[Numpy-discussion] Speed performance on array constant set

Mark Heslep mark at mitre.org
Thu Jan 19 20:24:01 CST 2006


Travis Oliphant wrote:

> This is actually a bit surprising that opencv can create and fill so 
> quickly.  Perhaps they are using  optimized SSE functions for the 
> Intel platform, or something?
> -Travis
>
Ah, sorry, Im an unintentional fraud.  Yes I have Intel's optimization 
library IPP turned on and had forgotten about it.  So one more time:

With IPP on as before.  UseOptimized = # of Cv functions available w/  IPP

> python -m timeit -s "import opencv.cv as cv; print 
> cv.cvUseOptimized(1); im =cv.cvCreateImage(cv.cvSize(1000,1000), 8, 
> 1)" "cv.cvSet( im, cv.cvRealScalar( 7 ) )"
> 305
> 305
> 305
> 305
> 305
> 100 loops, best of 3: 2.24 msec per loop

And without:

> python -m timeit -s "import opencv.cv as cv; print 
> cv.cvUseOptimized(0); im =cv.cvCreateImage(cv.cvSize(1000,1000), 8, 
> 1)" "cv.cvSet( im, cv.cvRealScalar( 7 ) )"
> 0
> 0
> 0
> 0
> 0
> 100 loops, best of 3: 6.94 msec per loop

So IPP gives me 3X, which leads me to ask about plans for IPP / SSE for 
NumPy, no offense intended to non Intel users.  I believe I recall some 
post that auto code generation in NumArray was the road block? 

Mark





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