Re: [Numpydiscussion] Performance issue in covariance function in Numpy 1.9 and later
Hi Ralf, Thank you so much for your answer. I finally figured out that the problem was because Numpy 1.9 was not linked to BLAS. I do not know why because I simply installed numpy 1.9 via the commands: aptget install python3numpy If anybody has the same problem, you may want to take a look into this: http://osdf.github.io/blog/numpyscipywithopenblasforubuntu1204secondt... Best Regards, Ecem On Fri, Jul 22, 2016 at 5:19 PM Ecem sogancıoglu <ecemsogancioglu@gmail.com> wrote:
Dear Ralf,
Thank you so much for your answer.
I finally figured out that the problem was because Numpy 1.9 was not linked to BLAS. I do not know why because I simply installed numpy 1.9 via the commands:
aptget install python3numpy
If anybody has the same problem, you may want to take a look into this: http://osdf.github.io/blog/numpyscipywithopenblasforubuntu1204secondt...
Best Regards, Ecem
On Tue, Jul 19, 2016 at 9:44 PM Ralf Gommers <ralf.gommers@gmail.com> wrote:
On Tue, Jul 19, 2016 at 3:53 PM, Ecem sogancıoglu < ecemsogancioglu@gmail.com> wrote:
Hello All,
there seems to be a performance issue with the covariance function in numpy 1.9 and later.
Code example: *np.cov(np.random.randn(700,37000))*
In numpy 1.8, this line of code requires 4.5755 seconds. In numpy 1.9 and later, the same line of code requires more than 30.3709 s execution time.
Hi Ecem, can you make sure to use the exact same random array as input to np.cov when testing this? Also timing just the function call you're interested in would be good; the creating of your 2D array takes longer than the np.cov call:
In [5]: np.random.seed(1234)
In [6]: x = np.random.randn(700,37000)
In [7]: %timeit np.cov(x) 1 loops, best of 3: 572 ms per loop
In [8]: %timeit np.random.randn(700, 37000) 1 loops, best of 3: 1.26 s per loop
Cheers, Ralf
Has anyone else observed this problem and is there a known bugfix?
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Ecem sogancıoglu