On Sep 26, 5:45 am, "David Cournapeau" <[EMAIL PROTECTED]> 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. -- http://mail.python.org/mailman/listinfo/python-list
