Fang Fang wrote:
> Thanks for your reply. The simplified code is as follows. It takes 7
> seconds to process 1000 rows, which is tolerable, but I wonder why it
> takes so long. Isn't vectorized operation supposed to run very quickly.
>
> from numpy import *
>
> componentcount = 300000
> currSum = zeros(componentcount)
> row = zeros(componentcount) #current row
> rowcount = 50000
> for i in range(1,rowcount):
> row[:] = 1
> currSum = currSum + row
>
In this case, you can save a significant chunk of time (about 30% on my
machine) by replacing the last line with:
currSum += row
I suspect that the bulk of the remaining time is being chewed up moving
things in and out of the cache since your arrays are large. There are
some things you could try to alleviate that, but I don't know that I'd
worry about it as I suspect that your overall time in your original
problem is going to be dominated by pulling stuff out out of the
dictionaries and populating row.
-tim
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