Em Seg, 2008-12-08 às 05:50 -0800, mabshoff escreveu:
>
> > > As I saw matplotlib/pylab being mentioned, in combination with
> slowdowns, I
> > > thought I'd mention that plotting in pylab itself goes
> progressively slower.
> > > On a system here, running the following:
> >
> > > import pylab
> > > import time
> > > n=300
> > > m=100
> > > for i in range(n):
> > > start=time.time()
> > > pylab.plot(range(m), [pylab.sin(pylab.pi*j/(m+0.0)) for j in
> range(m)])
> > > pylab.savefig("name%04i.eps"%i)
> > > print "plotting took %f sec"%(time.time()-start)
> >
> > > Will print "plotting took 0.051967 sec" for the second run (first
> goes
> > > slower), but then the time to plot the next plots goes slowly up,
> to about
> > > 0.55 sec after 300 plots.
> >
> > Thanks. That is more than a 10-fold increase!
> >
> > Michael: could this contribute significantly to the slowness of the
> > plot Fourier
> > series you mentioned in the other thread?
>
I did some tests here, putting an os.system('free -m') in the loop and
noticed that the memory usage keeps going up, which may explain the
slowdown.
However, more importantly, I noticed some other things in the tests. I
Ctrl+C'ed the test and quit ipython, but my CPU was still at 100% usage.
Investigating, I found that there were a bunch of gs processes running,
converting files to .eps. Those files were the ones generated at the
loop with savefig(). So, the problem really lies in that saving the plot
is very slow and doesn't lock the interpreter (which is good), but it
doesn't reveal where the bottleneck is. Then, doing a loop and putting a
lot of those in the background will inevitably degrade performance
either because of mem usage or because of increased CPU usage by
background processes.
Ronan
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