On Mon, Feb 17, 2014 at 10:03 AM, alex <argriffi@ncsu.edu> wrote:
On Mon, Feb 17, 2014 at 4:49 AM, Dave Hirschfeld <novin01@gmail.com> wrote:
alex <argriffi <at> ncsu.edu> writes:
Hello list,
Here's another idea resurrection from numpy github comments that I've been advised could be posted here for re-discussion.
The proposal would be to make np.linalg.svd more like scipy.linalg.svd with respect to input checking. The argument against the change is raw speed; if you know that you will never feed non-finite input to svd, then np.linalg.svd is a bit faster than scipy.linalg.svd. An argument for the change could be to avoid issues reported on github like crashes, hangs, spurious non-convergence exceptions, etc. from the undefined behavior of svd of non-finite input.
""" [...] the following numpy code hangs until I `kill -9` it.
``` $ python runtests.py --shell $ python Python 2.7.5+ [GCC 4.8.1] on linux2
import numpy as np np.__version__ '1.9.0.dev-e3f0f53' A = np.array([[1e3, 0], [0, 1]]) B = np.array([[1e300, 0], [0, 1]]) C = np.array([[1e3000, 0], [0, 1]]) np.linalg.svd(A) (array([[ 1., 0.], [ 0., 1.]]), array([ 1000., 1.]), array([[ 1., 0.], [ 0., 1.]])) np.linalg.svd(B) (array([[ 1., 0.], [ 0., 1.]]), array([ 1.00000000e+300, 1.00000000e+000]), array([[ 1., 0.], [ 0., 1.]])) np.linalg.svd(C) [hangs forever]
""" AlexI'm -1 on checking finiteness - if there's one place you usually want maximum performance it's linear algebra operations.
It certainly shouldn't crash or hang though and for me at least it doesn't - it returns NaN
btw when I use the python/numpy/openblas packaged for ubuntu, I also get NaN. The infinite loop appears when I build numpy letting it use its lapack lite. I don't know which LAPACK Josef uses to get the weird behavior he observes "13% cpu usage for a hanging process".
I use official numpy release for development, Windows, 32bit python, i.e. MingW 3.5 and whatever old ATLAS the release includes. a constant 13% cpu usage is 1/8 th of my 8 virtual cores. If it were in a loop doing some work, then cpu usage fluctuates (between 12 and 13% in a busy loop). +/- 1 Josef
This is consistent with the scipy svd docstring describing its check_finite flag, where it warns "Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs." I think this caveat also applies to most numpy linalg functions that connect more or less directly to lapack. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion