John Hunter wrote:
> On Nov 15, 2007 12:53 PM, Michael Droettboom <[EMAIL PROTECTED]> wrote:
>
>> Thought some of you may be interested to know that the speed on the
>> branch is getting much better. Whereas earlier the branch was about 2x
>> slower than the trunk, now most things are close to equal with the trunk
>> speed-wise (with a few outliers for some things such as auto legends,
>> quivers and the pcolor stuff that Eric and I have been working on).
>
> Hey Michael, this is very encouraging I just wanted to let know about
> another important use case which I think you are aware of because
> you've referred to optimized marker drawing in the past, but this is
> something I put a lot of effort into (the agg cached marker rasters in
> extension code) because it is an important use case. The script below
> is a useful test, with performance numbers below
>
>
> import time
> import numpy as n
> import matplotlib
> matplotlib.use('Agg')
> from pylab import figure
>
> fig = figure()
> ax = fig.add_subplot(111)
> for i in range(1,7):
> N = 10**i
> x, y = n.random.rand(2,N)
> ax.cla()
> tstart = time.time()
> ax.plot(x, y, 'o')
> fig.canvas.draw()
> print 'N=%d; elapsed=%1.3f'%(N, time.time()-tstart)
>
>
> Trunk:
> N=10; elapsed=0.139
> N=100; elapsed=0.092
> N=1000; elapsed=0.082
> N=10000; elapsed=0.133
> N=100000; elapsed=0.594
> N=1000000; elapsed=5.193
>
>
> Branch:
> N=10; elapsed=0.207
> N=100; elapsed=0.118
> N=1000; elapsed=0.138
> N=10000; elapsed=0.280
> N=100000; elapsed=1.671
> N=1000000; elapsed=15.877
Very odd. I've been running my own very similar benchmark as I've been
going, and the two code bases perform quite similarly. The branch
continues to cache the markers in more or less the same way as on the
trunk. Here are my results with your benchmark:
Trunk:
N=10; elapsed=0.056
N=100; elapsed=0.039
N=1000; elapsed=0.042
N=10000; elapsed=0.067
N=100000; elapsed=0.326
N=1000000; elapsed=2.913
Branch:
N=10; elapsed=0.033
N=100; elapsed=0.028
N=1000; elapsed=0.030
N=10000; elapsed=0.055
N=100000; elapsed=0.310
N=1000000; elapsed=2.858
I wonder what environmental and/or hardware difference could cause this?
Perhaps a fresh rebuild would make a difference? (Due to distutils'
lack of dependency tracking...?)
>> log_demo.py 1.769 2.011 0.242 113%
>
>
> Here is another area where there is an important difference. Panning
> and zooming interactively with log scaling is much slower on the
> branch, presumably because you have to redo the non-affine part every
> time.
The non-affine part is not computed on every pan and zoom -- that was
one of the main design goals of the branch. (You can put a print
statement in Log10Transform.transform to see when it gets called.) I
can't feel a speed difference between the two, but...
> Also, the old grid line bug on log plots seems to be back, as
> evinced when you zoom from the "home" view.
...I should fix this bug first to have a fair comparison.
Cheers,
Mike
--
Michael Droettboom
Science Software Branch
Operations and Engineering Division
Space Telescope Science Institute
Operated by AURA for NASA
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