On Mon, Feb 17, 2014 at 10:03 AM, alex <argri...@ncsu.edu> wrote: > On Mon, Feb 17, 2014 at 4:49 AM, Dave Hirschfeld <novi...@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] >>> ``` >>> """ >>> >>> Alex >>> >> >> I'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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
