On Wed, Apr 16, 2014 at 12:51 PM, Julian Taylor
jtaylor.deb...@googlemail.com wrote:
Hi,
In NumPy what we want is the tracing, not the exchangeable allocators.
I don't think it is a good idea for the core of a whole stack of
C-extension based modules to replace the default allocator or
On Wed, Apr 16, 2014 at 7:35 PM, Victor Stinner
victor.stin...@gmail.com wrote:
Hi,
2014-04-16 7:51 GMT-04:00 Julian Taylor jtaylor.deb...@googlemail.com:
In NumPy what we want is the tracing, not the exchangeable allocators.
Did you read the PEP 445? Using the new malloc API, in fact you
Hi,
In NumPy what we want is the tracing, not the exchangeable allocators.
I don't think it is a good idea for the core of a whole stack of
C-extension based modules to replace the default allocator or allowing
other modules to replace the allocator NumPy uses.
I think it would be more useful if
Hi,
2014-04-16 7:51 GMT-04:00 Julian Taylor jtaylor.deb...@googlemail.com:
In NumPy what we want is the tracing, not the exchangeable allocators.
Did you read the PEP 445? Using the new malloc API, in fact you can
have both: install new allocators and set up hooks on allocators.
Indeed, that's very reasonable.
Please open an issue on the tracker!
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Hi,
2014-04-14 1:39 GMT-04:00 Nathaniel Smith n...@pobox.com:
The new tracemalloc infrastructure in python 3.4 is super-interesting
to numerical folks, because we really like memory profiling.
Cool, thanks :-)
calloc() is more awesome than malloc()+memset() (...)
I had a discussion with
On Tue, Apr 15, 2014 at 9:31 AM, Charles-François Natali
cf.nat...@gmail.com wrote:
Indeed, that's very reasonable.
Please open an issue on the tracker!
Done!
http://bugs.python.org/issue21233
I'll ping numpy-discussion and see if I can convince someone to do the work ;-).
-n
--
Nathaniel
Hi all,
The new tracemalloc infrastructure in python 3.4 is super-interesting
to numerical folks, because we really like memory profiling. Numerical
programs allocate a lot of memory, and sometimes it's not clear which
operations allocate memory (some numpy operations return views of the
original
On Sun, Apr 13, 2014, at 22:39, Nathaniel Smith wrote:
Hi all,
The new tracemalloc infrastructure in python 3.4 is super-interesting
to numerical folks, because we really like memory profiling. Numerical
programs allocate a lot of memory, and sometimes it's not clear which
operations
On 04/14/2014 08:36 AM, Benjamin Peterson wrote:
On Sun, Apr 13, 2014, at 22:39, Nathaniel Smith wrote:
SO, we'd like to route our allocations through PyMem_* in order to let
tracemalloc see them, but because there is no PyMem_*Calloc, doing
this would force us to give up on the calloc()
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