[Numpy-discussion] multiprocessing shared arrays and numpy
Nadav Horesh
nadavh at visionsense.com
Thu Mar 11 07:20:34 EST 2010
Here is a strange thing I am getting with multiprocessing and memory mapped array:
The below script generates the error message 30 times (for every slice access):
Exception AttributeError: AttributeError("'NoneType' object has no attribute 'tell'",) in <bound method memmap.__del__ of memmap(2949995000.0)> ignored
Although I get the correct answer eventually.
------------------------------------------------------
import numpy as N
import multiprocessing as MP
def average(cube):
return [plane.mean() for plane in cube]
N.arange(30*100*100, dtype=N.int32).tofile(open('30x100x100_int32.dat','w'))
data = N.memmap('30x100x100_int32.dat', dtype=N.int32, shape=(30,100,100))
pool = MP.Pool(processes=1)
job = pool.apply_async(average, [data,])
print job.get()
------------------------------------------------------
I use python 2.6.4 and numpy 1.4.0 on 64 bit linux (amd64)
Nadav
-----Original Message-----
From: numpy-discussion-bounces at scipy.org on behalf of Gael Varoquaux
Sent: Thu 11-Mar-10 11:36
To: Discussion of Numerical Python
Subject: Re: [Numpy-discussion] multiprocessing shared arrays and numpy
On Thu, Mar 11, 2010 at 10:04:36AM +0100, Francesc Alted wrote:
> As far as I know, memmap files (or better, the underlying OS) *use* all
> available RAM for loading data until RAM is exhausted and then start to use
> SWAP, so the "memory pressure" is still there. But I may be wrong...
I believe that your above assertion is 'half' right. First I think that
it is not SWAP that the memapped file uses, but the original disk space,
thus you avoid running out of SWAP. Second, if you open several times the
same data without memmapping, I believe that it will be duplicated in
memory. On the other hand, when you memapping, it is not duplicated, thus
if you are running several processing jobs on the same data, you save
memory. I am very much in this case.
Gaël
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