Python is slow?
sturlamolden at yahoo.no
Fri Sep 26 12:08:52 CEST 2008
On Sep 26, 5:45 am, "David Cournapeau" <courn... at gmail.com> wrote:
> I am fairly experienced in matlab (have been using it extensively for
> 5 years in academical context), and now with numpy, and generally,
> they are comparable speed-wise. Matlab has some niceties which makes
> it faster in some simple cases (JIT for loops, function calls faster,
> sometimes COW semantics means it faster),
I've used Matlab for 10 years.
Matlab has a horrible pass-by-value semantics, which means that arrays
are copied in and copied out form function calls. Yes there is a copy-
on-write optimization. But unless you are not careful, it will kill
performance and make memory usage skyrocket. Also, slicing creates a
new array, whereas in numpy slicing gives you a view of an existing
array. This generally makes Matlab slow, and it wastes terrible
amounts of memory.
For example I once tested D4 wavelet transforms on a 64 MB array of
doubles (i.e. length 2**23). Matlab R14 Service Pack 2 did this in 27
seconds, whereas Python 2.4 with NumPy 1.0 one required 3.4 seconds.
That is an order of magnitude speed difference in favour of Python.
Matlab also has a strange habit of fragmenting the heap. This can be
so bad that you have to stop the script, run a special function called
pack() do defragement, and restart. I've never seen that with Python.
I more or less stopped using Matlab 3 years ago, and I have not
renewed my subscription for maintenance of my personal Matlab license
since. Matlab is a nice tool, but I have grown past it.
Matlab's strongest side is data visualization though. Although we have
matplotlib, mayavi and possibility of interfacing with gnuplot, it's
not anywhere near the capabilities of Matlab.
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