Sasha wrote:

> I''ve just posted a patch at
> http://projects.scipy.org/scipy/numpy/wiki/PossibleOptimizationAreas
> that results in a 6-14x speed-up of  ndarray.fill for simple
> datatypes.  That's a bigger change than what I am confortable
> submiting to svn without a review.  Also since I am not familiar with
> record arrays, I am not sure whether or not this change would break
> anything in that area.  Finally, the patch only adresses a
> single-segment case, "strided" arrays would still use old code.
>   
> 
This looks like a good proceedure.    For those less familiar with the 
code.  Sasha added an additional "data-type-function" to the structure 
of data-type-specific functions.   Each builtin data-type has a pointer 
to a list of functions.  A while ago I moved these functions out from 
the data-type object so that they could grow.

This is probably a good fundamental operation for speeding up.

I would probably also not define the misaligned case, but just use the 
default code for that case as well.  This is consistent with the idea 
that misaligned data will have slower operations.

To make your code work for record arrays you need to handle the VOID 
case.  I would just not define it for the void case at this point.    
Several other data-type functions need to be improved to handle record 
arrays (look at setitem and getitem for guidance) better anyway.

You also need to add a check so that if the function pointer is NULL, 
the optimized function is not called.

But, generally, this is the right use of the data-type-functions.   Good 
job.

-Travis



-------------------------------------------------------
This SF.Net email is sponsored by xPML, a groundbreaking scripting language
that extends applications into web and mobile media. Attend the live webcast
and join the prime developer group breaking into this new coding territory!
http://sel.as-us.falkag.net/sel?cmd=lnk&kid=110944&bid=241720&dat=121642
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@[...].net
https://lists.sourceforge.net/lists/listinfo/numpy-discussion