-1][ndx2]+ONEINDELPENALTY, \
> Min(scoremat[ndx1-1][ndx2]+OPENGAPPENALTY+ONEINDELPENALTY, \
> insertmat[ndx1][ndx2] = Min(insertmat[ndx1][ndx2-1]+ONEINDELPENALTY, \
> Min(scoremat[ndx1][ndx2-1]+OPENGAPPENALTY+ONEINDELPENALTY, \
> scoremat[ndx1][ndx2] = Min(scoremat[ndx1-1][ndx2-1], \
> Min(delmat[ndx1-1][ndx2-1], insertmat[ndx1-1][ndx2-1])) + \
You should definitely be using Numeric Python for this.
> def Min(a,b):
> if a< b:
> return a
> return b
The builtin function "min" does exactly this, and probably does it quite a
> In C this is not a big deal, delmat, scoremat and insertmat are int
> matrices dynamically allocated and the loop-inside-a-loop is pretty fast.
> However, on python (2.3) it is very slow. So for comparison, my C version
> takes 8 seconds to execute and the python version takes around 730
> seconds. I have narrowed down through visual observation (print before and
> print after entry into the above nested loop) that most of the time is
> spent in there. I have also tried the Numeric package with their arrays.
> It speeded things up somewhat but not considerably.
Did you loop over the arrays and perform the same operations as DynAlign
above does? If so, that's not the best way to do it. Use the matrix
operations it provides. You'll know when you have written a good Numeric
solution when your code no longer has any `for' loops.
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