On Mon, Oct 25, 2010 at 6:48 AM, Citi, Luca <lc...@essex.ac.uk> wrote: > Hello, > I have noticed a significant speed difference between the array and the > matrix implementation of the dot product, especially for not-so-big matrices. > For example: > > In [1]: import numpy as np > In [2]: b = np.random.rand(104,1) > In [3]: bm = np.mat(b) > In [4]: a = np.random.rand(8, 104) > In [5]: am = np.mat(a) > In [6]: %timeit np.dot(a, b) > 1000000 loops, best of 3: 1.74 us per loop > In [7]: %timeit am * bm > 100000 loops, best of 3: 6.38 us per loop > > The results for two different PCs (PC1 with windows/EPD6.2-2 and PC2 with > ubuntu/numpy-1.3.0) and two different sizes are below: > > array matrix > > 8x104 * 104x1 > PC1 1.74us 6.38us > PC2 1.23us 5.85us > > 8x10 * 10x5 > PC1 2.38us 7.55us > PC2 1.56us 6.01us > > For bigger matrices the timings seem to asymptotically approach. > > Is it something worth trying to fix or should I just accept this as a fact > and, when working with small matrices, stick to array?
I think the fixed overhead comes from the subclassing of arrays. The subclassing is done in Python and if an operation creates a matrix then __array_finalize__ is called. All that adds up to overhead. http://github.com/numpy/numpy/blob/master/numpy/matrixlib/defmatrix.py I wrote a mean-variance optimizer with matrices. Switching to arrays gave me a big speed up. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
