There are a few places where we (improperly) directly call malloc()
instead of PyMem_Malloc(), so yes, you should rebuild numpy against
TCMalloc in addition to the Python interpreter.
How do I build against the -ltcmalloc library? Just not sure how to
input compile time arguments with the build script.
from numpy import arange, sum
for x in range(10000000):
inhibVal = sum(arange(15))
Memory usage stays constant with Ubuntu 11.04, 64-bit, using the numpy
1.5.1 package from ubuntu, and using 1.6.1.dev-a265004.
efiring@manini:~$ uname -a
Linux manini 2.6.38-8-generic #42-Ubuntu SMP Mon Apr 11 03:31:24 UTC
2011 x86_64 x86_64 x86_64 GNU/Linux
Eric
Also noticed that the same issue occurs as described with using the prod
instead of sum. Other commands like copy and reshape have no issues. I
have a machine with 11.04 on it so I will give that a go later to see
what goes.
Thanks, Jeff
Message: 2 Date: Sun, 22 May 2011 12:32:43 -0500 From: Robert Kern
<[email protected]> Subject: Re: [Numpy-discussion] Python memory
management issues using Linux. Maybe Numpy related. To: Discussion of
Numerical Python <[email protected]> Message-ID:
<[email protected]> Content-Type:
text/plain; charset=UTF-8 On Sat, May 21, 2011 at 23:27, Jeffrey Spencer
<[email protected]> wrote:
> I have had issues with Linux (Ubuntu 10.04 x86_64) not releasing memory
> back to the OS. I have code here the exhibits the issue:
>
>
http://stackoverflow.com/questions/5975255/memory-allocated-to-python-in-the-os-is-never-released-back-in-linux-even-after-g
>
> Then I read athttp://pushingtheweb.com/2010/06/python-and-tcmalloc/
> that this was a problem fixed by compiling your own version of Python
> using the TCMalloc library which I have now done. The issue is still
> exhibited so I was keen to know if possibly it was because numpy is
> compiled and linked to the default memory management library (not sure
> if this is true or how this works but would I also need to compile numpy
> linked to the TCMalloc library). Let me know if anyone has any insight
> on this issue. Or if it isn't related to numpy at all.
There are a few places where we (improperly) directly call malloc()
instead of PyMem_Malloc(), so yes, you should rebuild numpy against
TCMalloc in addition to the Python interpreter.
-- Robert Kern "I have come to believe that the whole world is an
enigma, a harmless enigma that is made terrible by our own mad attempt
to interpret it as though it had an underlying truth." ? -- Umberto Eco
--
________________________
Jeffrey Spencer
[email protected]
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